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Photo by CHUTTERSNAP on UnsplashGenerate Simulated Dataset for Linear Model in RMotivationIn these recent years, research about Machine Learning (ML) has increased along with the increased computation capability.

To overcome those problems, the researchers usually generate a simulated dataset that follows the model’s assumptions.

This simulated dataset can be used as a benchmark for the model or real-world dataset replacement in the modeling process, where the simulated dataset is cost-effective than the real-world dataset.

This article will explain how to generate a simulated dataset for a linear model using R.The ConceptThe process of generating a simulated dataset can be explained as fol…

What if I tell you that you can change these emojis to text?

import emot as eConverting emojis to textThis is the final step in which we will pass some text containing emojis to the emot library and convert emojis to text.

text = "I am a coder😎"con = e.emoji(text)conSource: By AuthorHere you can see how easily it converted the emoji to the text.

text = "😁😆😅😂😇🙃"con = e.emoji(text)conSource: By AuthorYou see how wonderful emot is, creating text out of emojis.

text = "🤡"con = e.emoji(text)conSource: By AuthorYou can use this to preprocess your text data before passing it to NLP models.

Data Science MythsPhoto by Sergio Capuzzimati on UnsplashIs machine learning the actual focus of data scientists’ everyday work?

Do you need to learn all the things to be a data scientist?

And, most importantly: Do data scientists have a sense of humor?

It seems there are enough myths about data science out there that there’s always something new for our guests to highlight.

It’s fascinating to see what these experts highlight from their experiences in data science.

Transfer Learning with VGG16 and KerasThe main goal of this article is to demonstrate with code and examples how can you use an already trained CNN (convolutional neural network) to solve your specific problem.

Convolutional Networks are great for image problems however, they are computationally expensive if you use a big architecture and don’t have a GPU.

That’s where Transfer Learning can help you achieve great results with less expensive computation.

Transfer LearningSo what is transfer learning?

To better explain that we must first understand the basic architecture of a CNN.

Presentation of the state of the art in speech synthesis research at the end of May 2021 with a focus on deep learning technologies.

In this paper, I will discuss the state of the art of speech synthesis research to date.

Speech synthesis systems based on Deep Neuronal Networks (DNNs) are now outperforming the so-called classical speech synthesis systems such as concatenative unit selection synthesis and HMMs that are (almost) no longer seen in studies.

[pdf]TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction (2021) Beliaev et al.

(2020)Breathing and Speech Planning in Spontaneous Speech Synthesis

How to delete your saved workspace in RWhen you exit RStudio, you’ll see a pop-up asking, “Save workspace image to ~/.RData?” If you’re unsure, you’ll probably select Save.

The next time you load RStudio, everything in your workspace (global environment) will be exactly how you left it.

By saving your workspace, R saves your global environment as .RData files.

This won’t actually delete your environment, but it will delete the saved .RData files.

With these solutions, hopefully your saved workspace will be gone for good.

Recently, one of our new hires at Atlan asked me, “What are the resources you recommend for me to stay on top of what’s happening in the modern data stack?”The modern data stack is messy and complicated, and it’s changing every day.

Modern Data Stack 101My blog post is a beginner’s guide to defining a modern data platform, the key building blocks of a modern data platform, and the top tools and companies at every stage of the stack.

A great, in-depth read from a16z about which technologies are winning in the modern data stack, based on interviews with 20+ practitioners.

Curated by Andrew Ermogenous, this newsletter shares blogs, guides, and podcasts on the modern data stack and data culture…

Building Reproducible Machine Learning PipelinesPhoto by JJ Ying on UnsplashReproducibility is the accountability required from businesses to further understand and trust the adoption of Machine Learning into our day-to-day lives.

Note: The lessons in this article are taken from my notes of the Deployment of Machine Learning Models course on Udemy.

Development Environment — In the development environment, Machine Learning Engineers seek to reproduce the machine learning pipeline developed in the research environment.

Reproducibility in Data GatheringAlways remember Data comes before Science; Without data machine learning models are insignificant.

Wrap UpData Scientists and Machine Learning …

When I first started using Jupyter Notebooks it took me longer than I’d like to admit to figure out how to get my conda environment kernels to show in the kernels list.

Method 1: “The Easy Way”This is my preferred method because it is simple.

As of the time of this writing, nb_conda_kernels does not yet support Python 3.9.

This only affects our base environment which we aren’t going to use for any of our work anyway, right?

Initially your kernel list (under Change kernel) will only show your current environment.

What can basketball wiz Kevin Durant teach about the probability of a probability?

P(H)=p is the chance of Durant’s coin landing heads (Durant scoring a three-pointer).

P(p=0.3) quantifies how probable is it that Durant’s probability of making a three-pointer is 0.3.

This hints there’s a probability distribution, probability distribution of probabilities to be precise, buried in the above plot.

While we focused on three-pointers, we could similarly model any other statistical category that follows a binomial distribution (coin toss characteristic).

Using fit or fit_transform everywhereLet’s start with the most serious mistake — a mistake that is related to data leakage.

Data leakage is subtle and can be destructive to model performance.

Data leakage causes models to give very optimistic results, even in cross-validation but perform terribly when testing on actual novel data.

Data leakage is common during data preprocessing, particularly if the training and test sets are not separated.

Then, call the transform method on the test set to transform it based on the information learned from the training data.

How to Build a Decentralized Data PlatformImage courtesy of Max on Unsplash.

Here’s how the Data Engineering team at Auto Trader built a data platform with both decentralized data ownership and reliability in mind.

“Decentralized data ownership means decentralized responsibility for data quality,” said Edward.

“Data observability helps us provide this platform capability.”The impact of decentralized data ownership at Auto TraderAs Auto Trader seeks to build trust in data while opening up access, data observability is key to ensuring data remains accurate and reliable.

From a tracking perspective, this visibility is hugely important for us as we move closer towards a decentralized data platf…

OK, but in all seriousness, Dplyr is pretty dang cool (another grand invention by the one and only Hadley Wickham).

Include a string of columns at once with ':'gapminder %>%select(country, year, lifeExp:gdpPercap) # 3.

Renaming in Dplyr is similar to using df.rename() and mutating is similar to df[new_col] = df[old_col1] * df[old_col2] used in Python.

Akin to other Dplyr functions, one can specify multiple filters by breaking up each argument with a , , & , or | sign.

When mixed with functions like group_by() , these functions can become incredibly useful in understanding subpopulations in the dataset.

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.

Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.

Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.

A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems

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?

Vanilla Gradient Ascent [2013](Vanilla) gradient ascent was presented in the Visualizing Image Classification Models and Saliency Maps [2013] paper.

Here’s how Guided Back-Propagation looks next to SmoothGrad:Standard Guided Back-Propagation (left) vs. SmoothGrad (right) on an image of a doberman.

Blur Integrated Gradients [2020]That’s what our final interpretation method, blur integrated gradients seeks to do.

The blur integrated gradients method works by measuring gradients along a series of increasingly blurry versions of the original input image (rather than dimmed versions of the image, as integrated gradients does).

CitationFor attribution in academic contexts or books, please cite th…

SourceOur purpose here is not to provide an explanation of the quantum eccentricities that occur underneath the hood of a quantum computer.

SourceVarious other algorithms under the umbrella of quantum machine learning have been formulated in the past few years as well.

This results in a model of computation that is closer to the one abstractly modeled by a quantum Turing machine.

Shifting gears back to our original discussion of Turing machines, a quantum Turing machine is the generalization or quantization of the classical Turing machine, where the head and tape are superposed.

Furthermore, the impressive trainability and dimensionality of quantum neural networks provide exciting new avenu…

2018; Geva, Goldberg, and Berant 2019), and BERT does exploit them (T. McCoy, Pavlick, and Linzen 2019; Jin et al.

The reason for that appears to be that the importance scores for most BERT heads are equally low.

If the success of BERT subnetworks is attributable to the linguistic knowledge they encode, the “super-survivors” should contain considerably more of it.

2020) confirm that the Lottery Ticket Hypothesis holds when using magnitude pruning on BERT: “good” subnetworks can be retrained to reach full model performance.

Thus it could be said that with structured pruning BERT has no “losing” tickets, even if it does not fully “win”.

Dr. Timnit Gebru is one of those few.

She is one of the founders of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), one of the most prestigious and well-known conferences related to machine learning ethics.

The entire team of FAccT founders, including Gebru, did a great job with this and helped change the field of machine learning in the process.

In response, she founded Black in AI, and over 500 Black machine learning researchers participated in the Black in AI workshop at NeurIPS 2017, just one year later.

I stand by you, Timnit.” I hope that we can all stand with Dr. Timnit Gebru now.

This essay provides a broad overview of the sub-field of machine learning interpretability.

Below, each section is operationalized by a concrete question we can ask of our machine learning model using a specific definition of interpretability.

For other conceptual surveys of the field, Definitions, methods, and applications in interpretable machine learning and Explainable Machine Learning for Scientific Insights and Discoveries.

He is interested in machine learning interpretability and, more broadly, AI safety.

"Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning."

As part of our series about reinforce…We discuss DeepMind’s bsuite framework for benchmarking reinforcement learning algorithms.

we learn how Google Research uses reinforcement learning to master football;we explore policy optimization in reinforcement learning methods;In this issue:✖ CloseThis site uses cookies.

To find out more, read our privacy policy

From training, personalization to computational resource consumption, the mobile deep learning paradigm presents many inefficiencies for mobile architectures.

The holy grail of mobile deep learning is to build models that can execute natively and efficiently in mobile devices.

These days, we have mobile deep learning frameworks in popular deep learning stacks like PyTorch or TensorFlow that easily allow you to adapt deep learning models to mobile architectures.

Finally, the developer experience from mobile deep learning is very limited compared to frameworks or platforms for models that execute in server-side topologies.

These releases keep signaling the importance of mobile deep learning f…

In this issue, we overview:challenges of choosing between the feature-related platforms;Pilosa-based Molecula platform and its four fundamental sets of capabilities;Ingesters, PSQL, and Consumption Interfaces in Molecula’s architecture.

💥 What’s New in AI: Molecula is a Feature Extraction and Storage Platform Designed for Enterprise ML WorkloadsFeatures are rapidly becoming one of the fastest-growing components in the relatively crowded machine learning space.

ShareFrom a functional standpoint, the Molecula platform enables four fundamental sets of capabilities:Feature Store: The central element of the Molecula platform, the feature store abstracts the calculation, storage, and management o…

I completed my first machine learning project back in 1988 as part of my master’s thesis at Carnegie Mellon University.

🛠 ML WorkYou are the CEO of one of the most important, and yet not very well known, organizations advancing AI research and development.

One of the things I find intriguing about AI2 is that it combines advanced AI research with practical implementations.

In your opinion, how big is the gap between AI research and engineering and what are good practices to bridge it?

Last, but not least, the evaluation of AI models and algorithms needs to be multifaceted.

In this issue:we explain what DQN reinforcement learning models are;we explore DeepMind’s RL agent that masters Quake III Capture the Flag;

Machine learning (ML) has challenged those conventional dynamics.

Let’s take the example of ML development frameworks, which are one of the foundational components of the artificial intelligence (AI) ecosystem.

This level of support from large AI labs has been super important for advancing open-source ML frameworks.

Not only AI incumbents are actively committing to engineering and financial resources to support their favorite ML stacks, but they are also adopting those frameworks in some of the most complex ML scenarios in the world.

Register🤖 Cool AI Tech ReleasesPyTorch at FacebookFacebook AI Research (FAIR) published a detailed blog post explaining the use of PyTorch across the organizat…

In this issue, we overview:the challenges of training models at scale;the core objective of Determined platform and its capabilities;what master-agent architecture and how it helps deliver great results.

Share TheSequence💥 What’s New in AI: Determined AI Tackles the Monster Challenge of Distributed TrainingTraining is one of those aspects of machine learning applicati…

💥 What’s New in AI: Determined AI Tackles the Monster Challenge of Distributed TrainingTraining is one of those aspects of machine learning applications that we tend to take for granted.

