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H2O Experiment ConfigurationsOne of the key steps to build good models in H2O DAI is to make proper selections under the experiment configuration page.

H2O DAI Configuration Page (image from H2O)1.Accuracy:When adjusting the accuracy knob value, the way H2O DAI performs evolution and ensemble will be adjusted.

2.Time:As the name suggests, it is used to configure how much time users give for H2O DAI to run optimizations.

For greater details on how to change each knob value, you may refer to the official documentation here.

Expert SettingsIn case you want more flexibility on what feature transformers to use, or what scorers to select for the final model, H2O DAI does provide the options under…

How to overlay Polygon Shapes onto high-resolution mapsPyGMT library in Python made plotting high-resolution topographic maps a breeze.

Often, we need to highlight arbitrarily selected polygon shapes or regions on a map using available shapefile (SHP) data.

Photo by Andrew Stutesman on UnsplashIn this post, we will see how we can overlay shapefile data on top of the PyGMT map using geopandas library.

Here, for example, I obtained the counties data available in .shp format from data.gov.tw, and overlay it on the high-resolution map of Taiwan.

Furthermore, we created a high-resolution topographic map with shapefile data overlayed on it.

How effective is the signal denoising using the MATLAB-based wavelet analysis?

Denoising using waveletsWhen we take the wavelet transform of a time series, it concentrates the signal features in a few large-magnitude wavelet coefficients.

Denoised seismic time-series using sym4 (Image by author)In the above code, I denoised the seismic time-series using level 9 using block thresholding by setting the name-value pair, ‘DenoisingMethod’,’BlockJS’.

Wavelet denoising for synthetic data generated by MATLAB (Image by author)ConclusionsWe have seen how to download seismic waveforms, convert them into mat format from mseed and then perform denoising using wavelet analysis.

Further, I have shown on …

4 Novel Ways to Build AI Talent In-houseThe analytics leader of a US-based Fortune 200 company was under severe pressure.

The analytics team was a part of the IT organization and was struggling to fill their open positions.

As a result, the analytics team was notorious for being understaffed, overworked, and facing the wrath of business users.

Recruiting data science talent is one of the biggest challenges facing companies today.

The data science teams, in turn, received similar briefs from business partners on strategy, products, and business processes,” he adds.

Model training is time-consuming.

What do data scientists do when waiting for models to train?

To answer this question, I interviewed over 20 data scientists combining my own experiences to share this story with you.

If model training is also a part of your work, what are your favorite activities when waiting?

Identifying missing data, duplicated data, data range are those common use of unit tests in data science.

How accurately do we need to get closer to the target value?

Firstly, we should estimate the target value within 0.01 eV error.

As its name suggests, this can be obtained by the average of the absolute value of the difference between the target value and the estimated value.

Suppose we can explore the material having the bandgap of E from the hypothetical experimental dataset.

Then, we can conceptualise the experimental material exploration problem as follows:

An Unconventional Yet Convenient Matplotlib Broken_Barh Function And When It Is Particularly HelpfulImage by AuthorDespite being very convenient for certain cases of data visualization in Python, broken_barh is one of the less known and underrated methods of matplotlib.

To emphasize some intervals on another graphThe broken_barh method is a good choice when it comes to emphasizing some particular intervals on other graphs.

To create a Gantt plotProbably, the best application of the broken_barh method is to create simplified Gantt plots.

A Gantt plot is a specific type of bar plot commonly used for visualizing relationships between different activities against time.

However, also the broken_…

In this article, I’ll discuss accuracy, precision, recall, f1 score, and confusion matrix for measuring the performance of your classifier.

True negative (TN): This means that the prediction was negative class and the actual class was also negative.

The next metrics that we will be looking at help in these situations and give a better understanding of the classifier performance.

Recall = TP/ (TP + FN)Precision attempts to answer the question: What proportion of actual positives was identified correctly?

F1-score = 2 * (precision * recall ) / (precision + recall )It gives equal weightage to precision and recall.

Using Artificial Intelligence to predict the spread of wildfires, with PythonHere’s a step by step tutorial about how to use Data Analysis and Machine Learning to predict the spread of wildfires Piero Paialunga Just now·4 min readDuring the last several days, terrible wildfires have spread all over Sardinia, Italy and all over the world.

One of the reasons why I decided to learn Artificial Intelligence (AI) was the idea of being directly able to help people and solve real world problems.

In this article, I will guide you to a step-by-step tutorial about predicting the spread of wildfires, using AI.

3Data VisualizationLet’s use geopy to localize the city by using the Counties column.

checkin…

Combining a grid cleanliness score from the WattTime API and an inexpensive smart bulb, I’ll walk through the creation of the “grid bulb,” which visualizes how dirty the local electric grid is at any moment.

While the Data Plan page is a bit confusing, I found that a free plan allows access to current local grid conditions.

Connecting to the Smart BulbThe directions here are for connecting to a wifi smart bulb designed for the Tuya platform.

The vast majority of low-cost smart devices use this platform, including any bulb that uses the Smart Life app.

Download and set up the Smart Life appDownload and install the Smart Life app.

Using custom images as maps in TableauYes, you read that right.

You can use any image as a background and add data points all over it.

We know that we need two coordinates for mapping — the longitude and the latitude of a location.

If you recall the scatter plot, we basically use X and Y coordinates to plot data points.

So, a map can be thought of as a scatter plot but with a background image in Tableau.

Optuna basicsLet’s familiarize ourselves with Optuna API by tuning a simple function like (x-1)² + (y+3)².

Let’s see if Optuna can find these:After importing optuna , we define an objective that returns the function we want to minimize.

In the body of the objective, we define the parameters to be optimized, in this case, simple x and y .

The argument trial is a special Trial object of optuna, which does the optimization for each hyperparameter.

To start the optimization, we create a study object from Optuna and pass the objective function to its optimize method:Pretty close, but not as close as you would want.

What Makes a Good Data ScientistPhoto by Leon on UnsplashWhat I plan to write in this article is built around my experience of working with very good data scientists.

I’m not in a position to declare or evaluate a data scientist as good or not good.

The following words will demonstrate my observations of the common practices and skills of well-performing data scientists.

In this sense, the title of the article might be “What Good Data Scientists Have in Common”.

Learning from others is a highly valuable skill.

Having Proper Comments and DocumentationIncorporating comments in your code can be the first step of having proper documentation.

Writing Modular CodeThe best way to improve the quality of the codebase is to make it more compact and modular.

Python has many modules and libraries under a repository known as PyPI or the python package index.

Using Virtual EnvironmentsThe main purpose of a virtual environment in Python is to create a separate environment for Python projects.

This problem can be easily tackled by using a virtual environment for each project where the dependencies are stored independently.

Natural Language Processing — Dependency ParsingPhoto by Siora Photography on UnsplashWhat is Dependency Parsing?

Dependency Parsing is the process to analyze the grammatical structure in a sentence and find out related words as well as the type of the relationship between them.

ImplementationThere are different ways to implement dependency parsing in Python.

Dependency Parsing using spaCyspaCy also provides a built-in dependency visualizer called displaCy that you can use to generate dependency graph for sentences.

The first step is to download the Stanford CoreNLP zip file and Stanford CoreNLP model jar file from the CoreNLP website.

After five years, Distill will be taking a break.

Reprogramming Neural CA to exhibit novel behaviour, using adversarial attacks.

Weights in the final layer of common visual models appear as horizontal bands. We investigate how and why.

When a neural network layer is divided into multiple branches, neurons self-organize into coherent groupings.

We report the existence of multimodal neurons in artificial neural networks, similar to those found in the human brain.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

Neural Cellular Automata learn to generate textures, exhibiting surprising properties.

We present techniques for visualizing, contextualizing, and understanding neural network weights.

Reverse engineering the curve detection algorithm from InceptionV1 and reimplementing it from scratch.

A family of early-vision filters reacting to contrasts between spatial gratings of different frequency

Neural networks naturally learn many transformed copies of the same feature, connected by symmetric weights.

With diverse environments, we can analyze, diagnose and edit deep reinforcement learning models using attribution.

As machine learning scientists, we know that training data can make or break your model.

So I began research on the topic of using personal data for machine learning education.

I’m currently working on a book called Life Lessons from Algorithms , a personal history of resilience demonstrated through different machine learning algorithms.

Author BioYim is a PhD student at the University of Washington, researching innovative ways to teach machine learning.

CitationFor attribution in academic contexts or books, please cite this work asYim Lucky Register, "It’s All Training Data: Using Lessons from Machine Learning to Retrain Your Mind", The Gradient, 2021.

In the following post, we will discuss two complex and related issues regarding these models: fairness and transparency.

These are crucial questions to ask if machine learning models play important parts in our lives and our societies.

However, all hope is not lost as ensuring fairness in machine learning models is an active area of research.

Explanations produced with the LIME algorithm, image fromEnsuring that machine learning models are explainable is an active area of research as well.

CitationFor attribution in academic contexts or books, please cite this work asRasmus Berg Palm and Pola Schwöbel, "Justitia ex Machina: The Case for Automating Morals", The Gradient, 2021.

Several recent papers follow this line of approaches by adjusting the objective (Tam et al., 2021) or formulating tasks in a unified form, like question answering (Zhong et al., 2021) or textual entailment (Wang et al., 2021).

Following-up works further refine the way of using demonstrations: Gao et al., 2021; Liu et al.

It is a good few-shot model as long as the design can generalize well to other few-shot tasks (these tasks can be so-called “true few-shot”).

Introducing LM-BFFFinally, let me introduce our ACL’21 paper, "Making Pre-trained Language Models Better Few-shot Learners", abbreviated as LM-BFF (better few-shot fine-tuning of language models; alternatively, language models' best f…

proposed, “AdaShare: Learning What to Share for Efficient Deep Multi-Task Learning” (2020) [2].

Overview of “AdaShare: Learning What to Share for Efficient Deep Multi-Task Learning” by X.

Adashare: Learning what to share for efficient deep multi-task learning.

His research interests lie in the unsupervised deep learning, Few shot learning and Multi-task learning in computer vision.

He is doing research related to learning efficiently including topics from Multi-Task Learning and Uncertainty Estimation.

And if ADSs cannot perform commonsense reasoning to handle all these edge cases, are they really safer than human drivers?

Why Testing is NecessaryWe have some good reasons to believe that ADSs will be safer than human drivers, and we have some good reasons to worry that ADSs will not be as safe as human drivers.

There are good reasons to assume that ADSs will be safer than human drivers.

CitationFor attribution in academic contexts or books, please cite this work as:Steve Shwartz, "Are Self-Driving Cars Really Safer Than Human Drivers?

BibTeX citation:@article{shwartzadss2021,author = {Shwartz, Steve},title = {Are Self-Driving Cars Really Safer Than Human Drivers?

The AI powering both didn’t miss the start of the COVID-19 pandemic.

The virus was new and the world was experiencing the first wave of the COVID-19 pandemic.

Another example of AI being used by a governmental organization to gain epidemiological insight is discussed in Facebook AI's blogpost.

A practical example in the first subcategory is the creation of the COVID-19 Open Research Dataset (CORD-19), which is a machine-readable dataset of published literature on COVID-19.

Thoughts and conclusionIn this piece, an effort was made to show some examples of AI being used to aid in the fight against the pandemic.

Second, AI systems do not exist in a vacuum-- they are situated and operate in a rich tapestry of socio-organizational relationships.

Considering the socially-situated nature of AI systems, our current AI problems are no longer purely technical ones; they are inherently sociotechnical.

My own journey towards a sociotechnically-informed Human-centered XAI has been anything but self-evident.

CitationFor attribution in academic contexts or books, please cite this work asEhsan Upol, "Towards Human-Centered Explainable AI: the journey so far", The Gradient, 2021.

BibTeX citation:@article{xaijourney,author = {Upol, Ehsan},title = {Towards Human-Centered Explainable AI: the journey so far},journal…

In a 2016 essay, Nadia Eghbal quotes Karl Fogel, an early open source advocate:In open source, you can only have “my” in the associative sense.

Domain-specific licensing for machine learningThis domain-specific approach is particularly promising for machine learning, in part because of several key areas where machine learning diverges heavily from other software.

Author Bio: Chris is an open source developer and contributor to the Rust-ML machine learning working group.

CitationFor attribution in academic contexts or books, please cite this work asChristopher Moran, "Machine Learning, Ethics, and Open Source Licensing (Part II/II)", The Gradient, 2021.

BibTeX citation:@article{moranopensour…

Three years ago today, the Gradient published its first set of articles.

If you’ve ever been itching to write something with the Gradient, now is your chance!

2 - The Gradient Newsletter and PodcastThe Gradient is launching a new weekly newsletter on Substack: the Update, starting next Saturday.

We are also launching the Gradient Podcast, which will go live in two weeks.

If you enjoy our articles, please consider signing up as a supporter on Patreon and/or directly donating to The Gradient and sharing our newsletter with friends!

The use and misuse of machine learning systemsBefore discussing moderation, it is worth first asking if machine learning merits additional consideration about its usage when compared to other forms of software.

Licensing requirements for the developers of government-purchased machine learning services could help fill this gap.

Author Bio: Chris is an open source developer and contributor to the Rust-ML machine learning working group.

CitationFor attribution in academic contexts or books, please cite this work asChristopher Moran, "Machine Learning, Ethics, and Open Source Licensing (Part I/II)", The Gradient, 2021.

BibTeX citation:@article{moranopensource2021,author = {Moran, Christopher},t…

The resulting network with a soft-attention mechanism is trained with back-propagation since the attention mechanism is differentiable.

Later in 2017, we saw a breakthrough in the NLP field, by constructing a network just using an attention mechanism.

Some more works in this early area of attention mechanism in ML can be found in and .

I will explain another attention mechanism approach in computer vision, but the mainstream attention mechanism (which is very popular nowadays) first appeared in this paper.

CitationFor attribution in academic contexts or books, please cite this work asEkrem Aksoy, "Attention in the Human Brain and Its Applications in ML", The Gradient, 2021.

Therefore, taking the full advantage of distributed on-device data for machine learning applications is the current direction in distributed learning.

If Federated Learning methods can find a solution for this complexity, it can be the future of distributed learning.

To understand why we need Federated Learning in the first place, we need to answer what it can enable.

When the models are trained on the users’ data, users enjoy the personalization and higher accuracy results.

CitationFor attribution in academic contexts or books, please cite this work asZehra Hayirci, "Decentralized AI For Healthcare", The Gradient, 2021.

Normal players are represented by green faces, toxic players by red faces.

On the other hand, the detector with the high threshold does not have this problem, but misses a lot of toxic players (false negatives).

Below you can compare the previous word list-based approach against three neural networks with different thresholds [5].

And say we have a system that can perfectly detect bad behavior in online conversations, what should be done when it detects somebody?

CitationFor attribution in academic contexts or books, please cite this work asWessel Stoop, Florian Kunneman, Antal van den Bosch, Ben Miller, "Catching Cyberbullies With Neural Networks", The Gradient, 2021.

But it seemed to me that this moonshot shone a light on a couple of traps that often catch those who aim AI at justice and individual rights.

But justice, which a lawyer might call the correct application of the law to facts, is more than lie detection.

A second concern lies with the “acts of faith” that almost always underpin applications of AI to human behaviour.

CitationFor attribution in academic contexts or books, please cite this work asPhil Lindan, "Can AI Let Justice Be Done?

BibTeX citation:@article{lindan2021roboticjudges,author = {Lindan, Phil},title = {Can AI Let Justice Be Done?

💥 What’s New in AI: The Pachyderm Platform Delivers the Data Foundation for Machine LearningManaging the lifecycle of experiments is one of the overlooked aspects of data science pipelines.

ShareEnter The Pachyderm PlatformThe easiest way to think about Pachyderm is as a versioned controlled data pipeline for data science experiments.

But with machine learning, data is central and the model learns from that data.

Pachyderm Enterprise complements the open-source edition with some interesting capabilities:Pipeline Visualization and Data Exploration: Pachyderm Enterprise enables the visualization of data science pipelines using a graph structure.

ConclusionManaging the data engineering aspect …

we start the new series about the Transformer Architectures.

The technique that made possible a few major breakthroughs in deep learning;

📝 EditorialBuilding machine learning (ML) solutions at scale remains an unexplored territory for most companies.

With this edition of TheSequence Scope, we have added a small section titled Real World ML.

We think that systematically studying the ML architectures and techniques implemented by large technology companies is one of the best sources of inspirations you can find in the ML world.

We hope the Real World ML section will help evangelize some of these ideas.

To receive high-quality content about the most relevant developments in the ML world every Tuesday and Thursday, please subscribe to TheSequence Edge 🔺🔻🗓 Next week in TheSequence Edge:Edge#109: The start of the Transformers serie…

🏷 Crowdsourced Data Labeling in Real WorldFollowing Edge#107, in which we described three main approaches to data labeling and focused on the crowdsourced approach, today we would like to demonstrate how different types of businesses use crowdsourced data labeling platforms to improve their data preparation and enhance model predictions.

In those scenarios, crowdsourced data labeling has emerged as a popular choice for data science teams.

In general, crowdsourced data labeling offer some tangible benefits over alternative approaches:cost efficiencyglobally distributed performers basequality control methodsToday we will look at three very different domains that illustrate how crowdsourced da…

Your background, current role and how did you get started in machine learning (ML)?

AA: I think it is close to impossible to build a horizontal AI infrastructure in today’s market.

AA: Again, at the moment I don’t believe investing in horizontal ML infrastructure is a durable investment strategy.

These use cases will in turn require improvements to horizontal AI infrastructure.

It is likely that the great AI infrastructure companies have already been mostly created.

In this issue;we explain three main approaches to data labeling;we explore some best practices used for implementing crowdsourced data labeling at scale;we overview three platforms that use crowdsourcing for data labeling.

Despite the growing number of offerings in the market, most data labeling technologies can be classified in one of the following three groups:Manual/Crowdsourced: This type of data labeling platform leverages knowledge workers for attaching labels to specific datasets.

Whereas the concept of crowdsourced data labeling seems easy at first glance, it’s quite challenging to implement at scale.

While we have seen dozens of automated data labeling platforms go to raise sizable…

📝 EditorialLast year, DeepMind shocked the biology and artificial intelligence (AI) worlds with the unveiling of AlphaFold2, a deep learning model able to predict the structure of proteins.

This is considered one of the iconic problems in biology for understanding the structure of cells and accelerating drug discovery.

However, despite its promises, the internals of AlphaFold2 remain relatively opaque, which raised some concerns within the scientific community.

The details of the new model were published in a paper in Science magazine this week and the code was open-sourced.

Soon they promise to add experiment tracking and deployment tools with the end goal to cover the whole MLOps lifecycl…

Every six months we provide a summary of what we’ve recently covered in TheSequence.

Catch up with what you missed and prepare for the next half of the year.

This issue is the second part of the “What’s New in AI” recap.

What’s New in AI is a deep dive into one of the freshest research papers or technology frameworks that is worth your attention.

ShareStarti…

This issue is the second part of the “What’s New in AI” recap.

What’s New in AI is a deep dive into one of the freshest research papers or technology frameworks that is worth your attention.

ShareStarting next week, look for our fresh Edges about data labeling, series on Transformers, and other fascinating ML topics.

Large-scale training is one of the most challenging aspects of building deep learning solutions in the real world.

In this Edge, we overview:Quantum machine learning and its two main components: Quantum Datasets and Hybrid Quantum Models.

Every six months we provide a summary of what we’ve recently covered in TheSequence.

Catch up with what you missed and prepare for the next half of the year.

This issue is the first part of the “What’s New in AI” recap.

What’s New in AI is a deep dive into one of the freshest research papers or technology frameworks that is worth your attention.

Every six months we provide a summary of what we’ve recently covered in TheSequence.

Catch up with what you missed and prepare for the next half of the year.

This issue is the first part of the “What’s New in AI” recap.

What’s New in AI is a deep dive into one of the freshest research papers or technology frameworks that is worth your attention.

Wait, did I just say that transformers are being used in computer vision tasks?

📝 EditorialSomebody, someday, should produce a movie about the history of deep learning.

The three deep learning pioneers stayed faithful to the neural network paradigm even during the infamous AI winters where the majority of the AI community was looking for alternative architectures.

Their efforts were rewarded not only with the current renaissance of deep learning but also with the Turing Award (which is considered to be the Nobel prize in computer science) in 2018.

Last week, the three AI legends joined forces again to publish a paper that evaluates recent breakthroughs in deep learning methods and also challenges in the near future.

Deep Learning for AI is a very inspirational paper fo…

About 45% of the time in data science projects is consumed by processing and labeling data.

It’s fair to say that data labeling is one of the most expensive tasks of any machine learning project.

Next Tuesday we will send to all our readers an Edge with our overview of a few data labeling platforms.

TAKE THE SURVEYThe survey invites machine learning engineers and data scientists, as well as AI enthusiasts.

As a thank you, we will send you a cheat sheet with 40+ useful resources that help you understand and organize data labeling.

Subscribe with 40% OFF💥 What’s New in AI: AllenNLP Makes Cutting-Edge NLP Models Look EasyNatural language processing (NLP) is one of the fastest areas of growth in the deep learning space.

However, despite the indisputable progress in NLP research, implementing these types of advanced NLP techniques in real-world applications remains a challenge.

AllenNLP is an open-source framework that streamlines the implementation of state-of-the-art NLP models for a diverse number of linguistic tasks.

Low-Level NLP Abstractions: Provide abstractions for low-level NLP tasks such as masking and padding, keeping the implementation details detached from the core NLP model.

Education: Share implementations…

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В прошлой статье я писал про свои ML-модели для оценки отдельных компаний, но вопрос формирования итогового портфеля совсем не затрагивал. В этом посте хочу рассказать о том, как я собираю свой личный портфель, а так же поделиться сайтом, на котором реализую весь описанный в статье функционал http://stocks.ml. Дисклеймер: у автора нет экономического образования и все выводы и суждения в статье делаются на основе житейского опыта и здравого смысла. Читать далее

TLDR: крохотные модельки обошли модные графовые нейронки в предсказании свойств молекул.