Machine learning models have training infrastructure requirements that are highly different from those of traditional distributed systems architectures.

In order to achieve mainstream adoption, machine learning infrastructure should become as transparent and ubiquitous as web servers and databases became for mobile and web applications.

Unfortunately, machine learning infrastructure platforms do not receive the same level of attention as development stacks.

Other capabilities of Determined include visualiza…

In this issue:we explain what Q-Learning models are;we explore how Google SEED RL architecture enables highly scalable RL tasks;we discuss Facebook’s ReAgent that is used for building reinforcement learning systems.

Give a gift subscription💡 ML Concept of the Day: What is Q-Learning?

Continuing our series in reinforcement learning (RL), today we would like to explore one o…

This week we saw a massive step in that direction with the announcement of the PyTorch Enterprise Support program enabled by a partnership between Microsoft and Facebook.

As its name indicates, the program will provide support for enterprise users building production applications in PyTorch.

As part of the program, Microsoft announced the release of PyTorch Enterprise on Microsoft Azure, which delivers the enterprise-grade feature to PyTorch users.

From a market perspective, Microsoft could be the ideal partner to expand PyTorch into enterprise environments.

For now, this is a clear indication that PyTorch is getting serious about the enterprise.

In this issue:we explain what human-in-the-loop (HIL) data labeling is;we overview model-assisted labeling (MAL);we explore Cogito Data Annotation Solutions.

Human-in-the-Loop (HIL) data annotation looks for a middle ground between the purely automated and manual data labeling approaches.

Cogito Data Annotation SolutionsCogito is a workforce solution provider for data labeling and annotations based on HIL techniques.

From a data scientist’s perspective, Cogito is a nice complement to automated data labeling platforms.

Cogito is certainly one of the data annotation solutions that provides an easy entry point for data science teams dabbling in the HIL data labeling space.

HK: I’d like to actually modify the first part of the sentence: true data labeling automation is looking to help relieve most of the financial and time burdens associated with manual and even AI-assisted data preparation workflows.

Automated data labeling seems fundamentally different for different types of datasets such as text, images or videos.

Data labeling automation is a big undertake but almost equally important is to estimate the effectiveness of labels in the training process.

And we also agree that automation for data labeling can come in many different forms.

Custom Auto-Labeling ProcessImage credit: Superb AIOne of the biggest challenges of automated data labeling techniques is …

In this issue:we discuss what is model-free reinforcement learning (MFRL);we explore Agent57, an MFRL agent that outperformed the standard human benchmark on all 57 Atari games;

📝 EditorialMicrosoft, Amazon and Google are embarked on a frantic race for artificial intelligence (AI) cloud dominance.

While in other areas of the cloud space, these technology giants match each other literally feature by feature, machine learning might be the area where they all exhibit highly differentiated capabilities.

AWS and Azure are certainly ahead in terms of general customer adoption but machine learning remains one of the strongholds of the Google Cloud offering.

This week, at its annual I/O conference, Google unveiled a new series of capabilities that continue to enhance its already robust machine learning offering.

Vertex AI is a particularly interesting release and it shows …

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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…

Data Fest пройдет в этом году в онлайн формате 19 и 20 сентября 2020. Фестиваль организован сообществом Open Data Science и как обычно соберет исследователей, инженеров и разработчиков в области анализа данных, искусственного интеллекта и машинного обучения. Регистрация. Ну а дальше к деталям. Читать дальше →

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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…

This is the second post in a 2-part series about ML monitoring systems.

Today he’s leveraging his ML monitoring experience by leading the development of a monitoring platform at Mona.

In our interview we spoke about his experiences monitoring machine learning, what ML has to learn from application performance management (APM), and who should own ML monitoring within an organization.

Let’s talk about the technical-side of monitoring ML and building monitoring platforms.

There are a lot of open questions about the people and process involved in monitoring ML.

Reducing this inherent risk requires continuously monitoring an ML system to ensure that it’s operating effectively.

The question then is: what qualities should we look for in an ML monitoring system?

Itai brings a wealth of experience in both ML engineering and production monitoring, having spent 4 years as a tech leader at Google Trends, and the last year and a half building an ML monitoring system at Mona.

Find and resolve issues faster First and foremost, a good monitoring system helps ML teams go from “flying blind” and “reactive” to “full visibility” and “proactive”.

Version Benchmarking Last but not least, a good ML monitoring system should include comprehensive benchmarking function…

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 \(25\%\).

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 …

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.

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.

Paralleling the state of affairs in optimization, we have a variety of (provably efficient) algorithms for sampling from log-concave distributions, under a variety of access models to the distribution.

Formalizing the sampling problemThe formulation of the sampling problem we will consider is as follows:Problem: Sample from a distribution $p(x) \propto e^{-f(x)}$ given black-box access to $f$ and $abla f$.

Similarly, for sampling, when $p$ is log-concave, the distribution is unimodal and a Markov Chain which is a close relative of gradient descent — Langevin Monte Carlo — is efficient.

[](http://www.andrew.cmu.edu/user/aristesk/table_opt.jpg)Before we move on to non-log-concave distribution…

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…

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.

Model-free off-policy actor-critic methods are an efficient solution to complex continuous control tasks.

However, these algorithms rely on a number of design tricks and many hyperparameters, making their applications to new domains difficult and computationally expensive...

This paper creates an evolutionary approach that automatically tunes these design decisions and eliminates the RL-specific hyperparameters from the Soft Actor-Critic algorithm.

Empirically, we show that our agent outperforms well-tuned hyperparameter settings in popular benchmarks from the DeepMind Control Suite.

We then apply it to new control tasks to find high-performance solutions with minimal compute and research e…

Non-autoregressive (NAR) models have achieved a large inference computation reduction and comparable results with autoregressive (AR) models on various sequence to sequence tasks.

However, there has been limited research aiming to explore the NAR approaches on sequence to multi-sequence problems, like multi-speaker automatic speech recognition (ASR)...

In this study, we extend our proposed conditional chain model to NAR multi-speaker ASR.

Specifically, the output of each speaker is inferred one-by-one using both the input mixture speech and previously-estimated conditional speaker features.

Besides, we also adopt the Conformer and incorporate an intermediate CTC loss to improve the performa…

Molecular property prediction is gaining increasing attention due to its diverse applications.

One task of particular interests and importance is to predict quantum chemical properties without 3D equilibrium structures...

This is practically favorable since obtaining 3D equilibrium structures requires extremely expensive calculations.

In this work, we design a deep graph neural network to predict quantum properties by directly learning from 2D molecular graphs.

In addition, we propose a 3D graph neural network to learn from low-cost conformer sets, which can be obtained with open-source tools using an affordable budget.

This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge.

We produce synthetic and natural spoken versions of a well known semantic textual similarity database and show that our VGS model produces embeddings that correlate well with human semantic similarity judgements... Our results show that a model trained on a small image-caption database outperforms two models trained on much larger databases, indicating that database size is not all that matters.

We also investigate the importance of having multiple captions per image and find that this is indeed helpful even if the …

The carbon pump of the world's ocean plays a vital role in the biosphere and climate of the earth, urging improved understanding of the functions and influences of the ocean for climate change analyses.

State-of-the-art techniques are required to develop models that can capture the complexity of ocean currents and temperature flows...

This work explores the benefits of using physics-informed neural networks (PINNs) for solving partial differential equations related to ocean modeling; such as the Burgers, wave, and advection-diffusion equations.

We explore the trade-offs of using data vs. physical models in PINNs for solving partial differential equations.

PINNs account for the deviation fro…

Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs.

The existing approaches utilize either reasoning-based or semantic embedding-based techniques, but few studies explore their combination...

In this demonstration, we present PRASEMap, an unsupervised KG alignment system that iteratively computes the Mappings with both Probabilistic Reasoning (PR) And Semantic Embedding (SE) techniques.

PRASEMap can support various embedding-based KG alignment approaches as the SE module, and enables easy human computer interaction that additionally provides an option for users to feed the mapping annotations back to the system for better result…

Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs.

In this work, we present a suite of simulated environments tailored towards tactile robotics and reinforcement learning.

A simple and fast method of simulating optical tactile sensors is provided, where high-resolution contact geometry is represented as depth images.

A data-driven approach enables translation of the current state of a real tactile sensor to corresponding simulated depth images.

This policy is implemented within a real-time control loop on a physical robot to demonstrate zero-shot sim-to-real policy t…

Nevertheless, due to various special challenges in UAV tracking, \textit{e.g.

}, severe occlusion, and fast motion, most existing Siamese-based trackers hardly combine superior performance with high efficiency... To this concern, in this paper, a novel attentional Siamese tracker (SiamAPN++) is proposed for real-time UAV tracking.

By virtue of the attention mechanism, the attentional aggregation network (AAN) is conducted with self-AAN and cross-AAN, raising the expression ability of features eventually.

Experiments on two well-known authoritative benchmarks are conducted, where SiamAPN++ outperforms its baseline SiamAPN and other SOTA trackers.

Besides, real-world tests onboard a typical e…

This case corresponds to a partially observable Markov decision process (POMDP)... One strategy to seek good performance in POMDPs is to endow the agent with a finite memory, whose update is governed by the policy.

However, policy optimization is non-convex in that case and can lead to poor training performance for random initialization.

The performance can be empirically improved by constraining the memory architecture, then sacrificing optimality to facilitate training.

For (i), the probability $q$ to play the worst arm is known to be exponentially small in $M$ for the optimal policy.

Interestingly, we observe empirically that training from random initialization leads to very poor results…

Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution.

In particular, contrastive learning has shown great promises by (almost) matching the performance of fully-supervised CNN on vision tasks.

Nonetheless, this method does not take advantage of available meta-data, such as participant's age, viewed as prior knowledge.

Here, we propose to leverage continuous proxy metadata, in the contrastive learning framework, by introducing a new loss called y-Aware InfoNCE loss.

When fine-tuned, it also outperforms 3D CNN trained from scratch on these tasks, as well as state-of-the-art self-supervised methods.

Variational autoencoders (VAEs) have been widely applied for text modeling.

The former arises as only the last hidden state of LSTM encoder is transformed into the latent space, which is generally insufficient to summarize the data.

In this paper, we propose Discrete Auto-regressive Variational Attention Model (DAVAM) to address the challenges.

Specifically, we introduce an auto-regressive variational attention approach to enrich the latent space by effectively capturing the semantic dependency from the input.

We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.

In this paper, we propose an implementation of both Large Deformation Diffeomorphic Metric Mapping (LDDMM) and Metamorphosis image registration using a semi-Lagrangian scheme for geodesic shooting.

We propose to solve both problems as an inexact matching providing a single and unifying cost function... We demonstrate that for image registration the use of a semi-Lagrangian scheme is more stable than a standard Eulerian scheme.

Our GPU implementation is based on PyTorch, which greatly simplifies and accelerates the computations thanks to its powerful automatic differentiation engine.

It will be freely available at https://github.com/antonfrancois/Demeter_metamorphosis.

read morePDFAbstract

Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5.

However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems...

In this paper, we show that by applying \textsc{Gm} to only a judiciously chosen block of coordinates at a time and using a memory mechanism, one can retain the breakdown point of 0.5 for smooth non-convex problems, with non-asymptotic convergence rates comparable to the SGD with \textsc{Gm}.

read morePDFAbstract

However, the traditional TL cannot search reliable unseen disentangled representations due to the unavailability of unseen classes in ZSL.

To alleviate this drawback, we propose in this work a multi-modal triplet loss (MMTL) which utilizes multimodal information to search a disentangled representation space.

As such, all classes can interplay which can benefit learning disentangled class representations in the searched space.

Furthermore, we develop a novel model called Disentangling Class Representation Generative Adversarial Network (DCR-GAN) focusing on exploiting the disentangled representations in training, feature synthesis, and final recognition stages.

Benefiting from the disentangl…

The versatility of recent machine learning approaches makes them ideal for improvement of next generation video compression solutions.

Unfortunately, these approaches typically bring significant increases in computational complexity and are difficult to interpret into explainable models, affecting their potential for implementation within practical video coding applications...

This paper introduces a novel explainable neural network-based inter-prediction scheme, to improve the interpolation of reference samples needed for fractional precision motion compensation.

The approach requires a single neural network to be trained from which a full quarter-pixel interpolation filter set is derived,…

This paper introduces RyanSpeech, a new speech corpus for research on automated text-to-speech (TTS) systems.

Publicly available TTS corpora are often noisy, recorded with multiple speakers, or lack quality male speech data...

In order to meet the need for a high quality, publicly available male speech corpus within the field of speech recognition, we have designed and created RyanSpeech which contains textual materials from real-world conversational settings.

These materials contain over 10 hours of a professional male voice actor's speech recorded at 44.1 kHz.

This corpus's design and pipeline make RyanSpeech ideal for developing TTS systems in real-world applications.

Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems.

We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniques, thereby facilitating an improved interpretability of neural networks...

This study presents the discrimination of top quark signal over QCD background processes using a Matrix Product State classifier.

We show that entanglement entropy can be used to interpret what a network learns, which can be used to reduce the complexity of the network and feature space without loss of generality or performance.

For the optimisation of the network, we c…

The development of deep neural networks and the emergence of pre-trained language models such as BERT allow to increase performance on many NLP tasks.

This extractive model combines in an original way pre-trained language models and vocabulary frequency-based representations to predict tweet salience.

An additional advantage of the model is that it automatically adapts the size of the output summary according to the input tweet stream.

We conducted experiments using two different Twitter collections, and promising results are observed in comparison with state-of-the-art baselines.

read morePDFAbstract

Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers.

Importantly, the deterrence effect of patrols on adversaries' future behavior makes patrol planning a sequential decision-making problem...

Therefore, we focus on robust sequential patrol planning for green security following the minimax regret criterion, which has not been considered in the literature.

We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.

MIRROR uses two reinforcement learning-bas…

In this paper we propose \emph{counterfactual graphs} as a way to produce local post-hoc explanations of any black-box graph classifier.

Given a graph and a black-box, a counterfactual is a graph which, while having high structural similarity with the original graph, is classified by the black-box in a different class.

We propose and empirically compare several strategies for counterfactual graph search.

Our experiments against a white-box classifier with known optimal counterfactual, show that our methods, although heuristic, can produce counterfactuals very close to the optimal one.

Finally, we show how to use counterfactual graphs to build global explanations correctly capturing the beha…

In this work, we address the task of referring image segmentation (RIS), which aims at predicting a segmentation mask for the object described by a natural language expression.

Most existing methods focus on establishing unidirectional or directional relationships between visual and linguistic features to associate two modalities together, while the multi-scale context is ignored or insufficiently modeled... Multi-scale context is crucial to localize and segment those objects that have large scale variations during the multi-modal fusion process.

To solve this problem, we propose a simple yet effective Cascaded Multi-modal Fusion (CMF) module, which stacks multiple atrous convolutional laye…

Due to advances in deep learning, the performance of automatic beat and downbeat tracking in musical audio signals has seen great improvement in recent years.

In training such deep learning based models, data augmentation has been found an important technique...

However, existing data augmentation methods for this task mainly target at balancing the distribution of the training data with respect to their tempo.

In this paper, we investigate another approach for data augmentation, to account for the composition of the training data in terms of the percussive and non-percussive sound sources.

We report experiments on four completely unseen test sets, validating the effectiveness of the propos…

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals.

The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism...

This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components.

Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outper…

Episodic memory lets reinforcement learning algorithms remember and exploit promising experience from the past to improve agent performance.

Previous works on memory mechanisms show benefits of using episodic-based data structures for discrete action problems in terms of sample-efficiency...

The application of episodic memory for continuous control with a large action space is not trivial.

Our study aims to answer the question: can episodic memory be used to improve agent's performance in continuous control?

Our proposed algorithm combines episodic memory with Actor-Critic architecture by modifying critic's objective.

Out-of-scope intent detection is of practical importance in task-oriented dialogue systems.

Since the distribution of outlier utterances is arbitrary and unknown in the training stage, existing methods commonly rely on strong assumptions on data distribution such as mixture of Gaussians to make inference, resulting in either complex multi-step training procedures or hand-crafted rules such as confidence threshold selection for outlier detection...

In this paper, we propose a simple yet effective method to train an out-of-scope intent classifier in a fully end-to-end manner by simulating the test scenario in training, which requires no assumption on data distribution and no additional post-p…

Multi-task learning (MTL) aims to improve the generalization of several related tasks by learning them jointly.

As a comparison, in addition to the joint training scheme, modern meta-learning allows unseen tasks with limited labels during the test phase, in the hope of fast adaptation over them...

In this paper, we take one important step further to understand the close connection between these two learning paradigms, through both theoretical analysis and empirical investigation.

Theoretically, we first demonstrate that MTL shares the same optimization formulation with a class of gradient-based meta-learning (GBML) algorithms.

We believe this work could help bridge the gap between these two…

Short Message Service (SMS) is a very popular service used for communication by mobile users.

In this work, a classification performance comparison was conducted between three automatic ML tools for SMS spam message filtering.

These tools are mljar-supervised AutoML, H2O AutoML, and Tree-based Pipeline Optimization Tool (TPOT) AutoML.

The Stacked Ensemble model, which was built using H2O AutoML, achieved the best performance in terms of Log Loss (0.8370), true positive (1088/1116), and true negative (281/287) metrics.

The satisfactory filtering performance achieved with AutoML tools provides a potential application for AutoML tools to automatically determine the best ML model that can perfo…

Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks.

This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals).

We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.

The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, t…

However, many acoustic environments are noisy and reverberant, severely restricting the applicability of popular VC methods to such scenarios.

To address this limitation, we propose Voicy, a new VC framework particularly tailored for noisy speech.

Importantly, Voicy is capable of performing non-parallel zero-shot VC, an important requirement for any VC system that needs to work on speakers not seen during training.

We have validated our approach using a noisy reverberant version of the LibriSpeech dataset.

Experimental results show that Voicy outperforms other tested VC techniques in terms of naturalness and target speaker similarity in noisy reverberant environments.

It is no secret amongst deep learning researchers that finding the right data augmentation strategy during training can mean the difference between a state-of-the-art result and a run-of-the-mill ranking.

To that end, the community has seen many efforts to automate the process of finding the perfect augmentation procedure for any task at hand...

Unfortunately, even recent cutting-edge methods bring massive computational overhead, requiring as many as 100 full model trainings to settle on an ideal configuration.

We show how to achieve even better performance in just 7: with Random Unidimensional Augmentation.

Source code is available at https://github.com/fastestimator/RUA read morePDFAbstra…

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.

Graph Neural Networks extend the learning bias imposed by Convolutional Neural Networks and Recurrent Neural Networks by generalising the concept of “proximity”, allowing us to have arbitrarily complex connections to handle not only traffic ahead or behind us, but also along adjacent and intersecting roads.

These mechanisms allow Graph Neural Networks to capitalise on the connectivity structure of the road network more effectively.

This ability of Graph Neural Networks to generalise over combinatorial spaces is what grants our modeling technique its power.

We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum - the primary cause of this in…

This prompts us to ask, can we learn a more accurate physics simulator from a handful of real robot trajectories?

Comparison between a conventional simulator and our hybrid simulator.

In this case, the learnable hybrid simulator serves as the GAN generator, while the GAN discriminator provides the similarity scores.

Using Reinforcement Learning (RL) to Learn the Simulator and Refine the PolicyPutting everything together, we formulate simulation learning as an RL problem.

To achieve this, we augment a classical physics simulator with learnable components, and train this hybrid simulator using adversarial reinforcement learning.

Today, we introduce the More Inclusive Annotations for People (MIAP) dataset in the Open Images Extended collection.

In each subfigure the magenta boxes are from the original Open Images dataset, while the yellow boxes are additional boxes added by the MIAP Dataset.

Annotations in Open ImagesEach image in the original Open Images dataset contains image-level annotations that broadly describe the image and bounding boxes drawn around specific objects.

The MIAP dataset addresses the five classes that are part of the person hierarchy in the original Open Images dataset: person, man, woman, boy, girl.

Comparison of number of person bounding boxes between the original Open Images and the new MIA…

Financial institutions across the globe rely on real-time indices to inform real-time portfolio valuations, to provide benchmarks for other investments, and as a basis for passive investment instruments including exchange-traded products (ETPs). This reliance is growing—the index industry dramatically expanded in 2020, reaching revenues of $4.08 billion.Today, indices are calculated and distributed by index providers with proximity and access to underlying asset data, and with differentiating real-time data processing capabilities. These providers offer subscriptions to real-time feeds of index prices and publish the constituents, calculation methodology, and update frequency for each index…

Ever since jeweler Louis-Francois Cartier opened his first workshop in Paris in 1847, the name “Cartier” has been synonymous with exceptional quality. Almost two centuries later, the luxury Maison has popularized wristwatches for men, been dubbed the “jeweler of kings and king of jewelers” by King Edward VII, and continues to design, manufacture, and sell jewelry and watchmaking creations globally renowned for timeless design. While Maison Cartier prides itself on the vastness of its collection, manually browsing its catalog to find specific models, or comparing several models at once, could sometimes take quite some time for a sales associate at one of the Maison’s 265 boutiques. This was …

Many companies today are actively using AI or have plans to incorporate it into their future strategies — 76% of enterprises are now prioritizing AI and ML over other initiatives in their IT budgets and the global AI industry is expected to reach over $260 billion by 2027. But as AI and advanced analytics become more pervasive, the need for more transparency around how AI technologies work will be paramount. In this post, we’ll explore why explainable AI (XAI) is essential to widespread AI adoption, common XAI methods, and how Google Cloud can help. Why you need to explain ML modelsAI technology suffers from what we call a black box problem. In other words, you might know the question or th…

Research into robotics systems and their applications to the real world requires a rethinking of this experiment design.

Although these are classical research methods, they are not generally employed by default in robotics research — yet, they are critical to producing meaningful and measurable scientific results for robotics in real-world scenarios.

If the confidence intervals contain zero, that indicates the success rate is statistically similar to the success rate of baseline.

However, the statistical differences we’ve measured between the experiment and baseline are sound and robust to reproduction.

In this blog post, we show that robotics research can benefit from using this same metho…

Last year, we introduced a benchmark for comparing speech representations and a new, generally-useful speech representation model (TRILL).

The dashed line shows the student model output.

Choosing the Best Student ModelWe perform distillation with a variety of student models, each trained with a specific combination of architecture choices (explained below).

To measure each student model’s latency, we leverage TensorFlow Lite (TFLite), a framework that enables execution of TensorFlow models on edge devices.

Linear regression weight magnitudes for predicting model quality, latency, and size.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as …

Information in a written document is not only conveyed by the meaning of the words contained in it, but also by the overall document layout.

In an attempt to solve this challenge, some have proposed machine learning (ML) techniques to synthesize document layouts.

The resulting Variational Transformer Network (VTN) model is able to extract meaningful relationships between the layout elements (paragraphs, tables, images, etc.

In terms of design, layout elements are often represented by the coordinates of their enclosing bounding boxes.

The results below show that LayoutVAE struggles to comply with design rules, like strict alignments, as in the case of PubLayNet.

At Google I/O this year, we introduced Vertex AI to bring together all our ML offerings into a single environment that lets you build and manage the lifecycle of ML projects. In a previous post, we gave you an overview of Vertex AI, sharing how it supports your entire ML workflow—from data management all the way to predictions. Today, we’ll talk a little about how to manage ML datasets with Vertex AI.Many enterprises want to use data to make meaningful predictions that can bolster their business or help them venture into new markets. This often requires using custom machine learning models—something not every business knows how to create or use. This is where Vertex AI can help. Vertex AI p…

In “Supervised Contrastive Learning”, presented at NeurIPS 2020, we propose a novel loss function, called SupCon, that bridges the gap between self-supervised learning and fully supervised learning and enables contrastive learning to be applied in the supervised setting.