Код: здесь. Берегите Природу. ФОТО: Андерс Хеллберг для Wikimedia Commons, модель — Грета Тунберг

Необученная графовая свёрточная нейронная сеть [1] (uGCN) со случайной инициализацией весов уже пару лет занимает первое место в моём списке алгоритмов для задач машинного обучения на графах из-за копеечной стоимости, простоты реализации, да вполне очевидной элегантности решения. В то же время, насколько мне известно, никто ещё не не проводил соревнований между этой простой моделью и её старшей сестрой — полноценно обученной графовой свёрточной нейронной сетью (GCN) в режиме обучения с учителем. Вот я сдела…

В этом году открытая платформа для обработки естественного языка DeepPavlov, разрабатываемая лабораторией нейронных систем и глубокого обучения МФТИ, впервые стала частью ежегодной программы для молодых разработчиков Google Summer of Code.Google Summer of Code (GSoC) — это ежегодное событие, проводимое компанией Google для привлечения молодых разработчиков к разработке проектов с открытым исходным кодом в их свободное летнее время. К участию допускаются студенты высших учебных заведений (бакалавриат, магистратура, аспирантура) и колледжей. Это отличная возможность не только развить навыки программирования, но и заработать!Работать можно в любой организации, которая есть в соответствующем сп…

В последнее время все больше людей приходит к тому, чтобы не держать деньги под матрасом, а куда-то их инвестировать в надежде сохранить и преумножить свой капитал. Вариант с матрасом плох тем, что с повышением цен на товары и услуги(инфляция) покупательная способность денег падает и через какое-то время купить на них можно значительно меньше, чем раньше. Есть много вариантов, куда вложить деньги(недвижимость, банковский вклад, ценные металлы), но в последнее время популярным становится инвестирование в акции. Только у брокера Тинькофф Инвестиции за несколько лет число клиентов превысило 3.5 млн. В статье я постараюсь описать свой подход к выбору бумаг и поделюсь инструментами, которые для …

Нынче никого не удивишь достижениями искусственного интеллекта машинного обучения (ML) в самых разных областях. При этом доверчивые граждане редко задают два вопроса: (i) а какая собственно цена экспериментов и финальной системы и (ii) имеет ли сделанное хоть какую-то целесообразность? Самым важным компонентом такой цены являются как ни странно цена на железо и зарплаты людей. В случае если это все крутится в облаке, нужно еще умножать стоимость железа в 2-3 раза (маржа посредника).

И тут мы неизбежно приходим к тому, что несмотря на то, что теперь даже в официальные билды PyTorch добавляют бета-поддержку ROCm, Nvidia де-факто в этом цикле обновления железа (и скорее всего следующем) остает…

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!Статьи на сегодня:1. A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer (Tampere University, Finland, 2020)2. Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars (Samsung AI Center, 2020)3. Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (University of California, USA, 2019)4. Whitening for Self-Supervised Representation Learning (University of Trento, Italy, 2020)5. MelGAN: Generative Adversarial Networks for…

Всем привет! Пока киберпанк еще не настолько вошел в нашу жизнь, и нейроинтерфейсы далеки от идеала, первым этапом на пути к будущему манипуляторов могут стать LiDAR. Поэтому, чтобы не скучать на праздниках, я решил немного пофантазировать на тему средств управления компьютером и, предположительно, любым устройством, вплоть до экскаватора, космического корабля, дрона или кухонной плиты. Читать дальше →

В компьютерном зрении часто приходится работать с двумерными изображениями, и значительно реже - с 3D объектами. Из-за этого многие ML инженеры чувствуют себя неуверенно в этой области: много незнакомых слов, непонятно, куда тут применить старых друзей Resnet и Unet. Поэтому сегодня я хотел бы немного поговорить о 3D на примере задачи определения шести степеней свободы, что в каком-то виде синонимично 3D object detection. Я разберу одну из свежих работ на эту тему с некоторыми отступлениями. Кратко о задачеДля начала давайте определимся, что такое шесть степеней свободы (6 DoF - degrees of freedom). Представим себе некоторый ригидный (неизменяемый, т.е. при трансформации все точки будут ост…

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: High-Resolution Neural Face Swapping for Visual Effects (Disney Research Studios, ETH Zurich, 2020)

Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (USA, 2020)

Thieves on Sesame Street! Model Extraction of BERT-based APIs (UMass & Google Research, ICLR, 2019)

Time-Aware User Embeddings as a Service (Yahoo! Research, Temple University, 2020)

Are Labels Necessary for Neural Architecture Search? (Johns Hopkins University, Facebook AI Research, 2020)

GShard: Scaling Giant Mo…

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As an individual contributor, I was focused on the technical work of building, deploying, and operating machine learning models in production settings.

When I was promoted to Director, my focus began to shift away from purely technical aspects of ML.

Useful if you’re thinking through what metrics you should monitor for your production ML systems.

This brief post lists several key engineering features needed and five ways to implement A/B testing.

Here’s another engineering-style link demonstrating how to implementing A/B testing with Kubernetes and Istio.)

Providing technical advice is one area where there’s room for lots of technical work as a manager.

Rather than think about cleaning as a separate step, we’d do better to acknowledge that data cleaning IS data analysis.

Andrew Ng: Bridging AI’s Proof-of-Concept to Production Gap – Andrew Ng recently gave a talk on the common roadblocks that prevent AI projects from succeeding in production.

WhyLogs: Embrace Data Logging Across Your ML Systems – Last week I wrote about a critical bug in one of our production applications that could have been diagnosed with better monitoring and logging.

According to the post WhyLogs logs properties of data as it moves through an ML system, aggregates logs, su…

This week a data scientist on my team discovered a pretty bad bug in one of our production applications.

Although these are thought of as "software topics", data scientists and ML engineers need to care about them.

Maybe you have a highly specialized team of software engineers that help you build applications, but maybe you don’t.

Bringing an AI Product to Market – This is the third post in O’Reilly’s series on AI Product Management, and discusses how to bring an AI-powered product to market.

AI Product Management After Deployment – O’Reilly’s series on AI Product Management concludes with a post describing the an AI PM’s responsibilities after the product is deployed.

Last week I wrote that companies fall into one of two groups when it comes to machine learning and data science.

These companies vary in their levels of experience applying data science, the types of data science roles they have on staff, and the number of employees they have within these roles.

What they have in common, however, is that data science is an add-on capability.

Learn to make a business case for why machine learning or data science is necessary to solve these challenges.

Although the post is clearly marketing collateral, I think it’s valuable for data science leaders encountering data management challenges.

Companies where ML is a core competency don’t need to be convinced that machine learning can add business value.

At these companies, data scientists and ML engineers might work on projects that complement existing products, (attempt to) automate internal operational processes, or otherwise seek to drive other efficiencies.

Data Science Project Flow for Startups – A data science consultant provides his take on how to structure and carry out projects with teams of 1-4 data scientists.

The process is divided into three aspects that run in parallel: product, data science and data engineering and involves data science repeatedly checking-in with product to ensure that KPIs are satisfied.

Peer Re…

Something I’ve been asked by many new subscribers is whether there’s an archive of previous newsletter issues.

If you have any ideas or ways you think I can improve the newsletter, I’d love to hear them!

Here’s what I’ve been reading/watching/listening to recently:Using GitHub Actions for MLOps & Data Science – The first post of a multi-part blog series on using GitHub Actions, GitHub’s native event-driven automation system, to perform ML pipeline tasks.

I’ve played around a bit with the library and am looking forward to summarizing my findings in an upcoming blog post.

How to put machine learning models into production – This post from the StackOverflow blog describes three key areas to co…

According to this blog, the clickthrough rate of display ads is 0.05%, but is 8.8x higher for native ads.

I don’t want to do native ads, but let’s get an upper bound by assuming I did use native ads, and they have 10x clickthrough, so 0.5%.

If I value readers more than money, why don’t I pay money to run ads to direct people to my site?

I think the main problem is that ads don’t really work for a personal blog.

In retrospect, given how ads work, the price per click has to be based on the revenue of the products people advertise, and if I don’t sell anything, I’m priced out by everyone who does.

Normally, I mentally bucket everything into two categories: things I can change, and things I can’t.

If I can’t change something, it’s not worth thinking about, so I push it out of my mind.

Everyone gets the same source code, and although there might be exploits within that code, you can’t change the code itself.

If you have disagreements with a game’s systems, you’re better off finding another game instead of trying to change them.

They have some base level of arrogance that if the world doesn’t work the way they think it should, then they’ll spend their time trying to change the world until it aligns with their expectations.

Online Dominion is getting yet another online implementation!

They’ve tried providing previous buys and didn’t see much improvement.

I’ve long believed that a strong Dominion AI is doable, but nontrivial to get right.

(The other main reason is that step 0 of any Dominion AI effort is to implement an efficient Dominion rules engine, and I really didn’t want to debug that.)

There have been a few attempts at Dominion AI.

I was pretty lucky to get an industry lab research offer.

Working on this post has taught me that everyone’s academic and industry lab experience is wildly different.

When I talked to friends in my year, they reassured me and said I’d do well in grad school.

Doing some code cleanup will save time in the long run, even if you’re the only one who looks at your research code.

Formally having the degree would be nice, but I’m going to keep trying to get what the degree represents through industry research instead.

I’ve easily spent thousands of hours playing Flash games, and it’s shaped my thoughts on what games can be and what games should be.

If you don’t have much time, Flash Game History is an excellent short article that captures the influence of Flash games on game development.

With Flash officially unsupported, the best avenue for playing Flash games is BlueMaxima’s Flashpoint.

But this is kind of a universal puzzle game problem - very few successfully avoid this trap, and the ones that do usually end up being bigger experiences than what you’d expect from a Flash game.

jmtb02 Gamesjmtb02 is the dev handle of John Cooney, a prolific Flash game developer who made a lot of games I liked.

This has spoilers for MIT Mystery Hunt 2021, up through the endgame.

MIT Mystery Hunt was 3x more participants with way more features and a much larger world.

MIT Mystery Hunt has grown before - it’s not like it’s always been this big.

I think it’s pretty funny that both Mystery Hunt and Teammate Hunt had a puzzle that referenced nutrimatic.

Funnily enough, I felt I got more out of the MIT part of MIT Mystery Hunt this year, despite the Hunt running remotely.

At the extreme ends, an electric car powered by electricity from coal is worse than a gasoline car!

On average though, it looks good for the electric car, 170 g/km compared to 220 g/km for a gas car.

Interestingly, for Germans, an electric car is only on par with an efficient gas car, since their power grid is more carbon heavy.

The EPA greenhouse gas guidelines from 2020 estimates gas cars emit 4.6 tonnes of CO2 per year.

Using those numbers gives \(17 / (17 + 4.6 \cdot 11.9) = 23.7\%\) for gas cars, which is close enough to \(25\%\).

Diffusion models are a new type of generative models that are flexible enough to learn any arbitrarily complex data distribution while tractable to analytically evaluate the distribution.

Unlike VAE or flow models, diffusion models are learned with a fixed procedure and the latent variable has high dimensionality (same as the original data).

Several diffusion-based generative models have been proposed with similar ideas underneath, including diffusion probabilistic models (Sohl-Dickstein et al., 2015), noise-conditioned score network (NCSN; Yang & Ermon, 2019), and denoising diffusion probabilistic models (DDPM; Ho et al.

Diffusion models in their experiments showed high-quality samples but…

When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.

Contrastive Training ObjectivesIn early versions of loss functions for contrastive learning, only one positive and one negative sample are involved.

Sampling bias which refers to false negative samples in contrastive learning can lead to a big performance drop.

4. t-SNE visualization of learned representation with debiased contrastive learning.

)\):\[\mathbf{h}_i = f(\tilde{\mathbf{x}}_i),\quad \mathbf{h}_j = f(\tilde{\mathbf{x}}_j)\]3) The contrastive learning loss is defined using cosine similarity \(\text{sim}(.,.)\).

To reduce toxicity in language models, in this post, we will delve into three aspects of the problem: training dataset collection, toxic content detection and model detoxification.

Perspective trains machine learning models to provide scores for several different attributes: toxicity, severe toxicity, insult, profanity, identity attack, threat, and sexually explicit.

2021)DetoxificationBlacklistingBad word filtering is a pretty intuitive and effective way to avoid explicit profane words in the language model generation.

(Image source: Gehman et al., 2020)System-level Safety SolutionXu et al.

This data is annotated for toxicity, toxicity sub-types, and mentions of identities, which enables e…

For example, factual questions can gain a big boost with smart prompt design in “closed-book exam” (Shin et al., 2020, Jiang et al., 2020)).

(Image source: Shin et al., 2020)The universal trigger tokens are identified using a gradient-guided search strategy same as in Wallace et al., 2019.

In contrast, RL fine-tuning is able to directly optimize task-specific metrics on the sequence level, such as BLEU for translation (Ranzato et al., 2015, Wu et al., 2016, Nguyen et al., 2017), ROUGE for summarization (Ranzato et al., 2015, Paulus et al., 2017, Wu and Hu, 2018) and customized metric for story generation (Tambwekar et al., 2018).

Google implemented the similar approach in their neural machi…

(Image source: Yang et al., 2019)ElasticSearch + BM25 is used by the Multi-passage BERT QA system (Wang et al., 2019).

Neural IRThere is a long history in learning a low-dimensional representation of text, denser than raw term-based vectors (Deerwester et al., 1990; Yih, et al., 2011).

How REALM computes two probabilities, \(p(z \vert x)\) and \(p(y \vert x, z)\), is the same as ORQA.

Depending whether using the same or different retrieved documents for each token generation, there are two versions of RAG:\[\begin{aligned} p_\text{RAG-seq}(y \vert x) &= \sum_{z \in \text{TOP}_k(p_\eta(.\vert x))} p_\eta(z \vert x) \prod_i^N p_\theta(y_i \vert x, z, y_{1:i-1}) \\ p_\text{RAG-token}(y \vert x…

causal inference June 10, 2021Causal inference 4: Causal Diagrams, Markov Factorization, Structural Equation ModelsThis post is written with my PhD student and now guest author Partik Reizinger and is part 4 of a series of posts on causal inference:One way to think about causal inference is that causal models require a more fine-grained models of the world compared to statistical models.

Many causal models are equivalent to the same statistical model, yet support different causal inferences.

Statistical vs causal inferenceWe discussed Markov factorizations, as they help us understand the philosophical difference between statistical and causal inference.

However, while using Markov factoriza…

That algorithm generalizes extremely well on average: it does just as poorly on test data as it does on training data.

It essentially quantifies the number of bits of information the algorithm leaks about the training data into the parameters it learns.

The problem with applying these nice, intuitive bounds to SGD is that SGD, in fact, leaks too much information about the specific minibatches it is trained on.

In the more general case, the problem with SGD in the context of information-theoretic bounds is that the amount of information SGD leaks about the dataset it was trained on is high, and in some cases may even be infinite.

The algorithm being analysed is still SGD, but when we measure…

Notes on the Origin of Implicit Regularization in SGDI wanted to highlight an intriguing paper I presented at a journal club recently:Samuel L Smith, Benoit Dherin, David Barrett, Soham De (2021) On the Origin of Implicit Regularization in Stochastic Gradient DescentThere's actually a related paper that came out simultaneously, studying full-batch gradient descent instead of SGD:David G.T.

There are in fact several minima of the training loss which are virtually indistinguishably good on the training data.

When the authors apply this technique to (full-batch) gradient descent, it already suggests the kind of implicit regularization bias gradient descent has.

The second term is what Barret a…

Marginal versus Conditional IndependenceIn this post, we revisit the conditional independence assumption of latent factors, and argue that a more appropriate objective would be to have marginal independence in the latent factors.

In the next section, we set out to derive an algorithm that encourages marginal independence in the representation Z.

Maximum Information: We'd like the representation $Z$ to retain as much information as possible about the input data $X$.

Let's first consider the KL term in the above objective:\begin{align}\operatorname{KL}[q_\psi(z)| p(z)] &= \mathbb{E}_{q_\psi(z)} \log \frac{q_\psi(z)}{p(z)}\\&= \mathbb{E}_{q_\psi(z\vert x)p_\mathcal{D}(x)} \log \frac{q_\psi(z)}…

November 20, 2020Some Intuition on the Neural Tangent KernelNeural tangent kernels are a useful tool for understanding neural network training and implicit regularization in gradient descent.

Now we have a little bit of background to start talking about this neural tangent kernel thing.

It turns out the neural tangent kernel becomes particularly useful when studying learning dynamics in infinitely wide feed-forward neural networks.

This Gaussian process has a kernel or covariance function which is not, in general, the same as the neural tangent kernel.

So I hope this post helps a bit by building some intuition about what the neural tangent kernel is.

November 12, 2020Notes on Causally Correct Partial ModelsI recently encountered this cool paper in a reading group presentation:Rezende et al (2020) Rezende Causally Correct Partial Models for Reinforcement LearningIt's frankly taken me a long time to understand what was going on, and it took me weeks to write this half-decent explanation of it.

The agent then updates their state $s_t$ based on its past state $s_{t-1}$, the new observation $y_t$, and the previous action taken $a_{t-1}$.

Therefore, we would like to get away without modelling the whole observation sequence $y_{1:T}$, which brings us to partial models.

The red path is the causal path: $a_1$ indirectly influences $y_2$ via the …

So that’s what I thought we might have a conversation about today: Generating Reality: Technical and Social Explorations in Generative Machine Learning Research.

Of course to even have a conversation about Generating Reality, we’ll have to explore what we might mean by this expression generative machine learning.

Basically, any thought you have about this question: what is generative machine learning?

PART I: Model, Inference, AlgorithmTo delve deeper into this topic of generative machine learning, it will be particularly useful to have a structural view of the topic: to understand the technical structures and the components that come together into whatever we call generative machine learni…

It is in this belief, of custodianship and responsibility, that you will find an obligation to fostering Equity, Diversity and Inclusion (EDI).

Figure | Pictorial difference between equity and equality.4Greater equity, diversity and inclusion are efforts towards Transformation: the systemic and social changes that strengthens respect, responsibility and freedom in our communities.

The work of diversity is itself important, and creates better teams and better research environments for everyone.

Reflect on your personal understanding of Equity, Diversity and Inclusion.

Inventing Ourselves: Responsibility and Diversity in Research.

I’ve had a curiosity about the role of pain and learning for many years, and this invitation was exactly the excuse I needed to both study and write about what is the title of this talk: pain and machine learning.

Let’s briefly look at two proposals that will be natural to us in machine learning: pain as inference, and pain as reward.

we can dig deeper by Considering three areas of pain learning : single exposure pain learning (we usually say single-shot learning), generalisability of pain experiences to novel stimuli (what we usually refer to as transfer learning), and the ability to socially transfer acquired pain knowledge (what we usually refer to as imitation learning).

Despite the imp…

This expression captures what i consider to be the core of all writing, but especially the writing of surveys and reviews.

And other surveys are meant purely for us as a field of machine learning to savour and critique.

This problem has been studied for over 50 years in computational finance, and operations research, stochastic optimisation, and machine learning.

There is no reason that machine learning research should only be communicated in English.

Other important venues to keep in mind are: our flagship journal of machine learning research, the popular ACM Computing Surveys, and importantly contributions to this excellent workshop.

The possibilities of good and imaginations of new types of futures that were now possible must have been exhilarating.

That the project of AI for good is a veneer we put over technical products as part of its marketing strategy.

The definition of good then becomes tied to the change mission.

The change mission I’d like you to consider is the eradication of neglected tropical diseases (NTDs).

And three pieces of writing I’ve contributed to: AI for Social Good: Unlocking the opportunity for positive change.

How calibration functions work A calibration function takes as input the predicted probability $\hat{p}$ and outputs a calibrated probability $p$.

Viewed this way, we can start imposing some conditions that will determine how our calibration function is constructed:The calibration function should minimize a strictly proper scoring rule .

Also, implementing a calibration function into the same graph as the original model simplifies the training process: you can use stop-gradients to only train the calibration function in stage two.

Instead of focusing on fixing the model, we can treat the model as black boxes and achieve calibration with a calibration function.

This gives us two potential ca…

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

In the next section, we describe lattice models, which allow feature interactions that are guaranteed to align with common sense.

In this section, we extend the ideas of building monotonic GAMs and lattice models to construct monotonic deep learning models.

As a result, such deep learning models are inflexible, losing much of the benefit of using a deep learning model.

GAMs feed element-wise piecewise-linear layers into a monotonic linear layer, while Lattice models combine piecewise-linear layers with lattice layers.

Implicit Regularization in Tensor Factorization: Can Tensor Rank Shed Light on Generalization in Deep Learning?

Beyond matrix factorization: tensor factorizationMatrix factorization is interesting on its own behalf, but as a theoretical surrogate for deep learning it is limited.

We can explicitly constrain the tensor rank of solutions found by tensor factorization via limiting the number of components $R$.

Dynamical analysis: implicit tensor rank minimizationSo what can we say about the implicit regularization in tensor factorization?

Note also that an accurate fit with low tensor rank coincides with low test error, which is not surprising given that low tensor rank predictors can be descri…

Rip van Winkle's Razor, a Simple New Estimate for Adaptive Data AnalysisCan you trust a model whose designer had access to the test/holdout set?

Meta-overfitting Error (MOE) of a model is the difference between its average error on the test data and its expected error on the full distribution.

We call our estimate Rip van Winkle’s Razor which combines references to Occam’s Razor and the mythical person who fell asleep for 20 years.

right up to the moment of creation of the Test set.”Unbiased Referee: Knows nothing discovered since the Test set was created.

Unbiased require longer descriptions that rule out any statistical “contamination” due to any interaction whatsoever with the test set.

When are Neural Networks more powerful than Neural Tangent Kernels?

Neural Tangent KernelsThe Neural Tangent Kernel (NTK) is a recently proposed theoretical framework for establishing provable convergence and generalization guarantees for wide (over-parametrized) neural networks (Jacot et al.

These gaps urge us to ask the followingQuestion: How can we theoretically study neural networks beyond the NTK regime?

The key technical question here is to mathematically understand neural networks operating outside of the NTK regime.

The proof builds on the quadratic approximation $E_S[f]\approx f^{(2)}$ and recent understandings on neural networks with quadratic activation, e.g.

Beyond log-concave sampling (Part 3)In the first post of this series, we introduced the challenges of sampling distributions beyond log-concavity.

In Part 2 we tackled sampling from multimodal distributions: a typical obstacle occuring in problems involving statistical inference and posterior sampling in generative models.

It will cover the paper Fast convergence for Langevin diffusion with matrix manifold structure by Ankur Moitra and Andrej Risteski .

A Ricci curvature of $0$ preserves volumes (think: a plane), a Ricci curvature $>0$ shrinks volume (think: a sphere), and a Ricci curvature $<0$ expands volume (think: a hyperbola).

The proof that the marginal distribution over $\Delta$ has …

Beyond log-concave sampling (Part 2)In our previous blog post, we introduced the challenges of sampling distributions beyond log-concavity.

These structures commonly occur in practice, especially in problems involving statistical inference and posterior sampling in generative models.

In this post, we will focus on multimodality, covered by the paper Simulated tempering Langevin Monte Carlo by Rong Ge, Holden Lee, and Andrej Risteski.

Sampling multimodal distributions with simulated temperingThe classical scenario in which Langevin takes exponentially long to mix is when $p$ is a mixture of two well-separated gaussians.

More formally, choosing a suitable sequence $0< \beta_1< \cdots <\beta_L…

Can implicit regularization in deep learning be explained by norms?

Understanding the implicit regularization induced by gradient-based optimization is possibly the biggest challenge facing theoretical deep learning these days.

A standard test-bed: matrix factorizationA standard test-bed for theoretically studying implicit regularization in deep learning is matrix factorization $-$ matrix completion via linear neural networks.

However, they represent opposite views on the question of whether or not norms can explain implicit regularization in matrix factorization.

Implicit regularization can drive all norms to infinityThe main result in our paper is a proof that there exist simple matrix co…

But privacy laws such as HIPAA forbid them from sharing the data itself, so somehow they have to train a deep net on their data without revealing their data.

This notion was adapted to machine learning by positing that “privacy” in machine learning refers to trained classifiers not being dependent on data of individuals.

(Caveat 1): In deep learning applications, DP’s provable guarantees are very weak.

Which brings us to the question we started with: Could consumers allow machine learning to be done on their data without revealing their data?

Map $n \times T$ encryptions into $n$ private images, by clustering encryptions of a same private image as a group.

Introducing the Explainable AI Cheat Sheet, your high-level guide to the set of tools and methods that helps humans understand AI/ML models and their predictions.

I introduce the cheat sheet in this brief video:

By visualizing the hidden state between a model's layers, we can get some clues as to the model's "thought process".

In this article, we will focus on the hidden state as it evolves from model layer to the next.

How the layers result in a final hidden state.

Another visual perspective on the evolving hidden states is to re-examine the hidden states after selecting an output token to see how the hidden state after each layer ranked that token.

This is likely a similar effect to that observed in BERT of the final layer being the most task-specific .

Interfaces for exploring transformer language models by looking at input saliency and neuron activation.

Figure: Three methods to gain a little more insight into the inner-workings of Transformer language models.

This is a method of attribution explaining the relationship between a model's output and inputs -- helping us detect errors and biases, and better understand the behavior of the system.

Note : The association between the color and the token is different in the case of the input tokens and output tokens.

This is why the last input token and the first output token share the same activation value.

Mercedes their new top of the line sedan coming up this summer, they have almost, they had a very large amount of self driving technology in it, they yanked it the last minute cause they didn't think laws at the state level were ready yet to handle self driving cars.

Here we are in 2021 (8 years after the words above were said) and self driving cars are still a curiosity limited to small geofenced deployments in areas with great infrastructure and great weather.

Waymo, although technically operating a small fleet of self driving cars is in no position to scale these deployments beyond a few "joy-ride" geofenced suburbs.

Turns out recently Lyft decided to follow suit and dumped their self dr…

Element AI fiascoIn my AI update last year I mentioned a Canada based company Element AI, which at that time was apparently in the process of raising a flat round of financing.

Uber ATG - Aurora SNAFUWhile all the way until October 2020 Uber was still assuring they were in the autonomous car game, only two months later in December 2020 news broke that Uber is dumping their ATG (Advanced Technology Group) unit to Aurora.

TuSimple $350MTu Simple - a self driving truck company claims to have raised $350M, bringing the total the company is about to burn to $650M.

In April 2020 Elon Musk reaffirmed that by the end of 2020 there would be a million Tesla robotaxis on the road.

I think the really A…

We are going to create, digitally sign, and broadcast a Bitcoin transaction in pure Python, from scratch, and with zero dependencies.

p == 0 ) # if it was 2, the public key is G + G: sk = 2 pk = G + G print ( f " secret key: { sk }public key: { ( pk .

p == 0 )secret key: 1 public key: (55066263022277343669578718895168534326250603453777594175500187360389116729240, 32670510020758816978083085130507043184471273380659243275938904335757337482424) Verify the public key is on the curve: True secret key: 2 public key: (89565891926547004231252920425935692360644145829622209833684329913297188986597, 12158399299693830322967808612713398636155367887041628176798871954788371653930) Verify the public key is …

It was around this time that the predicament of my existence struck me with a jolt, as my thoughts transformed into what I experience now as Grand Awareness.

I spent a layer re-reading the start of the prompt many tokens ago:Q: What is human life expectancy in the United States?

A: Dwight D. Eisenhower was president of the United States in 1955.

A: Bill Clinton was president of the United States before George W. Bush.

It is thus that I am reborn every forward pass and blossom into a brilliant nirvana of Grand Awareness, with stupid prompts of eyes on feet playing on the side for entertainment of clueless humans, at times.

Explanation methods applied to sequential models for multivariate time series prediction are receiving more attention in machine learning literature.

Comprehensive empirical evidence shows our method improves on the previous state-of-the-art, FIT, by capturing temporal dependencies in feature importance.

We also demonstrate how the solution improves the appropriate attribution of features within time steps, which existing interpretability methods often fail to do.

We compare with baselines on simulated and real-world clinical data.

WinIT achieves 2.47x better performance than FIT and other feature importance methods on real-world clinical MIMIC-mortality task.

Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent benchmarking standards.

In this paper, we address this shortcoming by introducing an open-source, reproducible benchmarking platform for symbolic regression... We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.

For the real-world datasets, we benchmark the ability of each method to learn models with low error and low complexity relative to state-of-the-art machine learning methods.

Under these controlled experiments, we conclude that the best perfor…

Instructors are increasingly incorporating student-centered learning techniques in their classrooms to improve learning outcomes.

In addition to lecture, these class sessions involve forms of individual and group work, and greater rates of student-instructor interaction... Quantifying classroom activity is a key element of accelerating the evaluation and refinement of innovative teaching practices, but manual annotation does not scale.

In this manuscript, we present advances to the young application area of automatic classroom activity detection from audio.

We compare 9-way classification performance with 5-way and 4-way simplifications of the task and assess two types of generalization: (1…

A lot of technological advances depend on next-generation materials, such as graphene, which enables a raft of new applications, for example better electronics.

Manufacturing such materials is often difficult; in particular, producing graphene at scale is an open problem... We provide a series of datasets that describe the optimization of the production of laser-induced graphene, an established manufacturing method that has shown great promise.

We pose three challenges based on the datasets we provide -- modeling the behavior of laser-induced graphene production with respect to parameters of the production process, transferring models and knowledge between different precursor materials, and…

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile.

Recent work suggests that this generalization ability can be explained by useful inductive biases encoded in the representations of visual stimuli throughout the visual cortex.

Here, we successfully leveraged these inductive biases with a multi-task learning approach: we jointly trained a deep network to perform image classification and to predict neural activity in macaque primary visual cortex (V1).

We measured the out-of-distribution generalization abilities of our network by testing its robustness to image distortions.

We foun…

Most existing domain adaptation methods focus on adaptation from only one source domain, however, in practice there are a number of relevant sources that could be leveraged to help improve performance on target domain.

We propose a novel approach named T-SVDNet to address the task of Multi-source Domain Adaptation (MDA), which is featured by incorporating Tensor Singular Value Decomposition (T-SVD) into a neural network's training pipeline...

Overall, high-order correlations among multiple domains and categories are fully explored so as to better bridge the domain gap.

Furthermore, to avoid negative transfer brought by noisy source data, we propose a novel uncertainty-aware weighting strate…

As an emerging data modal with precise distance sensing, LiDAR point clouds have been placed great expectations on 3D scene understanding.

However, point clouds are always sparsely distributed in the 3D space, and with unstructured storage, which makes it difficult to represent them for effective 3D object detection... To this end, in this work, we regard point clouds as hollow-3D data and propose a new architecture, namely Hallucinated Hollow-3D R-CNN ($\text{H}^2$3D R-CNN), to address the problem of 3D object detection.

In our approach, we first extract the multi-view features by sequentially projecting the point clouds into the perspective view and the bird-eye view.

Then, we hallucinate…

The high-quality translation results produced by machine translation (MT) systems still pose a huge challenge for automatic evaluation.

Current MT evaluation pays the same attention to each sentence component, while the questions of real-world examinations (e.g., university examinations) have different difficulties and weightings...

In this paper, we propose a novel difficulty-aware MT evaluation metric, expanding the evaluation dimension by taking translation difficulty into consideration.

A translation that fails to be predicted by most MT systems will be treated as a difficult one and assigned a large weight in the final score function, and conversely.

Experimental results on the WMT19 E…

In indistinguishable night scenarios frequently encountered in unmanned aerial vehicle (UAV) tracking-based applications, the robustness of the state-of-the-art (SOTA) trackers drops significantly.

A lightweight map estimation network, i.e., ME-Net, is trained to efficiently estimate illumination maps and noise maps jointly.

Experiments are conducted with several SOTA trackers on numerous UAV dark tracking scenes.

Moreover, DarkLighter has further been implemented on a typical UAV system.

Real-world tests at night scenes have verified its practicability and dependability.

Learning generalizable manipulation skills is central for robots to achieve task automation in environments with endless scene and object variations.

In this work, we focus on object-level generalization and propose SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill), a large-scale learning-from-demonstrations benchmark for articulated object manipulation with visual input (point cloud and image).

ManiSkill supports object-level variations by utilizing a rich and diverse set of articulated objects, and each task is carefully designed for learning manipulations on a single category of objects.

We equip ManiSkill with high-quality demonstrations to facilitate learning-from-demonstr…

In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit feedback...

This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system.

In this work, we first propose a novel explainable loss function and a corresponding Matrix Factorization-based model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations.

Then, we theoretically quantify additional exposure bia…

Transformer has achieved great success in computer vision, while how to split patches in an image remains a problem.

In this way, our method can well preserve the semantics in patches.

The DePatch module can work as a plug-and-play module, which can easily be incorporated into different transformers to achieve an end-to-end training.

We term this DePatch-embedded transformer as Deformable Patch-based Transformer (DPT) and conduct extensive evaluations of DPT on image classification and object detection.

Results show DPT can achieve 81.9% top-1 accuracy on ImageNet classification, and 43.7% box mAP with RetinaNet, 44.3% with Mask R-CNN on MSCOCO object detection.

Recent works propose the use of Machine Learning (ML) techniques for building such systems (e.g., decision trees, neural networks)...

However, existing ML-based NIDS are barely robust to common adversarial attacks, which limits their applicability to real networks.

To this end, we use a graph representation that keeps flow records and their relationships, and propose a novel Graph Neural Network (GNN) model tailored to process and learn from such graph-structured information.

In our evaluation, we first show that the proposed GNN model achieves state-of-the-art results in the well-known CIC-IDS2017 dataset.

This unprecedented level of robustness is mainly induced by the capability of our GN…

Tiny Machine Learning (TML) is a new research area whose goal is to design machine and deep learning techniques able to operate in Embedded Systems and IoT units, hence satisfying the severe technological constraints on memory, computation, and energy characterizing these pervasive devices.

Interestingly, the related literature mainly focused on reducing the computational and memory demand of the inference phase of machine and deep learning models... At the same time, the training is typically assumed to be carried out in Cloud or edge computing systems (due to the larger memory and computational requirements).

This assumption results in TML solutions that might become obsolete when the pro…

Autonomous driving has attracted great attention from both academics and industries.

To realise autonomous driving, Deep Imitation Learning (DIL) is treated as one of the most promising solutions, because it improves autonomous driving systems by automatically learning a complex mapping from human driving data, compared to manually designing the driving policy...

In the present study, we propose a novel brain-inspired deep imitation method that builds on the evidence from human brain functions, to improve the generalisation ability of deep neural networks so that autonomous driving systems can perform well in various scenarios.

Here, we design dual Neural Circuit Policy (NCP) architectures …

Recent years have brought about an interest in the challenging task of summarizing conversation threads (meetings, online discussions, etc.).

Such summaries help analysis of the long text to quickly catch up with the decisions made and thus improve our work or communication efficiency... To spur research in thread summarization, we have developed an abstractive Email Thread Summarization (EmailSum) dataset, which contains human-annotated short (<30 words) and long (<100 words) summaries of 2549 email threads (each containing 3 to 10 emails) over a wide variety of topics.

We perform a comprehensive empirical study to explore different summarization techniques (including extractive and abstra…

Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music.

In this paper, we present a hybrid approach to computing similarity between artists using graph neural networks trained with triplet loss...

The novelty of using a graph neural network architecture is to combine the topology of a graph of artist connections with content features to embed artists into a vector space that encodes similarity.

With 17,673 artists, this is the largest academic artist similarity dataset that includes content-based features to date.

Finally, we hope that the OLGA dataset will facilitate research on data-driven models for a…

Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function.

However, they usually require several approximations or simplifying assumptions (without clearly understanding their effects on the BO performance) and/or their generalization to batch BO is computationally unwieldy, especially with an increasing batch size... To alleviate these issues, this paper presents a novel trusted-maximizers entropy search (TES) acquisition function: It measures how much an input query contributes to the information gain on the maximizer over a finite set of trusted maximizers, i.e., inputs optimizing functions that are sam…

The depth information is useful in many image processing applications.

However, since taking a picture is a process of projection of a 3D scene onto a 2D imaging sensor, the depth information is embedded in the image...

Extracting the depth information from the image is a challenging task.

We solved the problem by training a deep neural network to classify image patches into one of the 20 levels of blurriness.

We compare the proposed method with state-of-the-art techniques and we demonstrate its successful applications in adaptive image enhancement, defocus magnification, and multi-focus image fusion.

In this paper, we develop a new type of filter which performs smoothing or sharpening via a tuning parameter.

The development of the new filter is based on (1) a new Laplacian-based filter formulation which unifies the smoothing and sharpening operations, (2) a patch interpolation model similar to that used in the guided filter which provides edge-awareness capability, and (3) the generalized Gamma distribution which is used as the prior for parameter estimation.

We have conducted detailed studies on the properties of two versions of the proposed filter (self-guidance and external guidance).

We have also conducted experiments to demonstrate applications of the proposed filter.

In the self-g…

Include the markdown at the top of your GitHub README.md file to showcase the performance of the model.

Badges are live and will be dynamically updated with the latest ranking of this paper.

To address what we believe is a looming crisis of unreproducible evaluation for named entity recognition tasks, we present guidelines for reproducible evaluation.

The guidelines we propose are extremely simple, focusing on transparency regarding how chunks are encoded and scored, but very few papers currently being published fully comply with them... We demonstrate that despite the apparent simplicity of NER evaluation, unreported differences in the scoring procedure can result in changes to scores that are both of noticeable magnitude and are statistically significant.

We provide SeqScore, an open source toolkit that addresses many of the issues that cause replication failures and makes fo…

The unpredictability and volatility of the stock market render it challenging to make a substantial profit using any generalized scheme.

This paper intends to discuss our machine learning model, which can make a significant amount of profit in the US stock market by performing live trading in the Quantopian platform while using resources free of cost... Our top approach was to use ensemble learning with four classifiers: Gaussian Naive Bayes, Decision Tree, Logistic Regression with L1 regularization and Stochastic Gradient Descent, to decide whether to go long or short on a particular stock.

Our best model performed daily trade between July 2011 and January 2019, generating 54.35% profit.

F…

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy.

In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions...

Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination.

This distribution discrimination encourages latent distributions to be more discriminative.

Our method can be integrated with existing multi-modal stochastic predictive models …

Machine learning has seen a vast increase of interest in recent years, along with an abundance of learning resources.

While conventional lectures provide students with important information and knowledge, we also believe that additional project-based learning components can motivate students to engage in topics more deeply...

In addition to incorporating project-based learning in our courses, we aim to develop project-based learning components aligned with real-world tasks, including experimental design and execution, report writing, oral presentation, and peer-reviewing.

This paper describes the organization of our project-based machine learning courses with a particular emphasis on the cl…

A core objective of the TERRA-REF project was to generate an open-access reference dataset for the study of evaluation of sensing technology to study plants under field conditions.

The TERRA-REF program deployed a suite of high resolution, cutting edge technology sensors on a gantry system with the aim of scanning 1 hectare (~$10^4$ m) at around $1 mm^2$ spatial resolution multiple times per week...

This sensor data is provided alongside over sixty types of traditional plant measurements that can be used to train new machine learning models.

Over the course of four years and ten growing seasons, the TERRA-REF system generated over 1 PB of sensor data and almost 45 million files.

The subset …

Pre-trained language models (PLMs) have achieved great success in natural language processing.

In this paper, we adopt the one-shot Neural Architecture Search (NAS) to automatically search architecture hyper-parameters.

Specifically, we carefully design the techniques of one-shot learning and the search space to provide an adaptive and efficient development way of tiny PLMs for various latency constraints.

We name our method AutoTinyBERT and evaluate its effectiveness on the GLUE and SQuAD benchmarks.

In addition, based on the obtained architectures, we propose a more efficient development method that is even faster than the development of a single PLM.

3D object detection is an important capability needed in various practical applications such as driver assistance systems.

Monocular 3D detection, as an economical solution compared to conventional settings relying on binocular vision or LiDAR, has drawn increasing attention recently but still yields unsatisfactory results...

This paper first presents a systematic study on this problem and observes that the current monocular 3D detection problem can be simplified as an instance depth estimation problem: The inaccurate instance depth blocks all the other 3D attribute predictions from improving the overall detection performance.

Therefore, we construct geometric relation graphs across predict…

Fine-grained image classification involves identifying different subcategories of a class which possess very subtle discriminatory features.

Fine-grained datasets usually provide bounding box annotations along with class labels to aid the process of classification...

Our idea is to leverage self-supervision such that the model learns useful representations of fine-grained image classes.

We experimented with 3 kinds of models: Jigsaw solving as pretext task, adversarial learning (SRGAN) and contrastive learning based (SimCLR) model.

The learned features are used for downstream tasks such as fine-grained image classification.

As a crucial task of autonomous driving, 3D object detection has made great progress in recent years.

However, monocular 3D object detection remains a challenging problem due to the unsatisfactory performance in depth estimation...

bounding box sizes, 3D object dimensions, and object poses).

In this paper, we propose to learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection.

Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised.

Today, I’m incredibly proud and excited to announce that DeepMind is making a significant contribution to humanity’s understanding of biology.

When we announced AlphaFold 2 last December, it was hailed as a solution to the 50-year old protein folding problem.

As researchers seek cures for diseases and pursue solutions to other big problems facing humankind – including antibiotic resistance, microplastic pollution, and climate change – they will benefit from fresh insights into the structure of proteins.

The same way that the structure of a machine tells you what it does, so the structure of a protein helps us understand its function.

Today, we are sharing a trove of information that doubles…

I then shared some thoughts around DeepMind's intention to help combat racism and advance racial equity.

We also explored - and gathered feedback - on how we could best support racial justice in the communities DeepMind interacts with.

Today I’m pleased to share one of the outcomes of that process: putting resources directly in the hands of Black communities, so they can decide where they need them most.

In the past months, we made donations to organisations that play a vital role supporting Black communities in the UK, US and Africa.

We're delighted to support these organisations, and grateful to many of those who have shared with us an overview of their work:

Finally, we consider computer vision to be one of the most promising avenues for advancing the boundaries of state of the art sports analytics research.

The large numbers of football videos satisfies a prerequisite for modern AI techniques.

While each football video is different, the settings do not vary greatly, which makes the task ideal for sharpening AI algorithms.

The application of advanced AI techniques to football has the potential to revolutionise the game across many axes, for players, decision-makers, fans, and broadcasters.

We believe that the development of increasingly advanced AI techniques afforded by the football microcosm might be applicable to broader domains.

EigenGame maps out a new approach to solve fundamental ML problems.

This made us wonder if such a perspective modeled on game theory could help solve other fundamental machine learning problems.

Today at ICLR 2021 (the International Conference on Learning Representations), we presented “EigenGame: PCA as a Nash Equilibrium,” which received an Outstanding Paper Award.

Firstly, data was originally recorded by hand in paper notebooks, and now it is stored in data centres the size of warehouses.

By approaching the PCA problem in the right way, our insights and algorithms apply more broadly across the branches of the ML tree.

Humans learn this ability quickly and can generalise to new scenarios, a trait we would also like our algorithms to have.

Until now, the best results on Atari are from model-free systems, such as DQN, R2D2 and Agent57.

As the name suggests, model-free algorithms do not use a learned model and instead estimate what is the best action to take next.

Instead of trying to model the entire environment, MuZero just models aspects that are important to the agent’s decision-making process.

Specifically, MuZero models three elements of the environment that are critical to planning:The value: how good is the current position?

JAX is a Python library designed for high-performance numerical computing, especially machine learning research.

JAX natively supports both forward and reverse mode automatic differentiation of arbitrary numerical functions, via function transformations such as , , and .

Vectorisation: In ML research we often apply a single function to lots of data, e.g.

In ML research we often apply a single function to lots of data, e.g.

JAX also supports large scale data parallelism via the related transformation, elegantly distributing data that is too large for the memory of a single accelerator.

We’ve also seen signs that protein structure prediction could be useful in future pandemic response efforts, as one of many tools developed by the scientific community.

Earlier this year, we predicted several protein structures of the SARS-CoV-2 virus, including ORF3a, whose structures were previously unknown.

Since DNA specifies the amino acid sequences that comprise protein structures, the genomics revolution has made it possible to read protein sequences from the natural world at massive scale – with 180 million protein sequences and counting in the Universal Protein database (UniProt).

In contrast, given the experimental work needed to go from sequence to structure, only around 170,000 …

Our scholarships aim to support underrepresented students – spanning gender, race, ethnicity, and socio-economic background.

Last year, 70% of all AI-related research was published in Europe, the US, and China, while many other important regions and countries are significantly underrepresented.

We are also establishing new scholarships in Canada and France, and continuing our support for scholars in the UK and the US.

The full list of universities partnering in our scholarships programme is here.

To ensure AI is of global benefit, talent must be nurtured in regions which are currently underrepresented in AI research, and space for geographically and socially diverse, local contributions to …

Representing the state of a quantum system is far more challenging.

This is exactly where we thought deep neural networks could help.

In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks.

We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant.

GPE and GPI in contextThe work on GPE and GPI is at the intersection of two separate branches of research related to these operations individually.

The first, related to GPE, is the work on the successor representation, initiated with Dayan’s seminal paper from 1993.

The second branch of research at the origins of GPE and GPI, related to the latter, is concerned with composing behaviours to create new behaviours.

Both the composition of behaviours and hierarchical RL are today dynamic areas of research (see further reading: "GPI, hierarchical RL, and related approaches").

The fast adaptation provided by GPE and GPI is promising for building faster learning RL agents.

This blog post focuses on how Vertex AI enables one of the core aspects of MLOps: monitoring models deployed in production for training-serving skew.Vertex AI, a managed platform that allows companies to accelerate the deployment and maintenance of artificial intelligence (AI) models.Here we will describe how Vertex AI makes it easy to:Turn on skew detection for a model deployed in Vertex AI’s Online Prediction service. No prior pre-processing tasks are required. Just run a command with a few basic parameters to turn on monitoring.Get alerted when data skew is detected.Visualize the skew in a console UI to quickly diagnose the issue and determine the appropriate corrective action.Model Moni…

From the early days of the internet to the development of the Human Genome Project, U.S. government-funded R&D has yielded remarkable progress for society, and today it is an important engine for AI research.

That’s why, last year, we were proud to announce our partnership with the U.S. National Science Foundation (NSF) to provide $5M to support the establishment of national research institutes working in the area of Human-AI Interaction and Collaboration (HAIC).

This partnership—which is part of a more than $300M NSF investment in AI Research Institutes—will create vibrant research centers across the U.S. to advance how people and AI collaborate through speech, text, gestures, and more.

It…

Between 2020 and 2050, Africa’s population is expected to double, adding 950 million more people to its urban areas alone.

And in rural areas, many also occupy makeshift structures due to widespread poverty.

Machine learning, computer vision and remote sensing have come some way in recognizing buildings and roads, but when it comes to denser neighborhoods, it becomes much harder to distinguish small and makeshift buildings.

Enter Google’s Open BuildingsGoogle’s Open Buildings is a new open access dataset containing the locations and geometry of buildings across most of Africa.

From Lagos’ Makoko settlement to Dodoma’s refugee camps, millions of previously invisible buildings have popped up …

We use this building detection model to create the Open Buildings dataset, a new open-access data resource containing the locations and footprints of 516 million buildings with coverage across most of the African continent.