Cross-entropy, self-supervised contrastive loss and supervised contrastive loss Left: The cross-entropy loss uses labels and a softmax loss to train a classifier.

Middle: The self-supervised contrastive loss uses a contrastive loss and data augmentations to learn representations.

Right: The supervised contrastive loss also learns representations using a contrastive loss, but uses label information to sample positives in add…

Data is a foundational aspect of machine learning (ML) that can impact performance, fairness, robustness, and scalability of ML systems.

In “‘Everyone wants to do the model work, not the data work’: Data Cascades in High-Stakes AI”, published at the 2021 ACM CHI Conference, we study and validate downstream effects from data issues that result in technical debt over time (defined as "data cascades").

We further discuss the opportunity presented by a collective re-imagining of ML data as a high priority, including rewarding ML data work and workers, recognizing the scientific empiricism in ML data research, improving the visibility of data pipelines, and improving data equity around the world…

Editor’s note: Mr. Cooper Group is an industry-leading mortgage services provider serving customers through servicing, originations, and digital real estate solutions. Using Google Cloud AI and ML solutions, they created a highly reliable, cloud native document analysis and processing platform to process lending documents and unlocked new levels of accuracy and operational efficiency that help them to scale and control the cost at the same time. Read on to hear how they did it. Mr. Cooper is one of the largest home loan servicers in the country focused on delivering a variety of servicing and lending products, services and technologies to homeowners. Our goal is to shorten the time for loan…

Good artists copy, great artists steal, and smart software developers use other people’s machine learning models.If you’ve trained ML models before, you know that one of the most time-consuming and cumbersome parts of the process is collecting and curating data to train those models. But for lots of problems, you can skip that step by instead using somebody else’s model that’s already been trained to do what you want--like detect spam, convert speech to text, or label objects in images. All the better if that model is built and maintained by folks with access to big datasets, powerful training rigs, and machine learning expertise.One great place to find these types of “pre-trained” models i…

A limitation for their use in sim-to-real transfer, however, is that because GANs translate images at the pixel-level, multi-pixel features or structures that are necessary for robot task learning may be arbitrarily modified or even removed.

With RL-CycleGAN, we describe our sim-to-real transfer methodology and demonstrate state-of-the-art performance on real world grasping tasks trained with RL.

RL-CycleGANIn “RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real”, we leverage a variation of CycleGAN for sim-to-real adaptation by ensuring consistency of task-relevant features between real and simulated images.

Three examples of the real robot successfully opening conference room doo…

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.

OpenAI released its first commercial product back in June: an API for developers to access advanced technologies for building new applications and services.

The API features a powerful general purpose language model, GPT-3, and has received tens of thousands of applications to date.

In addition to offering GPT-3 and future models via the OpenAI API, and as part of a multiyear partnership announced last year, OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.

GPT-3 is the most powerful model behind the API today, with 175 billion parameters.

Today, the API remains in a limited beta as OpenAI and academic partners test and assess the capabilities and limitati…

We've applied reinforcement learning from human feedback to train language models that are better at summarization.

Our approach follows directly from our previous work on learning from human feedback.

In particular, our 1.3 billion parameter (1.3B) model trained with human feedback outperforms our 12B model trained only with supervised learning.

Note that our human feedback models generate summaries that are significantly shorter than summaries from models trained on CNN/DM.

This suggests that our human feedback models have learned something more general about how to summarize text, and are not specific to Reddit posts.

In this blog post, we introduce SOLOIST (TaSk-Oriented DiaLOg wIth A Single Pre-Trained Model) to enable building task bots at scale with transfer learning and machine teaching.

For further details please refer to the research paper, “SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching,” and check out the code on the GitHub repository.

Figure 3: Machine teaching performance of different iterations in the Restaurant domainFigure 3 demonstrates the performance of SOLOIST in the Restaurant domain by repeating the previously mentioned machine teaching process in multiple iterations.

We observe that in the second iteration of machine teaching, SOLOIST plus Teach impr…

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Um, but I think that’s approximately how many behavioral economists think about this.

And so I think that’s the distinction, and that illustrates how you might still make suboptimal choices even if you’re optimally allocating your mental bandwidth.

So Jerry Seinfeld has this great bit—I don’t know if you’ve seen it—about Morning Guy and Night Guy.

There’s a lot of underlying potential reasons why this might be the case, and I think there’s more to do there.

And so I think that’s what’s generated the real concern, among regulators at the state and national levels.

The power of 5G, IoT, and real-time AI will unlock new and innovative services for enterprises across the world to accelerate their transformation toward Industry 4.0 as they evolve and adopt diverse new business models. Today, we’re introducing Azure private multi-access edge compute (MEC), new services to accelerate 5G and edge monetization, and a new partner initiative to empower operators, SIs, and ISVs to develop Microsoft-validated Azure private MEC customer solutions.

The lower-level hidden layers \(S_l^*\) are responsible for generating weights that are used to paint lower-resolution images; and the higher-level hidden layers responsible for painting higher-resolution images.

In a generative model, we attribute image sharpness to a hierarchical sparse coding property of hidden layers.

Learn the output deconvolution layers via moment matchingTo learn the output deconvolution layers (i.e.

Learn the first hidden layer via moment matching and sparse decodingTo learn the first hidden layer (i.e.

Thus from a theoretical standpoint, it suffices to perform layer-wise learning: namely, first learn the distribution \(X_1\) and hidden layer \(S_1^*\) , then learn …

And so it was really—it was a great experience, um, and really, uh, led me on a great pathway.

All of those type of things, um, are very simple, very descriptive, and seemed very necessary.

It’s that they’re separated into worlds which are describing different things.

And, uh, these have been really tricky and difficult questions for the mainstream media, uh, to make decisions on.

So, as you look into the next decade, do you think our media ecosystem is gonna become better or worse?

In this blog post, we share two papers that explore reinforcement learning methods to improve semantic understanding in text agents, a key process by which AI understands and reacts to text-based input.

Leveraging human priors for action selectionAnother way to build in semantic language understanding is to leverage it for action generation.

Our recent work highlights the need for careful examination of the handicaps as they relate to the development of semantic understanding the text agents trained within.

Our findings show that handicaps such as valid-action identification can allow agents to bypass the challenges of developing semantic language understanding.

We outlined two different wa…

So one kind of major set of applications is exactly this, uh, sort of context-dependent treatment or heterogeneous treatment effects.

And so I think that’s how I think about the storytelling—and the value of storytelling.

LEWIS: So, I think that we’re very early there, and I actually think that’s sort of one of the weaknesses of this area.

So, one of the things that’s appealing for policy evaluation of some of these causal machine learning tools is that they automate model selection.

I have, um, data that’s actually data.

Today, Azure announces the general availability of the Azure ND A100 v4 Cloud GPU instances—powered by NVIDIA A100 Tensor Core GPUs—achieving leadership-class supercomputing scalability in a public cloud. For demanding customers chasing the next frontier of AI and high-performance computing (HPC), scalability is the key to unlocking improved total cost of ownership and time-to-solution.

Explore moreProgramming LanguagesBosqueSample code in Bosque, a new programming language created by Microsoft researchers and their colleaguesThe Bosque programming language is an experiment in regularized programming language design for a machine-assisted rapid and reliable software development lifecycle.

Explore moreQ#Documentation Get started with the Microsoft Quantum Development KitQ# is a domain-specific programming language designed for quantum application development.

An important aspect of this is making all kinds of software development more accessible, inclusive and sustainable.

Well-being: Investigating the intersections of happiness, satisfaction and personal value with softwar…

At digital Microsoft Build this week, we announced a host of new capabilities that help developers create intelligent, connected, and secure cloud-native apps that harness the power of data and AI and run anywhere. Below are the key stories we landed this week at Microsoft Build, with ways to explore for more details.

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Our commitment to developers is to make Azure the best cloud for developing intelligent applications that harness the power of data and AI. At Microsoft Build, we are announcing several exciting new capabilities and offers that make it easy and cost-effective for developers to get started with Azure data and AI services.

In addition, by splitting the inference workload across multiple GPUs, multi-GPU inference can also reduce inference latency to meet the stringent latency requirements of production workloads.

DeepSpeed Inference offers inference kernels for Transformer blocks with two innovative optimizations that address these challenges to achieve significant latency reduction and throughput improvement.

Therefore, to achieve the best performance, DeepSpeed Inference kernels are fine-tuned to maximize the memory bandwidth utilization for loading the parameters.

DeepSpeed Inference also supports fast inference through automated tensor-slicing model parallelism across multiple GPUs.

DeepSpeed Inference rel…

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.

Often referred to as the First Law of Operations, Little’s Law is named for John D.C. Little, a professor post tenure at MIT Sloan and an MIT Institute Professor Emeritus.

Federico Crespo, CEO of fast-growing industrial tech company Valiot.io, and Miguel Aguilera, supply chain digital transformation and innovation manager at Heineken México, first met at the MIT Sloan Executive Education program Implementing Industry 4.0: Leading Change in Manufacturing and Operations .

Global beer manufacturer Heineken is the second-largest brewer in the world.

Crespo and Aguilera applied Little’s Law and worked backward through the entire production process, examining cycle times to assess wait times and …

Often referred to as the First Law of Operations, Little’s Law is named for John D.C. Little, a professor post tenure at MIT Sloan and an MIT Institute Professor Emeritus.

Federico Crespo, CEO of fast-growing industrial tech company Valiot.io, and Miguel Aguilera, supply chain digital transformation and innovation manager at Heineken México, first met at the MIT Sloan Executive Education program Implementing Industry 4.0: Leading Change in Manufacturing and Operations .

Global beer manufacturer Heineken is the second-largest brewer in the world.

Crespo and Aguilera applied Little’s Law and worked backward through the entire production process, examining cycle times to assess wait times and …

His explorer mentality has brought him to at least one edge of the unknown — where he seeks to determine how machine learning, used in increasingly diverse and numerous applications, actually works.

“I decided pretty early on that computer science was definitely not cool,” he says.

Eventually I came to discover computer science and mathematics on my own and fell in love with them.”Moitra received his bachelor’s degree in electrical and computer engineering from Cornell University in 2007.

He earned his master’s and PhD from MIT in computer science, in 2009 and 2011, and joined the MIT faculty in 2013.

In 2018, Moitra won a School of Science teaching prize for his graduate-level course 18.40…

In the inaugural session on April 28, Lincoln Laboratory researchers presented three topics inherently linked to each other — those of climate change, disaster response, and global health.

To help foster these discussions, Pitts and Mischa Shattuck, who serves as the senior humanitarian advisor at Lincoln Laboratory, recently launched a new lecture series, called the Future of Humanitarian Technology .

"We need to discuss innovative ways that advanced technology can address some of these most pressing humanitarian, climate, and health challenges," says Jon Pitts, who leads Lincoln Laboratory's Humanitarian Assistance and Disaster Relief Systems Group .

The Future of Humanitarian Technology:…

MIT researchers have created the first fiber with digital capabilities, able to sense, store, analyze, and infer activity after being sewn into a shirt.

Fink and his colleagues describe the features of the digital fiber today in Nature Communications.

Memory and moreThe new fiber was created by placing hundreds of square silicon microscale digital chips into a preform that was then used to create a polymer fiber.

A digital fiber can also store a lot of information in memory.

Gitelson-Kahn incorporated the digital fibers into a knitted garment sleeve, thus paving the way to creating the first digital garment.

Improved computing power and an exponential increase in data have helped fuel the rapid rise of artificial intelligence.

To solve the problem, MIT spinout Lightelligence is developing the next generation of computing hardware.

Compared to traditional architectures, the optical chips made by Lightelligence offer orders of magnitude improvement in terms of high speed, low latency, and low power consumption.