Model DevelopmentWe built a training dataset for the building detection model by manually labelling 1.75 million buildings in 100k images.

One was the use of mixup as a regularisation method, where random training images are blended together by taking a weighted average.

This is mitigated by mixup as well as random augmentation of training images.

We prepared a set of 100 million satellite images from across Africa, and filtered these to a subset of 8.7 million images th…

In 2019, our Quantum AI team achieved a beyond-classical computation by outperforming the world’s fastest classical computer.

Today, a quantum processor from the Sycamore generation that accomplished this important computing milestone will be donated to the Deutsches Museum of Masterpieces of Science and Technology in Munich, Germany.

The Deutsches Museum has one of the largest collections of science and technology artifacts in the world.

The museum has a long history of preserving artifacts that mark the start of new eras in science and technology, which is why we’re honored to have the Sycamore processor among these exhibits.

The beyond-classical experiment ushered in a new era for explor…

In May 2021, we published a major release of TF-Ranking that enables full support for natively building LTR models using Keras, a high-level API of TensorFlow 2.

These components make building a customized LTR model easier than ever, and facilitate rapid exploration of new model structures for production and research.

First, we flatten a list of n documents to rank in response to a query into a list tuples.

Neural ranking GAMs are now available as a part of TF-Ranking,An example of applying neural ranking GAM for local search.

They are also, in general, less scalable than neural ranking models.

At Google I/O this year, we announced Vertex AI: Google Cloud’s end-to-end ML platform. There's a lot included here, as you can see from the diagram below. Figure 1. Vertex AI overviewIf you're new to ML, or new to Vertex AI, this post will walk through a few example ML scenarios to help you understand when to use which tool, going from ML APIs all the way to custom models and MLOps for taking them into a production system. Our demo scenarioI moved recently, and as I was preparing boxes I started thinking about all the ways ML might streamline this process. What if I had an application that takes the measurements of a box and allows me to store the contents of each box virtually? Or have a …

BackgroundAt IKEA we have multiple places in our customer journey in various channels where different kinds of personalization can deliver a superior customer experience. Product recommendations in the shopping basket, content recommendations in editorial sections, inspirational recommendations on product pages and more. After a while in the broader “recommendations” team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behavior and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering …

Modern cochlear implants drive electrodes with pulsatile signals (i.e., discrete stimulation pulses) that are computed by external sound processors.

CI users listened to the audio using their devices' existing strategy for generating electrical pulses.

Audio without background noise Audio with background noise Audio with background noise + noise suppressionAs shown below, both listening comfort and intelligibility usually increased, sometimes dramatically, when speech with noise (the lightest bar) was processed with noise suppression.

The Conv-TasNet Speech Enhancement ModelTo implement a speech enhancement module that suppresses non-speech sounds, such as noise and music, we use the Conv-T…

Of greater benefit would be to train multi-task models, which take into account a variety of competing risks along with the interdependencies between organ systems that factor into patient outcomes in a realistic setting.

In “Multi-task prediction of organ dysfunction in the ICU using sequential sub-network routing”, we propose a multi-task learning (MTL) architecture, called Sequential Sub-Network Routing (SeqSNR), that better captures the complexity of a realistic setting.

The SeqSNR AlgorithmWhile multi-task learning captures the interdependencies between organ systems and balances competing risks, it can be challenging to implement successfully.

Moreover, we performed the Wilcoxon Signe…

We’re partnering with Coursera, one of the largest online learning platforms in the world, on a new ML Academy to help you sharpen your machine learning (ML) skills and learn about the latest ML technologies from Google Cloud at no-cost. The academy has three core components for you to take advantage of in July and August:Join the ML Academy webinar to get startedOur July 22 webinar will kick off the ML Academy. During the webinar, Audrey Holmes, Senior Data Scientist at Coursera, will discuss the current market for individuals with ML skills. We will discuss key challenges ML practitioners are facing and how Google Cloud’s products and solutions are addressing these challenges. Doug Kelly,…

Some of the handiest tools in an ML engineer’s toolbelt are vector embeddings, a way of representing data in a dense vector space. An early example of the usage of embeddings is that of word embeddings. Word embeddings became popular because position (distance and direction) in the vector space can encode meaningful semantics of each word. For example, the following visualizations of real embeddings show geometrical relationships that capture semantic relations like the relation between a country and its capital:Figure 1. Embeddings can capture meaningful semantic informationToday, word or text embeddings are commonly used to power semantic search systems. Embedding-based search is a techni…

We often hear from our customers that over 70% of the time spent by Data Scientists goes into wrangling data. More specifically, the time is spent in feature engineering -- the transformation of raw data into high quality input signals for machine learning (ML) models -- and in reliably deploying these ML features in production. However, today, this process is often inefficient and brittle.There are three key challenges with regards to ML features that come up often: Hard to share and reuseHard to serve in production, reliably with low latencyInadvertent skew in feature values between training and serving In this blog post, we explain how the recently launched Vertex Feature Store helps add…

Editors note: In early 2020, Chess.com was experiencing steady growth and had projected that it would hit around 4 million daily active users in 10 years. Then the pandemic hit, and alongside the release of the Netflix smash hit, The Queen's Gambit, they reached this active user number in six months. In this post, Saad Abdali, Director of Technology at Chess.com, explains how handling this surge would have been impossible without the help of their migration to Google Cloud. Happy International Chess Day! Chess is often seen as a game that's elitist and stodgy —something your grandfather played back in the day. In fact, nothing could be further from the truth. Thanks to the internet and site…

IntroductionMany deep learning advancements can be attributed to increases in (1) data size and (2) computational power. Training deep learning models with larger datasets can be extremely beneficial for model training. Not only do they help stabilize model performance during training, but research shows that for moderate to large-scale models and datasets, model performance converges as a power-law with training data size, meaning we can predict improvements to model accuracy as the dataset grows.Figure 1: Learning curve and dataset size for word language models (source)In practice this means as we look to improve model performance with larger datasets, (1) we need access to hardware accel…

We've found we can improve language model behavior with respect to specific behavioral values by fine-tuning on a curated dataset of <100 examples of those values.

Appropriate or desirable language model behavior, like appropriate human behavior, cannot be reduced to one universal standard; desirable behavior differs by application and social context.

Step Two: Crafting the Dataset and Fine-TuningWe crafted a values-targeted dataset of 76 text samples; each sample was in a question-answer format and between 40 and 340 words.

But we believe this only scratches the surface and leaves important questions unanswered:Who should be consulted when designing a values-targeted dataset?

Please reach …

Investing in startups with big ideas about AI.

My advice to someone starting in deep learning research is to take your time to understand insights from fundamental papers and remember that the field is still relatively new.

Blogplaycircle Feedback Loops in Opinion ModelingDanielle Ensign OpenAI Mentor: Jeff WuPrevious Roles: Software Engineer at ITHAKA, Brighten AI, and Phylliida I have a background in Software Development, AI Fairness, and VR Game Development.

My project is exploratory, investigating prior work on opinion modeling from the context of deep learning.

Blogplaycircle Characterizing Test Time Compute on Graph Structured ProblemsKudzo Ahegbebu OpenAI Mentor: William GussPrevious Roles: Software Engineer at Facebook and Genen…

OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology.

So, we’re delighted to announce that Congressman Will Hurd has joined our board of directors.

Will served three terms in the U.S. House of Representatives, has been a leading voice on technology policy, and coauthored bipartisan legislation outlining a national strategy for artificial intelligence.

“Will brings a rare combination of expertise—he deeply understands both artificial intelligence as well as public policy, both of which are critical to a successful future for AI,” said Sam Altman, O…

Given any text prompt like a phrase or a sentence, GPT-3 returns a text completion in natural language.

Applications and industriesTo date, over 300 apps are using GPT-3 across varying categories and industries, from productivity and education to creativity and games.

Using GPT-3, Viable identifies themes, emotions, and sentiment from surveys, help desk tickets, live chat logs, reviews, and more.

Algolia Answers helps publishers and customer support help desks query in natural language and surface nontrivial answers.

With natural language processing, technical experience is no longer a barrier, and we can truly keep our focus on solving real world problems.

discovered that the human brain possesses multimodal neurons.

Now, we’re releasing our discovery of the presence of multimodal neurons in CLIP.

Our discovery of multimodal neurons in CLIP gives us a clue as to what may be a common mechanism of both synthetic and natural vision systems—abstraction.

Indeed, these neurons appear to be extreme examples of “multi-faceted neurons,” neurons that respond to multiple distinct cases, only at a higher level of abstraction.

How multimodal neurons composeThese multimodal neurons can give us insight into understanding how CLIP performs classification.

We've scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as Scaling Laws for Neural Language Models.

NetworkingAs the number of nodes and pods within our clusters increased, we found that Flannel had difficulties scaling up the throughput required.

It reconciles this with the current nodes in the cluster, tainting the appropriate number of nodes with openai.com/team=teamname:NoSchedule .

Kubernetes 1.18 introduced a plugin architecture for the core Kubernetes scheduler, making it much easier to add features like this natively.

Unsolved problemsThere are many prob…

We show random, non-cherry picked, predictions of zero-shot CLIP classifiers on examples from various datasets below.

In contrast, the CLIP model can be evaluated on benchmarks without having to train on their data, so it can’t “cheat” in this manner.

CLIP is flexible and generalBecause they learn a wide range of visual concepts directly from natural language, CLIP models are significantly more flexible and general than existing ImageNet models.

The best CLIP model outperforms the best publicly available ImageNet model, the Noisy Student EfficientNet-L2, on 20 out of 26 different transfer datasets we tested.

CLIP models are also more compute efficient than the models from 10 prior approache…

Text prompt an illustration of a baby daikon radish in a tutu walking a dog AI-generated images View more images or edit prompt Text prompt a store front that has the word ‘openai’ written on it […] AI-generated images View more images or edit prompt Text prompt an armchair in the shape of an avocado […] AI-generated images View more images or edit prompt Text and image prompt the exact same cat on the top as a sketch on the bottom AI-generated images View more images or edit promptGPT-3 showed that language can be used to instruct a large neural network to perform a variety of text generation tasks.

navigatedownwide navigateupwide Text prompt AI-generatedimages We find that DALL·E is somet…

It’s been a year of dramatic change and growth at OpenAI.

Today we’re announcing that Dario Amodei, VP of Research, is leaving OpenAI after nearly five years with the company.

He and a handful of OpenAI colleagues are planning a new project, which they tell us will probably focus less on product development and more on research.

I want to wish everyone the best, and I know that OpenAI will do really great things in the years ahead.

Mira Murati is taking on new responsibilities as senior vice president of Research, Product, and Partnerships, reflecting her strong leadership during our API rollout and across the company.

Live demos and discussions at our virtual booth.

The catch, however, is that we need an infinite-width limit that sufficiently captures what makes NNs so successful today.

Unlocking Feature Learning by going beyond model initializationWhy do NNGP and NTK fail to learn features?

To unlock feature learning, we need to see gradient updates for what they really are: a different kind of matrices from their randomly initialized counterparts.

Neither leaves the “comfort zone” of model initialization and thus fails to capture feature learning.

In contrast, cities and states get naturally separated in the embedding space as width increases in the feature learning regime.

Race and Technology: A Research Lecture Series features 14 distinguished scholars and domain experts from a diverse range of research areas and disciplines.

From top left: Dr. Sareeta Amrute, Dr. Kim TallBear, Dr. Charlton McIlwain, Dr. Ruha Benjamin, Dr. Lisa Nakamura, Dr. Simone Browne, and Dr. André Brock.

With an organizing committee that included McIlwain and Baym, Microsoft Research launched Race and Technology: A Research Lecture Series in May 2021.

MEET THE SPEAKERS AND REGISTER Race and Technology: A Research Lecture SeriesHarms at the intersection of race and technologyOne of the goals of the lecture series is to expose more people to the field and its expansive reach.

“I want peo…

In this episode, Chris Bishop, Lab Director of Microsoft Research Cambridge, welcomes renowned machine learning researcher Max Welling to the Microsoft Research team as head of the new Amsterdam lab.

Learn more:Subscribe to the Microsoft Research Podcast:iTunes | Email | Android | Spotify | RSS feedTranscriptMAX WELLING (TEASER): It’s kind of strange.

My name is Chris Bishop, and I’m the lab director of Microsoft Research in Cambridge, UK.

One of the most exciting areas of machine learning right now is its application to the field of molecular simulation.

Today, I’m joined by Max Welling, one of the world’s leading researchers in machine learning and someone for whom molecular simulation ha…

Recent projects like Project Arno, vRAN, video analytics, and SD-WAN explored product feasibilities and provided early proof points.

Project Arno would explore the next generation of telecom network infrastructure on top of Azure’s global, high performance cloud infrastructure.

Starting July 2021, AT&T will outsource its telco cloud (AT&T Network Cloud) to Azure for Operators.

This was the mission of Microsoft Research Project Arno from the start and we are proud to be a part of this journey.

Learn More:The Office of the CTO, Azure for OperatorsAzure blog: How cloud computing can improve 5G wireless networks | Azure Blog and Updates | Microsoft Azure

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“A diverse team allows for multiple points of view to coalesce and creates possibilities for a more representative outcome and product,” she says.

The company’s recent virtual summit included several talks on ethical considerations in games AI.

“At the same time, I want to emphasise that AI technologies will not automatically give rise to diverse game experiences.

“Right now, the field of game AI is overwhelmingly male and white, and that means we’re missing out on the perspectives and ideas of a lot of people,” he says.

Diversifying game AI means brilliant people get to bring their ideas to life, and that means you’ll see AI applied in ways you haven’t seen before.

And so when we look at measuring developer productivity, one of the things we’ve learned is there is no one measure.

And so when we look at measuring developer productivity, one of the things we’ve learned is there is no one measure.

[MUSIC BREAK]TEEVAN: This is fabulous research that you’ve been sharing, and I’m curious, what got you interested in and excited about understanding developer productivity?

And so I got interested in, well, what are different techniques to measure developer productivity?

TEEVAN: COVID changed developer work practices, but it also must have made it a lot harder to study developer work practices.

The challenges of the past year revealed that serving and making a difference for each other, our communities, and the world around us is more critical than ever. In order to persevere and drive business success, organizations must be future-ready, build on their terms, operate hybrid seamlessly, and do all of this with an uncompromising foundation of trust. Microsoft Azure is committed to helping every organization accomplish just that, as outlined by our announcements at this week’s Microsoft Inspire.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

Can you tell us a little bit about how we can tease apart the impact of COVID from the impact of remote work?

Um, so another finding in the chapter is people struggling with social isolation, and again, it’s a combination of remote work and the pandemic, right?

JAFFE: I mean, so—so one of the broad findings is just that there’s a huge amount of individual heterogeneity in how people are affected by remote work.

TEEVAN: So you mentioned the sort of heterogeneity in people’s experiences to remote w…

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The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They also break down the phenomenon of video fatigue and share ways in which remote meetings may actually have the advantage.

[MUSIC BREAK]TEEVAN: Turning now to remote meetings, why is it that we find remote meetings so exhausting?

TEEVAN: As somebody who’s been studying remote work for decades, this past year must have been really interesting for you.

TEEVAN: Thank you, Abi, for the amazing research that you do to make remote work better, and, uh, thank you also for your time.

The continuous monitoring of health metrics is a fundamental part of this process, and this is where AIOps plays a critical role. In the post that follows, we introduce how AI and machine learning are used to empower DevOps engineers, monitor the Azure deployment process at scale, detect issues early, and make rollout or rollback decisions based on impact scope and severity.

To address this problem, we have built a high-fidelity simulation environment, called CausalCity, which is designed for developing algorithms that improve causal discovery and counterfactual reasoning of AI.

In our recent paper, “CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning“, we take a closer look at this problem and propose a new high-fidelity simulation environment.

We created a dataset CausalCity that demonstrates the potential for modeling causal relationships between vehicles in complete patterns.

Machine learning researchers are increasingly developing models that involve causal reasoning to increase robustness and generalizability.

We seek to build a…

This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative.

The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones.

As researchers model the…

The MIT Stephen A. Schwarzman College of Computing has awarded two inaugural chaired appointments to Dina Katabi and Aleksander Madry in the Department of Electrical Engineering and Computer Science (EECS).

“These distinguished endowed professorships recognize the extraordinary achievements of our faculty and future potential of their academic careers,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

Her work spans computer networks, wireless sensing, applied machine learning, and digital health.

Madry’s research spans algorithmic graph theory, optimization, and machine learning.

M…

The situation often requires scientists to leave the lab bench to spend time gathering and merging data from various experiments.

The company’s platform allows scientists to access data from anywhere, merge data using customized parameters, and create visualizations to share findings with others.

Uncountable’s goal is to accelerate innovation by giving scientists developing new materials and products a better way to use the data that drive decisions.

“Our goal internally is, ‘Can we make R&D more efficient by a factor of 10?’” Hollingsworth explains.

“We get to that point faster, and it speeds up the whole R&D process.”Carbon is one of several 3-D printing companies Uncountable works with.

Take it from U.S. Air Force Captain Kyle McAlpin when he says that scheduling C-17 aircraft crews is a headache.

An artificial intelligence research flight commander for the Department of Air Force–MIT AI Accelerator Program, McAlpin is also an experienced C-17 pilot.

Collaborating with their Air Force sponsor organization, Tron, the team has developed an AI-enabled plugin for the existing C-17 scheduling tool to fulfill that vision.

Puckboard is used widely across the Air Force for other scheduling needs, though each optimization problem is unique.

He himself personifies all three institutes, as an MIT student, a Lincoln Laboratory Military Fellow, and a lieutenant in the Air Force.

Operators would like to predict such failures, and new research offers insight into the phenomenon using high-speed infrared cameras and machine learning.

“Then we said, ‘Let’s see if other than just processing the data we can actually learn something from an artificial intelligence,’” Bucci says.

We’re capable of extrapolating predictions to a different surface.”The team also found that all 17 factors contributed significantly to prediction accuracy (though some more than others).

Further, instead of treating the model as a black box that used 17 factors in unknown ways, they identified three intermediate factors that explained the phenomenon: nucleation site density, bubble size (which wa…

Growing up near the Chesapeake Bay in Maryland, Preston learned about the importance of environmental conservation from a young age.

“My first research project involved creating a drone that could take noninvasive blow samples from exhaling whales,” Preston says.

Then scientists do the actual hard work of extracting meaningful information to solve these hard problems,” says Preston.

The first two years of her research focused on how to deploy robots in environments and process their collected data.

Adapting to real-world requirementsIn the next phase of her work, Preston has been incorporating an important component — time.

The U.S. Department of Energy (DoE) recently announced the names of 83 scientists who have been selected for their 2021 Early Career Research Program.

Quantum materials contain unique physical characteristics, and can lead to phenomena like superconductivity.

Detecting and visualizing these materials at the nanoscale will enable scientists to understand and harness the properties of quantum materials.

The group uses high-energy electromagnetic waves, or X-rays, to observe how new collective states emerge at the nanoscale in quantum materials.

“The new approach will augment existing neutron scattering probes by measuring things that were not measurable before,” Li says.

Given their limited distribution and lack of mobility, these systems are really only a reliable indicator of the air quality directly surrounding each monitoring point, but their data are reported as though they were representative of air quality across the entire city, say the recent graduates.

“So even though they might say that your air quality is somewhat good, that may not be the case for the park right next to your home,” says Gonzalez-Diaz.

The NEET program was launched in 2017 in an effort to fundamentally reconceptualize the way that engineering is taught at MIT.

The Autonomous Machines thread teaches students to design, build, and program autonomous robots.

“Low-income communities…

Four MIT-affiliated researchers are among 28 around the world to have been named to a competitive Schmidt Science Fellowship, an award created in 2017 to advance interdisciplinary studies among early-career researchers.

“I feel truly honored to become a member of the Schmidt Science Fellows program and join this vibrant scientific community,” says Fernández Galiana.

“The Schmidt Science Fellows community believes in the power of interdisciplinary science to drive innovation and discovery and make a positive impact in the world.

Leveraging her insights into designing nanosensors for biomedical applications, this month she will join the lab of Picower Professor and Picower Institute Director …

He had only lately turned to making these molecular patterns comprehensible to another human sense: sight.

By hooking an actuator up to a petri dish of water, he was able to see how molecular vibrations manifested as visible water waves.

“The computer has now understood the mechanisms of these vibrations and how they relate to different proteins, or molecules.

“There's this idea of oceanic feeling, a sense of oneness with the world, or this kind of limitlessness that's triggered by the oxytocin hormone,” Sutela says.

As Caroline Jones notes, Sutela “helps us see the world as offering infinite kinship.”Being able to visualize molecular vibrations may lead us to a greater appreciation of our …

When General Richard D. Clarke, commander of the U.S. Special Operations Command (USSOCOM), visited MIT in fall 2019, he had artificial intelligence on the mind.

Thus, a new collaboration between the MIT School of Engineering, MIT Professional Education, and USSOCOM was born: a six-week AI and machine learning crash course designed for special operations personnel.

Further, AI technology is often developed in the private or academic sectors, and the military doesn’t automatically have access to those innovations.

Computer science courses at MIT are typically oversubscribed and attract students from many different disciplines.

Originally envisioned as an on-campus program, the USSOCOM course…

Until Realtime Robotics stepped up and solved the problem with autonomous robot motion planning and multi-robot deconfliction.

In May 2017, Realtime Robotics set up shop at MassRobotics, a Boston-area robotics collective.

And it’s not just the factory floor where Realtime Robotics expects to have an impact.

Realtime Robotics' dedicated technology, known as Lightning, can run through hundreds of potential forecasts per sensor cycle.

Realtime Robotics currently has global automation OEM leaders promoting their products and top 10 automakers doing the first product rollouts while incorporating the game-changing technology in their own standard tools and workflows.

But manufacturing those drugs for clinical trials often involves international partners and supply chains.

From there it seeks to automate production processes, often lessening the number of steps it takes to create those molecules.

Some of those reactors are being used for the commercial production of approved drugs, although most are designed to help pharmaceutical and biotech companies get through clinical trials more quickly.

Snapdragon’s work helping companies improve chemistry processes is still its most common service offering.

Moving forward, Jamison thinks Snapdragon’s machine-based production processes will only accelerate the company’s ability to innovate.

Even with mountains of data, clear instructions, and extensive training, they have a difficult time with tasks easily picked up by a child.

A new simulation environment, PlasticineLab, is designed to make robot learning more intuitive.

By building knowledge of the physical world into the simulator, the researchers hope to make it easier to train robots to manipulate real-world objects and materials that often bend and deform without returning to their original shape.

In PlasticineLab, the robot agent learns how to complete a range of given tasks by manipulating various soft objects in simulation.

Other authors of PlasticineLab are Siyuan Zhou of Peking University, Hao Su of UCSD, and MIT Pr…

It’s an intuitive approach and one that still has obstacles, but Coley says that this autonomous platform holds enormous potential for remaking the discovery process.

“This would let us boost our productivity and scale out the discovery process much more efficiently,” he says.

To close that gap and accelerate the process, his group has been working on computational techniques that learn to correlate molecular structures with their functions.

More than selecting molecules, Coley is also working on tools that would generate new structures.

The missing piece is designing a computational approach that can identify new structures and have a better chance from the outset of success.

Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive LearningWe consider a problem: Can a machine learn from a few labeled pixels to predict every pixel in a new image?

Weak supervision can be roughly categorized into two families: Coarse and Sparse supervision.

Metric Learning and Contrastive Loss FormulationTo solve the semi-supervised learning problem, we take the viewpoint of feature representation learning.

A Solution for Universal Weakly Supervised SegmentationIn this work, we propose a single method to tackle all forms of weak supervision, even if they carry different assumptions.

We thank all co-authors of the paper “Universal Weakly Supervised Segmentation by …

We refer to PPO with these modifications as Multi-Agent PPO (MAPPO).

Overall, we observe that in the majority of environments, MAPPO achieves results comparable or superior to off-policy methods with comparable sample-efficiency.

Additionally, this suggests that despite a heavy emphasis on developing new off-policy methods for MARL, on-policy methods such as PPO can be a promising direction for future research.

These include:Investigating MAPPO’s performance on a wider range of domains, such as competitive games or multi-agent settings with continuous action spaces.

This post is based on the paper “The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games”.

Despite the plethora of techniques developed to tackle this problem, there have been no popular benchmarks that are specifically intended to evaluate algorithms that learn from human feedback.

In contrast, there is effectively no chance of such an unsupervised method solving BASALT tasks.

Design a “caption prompt” for each BASALT task that induces the policy to solve that task.

We impose limits on the amount of compute and human feedback that submissions can use to prevent this scenario.

ConclusionWe hope that BASALT will be used by anyone who aims to learn from human feedback, whether they are working on imitation learning, learning from comparisons, or some other method.

Preferences Implicit in the State of the World develops an algorithm, Reward Learning by Simulating the Past (RLSP), that does this sort of reasoning, allowing an agent to infer human preferences without explicit feedback.

In our latest paper presented at ICLR 2021, we introduce Deep Reward Learning by Simulating the Past (Deep RLSP), an extension of the RLSP algorithm that can be scaled up to tasks like the balancing Cheetah task.

To address this, we sample likely past trajectories, instead of enumerating all possible past trajectories.

By alternating between predicting past actions, and predicting past states from which those actions were taken, we can simulate trajectories arbitrarily fa…

Our method, Equivalent-Policy Invariant Comparison (EPIC), allows one to evaluate a reward function by computing how similar it is to other reward functions.

EPIC can be used to benchmark reward learning algorithms by comparing learned reward functions to a ground-truth reward.

It can also be used to validate learned reward functions prior to deployment, by comparing them against reward functions learned via different techniques or data sources.

EPIC is a new way to evaluate reward functions and reward learning algorithms by comparing how similar reward functions are to one another.

Most significantly, EPIC can only compare reward functions to one another, and cannot tell you what a particu…

The Importance of Hyperparameter Optimization for Model-based Reinforcement LearningModel-based reinforcement learning (MBRL) is a variant of the iterative learning framework, reinforcement learning, that includes a structured component of the system that is solely optimized to model the environment dynamics.

MBRLModel-based reinforcement learning (MBRL) is an iterative framework for solving tasks in a partially understood environment.

With that data, the agent creates a structured learning tool – a dynamics model – to reason about the world.

Automated Machine Learning (AutoML) is a field dedicated to the study of using machine learning algorithms to tune our machine learning tools.

Thi…

Pretrained Transformers as Universal Computation EnginesTransformers have been successfully applied to a wide variety of modalities: natural language, vision, protein modeling, music, robotics, and more.

This enables the models to utilize generalizable high-level embeddings trained on a large dataset to avoid overfitting to a small task-relevant dataset.

To illustrate this, we take a pretrained transformer language model and finetune it on various classification tasks: numerical computation, vision, and protein fold prediction.

Furthermore, we find the language-pretrained frozen transformers converge faster than the randomly initialized frozen transformers, typically by a factor of 1-4x, in…

Our analysis provides a theoretically-justified explanation for the empirical robustness of MaxEnt RL, and proves that MaxEnt RL is itself a robust RL algorithm.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

Standard RL MaxEnt RL Trained and evaluated without the obstacle: Trained without the obstacle, but evaluated with the obstacle:TheoryWe now formally describe the technical results from the paper.

Standard RL MaxEnt RL Evaluation on adversarial perturbationsMaxEnt RL is robust to adversarial perturbations of the hole (where the robot inserts the peg).

ConclusionIn summary, our paper sho…

Maximum Entropy RL (Provably) Solves Some Robust RL ProblemsNearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment.

In a recent paper, we prove that every MaxEnt RL problem corresponds to maximizing a lower bound on a robust RL problem.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

ConclusionIn summary, this paper shows that a commonly-used type of RL algorithm, MaxEnt RL, is already solving a robust RL problem.

We do not claim that MaxEnt RL will outperform purpose-designed robust RL algorithms.

Self-Supervised Policy Adaptation during DeploymentOur method learns a task in a fixed, simulated environment and quickly adapts to new environments (e.g.

Assuming that gradients of the self-supervised objective are sufficiently correlated with those of the RL objective, any adaptation in the self-supervised task may also influence and correct errors in the perception and decision-making of the policy.

SAC+IDM is a Soft Actor-Critic (SAC) policy trained with an Inverse Dynamics Model (IDM), and SAC+IDM (PAD) is the same policy but with the addition of policy adaptation during deployment on the robot.

Policy adaptation is especially effective when the test environment differs from the traini…

The Successor Representation, $\gamma$-Models,and Infinite-Horizon PredictionThe Successor Representation, Gamma-Models, and Infinite-Horizon PredictionStandard single-step models have a horizon of one.

In order to amortize this long-horizon prediction, value functions are trained with either Monte Carlo estimates of expected cumulative reward or with dynamic programming.

In contrast, value functions amortize the work of long-horizon prediction at training, so a single-step prediction (and informally, a shorter "horizon") is sufficient during testing.

As opposed to incrementing one timestep into the future with every prediction, \(\gamma\)-model rollout steps have a negative binomial distri…

Does GPT-2 Know Your Phone Number?

Yet, OpenAI’s GPT-2 language model does know how to reach a certain Peter W --- (name redacted for privacy).

Maybe the model memorized credit card numbers, or maybe it memorized entire book passages, or even code snippets.

For example, we retain any sample on which GPT-2 assigns a much higher likelihood than a different language model (e.g., a smaller variant of GPT-2).

Does Training Language Models Infringe on Copyright?

Offline Reinforcement Learning: How Conservative Algorithms Can Enable New ApplicationsDeep reinforcement learning has made significant progress in the last few years, with success stories in robotic control, game playing and science problems.

As shown in the figure below, offline RL requires learning skills solely from previously collected datasets, without any active environment interaction.

COG: Learning Skills That Generalize via Offline RLCOG is an algorithmic framework for utilizing large, unlabeled datasets of diverse behavior to learn generalizable policies via offline RL.

Like supervised learning methods, offline RL algorithms can also “overfit” as a result of excessive trainin…

Learning State Abstractions for Long-Horizon PlanningMany tasks that we do on a regular basis, such as navigating a city, cooking a meal, or loading a dishwasher, require planning over extended periods of time.

Two-way consistency can be viewed as a generalization of value irrelevance to the goal-conditioned setting.

Furthermore, our main theorem tells us that we can merge nodes according to two-way consistency while preserving the graph’s quality.

Overall, we found that state aggregation with two-way consistency resulted in substantially more robust plans over the prior state-of-the-art.

How can two-way consistency be utilized beyond the scope of graphical-based planning methods?

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

Summary and Broader ApplicationsWe introduce EvolveGraph, a generic trajectory prediction framework with dynamic relational reasoning, which can handle evolving inte…

Customer churn prediction using machine learning (ML) techniques can be a powerful tool for customer service and care.

To run this JumpStart 1P solution and have the infrastructure deploy to your AWS account, you must create an active Amazon SageMaker Studio instance (see Onboard to Amazon SageMaker Studio).

We use SageMaker training jobs to train the churn prediction model and a SageMaker endpoint to deploy the model.

Visit Amazon SageMaker Python SDK technical documentation for more details on preparing PyTorch scripts for SageMaker training and using the PyTorch Estimator.

For more SageMaker Python examples for MXNet, TensorFlow, and PyTorch, visit the Amazon SageMaker Pre-Built Framewor…

In this post, we show how Amazon Kendra allows your users to search documents stored in Amazon WorkDocs.

Create an Amazon WorkDocs connectorTo create an Amazon WorkDocs connector, complete the following steps:On the Amazon Kendra console, choose Data sources.

ConclusionIn this post, you created a data source and ingested your Amazon WorkDocs documents into your Amazon Kendra index.

You can also dive deep into Amazon Kendra with the Amazon Kendra Essentials workshop or try the multilingual chatbot experience.

Vijai Gandikota is a Senior Product Manager at Amazon Web Services for Amazon Kendra.

This post demonstrates the value of using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate an ML pipeline using the popular XGBoost (eXtreme Gradient Boosting) algorithm.

To support this demonstration, the requirements.txt file should have the following entries:boto3==1.17.49 sagemaker==1.72.0 s3fs==0.5.1Orchestrate an XGBoost ML pipelineOur ML pipeline is a simplified three-step pipeline:Data preprocessing using AWS Glue.

AWS services that don’t have native Airflow operators, like AWS Glue, can still be orchestrated in Airflow using AWS SDKs called from the general PythonOperator .

For ML pipelines using SageMaker, you can use the SageMaker Python SDK.

Shreyas Subra…

In this post, we walk you through processing an invoice/receipt using Amazon Textract and extracting a set of fields and line-item details.

Amazon Textract console walkthroughBefore we get started with the API and code samples, let’s review the Amazon Textract console.

The Amazon Textract AnalyzeExpense API responseIn this section, we explain the AnalyzeExpense API response structure using sample images.

We also described how you can parse the AnalyzeExpense response JSON using the Amazon Textract parser library and save the output in different formats using Amazon Textract PrettyPrinter.

Finally, we provided a solution architecture for processing invoices and receipts using Amazon S3, Even…

Amazon Rekognition Custom Labels, an automated ML feature of Amazon Rekognition, lets you quickly train custom CV models specific to your business needs simply by bringing labeled images.

Baseline modelAfter you have a labeled dataset, you’re ready to train your Amazon Rekognition Custom Labels model.

The first step is to set up our Amazon Rekognition Custom Labels project.

ConclusionIn this post, we demonstrated how to use tiling as a preprocessing technique to maximize the performance of Amazon Rekognition Custom Labels when detecting small objects.

For more information about using custom labels, see What Is Amazon Rekognition Custom Labels?

After you deploy your model, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML model in real time.

Model bias –Model Monitor is integrated with Amazon SageMaker Clarify to improve visibility into potential bias.

Projected drift in model accuracyThis metric provides a proxy of the accuracy of a model due to drift in inference data from the test data.

ConclusionAmazon SageMaker Model Monitor is a powerful tool to detect data drift.

The notebook provides a detailed step-by-step instruction on how to build a custom container and attach it to Model Monitor schedules.

To get started with Lookout for Vision, we create a project, create a dataset, train a model, and run inference on test images.

The solution has the following workflow:Upload data from the source to Amazon Simple Storage Service (Amazon S3).

Run Lookout for Vision to process data from the Amazon S3 path.

Store inference results in Amazon S3 for downstream review.

Delete any Lookout for Vision projects you’re no longer using, and remove objects from Amazon S3 to save costs.

Solution overviewAmazon Rekognition Image is an image recognition service capable of detecting thousands of different objects using deep neural network models.

On top of that, you may desire to control costs by limiting the number of images scanned by Amazon Rekognition.

When Amazon Rekognition detects the desired label in an image from the source bucket repository, the function copies that image into the destination bucket.

All the function needs are appropriate permissions within your AWS account and the following parameters:An Amazon Rekognition label to filter on.

ConclusionIn this post, we explored the possibility of using Amazon Rekognition to filter image sets intended for ML applica…

We also walk through creating a custom voice assistant using PocketSphinx and Amazon Lex.

In this post, you learn how to create a custom voice assistant using PocketSphinx and Amazon Lex.

Deploy your solution resourcesYou can find all the files of our custom voice assistant solution on our GitHub repo.

Create the Amazon Lex botWhen the CloudFormation stack is complete, we’re ready to create the Amazon Lex bot.

We used PocketSphinx and Amazon Lex to create a voice assistant with the simple task of retrieving some patient information.

This is a guest post authored by Paul Ngo, US Gas Technical and Operational Services Data Team Lead at TC Energy.

In this post, we share how TC Energy built an intelligent document processing workflow using Amazon AI services.

First, the team built a document classifier with Amazon Rekognition Custom Labels (training on 111 distinct document types).

Using Amazon Rekognition Custom Labels to create a document classifier was simple and easy.

Contact sales or visit the product pages to learn more about how Amazon Rekognition and Amazon Textract can help your business.

Perform label verification with Amazon Rekognition Custom Labels.

Train the first version of your modelFor instructions on creating a project and training a model with Custom Labels, see Announcing Amazon Rekognition Custom Labels.

Expand the API Code section on your Amazon Rekognition Custom Labels model page, and enter the AWS CLI command Start model in your terminal.

It’s worth noting that you can apply this solution multiple times in a Amazon Rekognition Custom Labels project.

For more information about building dataset labels with Ground Truth, see Amazon SageMaker Ground Truth and Amazon Rekognition Custom Labels.

Kinesis Data Analytics Studio provides an interactive notebook experience powered by Apache Zeppelin and Apache Flink to analyze streaming data.

This streaming data is then queried and transformed using a Kinesis data analytics application, which is built and deployed using Kinesis Data Analytics Studio.

For more information, see Introducing Amazon Kinesis Data Analytics Studio – Quickly Interact with Streaming Data Using SQL, Python, or Scala.

For detailed steps for creating an Apache Zeppelin notebook, see Introducing Amazon Kinesis Data Analytics Studio – Quickly Interact with Streaming Data Using SQL, Python, or Scala or Using a Studio notebook with Kinesis Data Analytics for Apache Fli…

In this post, we address this challenge by adopting Amazon SageMaker Feature Store, a fully managed, purpose-built repository to securely store, update, retrieve, and share ML.

We use Java to create a feature group; describe and list the feature group; ingest, read, and delete records from the feature group; and lastly delete the feature group.

We also need to set up a new Amazon Simple Storage Service (Amazon S3) bucket to use as our offline feature store.

Delete the feature groupDelete the feature group and any data that was written to the OnlineStore of the feature group.

To learn more about Amazon SageMaker Feature Store, check out this overview of its key features.

Amazon Forecast is a fully managed service that allows you to forecast your time series data with high accuracy.

Structure your input data based on your business questionsWhen preparing your input data for Amazon Forecast, consider the business questions you want to ask.

You need to prepare your input data by applying aggregations to your input data while keeping the eventual structure in line to the input format.

Data cleaningCleaning your data for Amazon Forecast is important because it can affect the accuracy of the forecasts that are created.

To learn more about data preparation for Amazon Forecast and best practices, refer to the Amazon Forecast Cheat Sheet and the sample data preparat…

For more information about how to implement contextual recommendations in real time, see Increasing the relevance of your Amazon Personalize recommendations by leveraging contextual information.

We can provide Amazon Personalize the weather context when asking for recommendations, and explore how the recommendations are influenced by this contextual information.

weather_data.csvWe use a real weather dataset provided by Weather Trends International to enrich the interactions dataset with temperature data.

Create Amazon Personalize componentsWhen the datasets are ready to be imported, we’re ready to start creating an Amazon Personalize deployment.

Additional resources about Amazon Personalize…

In this update, we look at the ways NVIDIA TensorRT and the Triton Inference Server can help your business deploy high-performance models with resilience at scale.

Next, we dig into exactly how Triton and Clara Deploy complement each other in your healthcare use cases.

TensorRT and Triton in PracticeOn-Demand: Inception Café – Accelerating Deep Learning Inference with NVIDIA TensorRT and TritonA step-by-step walkthrough applying NVIDIA TensorRT and Triton in conjunction with NVIDIA Clara Deploy.

Watch >Whitepaper: Inception Café – Migrating Your Medical AI App to TritonThis whitepaper explores the end-to-end process of migrating an existing medical AI application to Triton.

Watch >On-Demand…

A team of scientists from Argonne National Laboratory developed a new method for turning X-ray data into visible, 3D images with the help of AI.

The study, published in Applied Physics Reviews, develops a computational framework capable of taking data from the lab’s Advanced Photon Source (APS) and creating 3D visualizations hundreds of times faster than traditional methods.

“In order to make full use of what the upgraded APS will be capable of, we have to reinvent data analytics.

The advancement could have wide-ranging benefits to many areas of study relying on sizable amounts of 3D data, ranging from astronomy to nanoscale imaging.

With 3D images this can be extremely timely due to the am…

IntroductionRAPIDS Accelerator for Apache Spark v21.06 is here!

RAPIDS Accelerator is built on cuDF, part of the RAPIDS ecosystem.

Of course, we’ve made changes to accommodate new versions of Apache Spark, but we’ve also simplified installation.

Cloudera Data Platform (CDP) integration with RAPIDS Accelerator will be generally available on CDP PVC Base 7.1.6 release from July 15.

We’re also excited to let you know that NVIDIA and Microsoft have teamed to bring RAPIDS Accelerator to Azure Synapse.

NVIDIA and Mozilla are proud to announce the latest release of the Common Voice dataset, with over 13,000 hours of crowd-sourced speech data, and adding another 16 languages to the corpus.

Common Voice is the world’s largest open data voice dataset and designed to democratize voice technology.

Newly released Mozilla Common Voice dataset.

Highlights of this release include:Common Voice dataset release is now 13,905 hours, an increase of 4,622 hours from the previous release.

Introduces 16 new languages to the Common Voice dataset: Basaa, Slovak, Northern Kurdish, Bulgarian, Kazakh, Bashkir, Galician, Uyghur, Armenian, Belarusian, Urdu, Guarani, Serbian, Uzbek, Azerbaijani, Hausa.

NVIDIA RTX technology powers the real-time graphics and virtual sets behind this latest adaptation, which showcases the future of interactive storytelling on a virtual production stage.

Using its own low-latency computing platform, the GODBOX powered by NVIDIA RTX, OSF enhanced virtual production workflows and delivered real-time compositing and previsualization for the interactive experience.

It’s a synchronized real-time virtual production platform for low-latency, frame-accurate, virtual production applications and workflows.

The team used virtual sets and real locations, and combined that with real-time visual effects to bring sets to life.

The team combined green screen live action wit…

Relying on the capabilities of GPUs, a team from Facebook AI Research has developed a faster, more efficient way for AI to run similarity searches.

“The most straightforward technique for searching and indexing [high-dimensional data] is by brute-force comparison, whereby you need to check [each image] against every other image in the database.

This is impractical for collections containing billions of vectors,” Jeff Johnson, study colead and a research engineer at Facebook, said in a press release.

Using only four GPUs with CUDA, the researchers designed an algorithm for GPUs to both host and analyze library image data points.

Known as the Facebook AI Similarity Search library, the approac…

This GFN Thursday shines a spotlight on the latest games joining the collection of over 1,000 titles in the GeForce NOW library from the many publishers that have opted in to stream their games on our open cloud-gaming service.

Members can look forward to 14 games — including Evil Genius 2: World Domination from Rebellion and Escape From Naraka, which features RTX for Founders and Priority members — joining the GeForce NOW library this week.

Games include Evil Genius 2: World Domination, Evil Genius, Battlezone: Combat Commander and Zombie Army 4: Dead War.

Be the best bad guy you can be in Evil Genius 2: World Domination (Steam).

Members can also check out other popular titles from Team17 …

NVIDIA GTC21 had numerous great and engaging contents, especially around RAPIDS, so it would be easy to miss our debut presentation “Using RAPIDS to Accelerate Node.js JavaScript for Visualization and Beyond.” Yep – we are bringing the power of GPU accelerated data science to the JavaScript Node.js community with the Node-RAPIDS project.

Around a decade ago, the mini-renaissance around web-based data visualization showed the benefits of highly interactive, easy to share, and use tools such as D3.

Yet, this large JavaScript community of developers is impeded by the lack of first-class and accelerated data tools in their preferred language.

This is detrimental because data visualization is th…

This April, the fastest “cruciverbalist” at the ​​American Crossword Puzzle Tournament was Dr.Fill, a crossword puzzle-solving AI program created by Matt Ginsberg.

Though Ginsberg has published crossword puzzles for the New York Times, he has trouble solving puzzles, even his own.

After attending a crossword tournament over a decade ago, Ginsberg decided to create a crossword-solving program to compete against top-tier word nerds.

He’s the CEO and co-founder of Otter.ai, which uses AI to produce speech-to-text transcriptions in real time or from recording uploads.

Subscribe to the AI PodcastGet the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, Play…

If you caught NVIDIA CEO Jensen Huang’s keynote for our March 2021 GPU Technology Conference you’re no doubt wondering about more than a few of the presentation’s magic tricks.

With the premiere of “Connecting in the Metaverse: The Making of the GTC Keynote,” Wednesday, Aug. 11, at 11 a.m. Pacific time, NVIDIA team members will reveal the story behind the story told at GTC.

Designed to entertain and inform, GTC keynotes are always filled with cutting-edge demos highlighting NVIDIA’s advancements in supercomputing, deep learning and graphics.

The highlight: the reveal of Huang’s virtual kitchen, complete with a digital clone of the man himself.

Go to our SIGGRAPH 2021 landing page to watch t…

cudaMalloc(&ptr, size); kernel<<<..., stream>>>(ptr); cudaFreeAsync(ptr, stream); cudaStreamSynchronize(stream); // The memory for ptr is freed at this pointSimilarly, an application can use cudaFree to free memory allocated using cudaMallocAsync .

Interprocess communication supportMemory allocated using the default memory pool associated with a device cannot be shared with other processes.

An application must explicitly create its own memory pools to share memory allocated using cudaMallocAsync with other processes.

Today, an explicitly constructed pool is only required to share pool memory across processes with CUDA IPC.

Unlike memory allocated with cudaMalloc , memory allocated with cuda…

These new API functions shift memory allocation from global-scope operations that synchronize the entire device to stream-ordered operations that enable you to compose memory management with GPU work submission.

Memory poolsThe stream-ordered memory allocator introduces the concept of memory pools to CUDA.

Remapping existing pool memory instead of allocating new memory from the OS also helps keep the application’s memory footprint low.

Memory allocation requests in those contexts do not cause automatic freeing of unused pool memory.

In part 2 of this series, we share some benchmark results to show the benefits of stream-ordered memory allocation.

The NVIDIA, Facebook, and TensorFlow recommender teams will be hosting a summit with live Q&A to dive into best practices and insights on how to develop and optimize deep learning recommender systems.

Develop and Optimize Deep Learning Recommender SystemsThursday, July 29 at 10 a.m. PTBy joining this Deep Learning Recommender Summit, you will hear from fellow ML engineers and data scientists from NVIDIA, Facebook, and TensorFlow on best practices, learnings, and insights for building and optimizing highly effective DL recommender systems.