Because AI algorithms are computationally intensive, AI compute takes up a large percentage of data center capacity.

Lightelligence is also the first company to have built a complete system of optical computing hardware, which it accomplished in April 2019.

Health care is at a junction, a point where artificial intelligence tools are being introduced to all areas of the space.

Researchers at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), an initiative to support AI research in health care, call for creating a robust infrastructure that can aid scientists and clinicians in pursuing this mission.

The clinical perspectiveThis call to action is a response to health care in 2020.

In health data, where trials often underrepresent certain populations, “minorities are the ones that look unique,” says Ghassemi.

Najat Khan, chief data science officer at Janssen R&D, encourages researchers to be “extremely systematic” w…

What is new, however, is the use of the internet and social media to spread these campaigns.

The spread of disinformation via social media has the power to change elections, strengthen conspiracy theories, and sow discord.

Their goal was to create a system that would automatically detect disinformation narratives as well as those individuals who are spreading the narratives within social media networks.

The project originated in 2014 when Smith and colleagues were studying how malicious groups could exploit social media.

Then, using the RIO system, they were able to detect disinformation accounts with 96 percent precision.

This way the algorithms could “learn” the difference between EEG readings predictive of consciousness and unconsciousness under propofol.

Then they used the algorithms to analyze EEG recorded from 27 real surgery patients who received propofol for general anesthesia.

As a third test, the team applied the algorithms to EEG recordings from 17 surgery patients who were anesthetized with sevoflurane.

The ability to predict unconsciousness across different drugs with the same mechanism of action is key, the authors said.

Altogether, a suite of well-trained and attuned algorithms could provide high accuracy that accounts for patient age and the drug in use.

“Protein Antibody” is harmonious and playful; “Viral Counterpoint” is foreboding, even sinister.

“Protein Antibody,” which is based on the part of the protein that attaches to SARS-CoV-2, runs for five minutes; “Viral Counterpoint,” which represents the virus’s entire spike protein, meanders for 50.

This spring, the two musicians teamed up again, with Buehler translating the coronavirus-attacking antibody protein into a score for a 10-piece orchestra.

“Protein Antibody in E Minor” is the sequel to “Viral Counterpoint of the Spike Protein,” a piece Buehler wrote last spring during the first wave of coronavirus infections.

Back at MIT, Buehler is planning several more “Protein Antibody” perfo…

Jeremy Kepner, a Lincoln Laboratory Fellow in the Cyber Security and Information Sciences Division and a research affiliate of the MIT Department of Mathematics, was named to the 2021 class of fellows of the Society for Industrial and Applied Mathematics (SIAM).

Since joining Lincoln Laboratory in 1998, Kepner has worked to expand the capabilities of computing at the laboratory and throughout the computing community.

"Jeremy has had two decades of contributing to the important field of high performance computing, including both supercomputers and embedded systems.

Kepner, who joined SIAM during his graduate days at Princeton University, has not only published books and articles through SIAM…

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…

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.

The model is expected to learn the criterion by itself and perform both edge type prediction and trajectory prediction.

Summary and Broader ApplicationsWe introduce …

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

Training on Test Inputs with Amortized Conditional Normalized Maximum LikelihoodCurrent machine learning methods provide unprecedented accuracy across a range of domains, from computer vision to natural language processing.

Different classifiers that work well on the training set can give different predictions on the query point.

The minimax optimal distribution given a particular input $x$ and training set $\mathcal D$ can be explicitly computed as follows:For each label $y$, we append $(x,y)$ to our training set and compute the new optimal parameters $\hat \theta_y$ for this modified training set.

Figure 2: Here, we show the heatmap of CNML predictions (left) and the predictions of the tr…

For this post, we use Python as the main language, but the code can be easily changed to other AWS CDK supported languages.

The AWS CDK is an open-source software development framework to model and provision your cloud application resources using familiar programming languages.

With the AWS CloudFormation native resource to create a Studio domain (AWS::SageMaker::Domain) and a user profile within the domain (AWS::SageMaker::UserProfile), you can automate the setup of Studio.

The constructs use cfn_inc.CfnInclude to call the native AWS CloudFormation resource with the appropriate parameters.

Deploy your AWS CDK stackTo deploy your AWS CDK stack, run the following commands in the location whe…

Amazon Lookout for Metrics uses machine learning (ML) to automatically detect and diagnose anomalies (outliers from the norm) without requiring any prior ML experience.

To implement our solution, we complete the following high-level steps:Create an anomaly detector with Lookout for Metrics.

Create an anomaly detector with Lookout for MetricsTo create your anomaly detector, complete the following steps:On the Lookout for Metrics console, choose Create detector.

ConclusionYou can seamlessly connect to your data in CloudWatch to set up a highly accurate anomaly detector across metrics, dimensions, and namespaces of your choice using Lookout for Metrics.

To get started with this capability, see…

This post shows you how to build, train, and deploy a fraud detection model and rules using Amazon Fraud Detector and integrate predictions with Amazon Connect in order to connect with customers in real time.

This function invokes your Amazon Fraud Detector model and predicts whether this transaction is fraudulent.

Create an Amazon Connect instanceThe first step is to create an Amazon Connect instance.

Build, train, and deploy the Amazon Fraud Detector modelIn this section, we build, train, and deploy the Amazon Fraud Detector model using an example Jupyter notebook.

We deployed a complete serverless architecture in which we integrated Amazon Fraud Detector with Amazon Connect to connect wi…

In this post, we show you an example of building a deep learning based patient outcome prediction model.

We build the model in Amazon SageMaker with MIMIC-III data stored in Amazon HealthLake and turn it into a lightweight application for visualization and interpretability.

Overview of solutionThe following architecture diagram illustrates the model training pipeline, inference pipeline, and information-rendering front end.

To learn more about HealthLake, see Amazon HealthLake resources and Making sense of your health data with Amazon HealthLake.

For more examples using HealthLake and population health, see Population health applications with Amazon HealthLake – Part 1: Analytics and monito…

Amazon Redshift ML simplifies the use of machine learning (ML) by using simple SQL statements to create and train ML models from data in Amazon Redshift.

You can use Amazon Redshift ML to solve binary classification, multi-class classification, and regression problems and can use either AutoML or XGBoost directly.

For more information about building regression using Amazon Redshift ML, see Build regression models with Amazon Redshift ML.

For the preliminary steps to get started, see Create, train, and deploy machine learning models in Amazon Redshift using SQL with Amazon Redshift ML.

For more information about building different models with Amazon Redshift ML, see Build regression models w…

In this post, we walk you through how to prepare for and run an AI music competition using AWS DeepComposer.

End of DAY 2Rules and guidelinesThe AWS DeepComposer Got Talent competition is intended to provide a fun backdrop for spending a day learning more about generative AI using AWS DeepComposer through the AWS DeepComposer console.

Overview of AWS DeepComposerIntroducing the AWS DeepComposer Music StudioMany of your participants may be unfamiliar with the AWS DeepComposer Music Studio, so spend 10–15 minutes walking through the steps necessary to create compositions.

Review the AWS DeepComposer algorithmsWe recommend providing a 100–200 level overview of each of the three algorithms util…

You can integrate a Data Wrangler data flow into your ML workflows to simplify and streamline data preprocessing and feature engineering using little to no coding.

Make sure that Data Wrangler automatically inferred the correct data types for your data columns.

Exploratory data analysis and feature engineeringExploratory data analysis is an important step when building ML models.

We can either run Autopilot directly on the raw data or feed it with the enhanced data set that we generated with Data Wrangler.

Learn more about Amazon SageMaker Data Wrangler and Amazon SageMaker Autopilot by visiting their product pages.

SageMaker kernel gateway app – A running instance of the container image on the particular instance type.

The costs incurred for running Studio notebooks, interactive shells, consoles, and terminals are based on Studio instance type usage.

When you run a Studio notebook, interactive shell, or image terminal within Studio, you must choose a kernel and an instance type.

ConclusionIn this post, we demonstrated how to reduce SageMaker costs by using an auto-shutdown Jupyter extension to shut down idle resources running within Studio.

Finally, we showed how the extension can reduce Data Wrangler costs by shutting down idle resources powering Data Wrangler.

In this post, we show how you can integrate Amazon Connect, Amazon Lex, and Salesforce to deliver exceptional customer experiences.

Amazon Connect captures the customer’s input and passes it to Amazon Lex along with the customer attributes fetched in Step 2.

Additionally, Amazon Connect can pass customer details in session attributes to Amazon Lex so that it can drive a personalized customer experience.

Give Amazon Connect Access to the Lambda functionTo grant the Amazon Connect instance permission to invoke the Lambda function previously created, complete the following steps.

If you need help with adding any of these Amazon Connect capabilities to your contact flows, reach out to one of th…

You can now use Snowflake as a data source in Data Wrangler to easily prepare data in Snowflake for ML.

We use Data Wrangler to transform and prepare the data for later use in ML models, first building a data flow in Data Wrangler, then exporting it to Amazon SageMaker Pipelines.

Add Snowflake as a data source in Data WranglerNext, we add Snowflake as a data source.

Clean upIf your work with Data Wrangler is complete, shut down your Data Wrangler instance to avoid incurring additional fees.

To get started with Data Wrangler, see Prepare ML Data with Amazon SageMaker Data Wrangler, and see the latest information on the Data Wrangler product page.

A near 3x speedup will be demonstrated for the optimized XGBoost model compared to the unoptimized one.

Deploy the unoptimized XGBoost model artifact to a SageMaker endpoint.

Create an Amazon CloudWatch Dashboard from the SageMaker notebook to monitor inference speed and performance under heavy load of both endpoints.

You can monitor the progression of the load test by clicking on the link generated by running the second cell.

This report’s metrics are an aggregate of the performances of both SageMaker endpoints, so in this case it’s not very useful to us.

Similarly, internal compliance teams may want to interpret a model’s behavior when validating decisions based on model predictions.

You can send model predictions and individual SHAP values from Clarify for review to internal compliance teams and customer-facing employees via Amazon A2I.

We then extract the SHAP values from the Clarify output and trigger the Amazon A2I review for predictions under a specific threshold.

Create a human review loop using Amazon A2I and supply the outcome and the plot of SHAP values to the Amazon A2I task template.

Prepare the ground truth data based on Amazon A2I resultsNext, we download that Amazon A2I result data and merge it with the batch data to generate …

In the following sections, we walk through building the DICOM data labeling workflow and performing ML model training using the output of the labeling jobs.

Upload DICOM images and prepare the input manifestYou can upload the DICOM images to the Orthanc server either through its web UI or the WADO-RS REST API.

After the DICOM images are uploaded, you can retrieve the DICOM instance IDs for them, and generate a manifest file with the instance IDs.

For more details on how to use the MONAI framework within SageMaker to build and deploy your medical image models, see Build a medical image analysis pipeline on Amazon SageMaker using the MONAI framework.

To learn about other custom data labeling …

Solution overviewWith S3 Object Lambda, organizations can transform S3 objects in-flight as they are being retrieved through a standard Amazon S3 GET request by using S3 Object Lambda Access Points.

Validate that permissions only allow the download through the S3 Object Lambda Access Points that correspond to the similarly named roles (for example, the Billing Redaction role can download from the Billing S3 Object Lambda Access Point, but not the Admin S3 Object Lambda Access Point or the Customer Support S3 Object Lambda Access Point).

Solution costUsing S3 Object Lambda for PII access control or redaction incurs costs from Amazon S3, Lambda, and Amazon Comprehend.

Create the Admin S3 Obje…

Researchers from University of Washington and Facebook used deep learning to convert still images into realistic animated looping videos.

I’m hoping that eventually the pictures that we share with our friends and family won’t be static images.

The researchers used the NVIDIA Pix2PixHD GAN model for motion estimation network training, as well as FlowNet2 and PWC-Net.

The training data included 1196 unique videos, 1096 for training, 50 for validation and 50 for testing.