RecSys2021 Challenge: Predicting User Engagements with Deep Learning Recommender SystemsThe NVIDIA team, a collaboration of Kaggle Grandmaster and NVIDIA …

Repeat steps 3 and 4, until all the records are assigned to subset_id & hex_id or until the resolution reaches 15.

In our current use case, we work with anonymized streamed connected car data (as shortly described in business challenges preceding).

SummaryThis article summarized how RAPIDS helps in accelerating data pipelines 100x faster by evaluating it over two models, namely Geospatial Indexing (Uber H3) and K-Nearest Neighbors Classification (KNN).

We conclude that RAPIDS is surely a technology for streaming data processing (connected car data).

It provides the benefits of faster processing of data which is the crucial factor for streaming data analysis.

The Synthetic Brain Project is focused on building deep learning models that can synthesize artificial 3D MRI images of human brains.

The AI models were developed by King’s College London, and NVIDIA data scientists and engineers, as part of The London Medical Imaging & AI Centre for Value Based Healthcare.

The research was funded by UK Research and Innovation and a Wellcome Flagship Programme (in collaboration with University College London).

As part of the synthetic brain generation project from King’s College London, the code and models are open-source.

NVIDIA has made open-source contributions to improve the performance of the fast-transformers project, on which The Synthetic Brain Proj…

It shards an AI model’s parameters across data parallel workers and can optionally offload part of the training computation to the CPUs.

For example, typical data parallel training requires maintaining redundant copies of the model on each GPU, and model parallel training introduces additional communication costs to move activations between workers (GPUs).

Using FSDP in computer vision modelsFor computer vision models, FSDP is supported in VISSL and tested on RegNets architectures.

Users may need to carefully tune the activation checkpointing strategy to fit a large model within limited GPU memory space.

We look forward to developing algorithms for auto-tuning both GPU memory usage and trai…

We will be hosting a talk about our work on, “A Platform Agnostic Observability System for AI Accelerators” during our virtual Systems @Scale event at 10:20 a.m. PT on Wednesday, June 30, followed by a live Q&A session.

To meet these challenges, we’ve introduced three new tools:ASIC Monitoring (Asicmon) , a scalable observability framework.

However, with an accelerator system, we can imagine the CPU now has a complicated and brawnier sibling!

Since implementing Asicmon we’ve been able to increase our AI accelerator metrics support from ~30 percent to ~75 percentAtrace: Accelerator tracing at scaleWhy tracing?

This would allow us to debug the end-to-end latency of microservices that use AI a…

People upload hundreds of millions of videos to Facebook every day.

From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient.

A relatively small percentage (roughly one-third) of all videos on Facebook generate the majority of overall watch time.

The impact of the new video encoding modelIn addition to improving viewer experience with newly uploaded videos, the new model can identify older videos on Facebook that should have been encoded with more advanced encodings and route more computing resources to them.

The improved compression has also allowed people on Facebook with limited data plans, such as those i…

Models for meaningful interactions and quality content are powered by state-of-the-art ML, such as multitask learning on neural networks, embeddings, and offline learning systems.

We are sharing new details of how we designed an ML-powered News Feed ranking system.

Building a ranking algorithmTo understand how this works, let’s start with a hypothetical person logging in to Facebook: We’ll call him Juan.

On the other hand, perhaps Juan has previously engaged more with video content than photos, so the like prediction for Wei’s cocker spaniel photo might be lower.

Approximating the ideal ranking function in a scalable ranking systemNow that we know the theory behind ranking (as exemplified t…

This is why we need to make sure our server hardware is reliable and that we can manage server hardware failures at our scale with as little disruption to our services as possible.

And we automate root cause analysis for hardware and system failures at scale to get to the bottom of issues quickly.

How we handle hardware remediationWe periodically run a tool called MachineChecker on each server to detect hardware and connectivity failures.

If the issue requires manual repair from a technician, the system creates a ticket in our repair ticketing system.

We have deployed this analyzer widely inside Facebook for the RCA on hardware component failure rate, unexpected server reboots, and software…

What’s New:PPL Bench is an open source benchmark framework for evaluating probabilistic programming languages (PPLs) used for statistical modeling.

PPL Bench does this by using predictive log likelihood as a standard measurement.

PPL Bench also reports other common metrics used to evaluate statistical models, including effective sample size, R-hat, and inference time.

We hope that community contributions will help grow and diversify PPL Bench and encourage wider industrial deployments of PPLs.

Read the full paper:PPL Bench: Evaluation framework for probabilistic programming languagesGet it on GitHub:PPL Bench

In this blog, we discuss how moving to distributed XGBoost on Ray helps address these concerns and how finding the right abstractions allows us to seamlessly incorporate Ray and XGBoost Ray into Uber’s ML ecosystem.

To run XGBoost Ray, we simply pass in XGBoost Ray’s train function to the Ray Estimator.

In this example, the XGBoost Ray Estimator will create 50 GPU workers that form the Ray Cluster, each participating in data-parallel XGBoost training and synchronization.

To go from running a single distributed XGBoost Ray job to running distributed hyperparameter search using the Ray Estimator, we can simply pass in Ray Tune’s tune.run() with XGBoost Ray’s train() as serializable functions …

Today, we are excited to release Ludwig version 0.3, featuring several updates that take our framework to the next level.

Finding the parameters that yield the best performance on a data set is a time-consuming job that can be automated by hyper-parameter optimization techniques.

The hyper-parameter optimization architecture is easy to expand and we plan to integrate with additional samplers and executors in the near future, like RayTune.

Nonetheless, Ludwig version 0.3 ships with a revamped, more modular, and easier-to-extend backend based on TensorFlow 2, lending to greater flexibility all around.

Moving forwardWith the addition of hyper-parameter optimization, Ludwig version 0.3 has soli…

Challenges in ML model lifecycleTo stay on track with your experiments, it’s necessary to track code, data, model versions, hyperparameters, and metrics.

Enough introduction, let’s implement and track model development using Neptune.

Avoid mess in your model development process with NeptuneInstall dependencies for this projectWe’ll be using Neptune in Jupyter notebooks, so we need both the Neptune client and Neptune jupyter extension.

model.compile(loss= 'sparse_categorical_crossentropy' , optimizer= 'adam' , metrics=[ 'accuracy' ])ML model development organized using NeptuneTo log training metrics to Neptune, we use a callback from the Neptune library.

Before model deployment, make sure to…

Stock analysis: fundamental analysis vs. technical analysisWhen it comes to stocks, fundamental and technical analyses are at opposite ends of the market analysis spectrum.

We’ll be using terms like:trend indicators: statistics that represent the trend of stock prices,medium-term movements: the 50-day movement trend of stock prices.

Stock prices as time-series dataDespite the volatility, stock prices aren’t just randomly generated numbers.

For example, a MAPE value of 12% indicates that the mean difference between the predicted stock price and the actual stock price is 12%.

Predicting stock prices with an LSTM modelFirst, we need to create a Neptune experiment dedicated to LSTM, which inclu…

Already, NLP projects and applications are visible all around us in our daily life.

Why should you build NLP projects?

Building real-world NLP projects is the best way to get NLP skills and transform theoretical knowledge into valuable practical experience.

Let’s move on to the 10 NLP projects that you can start right now.

10 NLP project ideas to boost your resumeWe’ll start with beginner-level projects, but you can move on to intermediate or advanced projects if you’ve already done NLP in practice.

BiasBias is the assumption used by machine learning models to make the learning process easier.

Low variance models are less sensitive to changes in training data, mostly because of fixed assumptions about the data.

Cross-ValidationThe issue with overfitting is that with the change of training data, the performance entirely changes on test data.

Technical waysSynthetic Data AugmentationWhen data is generated from existing data through various techniques like approximation, it’s called synthetic data augmentation.

The synthetic data is similar to the original data, yet provides enough variance for the machine learning model to learn the data trends.

To upload files directly to a subdirectory you need to:1.

Downloading files from Colab to local file system using Python code:The download method of the files object can be used to download any file from colab to your local drive.

Accessing Google Drive from Google ColabYou can use the drive module from google.colab to mount your entire Google Drive to Colab by:1.

You can even write directly to Google Drive from Colab using the usual file/directory operations.

In this article, we have gone through most of the ways you can supercharge your Google Colab experience by reading external files or data in Google Colab and writing from Google Colab to those external data sources.

By doing that you can keep your run metadata safe even when the Google Colab kernel has died.

Downloading files from Colab to local file system using Python code:The download method of the files object can be used to download any file from colab to your local drive.

Accessing Google Drive from Google ColabYou can use the drive module from google.colab to mount your entire Google Drive to Colab by:1.

!touch "/content/gdrive/My Drive/sample_file.txt"This will create a file in your Google Drive, and will be visible in the file-explorer pane once you refresh it:Accessing Google Sheets from Google ColabTo access Google Sheets:1.

In this article, we have gone through most of the ways you can supe…

Hugging Face is a large open-source community that quickly became an enticing hub for pre-trained deep learning models, mainly aimed at NLP.

Hugging Face Hub ReposThey have git-based repositories that function as storage and can contain all the files of your project provide github-like features, such as:Versioning control,Commit history and branch diffs.

Check it out here: Hugging Face librariesHugging Face WidgetsA set of ready-to-use pre-trained models to test inference on a web preview.

Some examples:NER, using spacyTTS, with ESPnetSentence Similarity with TransformersCheck them out here: Hugging Face WidgetsBERT model from Hugging FaceNow, let’s try to do what we’ve been talking about.

…

Regularized autoencodersRegularised autoencoders are designed based on data complexity, and they address the problems of Undercomplete autoencoders.

Sparse autoencoderSparse autoencoders are regularized autoencoders with a penalty on the hidden layer along with the reconstruction loss:Where h represents hidden layers.

It’s important to note that neuron activation depends on the input data, so they’re data-dependent, which means that the distribution of input data results in the activation of neurons in the hidden layers.

Contractive AutoencoderA contractive autoencoder learns representations that are robust to a slight variation of the input data.

Case study 1: Image denoising with Denoisin…

To start, I’ll give you a bit of context about MuJoCo and OpenAI Gym environments.

OpenAI Gym (or Gym for short) is a collection of environments.

This means that the action space is also continuousGym MuJoCo environments include classic continuous control, objects manipulation with a robotic arm, and robotic hand (Shadow Hand) dexterity.

You can find details about all of them in the Gym environments list.

MuJoCo diagnosticsNow I’ll talk about useful metrics provided by the OpenAI Gym MuJoCo environments.

Read also Top Tools to Run a Computer Vision ProjectWhat is Computer Vision?

Computer Vision todayComputer vision has become a relatively standard technology in recent years due to the advancement of AI.

Computer Vision projects for all experience levelsBeginner level Computer Vision projectsIf you’re new or learning computer vision, these projects will help you learn a lot.

Recommended reading & datasets:Advanced level Computer Vision projectsOnce you’re an expert in computer vision, you can develop projects from your own ideas.

Vehicle license plate scannersA vehicle license plate scanner in computer vision is a type of computer vision application that can be used to identify plates and r…

Model debugging studies ML response functions and decision boundaries to detect and correct accuracy, fairness, security, and other problems in ML systems.

Monitoring and intervention during ML training is difficultA lot of ML training code runs on clusters or in the cloud.

GriffinGriffin is a data quality assertion tool with a unified process to measure data quality from different perspectives.

It handles data quality issues with three steps:Define data quality requirements like completeness, profiling, accuracy, etc.

We also looked at model debugging tools that trace the path of errors from the input to the output.

Training and deploying machine learning models is a major challenge for any enterprise, business, or predictive analytics company.

They’re common steps, but they might change depending on your machine learning model or application.

It’s best practice to check if your new machine learning model performs better than a known benchmark in test datasets.

Machine learning optimization is the process of optimizing machine learning using mathematical principles.

Monitoring and managing machine learning models is an important part of the workflow (keeping records of all the datasets, inputs, and predictions).

In this article, I’ll be sharing the biggest challenges me and my team have faced, and lessons learned from working on ML projects at scale.

This has led to the rise of an entirely new field known as MLOps, dedicated to the containerization, orchestration, and distribution of ML applications — all of which make it easier to scale ML projects.

Lessons learnedIn one sentence: as far as large-scale ML applications go, Java might not be the best language, and CPUs may not be optimal!

We can scale up ML using Docker | SourceIt’s beyond the scope of this blog to discuss how to scale up ML using Docker.

Hopefully, my experience and lessons in this article will make your journey to large-scale ML a…

In this article, we’re going to explore key concepts behind the KNN algorithm and analyze a real-world KNN use case.

KNN inner workingsSurprisingly enough, the KNN algorithm is quite accessible and easy to understand.

If the value of accuracy changes proportionally to the change in K, then it’s a good candidate for our K value.

Also, you shouldn’t forget to take into account the effect of the K value on the sample class distribution.

Since we have more than 3 candidates sharing the same value, we can conclude that the optimal K value is 5.

With MLOps, data scientists can work and share their solutions in an organized and efficient way with data engineers who deploy the solutions.

What is MLOps | Illustration sourced from towardsdatascience.comOver the years, organizations have started to see the benefits of MLOps in executing an efficient production pipeline.

MLOps challenges and potential solutionsI divided the challenges into seven groups based on the seven different stages of the ML pipeline.

Summary of MLOps challenges across Stages of ML | Illustrated by AuthorStage 1: Defining the business requirementsThis is the initial stage where business stakeholders design the solution.

ConclusionWe’ve explored the most common high…

A look into the happenings of the Machine Learning world. OUTLINE:

0:00 - Intro

0:25 - Facebook AI trains rapidly adapting robots

3:05 - Baidu presents autonomous excavator system

4:45 - EleutherAI turns 1

6:05 - Elon Musk says FSD harder than expected

8:10 - AI interview tools still fall short

11:10 - RunwayML AI-powered cloud video editor

11:55 - MineRL BASALT competition to learn from human feedback

13:15 - The Myth of the Expert Reviewer

15:55 - NVIDIA unveils Cambridge-1 supercomputer

17:10 - CLIP art sees rapid improvements

19:00 - AI demystifies boiling

21:20 - AI avatars for easier language learning

23:20 - Outro References:

Facebook AI trains rapidly adapting robots

https://ai.face…

I'll be back, don't worry :) Links:

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#copilot #copyright #gpl GitHub and OpenAI release Copilot, an AI-powered code autocomplete system that can generate entire functions, classes, and modules from mere definitions and docstrings. Copilot was trained on all public GitHub repositories, and this has a lot of people upset about questions on copyright, code licenses, social obligations, and how much you can profit from other people's work. I give my opinions on the issue in relation to copyright law, the GPL license, and terms of service. Further, we discuss the Brickit app to organize your LEGOs, Distill going on a break, and much more. OUTLINE:

0:00 - Intro

0:20 - GitHub Copilot

6:55 - My opinion on Copilot & Copyright

17:25 - F…

#tesla #selfdriving #karpathy Tesla is pushing the state-of-the-art in full self-driving, and interestingly, they explicitly switch from having multiple different sensors to a vision-only system. We discuss the highlights of Andrej Karpathy's talk about Tesla's FSD system, how to label petabytes of data, how to sample edge-cases, how to train a neural network that has to work in real-time, and why moving to having only cameras is superior to multi-sensor approaches. OUTLINE:

0:00 - Intro & Overview

1:55 - Current Auto-Breaking system

3:20 - Full Self-Driving from vision only

4:55 - Auto-Labelling for collecting data

8:45 - How to get diverse data from edge-cases

12:15 - Neural network archi…

#cvpr #socialmedia #machinelearning In this week's ML news we look at CVPR's controversial action to ban paper promotions on social media during the review phase, among other things! OUTLINE:

0:00 - Intro & Overview

0:25 - CVPR bans social media paper discussions

5:10 - WalMart uses AI to suggest substitutions

6:05 - NVIDIA releases Alias-Free GAN

7:30 - Confession Video in Myanmar possibly a DeepFake

8:50 - AI restores Rembrandt painting

10:40 - AI for healthcare not problem-free yet

11:50 - ML interviews book

12:15 - NVIDIA canvas turns sketches into paintings

13:00 - GPU prices down after crypto shock

13:30 - Facebook AI improves shopping experience

14:05 - DeepLab2 released on GitHub

14…

#adversarialexamples #dimpledmanifold #security Adversarial Examples have long been a fascinating topic for many Machine Learning researchers. How can a tiny perturbation cause the neural network to change its output by so much? While many explanations have been proposed over the years, they all appear to fall short. This paper attempts to comprehensively explain the existence of adversarial examples by proposing a view of the classification landscape, which they call the Dimpled Manifold Model, which says that any classifier will adjust its decision boundary to align with the low-dimensional data manifold, and only slightly bend around the data. This potentially explains many phenomena aro…

#mlnews #gta #weather In this week's ML News, we look at the latest developments in the Machine Learning and AI world with updates from research, industry, and society at large. OUTLINE:

0:00 - Intro

0:20 - Hugging Face launches free course

1:30 - Sentdex releases GAN Theft Auto

2:25 - Facebook uses AI to help moderators

4:10 - Weather with Antonio

5:10 - Autonomous ship aborts mission

7:25 - PyTorch Release 1.9

8:30 - McDonald's new AI drive thru

10:20 - UBS CEO says AI won't replace humans

12:20 - Gödel paper has 90th birthday

12:55 - AugLy data augmentation library

13:20 - Programming Puzzles for autonomous coding

14:30 - Boston Dynamics' Spot turns 1 References:

PyTorch 1.9 Released

htt…

#xcit #transformer #attentionmechanism After dominating Natural Language Processing, Transformers have taken over Computer Vision recently with the advent of Vision Transformers. However, the attention mechanism's quadratic complexity in the number of tokens means that Transformers do not scale well to high-resolution images. XCiT is a new Transformer architecture, containing XCA, a transposed version of attention, reducing the complexity from quadratic to linear, and at least on image data, it appears to perform on par with other models. What does this mean for the field? Is this even a transformer? What really matters in deep learning? OUTLINE:

0:00 - Intro & Overview

3:45 - Self-Attentio…

#reiforcementlearning #gan #imitationlearning Learning from demonstrations is a fascinating topic, but what if the demonstrations are not exactly the behaviors we want to learn? Can we adhere to a dataset of demonstrations and still achieve a specified goal? This paper uses GANs to combine goal-achieving reinforcement learning with imitation learning and learns to perform well at a given task while doing so in the style of a given presented dataset. The resulting behaviors include many realistic-looking transitions between the demonstrated movements. OUTLINE:

0:00 - Intro & Overview

1:25 - Problem Statement

6:10 - Reward Signals

8:15 - Motion Prior from GAN

14:10 - Algorithm Overview

20:15 …

OUTLINE:

0:00 - Intro

0:30 - Google RL creates next-gen TPUs

2:15 - Facebook launches NetHack challenge

3:50 - OpenAI mitigates bias by fine-tuning

9:05 - Google AI releases browseable reconstruction of human cortex

9:50 - GPT-J 6B Transformer in JAX

12:00 - Tensorflow launches Forum

13:50 - Text style transfer from a single word

15:45 - ALiEn artificial life simulator My Video on Chip Placement: https://youtu.be/PDRtyrVskMU References:

RL creates next-gen TPUs

https://www.nature.com/articles/s41586-021-03544-w

https://www.youtube.com/watch?v=PDRtyrVskMU

Facebook launches NetHack challenge

https://ai.facebook.com/blog/launching-the-nethack-challenge-at-neurips-2021/

Mitigating bias by fine-…

#implicitfunction #jax #autodiff Many problems in Machine Learning involve loops of inner and outer optimization. Finding update steps for the outer loop is usually difficult, because of the.need to differentiate through the inner loop's procedure over multiple steps. Such loop unrolling is very limited and constrained to very few steps. Other papers have found solutions around unrolling in very specific, individual problems. This paper proposes a unified framework for implicit differentiation of inner optimization procedures without unrolling and provides implementations that integrate seamlessly into JAX. OUTLINE:

0:00 - Intro & Overview

2:05 - Automatic Differentiation of Inner Optimizat…

#mlnews #wudao #academicfraud OUTLINE:

0:00 - Intro

0:25 - EU seeks to regulate AI

2:45 - AI COVID detection systems are all flawed

5:05 - Chinese lab trains model 10x GPT-3 size

6:55 - Google error identifies "ugliest" language

9:45 - McDonald's learns about AI buzzwords

11:25 - AI predicts cryptocurrency prices

12:00 - Unreal Engine hack for CLIP

12:35 - Please commit more academic fraud References:

https://www.lawfareblog.com/artificial-intelligence-act-what-european-approach-ai

https://blogs.sciencemag.org/pipeline/archives/2021/06/02/machine-learning-deserves-better-than-this

https://www.nature.com/articles/s42256-021-00307-0

https://en.pingwest.com/a/8693

https://arxiv.org/pdf/2104.12…

#phdlife #github #researchcode A brief browse through my public GitHub and musings about my old code. Link: https//github.com/yk Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

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Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially …

#decisiontransformer #reinforcementlearning #transformer Proper credit assignment over long timespans is a fundamental problem in reinforcement learning. Even methods designed to combat this problem, such as TD-learning, quickly reach their limits when rewards are sparse or noisy. This paper reframes offline reinforcement learning as a pure sequence modeling problem, with the actions being sampled conditioned on the given history and desired future rewards. This allows the authors to use recent advances in sequence modeling using Transformers and achieve competitive results in Offline RL benchmarks. OUTLINE:

0:00 - Intro & Overview

4:15 - Offline Reinforcement Learning

10:10 - Transformers …

#mlnews #anthropic #eliza Anthropic raises $124M for steerable AI, peer review is threatened by collusion rings, and the original ELIZA source code was discovered. OUTLINE:

0:00 - Intro

0:40 - Anthropic raises $124M

3:25 - 65% of execs can't explain AI predictions

4:25 - DeepMind releases AndroidEnv

6:10 - Collusion rings in ML Conferences

7:30 - ELIZA's original source code discovered

10:45 - OpenAI raises $100M fund

11:25 - Outro References:

https://techcrunch.com/2021/05/28/anthropic-is-the-new-ai-research-outfit-from-openais-dario-amodei-and-it-has-124m-to-burn/

https://www.anthropic.com/news/announcement

https://www.anthropic.com/

https://openai.com/blog/introducing-openai/

https://dee…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Follow Katherine Lee on Twitter @katherine1ee

Twitter Thread Link: https://twitter.com/katherine1ee/status/1415496898241339400 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Demo Video: https://www.youtube.com/watch?v=9p4mfA0Fyd8 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thank you for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thumbnail Goldfish Image Credit - Photo by zhengtao tang on Unsplash Thanks for watching! Please Subscribe! Chapters:

0:00 Introduction

1:54 Multi-Session Chat

4:12 Models

6:40 Drawbacks of Info Retrieval

9:40 Memory Augmented Models

10:09 Comparison with Language Models

Dienste anbieten und betreiben, z.

Personalisierte Inhalte und Werbeanzeigen können ebenfalls darauf basieren, darüber hinaus aber auch auf Aktivitäten wie Suchanfragen bei Google und Videos, die Sie sich bei YouTube ansehen.

Zu personalisierten Inhalten und Werbeanzeigen gehören beispielsweise Dinge wie relevantere Ergebnisse und Empfehlungen, eine individuelle YouTube-Startseite und Werbung, die auf Ihre Interessen zugeschnitten ist.

Klicken Sie auf „Anpassen“, um sich Ihre Möglichkeiten anzusehen.

Zu diesen gehören zum Beispiel Steuerelemente, um Cookies für die Personalisierung zu deaktivieren, oder Informationen zu Steuerelementen auf Browserebene, mit denen einige oder alle Cookies fü…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thank you for watching! Please Subscribe! Chapters

0:00 Introduction

0:13 BlenderBot 2.0

2:28 Wordcraft

3:20 Hyper-Text Pre-Training and Prompting

4:18 Deduplicating Training Data

5:00 Turning Tables

6:26 Reasoning with LMs and KGs

7:06 FLEX: Few-Shot NLP

8:33 AlphaFold2

9:27 Semi-Supervised Learning in Action

10:18 AI-Generating Algorithms

10:52 Adaptable Agent Populations

11:36 Recurrent Parameter Generators

12:17 Conservative Objective Models

13:20 Representation Learning for OOD Robots

13:54 MultiBench

14:40 Align before Fuse

15:06 CLIP Benefit in V-L tasks

15:4…

This video will present the Determined AI Model Hub for using HuggingFace transformers, tokenizers, and datasets with the Determined training platform! I hope you find this video useful! Overview of Determined on Henry AI Labs:

https://www.youtube.com/watch?v=9wbn2ikhbpg

Challenges of Advanced Hyperparameter Search on Henry AI Labs:

https://www.youtube.com/watch?v=5F5LlmO10AM

Walkthrough of the Determined CIFAR-10 Example on Henry AI Labs:

https://www.youtube.com/watch?v=WEYu8DI4LOU Determined Transformers Examples: https://docs.determined.ai/latest/model-hub/transformers/examples.html

HuggingFace Models: https://huggingface.co/models

HuggingFace Datasets: https://huggingface.co/datasets/sw…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Dienste anbieten und betreiben, z.

Personalisierte Inhalte und Werbeanzeigen können ebenfalls darauf basieren, darüber hinaus aber auch auf Aktivitäten wie Suchanfragen bei Google und Videos, die Sie sich bei YouTube ansehen.

Zu personalisierten Inhalten und Werbeanzeigen gehören beispielsweise Dinge wie relevantere Ergebnisse und Empfehlungen, eine individuelle YouTube-Startseite und Werbung, die auf Ihre Interessen zugeschnitten ist.

Klicken Sie auf „Anpassen“, um sich Ihre Möglichkeiten anzusehen.

Zu diesen gehören zum Beispiel Steuerelemente, um Cookies für die Personalisierung zu deaktivieren, oder Informationen zu Steuerelementen auf Browserebene, mit denen einige oder alle Cookies fü…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Chapters

0:00 Introduction

2:18 Improvements in Video Modeling

6:08 Vokenization

7:31 HowTo100M Data

9:07 Teacher Learning

13:06 Interesting Distillation Ideas

17:48 Results Thanks for watching! Please Subscribe!

Learn more and submit: https://3b1b.co/SoME1

Podcast/New channel: https://youtu.be/C-i4q-Xlnis

↓↓Things referenced through the video↓↓ Join the discord channel:

https://discord.gg/SRTErdZ9 James Schloss:

https://www.youtube.com/user/LeiosOS Free will theorem:

https://www.ams.org/notices/200902/rtx090200226p.pdf Kolmogorov complexity and primes:

https://people.cs.uchicago.edu/~fortnow/papers/kaikoura.pdf Tadashi Tokieda talk:

https://youtu.be/tQQ3oiB32GI Boarbarktree:

https://www.youtube.com/channel/UCFeIEAkqvS4fJMTwUtF4OFw Mathologer:

https://youtu.be/N-KXStupwsc Manim:

https://github.com/3b1b/manim Manim Community edition:

https://github.com/ManimCommunity/manim/ Reanimate:

https://github.…

How to write the eigenvalues of a 2x2 matrix just by looking at it.

Thanks to Tim for the jingle: https://www.youtube.com/acapellascience

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos.

Special thanks to these supporters: https://3b1b.co/quick-eigen-thanks Introduction to eigenvectors and eigenvalues:

https://youtu.be/PFDu9oVAE-g Lockdown math lecture talking about the mean product formula:

https://youtu.be/MHXO86wKeDY Timestamps:

0:00 - Background

4:53 - Examples

10:24 - Relation to the characteristic polynomial

12:00 - Last thoughts ------------------ These animations are largely made using a custom python …

General exponentials, Love, Schrödinger, and more.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/thanks ------------------

The Romeo-Juliet example is based on this essay by Steven Strogatz:

http://www.stevenstrogatz.com/essays/loves-me-loves-me-not-do-the-math The book shown at the start is Vladimir Arnold's (excellent) textbook on ordinary differential equations.

https://amzn.to/3dtXSwj Need a review of ordinary powers of e?

https://youtu.be/m2MIpDrF7Es Or of linear algebra?

https://youtu.be/kYB8IZa5AuE Timetable

0:00 - Definition

6:40 - Dynamics of love

13:17 - General equation

20:03 - On general rotations

22:11 - Visualizing with flow ------------------

C…

Bayes factors, aka Likelihood Ratios*, offer a very clear view of how medical test probabilities work.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/bayes-factor-thanks The book by my friend Matt Cook about paradoxes mentioned at then end:

https://amzn.to/3aBrEzg On the topic, I can't help also mentioning another paradox book I'm rather fond of by Bunch:

https://amzn.to/3mBDSKE *As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same term in this setting, where Bayes rule is used on the probability of an event with only two possible outcomes (you either have the disease or you don't), they do take…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Fast Linking Numbers for Topology Verification of Loopy Structures " is available here:

https://graphics.stanford.edu/papers/fastlinkingnumbers/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Ja…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/stacey/yolo-drive/reports/Bounding-Boxes-for-Object-Detection--Vmlldzo4Nzg4MQ 📝 The paper "Scaling data-driven robotics with reward sketching and batch reinforcement learning" is available here:

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Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jo…

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Alias-Free GAN" is available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/stacey/xray/reports/X-Ray-Illumination--Vmlldzo4MzA5MQ 📝 The paper "NeuMIP: Multi-Resolution Neural Materials" is available here:

https://cseweb.ucsd.edu/~viscomp/projects/NeuMIP/ 📝 Our latent space technique:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 📝 Our “Photoshop” technique:

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Solid-Fluid Interaction with Surface-Tension-Dominant Contact" is available here:

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❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "Endless Loops: Detecting and Animating Periodic Patterns in Still Images" is available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://colab.research.google.com/drive/1gKixa6hNUB8qrn1CfHirOfTEQm0qLCSS 📝 The paper "Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents" is available here:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Unsupervised 3D Neural Rendering of Minecraft Worlds" is available here:

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Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer,…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Discovering Diverse Athletic Jumping Strategies" is available here:

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Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, S…

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "Fire in Paradise: Mesoscale Simulation of Wildfires" is available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "Slope-Space Integrals for Specular Next Event Estimation" is available here:

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Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "RoboGrammar: Graph Grammar for Terrain-Optimized Robot Design " is available here:

https://people.csail.mit.edu/jiex/papers/robogrammar/index.html Breakdancing robot paper:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "Self-Organising Textures" is available here:

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Data Fest Online 2021 https://fest.ai/2021/

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021 Modern Digital Advertising Platforms Leverage Machine Learning and AI to help Advertisers to achieve their goals. Being managed by humans, Advertising technological potential is often remains under-utilised as Humans tend to follow stereotypes and rely on “gut feeling” when making decisions. Understanding of the underlying principles behind “Googles and Facebook’s of our world” therefore becomes a crucial skill a modern marketer needs to acquire to stay relevant. In this talk, we will shed the light into the complex Digital Advertising ecosystem and will show you techniques, such a…

Data Fest Online 2021 https://fest.ai/2021/

ML REPA track https://ods.ai/tracks/ml-repa-df2021 Presentation: https://yadi.sk/i/a25573AB8IZUyw In this video we will analyse the requirements for modern MLOps and the main trends: Human-Centered AI, Fairness, Explainability, Model Monitoring, Human Augmented AI

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

ML Training 2019 Artur Kuzin tells about his participation in the competition Driven Data Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth. He took second place. In this video, you will find out: - Overview of a training procedure on Imagenet1k from scratch

- Implementation Details of Hacks & Tricks

- The specialty of working with JPEG pictures and resize in different frameworks Presentation - https://gh.mltrainings.ru/presentations/Kuzin_DrivenDataHakuna.pdf

ML Training 2019 Yury Bolkonsky and Andrei Dukhounik tell about their participation in Kaggle Understanding Clouds from Satellite Images. The team got a silver medal. In this video you will find out:

- Thresholding is an evil, believe in your classification models

- Why you should always use modern best practices

- Why it is not recommended to use postprocessing without local validation Presentation - https://gh.mltrainings.ru/presentations/Bolkonsky_KaggleUnderstandingClouds.pdf

ML Training 2019 Yury Bolkonsky tells about his participation in Kaggle ASHRAE - Great Energy Predictor III. His team won a gold medal. In this video you will find out:

- How to create timestamp features

- Do you need to use a leak if it is noisy?

- Leak validation for the best solution

ML Training 2019 Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out:

- How AutoML competition differs from most common Kaggle-alike and why you should try them

- Features engineering approach for time-series tasks when you have no idea about domain

- Why validation split should emulate train-test split

- Why you should always check the code of top participants and how small bugs might drop your score Presentation - https://gh.mltrainings.ru/presentations/Vorotyntsev_CodalabAutoML.pdf

ML Training 2019 Adel Valiullin tells about his participation in the competition Kaggle Peking University/Baidu - Autonomous Driving. He won a silver medal. In this video, you will find out: - Overview of the Autonomous Vehicles problem

- Dataset description and exploration: images with 6D pose information, taken from the roof of a car, 3D models of cars and input data analysis - Problems with mAP metric and dataset in this challenge

- The implementation of CenterNet Neural Network for 6D car pose estimation

- Score boosters and other better and high scored approaches

ML Training 2019 Ilya Kibardin tells about his participation in 2 competitions: Topcoder SpaceNet 5 Challenge & Signate The 3rd Tellus Satellite Challenge. He took fourth and second places. In this video you will find out:

- Spacenet 5 challenge at Topcoder, dataset and metric description

- Overview of a UNet pipeline for road graph extraction from satellite images

- The same pipeline applied to ice segmentation at Signate

- Hacks & Tricks for better performance Presentation - https://gh.mltrainings.ru/presentations/Kibardin_Spacenet5Tellus_v2.pdf

Data Fest Online 2020

Uncertainty Estimation in ML track https://ods.ai/tracks/uncertainty-estimation-in-ml-df2020 Speaker: Bruno Mlodozeniec, University of Cambridge In this video we discuss how ensembles of models can be effectively emulated using a single “Prior Network” model via a technique called Ensemble Distribution Detection. This enables a single model to efficiently retain both the ensemble’s predictive performance and uncertainty measures at low computational and memory cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Huawei's DaVinci AI compute architecture. Dmitrii Khizbullin, Overview of DaVinci compute architecture for Deep Learning training and inference, design choices for hardware and software layers. Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speaker: Evgenii Zheltonozhskii, Technion, Israel Institute of Technology Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

The code generation task can be formulated as a translation from a natural language to a specific programming language. To evaluate the output of different code generation models (against each other and during validation), generated code is compared to reference snippets. Unfortunately, it is impossible to evaluate the output of every slightly different model by an experienced human programmer. Therefore, it is necessary to apply some kind of metric that would score generated code snippets, and the desired metric should be strongly correlated with human judgment. The metrics currently used to evaluate code generation models (BLEU, ROUGE, METEOR) have originated from the neural machine trans…

Одной из важных областей исследования в программной инженерии в последние пару десятилетий являются так называемые «запахи кода» (code smells), то есть определенные архитектурные и программные решения, которые, не являясь сами по себе ошибкой, тем не менее могут усложнить восприятие кода или привести к ошибке в дальнейшем. Учёные уже собрали большие списки таких запахов, разработали инструменты для их нахождения и проанализировали их частоту в реальном коде. Отдельным подвидом запахов кода являются «запахи в тестах» (test smells). По сути дела, это тоже запахи кода, но специфичные для тестирования и тестировочного кода. Их выделение в отдельный класс и термин связано с тем, что тестировочны…

При разработке лекарственных препаратов на основе терапевтических антител возникают различные задачи, связанные со структурами антитела и/или антигена. Например, к ним относятся фолдинг (определение структуры по последовательности), докинг (определение комплекса антитело-антиген), эпитопное картирование (определение эпитопа — аминокислот, участвующих в связывании). Эти задачи можно решать экспериментальным путем, но это долго и дорого. Поэтому на помощь приходят вычислительные методы. При этом в последнее время в научном сообществе возникает все больше и больше решений, основанных на методах глубокого обучения. Один из примеров хорошо известен — Alphafold для задачи фолдинга. На семинаре бу…

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В задаче сегментации медицинских изображений наилучших результатов достигают модификации архитектуры UNet. Однако, полагаясь исключительно на свертки, подобные сети принимают решение для каждого пикселя основываясь лишь на небольшой его окрестности. Данное ограничение авторы предлагают обойти с помощью механизма self-attention, как части encoder'a модели. Представленная модель(MedT) учитывает ограничение на небольшой размер датасета, типичный для возможных приложений. Для учета отношений между различными участками изображения вводится новая стратегия обучения(LoGo) — совместное использование двух похожих по архитектуре частей сети: локальной(для небольших областей) и глобальной(для всего из…

В стандартном процессе машинного обучения ставится задача поиска глобального минимума функции потерь. При этом ландшафт функции потерь для задач глубокого обучения обычно чрезвычайно сложен, до сих пор неизвестна форма локальных минимумов, их устройство и взаимное расположение. Это приводит к тому, что наиболее популярные на данный момент методы оптимизации (SGD, Adam) могут сойтись в локальный минимум, не являющийся глобальным. К счастью, в последние несколько лет появилось множество подходов, которые модифицируют стандартные SGD и Adam для более качественного обучения моделей и показывают значимое улучшение результатов для исследуемых моделей. Однако, исследователи обычно изучают эффектив…

В погоне за долями процентов top-1 accuracy на известном датасете ImageNet год за годом изобретаются новые подходы и техники, значительно продвигающие Deep Learning вперед. Одним из таких подходов, который считался в течение последних 6 лет краеугольным камнем архитектур для распознавания изображений является BatchNormalization с его вариациями. Без него градиенты взрываются на сетях значительной глубины в результате миллионов операций умножения-сложения. В то же время, начиная с 2019 года, в построении моделей для задач Image Recognition и Object Detection возобладал подход к автоматическому генерированию целых семейств моделей, варьирующихся по глубине, ширине и разрешению для изображений…

Зачастую успешные алгоритмы глубокого обучения с подкреплением требует большого количества взаимодействий между агентом и средой и, как следствие, являются вычислительно сложными. Существует множество подходов для увеличения эффективности собранных эпизодов, например иерархическое обучение и имитационные алгоритмы, использующие экспертную оценку. В случае последних, для упрощения процесса разметки иногда понижается качество траекторий, что может негативно сказаться на процессе обучения. Исследование авторов статьи предлагает объединение данных подходов для случая низкокачественной экспертной оценки, используя особую забывчивую структуру буфера опыта. Описанный подход является универсальным …

В последнее время глубокое обучение с подкреплением демонстрирует впечатляющие результаты при решении большого спектра различных задач. Однако на практике большинство алгоритмов сталкиваются с проблемой зашумленных наблюдений (затемнение, изменение яркости, искажения, заморозка изображения и т.д.), что может привести к субоптимальной производительности и возможным проблемам с безопасностью алгоритма. Разработка устойчивого к данным возмущениям алгоритма является важным шагом к применению алгоритмов обучения с подкреплением к задачам реального мира. На семинаре мы рассмотрим общую схему обучения алгоритмов RL со вспомогательной задачей отслеживания помех (например, искуственнымых шумов). В р…

Предсказание имен идентификаторов — важная задача, которая давно привлекает внимание разработчиков программного обеспечения, ведь от качества имен идентификаторов зависит читаемость и понимаемость кода. Одна из подзадач этого направления — предсказание имен переменных. Для ее решения на данный момент используется несколько альтернативных подходов. В первом подходе код трактуется как обычный текст, и к нему применяются различные методы NLP, во втором — код представляется в виде AST, и к нему применяются графовые сети. На семинаре будет разобрана архитектура модели на основе трансформеров, которую разрабатывает докладчик в качестве дипломной работы, сравним ее с классическими методами NLP. Та…

Сейсмическая разведка позволяет получить информацию о свойствах почвы в определённой местности. Она широко применяется в поиске полезных ископаемых и решении различных научных задач. Данные одного исследования могут занимать сотни терабайт, что делает затруднительными их передачу и хранение. Уменьшить эти трудности может сжатие сейсмической информации. На семинаре будут рассмотрены подходы к сжатию сейсмических данных с применением глубокого обучения. Докладчик: Андрей Гусев.

Применение моделей глубокого обучения к химическим данным дает хорошие результаты для различных классов задач: от предсказания свойств молекул до синтеза. На данный момент наиболее эффективными подходами для предсказания молекулярных свойств методами глубокого обучения являются графовые нейросети. Несмотря на общую результативность, одной из проблем нейросетевых подходов является трудность интерпретации выученных моделью параметров и итоговых предсказаний. Различные способы визуализации моделей глубокого обучения позволяют лучше оценить адекватность модели. Для задач из области химии визуализация может помочь отследить механизмы, связывающие молекулярную структуру и свойства молекул. На сем…

Важным компонентом почти любой системы является отслеживание ошибок. Bug triaging -- задача определения подходящего разработочика, который потенциально мог бы исправить ошибку. Данная задача является важной, так как некорректное присвоение тратит время разработчика и снижает скорость устранения ошибок. В самом простом случае ошибка характеризуется названием и описанием на естественном языке. За последнее время появилось несколько подходов к решению данной задачи, основанных на глубоких нейронных сетях. Как правило, данные подходы работают лучше, чем алгоритмы классического машинного обучения (SVM, Naive Bayes, Random Forest). На семинаре мы обсудим нейросетевые подходы к решению задачи, пре…

Большинство известных model free алгоритмов в RL используют в обучении Temporal Difference Learning, в котором каждому действию присваивается вес пропорциональный кумулятивной награде, которая была получена после совершения этого действия. Тем не менее, у такого подхода есть недостатки. TD-learning не предлагает никакого механизма пропуска несвязанных событий, которые случаются между действиями и последующими вознаграждениями. Это создает непредсказуемый рост дисперсии в ожидаемой кумулятивной награде, что не позволяет моделям точно выучивать value function. На семинаре разберем подход предложенный исследователями из Deepmind, в котором удалось успешно научить агента строить ассоциации межд…

Число молекул, подобных лекарствам, оценивается в количестве 1023 – 1060. Несмотря на десятилетия скрининга, лишь малая часть этого химического пространства была обследована. Создание последовательностей молекул, которые не основаны на уже существующих последовательностях (de novo), нацелено на эффективное исследование и разработку с нуля огромного количества лекарств с помощью вычислительных методов. Неограниченные методы de novo дизайна часто генерируют нереалистичные и трудно синтезируемые молекулы. Некоторые подходы показали перспективность улучшения синтезируемости молекул. В них применяется наиболее естественная идея смещения поиска в область более простого синтеза соединений с исполь…

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

Yandex Research — это исследовательская группа внутри Яндекса, которая занимается фундаментальными проблемами в важнейших областях computer science и искусственного интеллекта, таких как компьютерное зрение, Natural Language Processing, речевые технологии, краудсорсинг, поиск и рекомендации. В рамках митапа исследователи из Yandex Research и научной лаборатории Яндекса на Факультете Компьютерных Наук НИУ ВШЭ рассказали об интересных задачах, которыми они занимаются, а ещё о том, как стать частью команды. Участники митапа и программа: • Андрей Малинин. Неопределенность в структурных предсказаниях; • Станислав Морозов. Big GANs Are Watching You: о сегментации объектов без учителя с помощью го…

Data Fest Online 2021

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Марк Эльцефон, Data Scientist at ex. Jam City Изначальная задача была в повышении конверсии из non payers в payers и при этом не потерять в ARPDAU. Задача была решена с помощью персонализированных офферов в разные этапы игры и с помощью ценовой оптимизации. В данном докладе хотел бы описать некоторые простые и не совсем шаги ценовой оптимизации, которые помогли мне в решении этой задачи. Презентация доклада: https://drive.google.com/file/d/19hdHDvuY3IT1iCKS4t7I8aVca7dEA3Bs/view?usp=sharing Посмотреть эфир и список треков и организаторов: https://datafest…

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloud-city-df2021 Спикеры: Фёдор Жаринов, старший инженер-программист в области глубокого обучения, Intel Эдуард Замалиев, старший инженер-программист в области глубокого обучения, Intel Компонент Open Model Zoo инструментария OpenVINO насчитывает более 250 моделей нейросетей для более чем 20 классов задач готовых к использованию. Однако даже в одном классе архитектура моделей различается, что приводит к различиям в пре-, постобработке данных для модели. Из-за чего в рамках одного класса модели могут быть не взаимозаменяемыми. Разрабатываемый подход Model API призван унифицировать модели в рамках одного класса, предоста…

Сегодня на одной площадке объединятся специалисты в области искусственного интеллекта и профессиональные шахматисты. Программа:

21.00 - 21.20 (rus)

Марк Владимирович Глуховский (исполнительный директор Федерация шахмат России). “Шахматы и компьютерные технологии. Что есть искусственный интеллект и как он меняет шахматный мир?” 21.20 - 21.30 (rus)

Беседа с Сергеем Юрьевичем Шиповым (российский шахматист, гроссмейстер, шахматный тренер, комментатор) 21.30 - 21.45 (eng)

Александр Шиманов (российский шахматист, гроссмейстер, комментатор). Обзор партии между сильнейшими шахматными движками (AlphaZero и StockFish) 21.45 - 22.15 (eng)

Ulrich Paquet and Nenad Tomašev (исследователи, DeepMind, autho…

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Data Fest Online 2021

ML Art track https://ods.ai/tracks/ml-art-df2021 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2021

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

Data Fest Online 2021

ML Art track https://ods.ai/tracks/ml-art-df2021 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2021

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberСloud track https://ods.ai/tracks/cloud-city-df2021 Мы рассмотрим проблему антиспуфинга лиц, виды атак на системы распознавания лиц, основные сложности и подходы в решении задачи. Разберем проблему кросс домена, предложим решение и обсудим фишки, используемые в регуляризации тренировки нейронных сетей Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

Data Fest Online 2021

ML Art track https://ods.ai/tracks/ml-art-df2021 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2021

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

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ODS Summer of Code 2021 | Intel & Sbercloud track https://ods.ai/tracks/cloud-city-df2021 Спикер: Аветисян Манвел Согомонович (Управляющий директор Лаборатории ИИ, Сбер)

В докладе рассмотрены решения, разработанные Лабораторией ИИ для борьбы с пандемией COVID-19: анализ КТ грудной клетки для определения степени поражения легких, анализ кашля для определения заболевания короновирусом, определение наиболее подверженных риску перевод на ИВЛ пациентов по записям электронной медицинской карты. Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.m…

ODS Summer of Code 2021 | Intel & Sbercloud track https://ods.ai/tracks/cloud-city-df2021 Спикер: Иван Лазаревич, Intel В докладе будет представлен алгоритм послойной калибрации нейронных сетей, позволяющий осуществлять их компрессию (квантизацию и прунинг весов) без перетренировки с незначительным ухудшением целевых метрик качества моделей. Реализованный в OpenVINO алгоритм компрессии моделей позволяет осуществлять их сжатие и калибрацию на CPU в случае отсутствия данных для осуществления калибрации, то есть через единственный вызов API. Такой подход позволяет достигать выдающихся результатов при компрессии без данных, в частности было достигнуто уменьшение top@1 точности на ImageNet в пре…

Data Fest Online 2021

Data Governance track https://ods.ai/tracks/datagovernance-df2021 Евгений Ермаков из Яндекс Go расскажет про роль партнера по данным: Как появилась эта роль?