Read the University of Washington news release for more >>The researchers’ paper is available here.

But at startup Opseyes, founder Bryan Arndt and data scientist Robin Schlenga are putting the AI that’s revolutionizing medical imaging to work on analyzing wastewater samples.

Arndt and Schlenga spoke with NVIDIA AI Podcast host Noah Kravitz about the inspiration for Opseyes, which began with Arndt’s career at wastewater industry leader Ramboll.

With Opseyes already in use at several wastewater plants, Arndt and Schlenga anticipate much more bacterial analysis in their future.

Tune in to the AI PodcastGet the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher…

With the new NVIDIA Jetson AGX Xavier Industrial module, NVIDIA is making it possible to deploy AI at the edge in harsh environments where safety and reliability are critical priorities.

Extending the capabilities of the Jetson AGX Xavier system-on-module, this new industrial module allows developers to build advanced, AI-enabled ruggedized systems.

And it’s pin-, software- and form-factor compatible with the existing Jetson AGX Xavier module, so upgrading is easy.

Jetson AGX Xavier Industrial provides the features necessary to bring autonomy to all of these machines.

Getting Started and AvailabilityThe new Jetson AGX Xavier Industrial module is available to order now and will be available …

Vyasa technologies can integrate external data sources (for example, Pubmed, patents, and clinical trials) with a client’s internal data sources including documents, images and database content.

Axon – A knowledge graph application that enables derivation of dynamically generated knowledge graphs directly from integrated data and documents sources integrated in a Layar data fabric.

Retina – An image analytics application that offers a wide range of deep learning image-related tasks, including management, annotation, and deep learning analytics on images.

Synapse – Provides “Smart Table Technology” that directly connects a user’s spreadsheet content to the analytical capabilities of Layar Da…

Horowitz founded AMP Robotics that year to harness AI run on NVIDIA GPUs to turn sorting out the trash into cash.

AMP Robotics relies on NVIDIA GeForce RTX or V100 GPUs, depending on the recycling center, to run this split-second inference.

Accelerated Business ProspectsAnother capability AMP Robotics provides is data collection and analytics.

AMP Robotics NVIDIA GPU-driven systems enable customers to bring costs down as well as sort plastics into different categories to achieve better returns.

Learn more about AMP Robotics from its GTC 2021 presentation.

NVIDIA RTX real-time ray tracing and AI helped the team enhance content creation workflows and produce photorealistic graphics for the documentary.

CMG also used Omniverse to let its artists create 3D models and high-fidelity renders for their immersive space environments.

NVIDIA Quadro RTX 8000 and NVIDIA RTX A6000 GPUs provided large video memory for loading massive amounts of data and reducing rendering times.

The company plans to use NVIDIA Omniverse running on NVIDIA RTX to create stunning photorealistic images, enhance production pipelines and build more complex scientific visualizations for film and television in the future.

Learn more about NVIDIA Omniverse and NVIDIA RTX.

A team at The Ohio State Center for Automotive Research (CAR) is building a Mobility Cyber Range (MCR) — a dedicated platform for cybersecurity testing — in a self-driving car.

The research pilot will initially focus on establishing standards and recommendations for best practices in AV safety and cybersecurity.

And by conducting this development work on NVIDIA DRIVE, CAR is also training students on AI compute technology that is widespread in the AV industry.

The NVIDIA DRIVE platform also comes with a comprehensive software stack for developers to build an AV system — from the DRIVE OS operating system, to DriveWorks middleware, to DRIVE AV and DRIVE IX autonomous driving and intelligent …

This blog post will show how simple it is to implement a GPU-accelerated Gauss rank transformation with drop-in replacements of Pandas and NumPy using RAPIDS cuDF and Chainer CuPy to deliver 100x speedup.

The idea of Gauss rank transformation was first introduced by Michael Jahrer in his winning solution of Porto Seguro’s Safe Driver Prediction challenge.

Figure 1: Gauss Rank Transformation.

Comparison of CuPy and NumPy implementations of Gauss rank transformation.

Comparison of CuPy and NumPy implementations of inverse Gauss rank transformation.

The main change is now to specify pretrained_weights as the path to pruned model and enable load_graph .

Because the model is being initialized with pruned model weights, the model converges faster.

# Retraining using the pruned model as model graph tlt bpnet train -e $SPECS_DIR/bpnet_retrain_m1_coco.yaml \ -r $USER_EXPERIMENT_DIR/models/exp_m1_retrain \ -k $KEY \ --gpus $NUM_GPUSYou can follow similar instructions as in the Evaluation and Model verification sections to evaluate and verify the pruned model.

One change would be that you now use $SPECS_DIR/infer_spec_retrained_strict.yaml as inference_spec and the model to use would be a pruned TLT model, INT8 engine, or FP16 engine.

Model ac…

Pose estimation demoOpen-source methods of developing pose estimation exist but are not optimal in terms of inference performance and are time consuming to integrate into production applications.

This post series walks you through the steps of training, optimizing, deploying a real-time high performance pose estimation model.

In part 1, you learn how to train a 2D pose estimation model using open-source COCO dataset.

In part 2, you learn how to optimize the model for inference throughput and then deploy the model using TLT CV inference pipeline.

To optimize the trained model for inference and deployment, see Training and Optimizing the 2D Pose Estimation Model, Part 2.

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

The U.K.’s Royal Academy of Engineering has elected Facebook Research Scientist Mark Harman as a Fellow for his achievements in academia and industry, including his work on search-based software engineering (SBSE), intelligent software testing tools, and web-enabled simulation (WES) approaches.

SBSE is an approach that uses search-based optimization algorithms to find solutions to highly complex software engineering problems.

Using the technique allows for smoother testing, design, and project management in software engineering.

For the next 25 years, he worked solely in academia, where he wrote, edited, and reviewed hundreds of papers, and authored books about software testing and programm…

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…

Geospatial data science is becoming an essential part of the data science landscape.

Geographic Information System(GIS) is a system for gathering, managing, manipulating, analyzing, storing, and visualizing geospatial data (data with geographic components).

The landscape of Geospatial Data Science includes:Data Engineering : help read, transform, format, clean, and enrich geospatial data.

This means that geospatial data science can be used in almost any industry, including:HealthcareTelecommunicationsUrban planning/developmentMarketingSocial servicesMilitaryNatural resource exploration and exploitationTransportationEducationWeatherAgricultureGeospatial Data Science toolkitsLet’s discuss pro…

The authors need to employ a piecewise modification of the original loss function to tackle this.

[Source]Code exampleimport tensorflow as tf def contrastive_loss (m) : def inner (y_true, d) : loss = tf.reduce_mean(y_true*d+( 1 -y_true)*tf.maximum(m-d, 0 )) return loss return innerTriplet LossBackground / motivationThe triplet loss is probably the best-known loss function for face recognition.

[Source]Code exampleimport tensorflow as tf def triplet_loss (m) : def inner (d_pos, d_neg) : loss = tf.square(tf.maximum(d_pos - d_neg + m, 0 )) loss = tf.reduce_mean(loss) return loss return innerCircle LossBackground / motivationMotivated by the disadvantage of the Triplet Loss mentioned above, the…

We’ll describe Graph Neural Networks (GNNs), cover popular GNN libraries, and we’ll finish with great learning resources to get you started in this field.

Prerequisites: This article assumes a basic understanding of Machine Learning (ML), Deep Learning (DL), and GNNs.

Graph Neural Networks (GNN) overviewGraph Neural Networks (GNNs) came to life quite recently.

2) Deep Graph Library (DGL)Deep Graph Library(DGL) is another easy-to-use, high-performance, and scalable Python library for deep learning on graphs.

It’s the product of a group of deep learning enthusiasts called the Distributed Deep Machine Learning Community.

In this article, we’re going to explain explainability, explore why it’s necessary, and talk about techniques and tools that simplify explainability.

The three most important aspects of model explainability are:Transparency Ability to question Ease of understandingApproaches to explainabilityYou can approach explainability in two ways:Globally – This is the overall explanation of model behavior.

Linear models: Linear models such as linear regression, SVMs with linear kernel, etc follow the linearity principle that two or more variables can be added together so that their sum is also a solution.

AI Explainability 360 (AIX360)The AI Explainability 360 toolkit is an open-source library from IB…

Machine Learning ArchitectOptimizes the architecture for Machine Learning models as part of production deployment.

Top 5 problems faced in Data Science and ML projectsIn data science projects, we might encounter the following five challenges:1.

Challenges in ML projectsThe big two challenges of ML projects are:Taking ML models to production, only 47% of models are fully deployed!

of Experiments Yes Yes No Yes Yes Custom Visualizations Yes Yes Yes Yes Yes5 MLOps tools1.

Employing pipelines helps you to regularly update Machine Learning models, test new models, and continuously roll out new models alongside your other apps & services.

You can monitor what could go wrong with your machine learning model in production at two levels:Functional level monitoring – monitoring model performance, inputs (data), and outputs (predictions).

Data drift refers to a meaningful change in distribution between the training data and production data.

The properties of your production data and your model’s performance in production to detect model staleness and degradation; can help with continuous training through triggers that automate the ML production pipelines to retrain models with new production data.

Ensure your production data is not vastly different from your training data, and your production and training data are processed the s…

Data Scientists, Data Analysts, Data Engineers, Machine Learning Engineers, AI engineers, Deep Learning Engineers, on and on the list goes.

Total of 3-6 years of experience in managing machine learning projects end-to-end with the last 18 months focused on MLOps.

Providing best practices, executing POC for automated and efficient model operations at scale.

MLOps Engineers work closely with Data Scientists and Data Engineers in the Data Science Team from the start of the project.

Best practicesTry multiple feature engineering methods that you choose based on your data and the business problem you’re solving.

Bringing knowledge graphs and machine learning (ML) together can systematically improve the accuracy of systems and extend the range of machine learning capabilities.

Thanks to knowledge graphs, results inferred from machine learning models will have better explainability and trustworthiness.

Knowledge graphs can help overcome this issue by mapping explanations to proper nodes in the graph and summarizing the decision-making process.

Anyway, to build knowledge graphs from text, it’s important to help our machine understand natural language.

It also manifests itself indirectly as it affects other operations, such as managing fast incremental updates to large-scale knowledge graphs.

The workflow for computer vision models, or any machine learning models, also follows a similar pattern.

MLOps level 2In level 2, the CI/CD pipeline is automated along with the ML pipeline.

Fig: an example machine learning pipeline devised by TPOT [Source]Auto-sklearn is an automated machine learning toolkit.

It uses an automated machine learning pipeline for version control, automation, logging, auditing, and containerization.

Fig: How Algorithmia operates [Source]Kubeflow is an open-source and free machine learning Kubernetes-native platform for developing, orchestrating, deploying, and running scalable and portable machine learning workloads.

You will also get a walkthrough of instances when MLOps is an organizational and communication problem and when it is a tech problem and how to resolve these challenges.

Despite the similarity between the core framework of MLOps and DevOps, some stark differences make MLOps much more unique and difficult to deal with.

Provisioning within budget – Sometimes, the development teams cannot use the company’s resources because of budget limitations or because the resource is shared across multiple teams.

– Sometimes, the development teams cannot use the company’s resources because of budget limitations or because the resource is shared across multiple teams.

By resolving some of the organizationa…

Online Food Ordering and Logistics In-House Machine Learning Platform Generic ML use cases at DoorDashDisclaimers:While this article is an in-depth guide into how these companies implement MLOps, it by no means provides an exhaustive detail of their processes.

How LUSH implements Machine Learning Operations (MLOps)The Machine Learning use case we will look into was deployed for LUSH by Datatonic.

How Uber implements Machine Learning Operations (MLOps)According to their engineering blog, Machine learning helps Uber make data-driven decisions.

Just like Uber, Netflix uses machine learning across a lot of areas in their product and deploys thousands of machine learning models.

How Netflix impl…

The API will consist of multiple views that process the input images at different stages.

We’ll also be taking a look at recent image techniques that accurately colorize old black and white photographs.

Let’s set up the Django PartInstall Django and Django Rest Framework:pip install django djangorestframeworkOnce the dependencies correctly installed, head to the root folder and initialize the Django app:django-admin startproject semantic-segNow your Django project is ready to go.

Basically, each output image can be saved in your Neptune platform and would inform about the model performance and accuracy.

For our particular case, we need to post images, apply the model inference to get the se…

Today there are 9k+ machine learning startups and companies according to Crunchbase.

But first, we need to decide how we’re going to rank different machine learning startups and companies:Results and performance – this is a no-brainer.

The company offers consultations to align your business needs with emerging technologies such as machine learning, AI, automation, and cybersecurity.

LITE is a combination of machine learning techniques such as inference, deep learning, natural language processing, and pattern recognition.

For more insights, you can also visit a few top machine learning conferences in 2021 or check out some amazing books and podcasts on machine learning and AI.

So, the Model Registry is a kind of a linchpin to make MLops work.

Model Registry platformsLet’s discuss the few best and most used tools for Model Registry.

Individuals and organizations use Neptune for experiment tracking and model registry to have control over their experimentation and model development.

in the central ML Model registry, version store, filter, sort, and group all your machine learning model training runs in a dashboard.

MLFlow Model Registry WorkflowYou can access the model registry via UI or API.

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

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

Minds: https://www.minds.com/ykilcher

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…

#reinforcementlearning #deepmind #agi What's the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion. OUTLINE:

0:00 - Intro & Outline

4:10 - Reward Maximization

10:10 - The Reward-is-Enough Hypothesis

13:15 - Abilities associated with intelligence

16:40 - My Criticism

26:15 - Reward Maximization through Reinforcement Learning

31:…

#facerecognition #emotiondetection #mindreading Face recognition has a bad rep in the ML community. While the technology continuously advances, so does the resistance against its applications, with good reasons: AI emotion analysis hints at a dystopian future where our lives are completely governed by algorithms. However, we must be realistic about what is and isn't possible with AI, and while current systems are not the most accurate, denying the link between your facial expression and your emotions is not productive either. https://twitter.com/jblefevre60/status/1395617615964475392 Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/…

#metarim #deeprl #catastrophicforgetting Reinforcement Learning is very tricky in environments where the objective shifts over time. This paper explores agents in multi-task environments that are usually subject to catastrophic forgetting. Building on the concept of Recurrent Independent Mechanisms (RIM), the authors propose to separate the learning procedures for the mechanism parameters (fast) and the attention parameters (slow) and achieve superior results and more stability, and even better zero-shot transfer performance. OUTLINE:

0:00 - Intro & Overview

3:30 - Recombining pieces of knowledge

11:30 - Controllers as recurrent neural networks

14:20 - Recurrent Independent Mechanisms

21:20…

#deepmind #google #mlnews DeepMind has reportedly failed to negotiate for greater independence from Google/Alphabet. While DeepMind wanted to set up a non-profit-like structure, Google seems to go for the opposite approach and seek tight integration. How is AI best served? Original Article: https://www.wsj.com/articles/google-unit-deepmind-triedand-failedto-win-ai-autonomy-from-parent-11621592951 Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

BitChute: https://www.bitchute.com/channel/yannic-kilcher

Minds: https://www.minds.com/ykilcher

Parler:…

#expirespan #nlp #facebookai Facebook AI (FAIR) researchers present Expire-Span, a variant of Transformer XL that dynamically assigns expiration dates to previously encountered signals. Because of this, Expire-Span can handle sequences of many thousand tokens, while keeping the memory and compute requirements at a manageable level. It severely matches or outperforms baseline systems, while consuming much less resources. We discuss its architecture, advantages, and shortcomings. OUTLINE:

0:00 - Intro & Overview

2:30 - Remembering the past in sequence models

5:45 - Learning to expire past memories

8:30 - Difference to local attention

10:00 - Architecture overview

13:45 - Comparison to Transfo…

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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.

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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…

#artificialintelligence #musicvideo #clip I used OpenAI's CLIP model and BigGAN to create a music video that goes along with the lyrics of a song that I wrote. The song lyrics are made from ImageNet class labels, and the song itself is performed by me on a looper. OUTLINE:

0:00 - Intro

1:00 - AI-generated music video for "be my weasel"

3:50 - How it was made

7:30 - My looping gear

9:35 - AI-generated music video #2

12:45 - Outro & Credits Code and references: https://github.com/yk/clip_music_video Links:

TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick

YouTube: https://www.youtube.com/c/yannickilcher

Twitter: https://twitter.com/ykilcher

Discord: https://discord.gg/4H8xxDF

…

#ddpm #diffusionmodels #openai GANs have dominated the image generation space for the majority of the last decade. This paper shows for the first time, how a non-GAN model, a DDPM, can be improved to overtake GANs at standard evaluation metrics for image generation. The produced samples look amazing and other than GANs, the new model has a formal probabilistic foundation. Is there a future for GANs or are Diffusion Models going to overtake them for good? OUTLINE:

0:00 - Intro & Overview

4:10 - Denoising Diffusion Probabilistic Models

11:30 - Formal derivation of the training loss

23:00 - Training in practice

27:55 - Learning the covariance

31:25 - Improving the noise schedule

33:35 - Reduci…

#icml #machinelearning #conference In a controversial move, ICML Area Chairs were instructed to raise the bar on acceptance to drop the acceptance rate by 10% from the previous trajectory. This raises a lot of questions about the pains of an academic peer review system under the load of an exponentially increasing field of study. Who draws the short stick? Usually not the big corporations. References:

https://www.reddit.com/r/MachineLearning/comments/n243qw/d_icml_conference_we_plan_to_reduce_the_number_of/

https://twitter.com/tomgoldsteincs/status/1388156022112624644

https://twitter.com/ryan_p_adams/status/1388164670410866692

https://github.com/lixin4ever/Conference-Acceptance-Rate Links:

…

Content Links Below:

Generative Models as a Data Source for Multi-View Representation Learning: https://arxiv.org/pdf/2106.05258.pdf

Learning to See by Looking at Noise: https://arxiv.org/pdf/2106.05963.pdf

Knowledge Distillation: A Good Teacher is Patient and Consistent: https://arxiv.org/abs/2106.05237

Does Knowledge Distillation Really Work? https://arxiv.org/abs/2106.05945

AdaMatch: https://arxiv.org/abs/2106.04732

Self-Damaging Contrastive Learning: https://arxiv.org/pdf/2106.02990.pdf

Masked Self-Supervised Transformer for Visual Representation: https://arxiv.org/pdf/2106.05656.pdf

Data-Efficient Instance Generation from Instance Discrimination: https://arxiv.org/pdf/2106.04566.pdf

Sc…

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Decision Transformer: https://arxiv.org/pdf/2106.01345.pdf

Trajectory Transformer (Sergey Levine's Twitter Thread): https://twitter.com/svlevine/status/1400547882973863939

Toward Generalized Sim-to-Real Transfer Robot Learning: https://ai.googleblog.com/2021/06/toward-generalized-sim-to-real-transfer.html

Consistency in LMs: https://arxiv.org/pdf/2102.01017.pdf

Implicit Representations of Meaning in Neural Language Models: https://arxiv.org/pdf/2106.00737.pdf

Database Reasoning Over Text: https://arxiv.org/pdf/2106.01074.pdf

Splinter: https://arxiv.org/abs/2101.00438

Zero-shot Fact Verification by Claim Generation: https://arxiv.org/abs/…

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Project CodeNet: https://arxiv.org/pdf/2105.12655.pdf

CoSQA: https://arxiv.org/pdf/2105.13239.pdf

SSL Bug Detection and Repair: https://arxiv.org/pdf/2105.12787.pdf

Reward is Enough: https://www.sciencedirect.com/science/article/pii/S0004370221000862

Reward is Enough (Yannic Kilcher's Video): https://www.youtube.com/watch?v=dmH1ZpcROMk

AndroidEnv: https://arxiv.org/abs/2105.13231

CogView: https://arxiv.org/pdf/2105.13290.pdf

Understanding VQ-VAE: https://ml.berkeley.edu/blog/posts/vq-vae/

Drawing Multiple Augmentation Samples: https://arxiv.org/pdf/2105.13343.pdf

On the Bias Against Inductive Biases: https://arxiv.org/abs/2105.14077

Medi…

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APPS: https://arxiv.org/pdf/2105.09938.pdf

Improving Code Autocomplete: https://arxiv.org/pdf/2105.05991.pdf

DeepDebug: https://arxiv.org/pdf/2105.09352.pdf

The Simplicity Bias: https://arxiv.org/pdf/2105.05612.pdf

Rethinking "Batch" in BatchNorm: https://arxiv.org/pdf/2105.07576.pdf

Divide and Contrast: https://arxiv.org/pdf/2105.08054.pdf

Ethan Perez on True Few-Shot Learning: https://twitter.com/EthanJPerez/status/1397015129506541570

KELM: https://ai.googleblog.com/2021/05/kelm-integrating-knowledge-graphs-with.html

Are Larger Pretrained Language Models Uniformly Better? https://arxiv.org/pdf/2105.06020.pdf

Pay Attention to MLPs: h…

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MoCoV3: https://arxiv.org/pdf/2104.02057.pdf

Revisiting Simple Neural Probabilistic Language Models: https://arxiv.org/pdf/2104.03474.pdf

Large-scale forecasting: Self-supervised learning framework for hyperparameter tuning: https://ai.facebook.com/blog/large-scale-forecasting-self-supervised-learning-framework-for-hyper-parameter-tuning

SiT: https://arxiv.org/pdf/2104.03602.pdf

GPV-I: https://arxiv.org/pdf/2104.00743.pdf

GAN Survey: https://www.youtube.com/watch?v=3ktD752xq5k

Regularizing GANs with Limited Data: https://arxiv.org/pdf/2104.03310.pdf

Transfer vs. Meta Learning: https://arxiv.org/pdf/2104.02638.pdf

CodeTrans: https://ar…

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…

Thank you for watching! Please Subscribe! Content Links:

Recursive Classification: https://ai.googleblog.com/2021/03/recursive-classification-replacing.html

Industrial Assembly via RL: https://arxiv.org/pdf/2103.11512.pdf

Model-based RL in Healthcare: https://twitter.com/christina_x_ji/status/1374815904790421508

Can ViTs learn w/o Natural Images?: https://arxiv.org/pdf/2103.13023.pdf

Universal Compute Engines: https://bair.berkeley.edu/blog/2021/03/23/universal-computation/

DeepViT: https://arxiv.org/pdf/2103.11886.pdf

Conv Designs in Visual Transformers: https://arxiv.org/pdf/2103.11816.pdf

Scaling Local Self-Attn. for Vision: https://arxiv.org/pdf/2103.12731.pdf

Visual PT w/ Contrastive D…

This video previews the content for the next AI Weekly Update - March 29th, 2021 (#30)!