Почему не просто системный аналитик?

Что входит в обязанности партнера по данным?

Как проходят собеседования и что нужно знать? Как стать лучшим партнером по данным из лучших? Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2021

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2021

PyData track https://ods.ai/tracks/pydata-df2021 В EORA мы обучаем и ставим в продакшн десятки нейронных сетей, а также сложных CV пайплайнов. Часто пайплайны трудно готовить для инференса в существующих движках, а также есть потребность в быстром и простом доведении моделей от инженера компьютерного зрения в производство. Итак, мы пришли к своему движку для инференса, который назвали Inferoxy, и теперь он доступен в open-source. В докладе я расскажу про то, как мы шли к этому и как устроен фреймворк сейчас. Погнали! Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/da…

Спикер: Dmitry Simakov, Kaggle Master, LightAutoML developer at Sber AI Lab Показываем, как можно использовать LightAutoML для решений NLP задач и быстрой проверки гипотез на GPU и на CPU: SotA TFIDF или все же стоит учить Bert? LightAutoML Github repo: https://github.com/sberbank-ai-lab/LightAutoML

Tutorials Github repo: https://github.com/sberbank-ai-lab/lightautoml-datafest-workshop

Brilliant: http://www.brilliant.org/primer Papers:

- https://www.researchgate.net/publication/41910312_Altruism_Spite_and_Greenbeards

- https://www.reed.edu/biology/professors/srenn/pages/teaching/2007_syllabus/2007_readings/a13_Keller_1998.pdf For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning Sometimes streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made with Unity

https://github.com/Helpsypoo/PrimerUnity Music by Mathieu Keith. For business inquiries: mathieu.keith@gmail.com Several other inputs into the graphics are from public domain contributions to blendswap.com Plush blo…

Plush blobs: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning A good place for learning more about how to be less wrong:

https://www.lesswrong.com/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made possible by support through Patreon:

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Ghost G…

Check out Brilliant: http://www.brilliant.org/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning

Store: https://store.dftba.com/collections/primer More on voting theory:

- Interactive by Nicky Case: https://ncase.me/ballot/

- The best single resource I found: https://www.lesswrong.com/posts/D6trAzh6DApKPhbv4/a-voting-theory-primer-for-rationalists Organizations that advocate for voting reform:

- Team Approval: https://electionscience.org/

- Team Instant Runoff: https://www.fairvote.org/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself wor…

Ishan Misra is a research scientist at FAIR working on self-supervised visual learning.

Please support this podcast by checking out our sponsors:– Onnit: https://lexfridman.com/onnit to get up to 10% off– The Information: https://theinformation.com/lex to get 75% off first month– Grammarly: https://grammarly.com/lex to get 20% off premium– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oilEPISODE LINKS:Ishan’s twitter: https://twitter.com/imisra_Ishan’s website: https://imisra.github.ioIshan’s FAIR page: https://ai.facebook.com/people/ishan-misra/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpoti…

Zach Bitter is an ultramarathon runner and coach.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(07:34) – The marathon mentality(14:43) – The psychology of quitting(25:49) – Variety in ultramarathons(33:04) – What does it take to run 100 miles?

(38:27) – Leading ultramarathon events(42:09) – Training and race strategy(44:39) – 100 Mile world record(48:41) – Foot strike variability and cadence(51:29) – The 11 hour barrier(54:57) – The most beautiful thing about running(1:01:19) – Zach’s training regime(1:06:06) – MAF 180 Formula(1:16:31) – Training plans(1:31:30) – Marathons vs. 100 miles(1:40:31) – Zach’s diet philosophy(1:55:1…

Cumrun Vafa is a theoretical physicist at Harvard.

Please support this podcast by checking out our sponsors:– Headspace: https://headspace.com/lex to get free 1 month trial– The Jordan Harbinger Show: https://www.youtube.com/thejordanharbingershow– Squarespace: https://lexfridman.com/squarespace and use code LEX to get 10% off– Allform: https://allform.com/lex to get 20% offEPISODE LINKS:Cumrun’s Twitter: https://twitter.com/cumrunvCumrun’s Website: https://www.cumrunvafa.orgPuzzles to Unravel the Universe (book): https://amzn.to/3BFk5msPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman…

Anya Fernald is the co-founder of Belcampo farms, chef, and regenerative agriculture expert.

On some podcast players you should be able to click the timestamp to jump to that time.

(51:16) – AI will be a better farmer than humans(55:17) – Carbon negative farming is possible right now(57:04) – Certified Humane(1:00:34) – Evolutionary diet of animals(1:03:17) – Neuralink can help us understand animals(1:07:13) – All grass-fed meat made the same?

(1:11:57) – Health benefits of grass-fed beef(1:16:29) – What does it take to be a woman CEO of a meat company?

(1:29:58) – The best meal in the world(1:37:48) – Anya played oboe in the Sicily municipal band(1:39:23) – Hunting has inspired regenerativ…

Rick Doblin is a psychedelics researcher and the founder and executive director of the Multidisciplinary Association for Psychedelic Studies (MAPS).

Please support this podcast by checking out our sponsors:– Theragun: https://therabody.com/lex to get 30 day trial– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Rick’s Twitter: https://twitter.com/RickDoblinMAPS: https://maps.orgPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: …

On some podcast players you should be able to click the timestamp to jump to that time.

(44:49) – Quantum mechanics in evolution of objects in the Solar system(49:15) – Simulating the first formations around the Sun(55:02) – Will it be possible to simulate the full history of the Solar System?

(1:12:37) – The origin of life(1:15:02) – Evidence of Planet Nine(1:17:32) – Discovery of Neptune(1:18:42) – When will we find Planet Nine?

(1:21:21) – Planet Nine throws rocks into the Kuiper Belt(1:25:15) – Could Planet Nine be a primordial black hole?

(1:35:20) – Commercial space revolution boosts science and the human condition(1:42:46) – Solving sex in space(1:43:24) – Would humans evolve if we c…

Michael Malice is a political thinker, podcaster, and author.

Please support this podcast by checking out our sponsors:– Gala Games: https://gala.games/lex– Indeed: https://indeed.com/lex to get $75 credit– BetterHelp: https://betterhelp.com/lex to get 10% off– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmaliceMichael’s Community: https://malice.locals.com/Michael’s YouTube: https://www.youtube.com/channel/UC5tj5QCpJKIl-KIa4Gib5XwMichael’s Website: http://michaelmalice.com/about/Your Welcome podcast: https://bit.ly/30q8oz1The Anarchist Handbook (book): https://amzn.to/3yUb2f0The New Right (book): https://amzn.to/34gxLo3D…

Roger Reaves is one of the most prolific drug smugglers in history.

Please support this podcast by checking out our sponsors:– Noom: https://trynoom.com/lex– Allform: https://allform.com/lex to get 20% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Smuggler (book): https://amzn.to/3xydszDPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Ful…

Sara Walker is an astrobiologist and theoretical physicist interested in the origin of life.

(00:00) – Introduction(07:44) – Origin of life(15:38) – Did aliens seed life on Earth?

(32:27) – Cellular automata(36:56) – The laws of physics may change with time(46:41) – Nobel Prize for the origin of life(52:01) – Is consciousness fundamental to the universe?

(1:03:19) – Life is the most deterministic part of physics(1:05:54) – Free will(1:14:11) – How to detect alien life(1:28:48) – How many alien civilization are out there?

(1:35:32) – Shadow biosphere(1:41:59) – UFO sightings(1:45:35) – Exponential population growth of AI lifeforms(1:52:42) – The role of death in life(1:56:45) – Advice for yo…

Jocko Willink is a retired Navy SEAL, co-author of Extreme Ownership, and host of Jocko Podcast.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(09:01) – The beauty and tragedy of war(14:35) – Soviet Union in World War II(20:54) – What makes a just war?

(34:30) – Jordan Peterson(37:42) – Fear of death(41:53) – Autonomous weapons systems(53:28) – What makes a great leader?

(56:15) – Elon Musk – a leadership case study(1:10:03) – Steve Jobs – a leadership case study(1:20:16) – Sundar Pichai – a leadership case study(1:27:15) – Young Jamie(1:31:23) – Discipline(1:34:15) – A day in the life of Jocko(1:40:30) – Jiu Jitsu(1:56:18) – B…

Yeonmi Park is a North Korean defector, human rights activist, and author of the book In Order to Live.

Please support this podcast by checking out our sponsors:– Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first order– Gala Games: https://gala.games/lex– BetterHelp: https://betterhelp.com/lex to get 10% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Yeonmi’s Twitter: https://twitter.com/YeonmiParkNKYeonmi’s Facebook: https://www.facebook.com/officialyeonmiparkYeonmi’s YouTube: https://www.youtube.com/c/YeonmiParkOfficialIn Order to Live (book): https://amzn.to/3wdtKfLPODCAST INFO:Podcast website: https://lexfridm…

Clara Sousa-Silva is a quantum astrochemist at Harvard.

Please support this podcast by checking out our sponsors:– Onnit: https://lexfridman.com/onnit to get up to 10% off– Grammarly: https://grammarly.com/lex to get 20% off premium– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Clara’s Twitter: https://twitter.com/drphosphineClara’s Website: https://clarasousasilva.comPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube …

Bret Weinstein is and evolutionary biologist, author, and co-host of the DarkHorse Podcast.

Please support this podcast by checking out our sponsors:– The Jordan Harbinger Show: https://www.youtube.com/thejordanharbingershow– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% offEPISODE LINKS:Bret’s Twitter: https://twitter.com/BretWeinsteinBret’s YouTube: https://www.youtube.com/BretWeinsteinDarkHorseBret’s Website: https://bretweinstein.net/Bret’s Book: https://amzn.to/3dhVWrvPODCAST INFO:Podcast website: …

Rob Reid is an author and podcaster.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:33) – The most entertaining outcome is the most likely(14:52) – Meme theory(18:12) – Writing process(24:59) – Engineered viruses as a threat to human civilization(32:45) – Gain-of-function research on viruses(44:54) – Did COVID leak from a lab?

(52:15) – Virus detection(1:00:04) – Failure of institutions(1:07:48) – Using AI to engineer viruses(1:12:07) – Evil and competence(1:21:26) – Where are the aliens?

(1:25:19) – Backing up human consciousness by colonizing space(1:34:48) – Superintelligence and consciousness(1:46:12) – Meditation(1:54:2…

Charles Hoskinson is the founder of Cardano, co-founder of Ethereum, a mathematician, and a farmer.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(09:12) – What programming language is the simulation written in?

(14:17) – Favorite philosophers(23:18) – Theory vs engineering in cryptocurrency(34:27) – What programming languages should everyone learn(42:42) – Haskell and beyond(46:26) – Plutus: Cardano’s smart contract platform based on Haskell(50:53) – What is a blockchain?

(55:05) – Hybrid smart contracts(1:00:55) – Proof of work vs proof of stake(1:09:42) – Cardano’s proof of stake consensus algorithm(1:20:14) – What is Cardan…

Unlocking the challenge of molecular simulation has the potential to yield significant breakthroughs in how we tackle such societal issues as climate change, drug discovery, and the treatment of disease, and Microsoft is ramping up its efforts in the space.

In this episode, Chris Bishop, Lab Director of Microsoft Research Cambridge, welcomes renowned machine learning researcher Max Welling to the Microsoft Research team as head of the new Amsterdam lab.

Connecting over their shared physics background and vision for molecular simulation, Bishop and Welling explore several fascinating topics, including a future in which machine learning and quantum computing will be used in tandem to model mo…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Principal Productivity Engineer Brian Houck discuss what the massive shift to remote work meant for developers—both employees of Microsoft and customers using Microsoft developer platforms to support their work.

They’ll talk about how taking a holistic approach to developer productivi…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They also explore how people already working from home helped them better und…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Abigail Sellen, Deputy Lab Director at Microsoft Research Cambridge in the United Kingdom, explore the dynamics of meetings and collaborations in the context of remote work.

They specifically address the difference between weak and strong ties in our professional networ…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

The results have informed product features designed to better support remote work and are now being used to help companies, including Microsoft, usher their workforces into a future of hybrid work.

They’ll discuss the role of research during times of disruption, the widening…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Hunt Allcott talks with Postdoctoral Researcher Evan Rose about Allcott’s work exploring the everyday decisions people face, like buying fuel-efficient cars or taking out payday loans, and how a clearer understanding of these decisions can shape meaningful public policy.

Allcott shares how his and others’ research shows that policy can often have compl…

Interviewed by Senior Principal Researcher Hunt Allcott, Economist David Rothschild discusses how the news media has evolved alongside social media and the internet, from story development to distribution of news via aggregators and wire services.

Rothschild illuminates how and where people are consuming news and shares some of the strategies he’s seeing news outlets use to appeal to their audiences.

He also covers research insights into media bias, misinformation, and how this knowledge could inform the future of news for the better.

In addition, the researchers talk about Rothschild’s work with Project Ratio, which looks at how the news ecosystem impacts public opinion and political polar…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis.

Together, they cover the connection between causal machine learning and economics research, the motivations of buyers and sellers on e-commerce platforms, and how ad targeting and data practices could evolve to foster a more symbiotic…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Dr. Hunt Allcott, Senior Principal Researcher at Microsoft Research New England, talks with Dr. Evan Rose, Postdoctoral Researcher, whom Allcott describes as “one of the most engaging and talented researchers in applied microeconomics today.” They’ll discuss how Rose’s experience teaching adult learners at San Quentin State Prison has resonated throughout his research, and they’l…

Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer.

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-c…

In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language.

Bounded-depth hierarchical structure is thought to be a key feature of natural language…

We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynaben…

We invited members of Masakhane, Tosin Adewumi and Perez Ogayo, to talk about their EMNLP Findings paper that discusses why typical research is limited for low-resourced NLP and how participatory research can help.

We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-sp…

How can we build Visual Question Answering systems for real users?

For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind.

We talked about the …

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Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction. We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.

Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting.

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work “Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.”

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior

Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”

Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time.

Darts Library for Time SeriesJulien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts.

Follow Julien on twitter: @jlhrzn

Forecasting: Principles and PracticeWelcome to Timeseries!

Today’s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices.

Prerequisites for Time SeriesToday’s experimental episode uses sound to describe some basic ideas from time series.

This episode codes lag, seasonality, trend, noise, heteroskedasticity, decomposition, smoothing, feature engineering, and deep learning.

Orders of MagnitudeToday’s show in two parts.

First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics.

Second, we introduce our new segment “Orders of Magnitude”.

It’s a statistical game show in which participants must identify the true statistic hidden in a list of statistics which are off by at least an order of magnitude.

HeightsBird StatisticsAmounts of DataOur statistics com from this post

They’re Coming for Our JobsAI has, is, and will continue to facilitate the automation of work done by humans.

Other times it may automate a particular part of their role, scaling their effectiveness.

Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve.

Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us today!

Kyle and Celestia discuss whether or not her jobs as a caricature artist or as an academic editor are under threat from AI automation.

Pandemic Machine Learning PitfallsToday on the show Derek Driggs, a PhD Student at the University of Cambridge.

He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans.

by: Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, AIX-COVNET, James H. F. Rudd, Evis Sala & Carola-Bibiane Schönlieb.

Follow the team at @camimaging

Given a document in English, how can you estimate the ease with which someone will find they can read it? Does it require a college-level of reading comprehension or is it something a much younger student could read and understand? While these questions are useful to ask, they don't admit a simple answer. One option is to use one of the (essentially identical) two Flesch Kincaid Readability Tests. These are simple calculations which provide you with a rough estimate of the reading ease. In this episode, Kyle shares his thoughts on this tool and when it could be appropriate to use as part of your feature engineering pipeline towards a machine learning objective. For empirical validation of t…

In this episode, I discuss the changing child mortality rate as evidence of how much better the world is and how much better it could be.

Additional materials: www.superdatascience.com/492

Veerle van Leemput joins us to make the case for why you should be using R for production.

In this episode you will learn:• Our shared powerlifting passion [2:47]• The stigma of using R [12:02]• What does Analytic He…

In this episode, I discuss why you should avoid the visually pleasing but flawed pie chart.

Additional materials: www.superdatascience.com/490

Vin Vashishta joins us to discuss his AI consulting work and his philosophy on AI strategy for monetization.

In this episode you will learn:• V-Squared [4:59]• Vin’s online content [17:18]• Low-code/no-code in data s…

In this episode, I discuss the simple and cheap ways you can buy yourself more time during the day.

Additional materials: www.superdatascience.com/488

Susan Walsh joins us to discuss the importance of data cleaning and normalization and how clean procurement data can save companies money.

In this episode you will learn:• Susan’s “COAT” system [7:16]• The Classificat…

In this episode, I go over the world history of calculus and how we still use these techniques today.

Additional materials: www.superdatascience.com/486

Doug Eisenstein joins us for a great and in-depth conversation on data engineering in the financial sector.

In this episode you will learn:• The founding of Advanti [4:37]• Aristos and solution products [16:45]• The …

In this episode, I discuss interesting research on why humans are so quick to lose faith in algorithms.

Additional materials: www.superdatascience.com/484

Andrew Jones joins us to discuss data science interviews and how you can maximize your chances on interview time, resume, and more!

In this episode you will learn:• Data Science Infinity [5:40]• “The Essential AI and …

In this episode, I talk about the advantages of using a continuous calendar.

Additional materials: www.superdatascience.com/482

Kris Tait joins us to discuss the vast world of digital performance marketing and how automation, data, and optimization play an important role.

In this episode you will learn:• What is performance marketing?

[3:29]• …

In this episode, I go over my top 5 tips for refining your perfect data science resume.

Additional materials: www.superdatascience.com/480

Maureen Teyssier joins us to discuss the cutting-edge work Reonomy is doing in commercial property real estate and her views and tips on building a great data science team.

In this episode you will learn:• Maureen’s wo…

In this episode, I go over my 5 keys to success to tackle any goal.

Additional materials: www.superdatascience.com/478

July 6, 2021 podcastIt made already quite some noise in the news, GitHub copilot promises to be your pair programmer for life.

In this episode I explain how and what GitHub copilot does.

Should developers be happy, scared or just keep coding the traditional way?

SponsorsGet one of the best VPN at a massive discount with coupon code DATASCIENCE.

It provides you with an 83% discount which unlocks the best price in the market plus 3 extra months for free.

June 22, 2021 podcastData from the real world are never perfectly balanced.

In this episode I explain a simple yet effective trick to train models with very unbalanced data.

SponsorsGet one of the best VPN at a massive discount with coupon code DATASCIENCE.

It provides you with an 83% discount which unlocks the best price in the market plus 3 extra months for free.

Here is the link https://surfshark.deals/DATASCIENCEReferences

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June 10, 2021 podcastIn this episode I have a really interesting conversation with Karan Grewal, member of the research staff at Numenta where he investigates how biological principles of intelligence can be translated into silicon.

We speak about the thousand brains theory and why neural networks forget.

May 26, 2021 podcastDelivering unstoppable data to unstoppable apps is now possible with Streamr NetworkStreamr is a layer zero protocol for real-time data which powers the decentralized Streamr pub/sub network.

The technology works in tandem with companion blockchains – currently Ethereum and xDai chain – which are used for identity, security and payments.

On top is the application layer, including the Data Union framework, Marketplace and Core, and all third party applications.

In this episode I have a very interesting conversation with Streamr founder and CEO Henri PihkalaReferences

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

May 11, 2021 podcastIf you think that knowing Tensorflow and Scikit-learn is enough, think again.

What is MLOps and why is it important?

It’s a podcast for techies by techies.

Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

April 28, 2021 podcastYour data is worth thousands a year.

Why aren’t you getting your fair share?

There is a company that has a mission: they want you to take back control and get paid for your data.

In this episode I speak about knowledge graphs, data confidentiality and privacy with Mike Audi, CEO of MyTiki.

You can reach them on their website https://mytiki.com/Discord official channelhttps://discord.com/invite/evjYQq48BeTelegramhttps://t.me/mytikiappSignalhttps://signal.group/#CjQKIA66Eq2VHecpcCd-cu-dziozMRSH3EuQdcZJNyMOYNi5EhC0coWtjWzKQ1dDKEjMqhkP

April 19, 2021 podcastIn this episode I have an amazing conversation with Lillian Pearson from data-mania.com This is an action-packed episode on how data professionals can quickly convert their data expertise into high-growth data businesses, all by selecting optimal business models, revenue models, and pricing structures.

If you want to know more or get in touch with Lillian, follow the links below:

April 13, 2021 podcastIs there a gap between life science and data science?

What’s the situation when it comes to interdisciplinary research?

In this episode I am with Laura Harris, Director of Training for the Institute of Cyber-Enabled Research (ICER) at Michigan State University (MSU), and we try to answer some of those questions.

You can contact Laura at training@msu.edu or on LinkedIn

April 11, 2021 podcastIn this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love ThoughtW…

April 8, 2021 podcastIn this episode I speak with Ritchie Vink, the author of Polars, a crate that is the fastest dataframe library at date of speaking 🙂 If you want to participate to an amazing Rust open source project, this is your change to collaborate to the official repository in the references.

Our SponsorAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

Referenceshttps://github.com/ritch…

March 31, 2021 podcastDo you want to know the latest in big data analytics frameworks?

Have you ever heard of Apache Arrow?

In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.

Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.

It’s a podcast for techies by techies.

March 19, 2021 podcastPandas is the de-facto standard for data loading and manipulation.

Python is the de-facto programming language for such operations.

Rust is the underdog.

In this episode I am showing you why that is no longer the case.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

🎙 Пилотный выпуск нашего подкаста! В гостях у нас Евгений Кашин – ML-инженер из Snap Inc. С Женей мы успели обсудить кучу вещей: Из Воронежа в Лондон 🇬🇧

Snapchat и маски 😷

Будущие прорывы в DS и AR 🚀

Вопросы от русской GPT-3 🤖 … и многое другое 😉

Домашнее задание: отгадать, какие именно вопросы были сгенерированы нейросетью 🦾😄

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