Thanks for watching and please subscribe! Content Links:

Recursive Classification: https://ai.googleblog.com/2021/03/recursive-classification-replacing.html

Industrial Assembly with RL: https://arxiv.org/pdf/2103.11512.pdf

Christina Ji's Twitter Thread on Model-Based RL in Healthcare: https://twitter.com/christina_x_ji/status/1374815904790421508

ADAPET: https://arxiv.org/pdf/2103.11955.pdf

Progress and Challenges in Long-Form Open-Domain Question Answering: https://ai.googleblog.com/2021/03/progress-and-challenges-in-long-form.html

Sebastian Ruder's Newsletter: http://newsletter.ruder.io/issues/qa-how-di…

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Revisiting ResNets: https://arxiv.org/pdf/2103.07579.pdf

Is it Enough to Optimize CNN Architectures on imageNet? https://arxiv.org/pdf/2103.09108.pdf

Learning to Resize Images for Computer Vision Tasks: https://arxiv.org/pdf/2103.09950.pdf

Large-Scale Zero-Shot Learning: https://arxiv.org/pdf/2103.09669.pdf

Training GANs with Stronger Augmentations via Contrastive Discriminator: https://arxiv.org/pdf/2103.09742.pdf

Using Latent Space Regression to Analyze and Leverage Compositionality in GANs: https://arxiv.org/pdf/2103.10426.pdf

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction: https://sites.google.com/view/…

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Behavior from the Void: https://arxiv.org/pdf/2103.04551.pdf

Barlow Twins: https://arxiv.org/pdf/2103.03230.pdf

Pretrained Transformers as Universal Compute Engines: https://arxiv.org/pdf/2103.05247.pdf

A New Lens on Understanding Generalization: https://ai.googleblog.com/2021/03/a-new-lens-on-understanding.html

Knowledge Evolution in Neural Networks: https://arxiv.org/pdf/2103.05152.pdf

COIN: https://arxiv.org/pdf/2103.03123.pdf

CheXseen: https://arxiv.org/pdf/2103.04590.pdf

Haystack: The State of Search in 2021: https://medium.com/deepset-ai/haystack-the-state-of-search-in-2021-7388ecb15dfb

Hurdles to Long-Form QA: https://arxiv.org…

This video explains another awesome Keras Code Example, this time implementing a cutting-edge technique for Data Augmentation. In my view, what makes MixUp so interesting is that it can be applied in data domains outside of images and Computer Vision. Say for NLP or Physiological data, it is very hard to define data augmentations and here is a great framework for getting started. You may also be interested in the video I made explaining MODALS - a recent ICLR 2021 paper exploring cutting-edge domain-agnostic data augmentation. Thanks for watching, please check out the rest of the Keras Code Examples playlist! Follow Sayak Paul on Twitter: https://twitter.com/RisingSayak Content Links:

Keras…

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…

Start with part 1: https://youtu.be/X8jsijhllIA

Ben Eater implementing Hamming codes on breadboards: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com…

A discovery-oriented introduction to error correction codes.

Part 2: https://youtu.be/b3NxrZOu_CE

Ben Eater:'s take: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks You can read Hamming's own perspective on his discovery of these codes in chapter 12 of "The Art of Doing Science and Engineering".

https://amzn.to/3lwcnmh ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by…

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Total Relighting: Learning to Relight Portraits for Background Replacement" is available here:

https://augmentedperception.github.io/total_relighting/ 🙏 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, 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:

http://moghs.csail.mit.edu/ Building grammar paper:

https://www.cg.tuwien.ac.at/research/publications/2015/Ilcik_2015_LAY/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

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Aleksandr Mashra…

❤️ 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:

https://distill.pub/selforg/2021/textures/ Game of Life animation source: https://copy.sh/life/

Game of Life image source: https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life 🙏 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 A…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Enhancing Photorealism Enhancement" is available here:

https://intel-isl.github.io/PhotorealismEnhancement/ 🙏 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, 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, Ste…

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Learning mesh-based simulation with Graph Networks" is available here:

https://arxiv.org/abs/2010.03409 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, 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, Rob…

❤️ 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/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly#System-4 📝 The paper "Wave Curves: Simulating Lagrangian water waves on dynamically deforming surfaces" is available here:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills" is available here:

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❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "Procedural Physically based BRDF for Real-Time Rendering of Glints" 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 "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" is available here:

https://yuval-alaluf.github.io/restyle-encoder/ 📝 Our material synthesis paper is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 📝 The font manifold paper is available here:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Learning and Exploring Motor Skills with Spacetime Bounds" is available here:

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❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery" is available here:

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Iterative Text-based Editing of Talking-heads Using Neural Retargeting" is available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/adv-dl/reports/An-Introduction-to-Adversarial-Examples-in-Deep-Learning--VmlldzoyMTQwODM 📝 The paper "Interactive Wood Combustion for Botanical Tree Models" is available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/text-recognition-crnn-ctc/reports/Text-Recognition-With-CRNN-CTC-Network--VmlldzoxNTI5NDI 📝 The paper "Freely orientable microstructures for designing deformable 3D prints" and the Shadertoy implementation are available here:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/authors/RayTune-dcgan/reports/Ray-Tune-Distributed-Hyperparameter-Optimization-at-Scale--VmlldzoyMDEwNDY 📝 The paper "A moving least square reproducing kernel particle method for unified multiphase continuum simulation" is available here:

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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/

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Anton Lokhmotov, ML Perf Engineer

Roman Vlasov, Huawei Engineer Register and get access to the tracks: https://ods.ai/events/datafest2020

Join the community: https://ods.ai/

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…

Одной из основных задач связывающих компьютерное зрение и обработку естественного языка является генерация изображений из текстовых описаний (text-to-image generation). Решение данной задачи имеет множество практических применений, а также способствует развитию исследований в области мультимодального обучения. Большинство известных на данный момент text-to-image подходов основаны на генеративно-состязательных сетях, которые показывают хорошие результаты. На семинаре будет рассмотрена статья "Zero-Shot Text-to-Image Generation", в которой описан новый подход к решению этой задачи, основанный на трансформере, который авторегрессивно моделирует текстовые токены и токены изображения как единый …

На семинаре мы рассмотрим один из текущих проектов лаборатории, Reflekt (https://github.com/JetBrains-Research/reflekt), который представляет собой плагин для Kotlin компилятора для compile-time рефлексии. В докладе мы разберем на примерах, что же такое рефлексия и какие задачи она может решать, рассмотрим существующие compile-time и runtime решения (для Java/Kotlin). После чего, рассмотрим сам Reflekt: поговорим, что это такое, как его использовать, как он устроен изнутри, а также про наши планы на будущее касательно этого проекта. Докладчик: Анастасия Бирилло.

One-class (OC) classification это вариант бинарной классификации, где во время обучения доступна только выборка одного из классов. Необходимость такой классификации возникает, например, в случаях когда данные одного из классов очень дорого и/или сложно получать. Positive-unlabeled (PU) learning тоже вариант бинарной классификации, но в этом сеттинге имеются уже две выборки: аналогичная OC из одного класса и неразмеченная выборка из смеси всех данных. Данные подходы очень похожи, но PU кажется более предпочтительным, так как во многих применениях OC неразмеченные данные либо сразу доступны, либо их просто можно получить. Несмотря на это, большинство работ фокусируются именно на OC подходах. …

Многие пациенты, страдающие сложными заболеваниями, для эффективного лечения вынуждены принимать несколько лекарств одновременно. Но такой подход несет большие риски, связанные с возникновением неожиданных побочных эффектов, которых могло не быть у препаратов по отдельности. Поэтому необходимо исследовать совместные побочные эффекты лекарств, то есть возникающие при их одновременном приеме. Традиционные подходы, основанные на экспериментальном скрининге, малоприменимы в условиях постоянно растущего числа препаратов. Поэтому сейчас активно развивается применение в этой области машинного обучения. В частности, современным подходом является использование knowledge graph embeddings моделей в ле…

Этот семинар будет несколько отличаться от предыдущих, потому что мы будем разбирать не конкретную проблему, а целый подход к развитию дальнейших AI технологий. CAIS (comprehensible AI services) — подход, представленный в одноименной работе Эриком Дрейкслером в 2019 году. Это внушительный и сложно составленный документ, в котором предлагается другой путь развития AI, отличный от классического взгляда на будущее AI. Вместо парадигмы развития в сторону единого агента, способного решать множество проблем на человеческом/сверхчеловеческом уровне, автор предлагает будущее как сеть сервисов, где каждый в отдельности справляется со своей задачей, и некоторый модуль, который находит необходимость в…

В классической постановке задачи обучения с подкреплением во время обучения агент руководствуется функцией награды, которая позволяет ему понять, насколько хорошо он умеет решать поставленную задачу. Конструирование такой функции зачастую является достаточно сложной задачей, поскольку требует достаточно больших знаний о предметной области. Альтернативой такому подходу является использование примеров, полученных при помощи экспертов, которые уже умеют решать проблему. На данный момент существует большое количество алгоритмов Imitation Learning, однако их использование накладывает серьезные ограничения на наблюдения, получаемые с помощью эксперта. Исследователи из OpenAI в своей недавней стат…

Использование демонстраций в обучении с подкреплением часто помогает агенту в решении трудных задач. Однако чрезмерная зависимость от некачественных демонстраций может наоборот помешать обучению и сильно сместить итоговую политику от оптимальной. В своей недавней работе исследователи из команды JetBrains Research предложили новый метод, который позволяет эффективно использовать демонстрации. Данный метод комбинирует через общий Replay Buffer алгоритм Self-Imitation Learning, использующий в обучении прошлый удачный опыт агента, и алгоритмы Imitation Learning, которые выучивают поведение эксперта по его демонстрациям. На предстоящем семинаре мы разберем предложенный алгоритм и посмотрим на по…

До сегодняшнего дня поведенческие характеристики человека при чтении текста представляли интерес в основном для изучения когнитивных функций мозга. Однако сигналы мозга при чтении, детектируемые на ЭЭГ, могут также использоваться в машинном обучении для NLP. Использование частотных сигналов мозга на ЭЭГ не исследовалось ранее в контексте машинного обучения для работы с текстом. В данной статье предложено мультимодальная архитектура, на вход которой совместно подается текст и фичи извлекаемые из ЭЭГ. Для множества word embedding types подмешивание данных ЭЭГ улучшает вторичную и третичную классификацию тональности, а также превосходит некоторые бейзлайн решения. Данный подход выглядит многоо…

Разработка беспилотных автомобилей остаётся очень сложной задачей, которая классически разделяется на четыре модуля: локализация, распознавание, управление и планирование. От подзадачи распознавания требуется точное определение объектов, находящихся рядом с беспилотным автомобилем: их класcа и расположения. Кроме точности, от таких моделей ожидаются устойчивость и высокая скорость работы, поскольку педполагается, что беспилотный автомобиль должен принимать решения в реальном времени при разных условиях окружающей среды (например, погода и время дня).

На практике “зрением” беспилотного автомобиля становятся различные сенсоры, которыми оборудован беспилотник. Чаще всего это радары, лидары и к…

Феномен коктейльной вечеринки заключается в способности слушателя фокусироваться на одном конкретном раздражителе, игнорируя другие. Большинство людей с легкостью справляются с этой задачей, чего нельзя сказать о системах автоматического распознавания речи. В статье, которая будет рассмотрена на семинаре, авторы представляют VoiceFilter Lite – одноканальную модель разделения речевых сигналов, являющуюся частью системы распознавания речи при потокой передаче сигнала. Модель принимает на вход перекрываемый шумом сигнал таргет-спикера и сохраняет его в чистом виде. На семинаре поймём: зачем авторы модели использовали асимметрическую лосс-функцию и механизм адаптивного подавления шума, а также …

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: Сергей Пономаренко, COO, Исполнительный директор at EORA Кейсы применения чат-ботов в задачах маркетинга: опросы, инструмент поддержки рекламной кампании, самостоятельный маркетинговый инструмент. Презентация: https://drive.google.com/file/d/1o3jlZWdkS-lxb6YaQ0wxdVb7xI7b-wyH/view?usp=sharing 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 in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Константин Коточигов, Head of Data Analytics at Weborama Russia В рамках доклада освещаются различные аспекты использования машинного обучения для оптимизации маркетинговых кампаний; рассматривается, как использовать множество разнородных источников данных и как оптимизируются кампании, когда одной метрики качества недостаточно Презентация: https://drive.google.com/file/d/1yItKiesUq3xH2BRh-VeQUjSf-bjzEAu2/view?usp=sharing Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест и получить доступ к трекам: http…

Data Fest Online 2021

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Никита Минаев, Data Scientist at X5 Расскажем о том, как строим модели look-alike на данных о покупках гостей Пятерочки и применяем их в маркетинговых коммуникациях Презентация: https://drive.google.com/file/d/1e65phuIqBKAvZtWjDsERnF0PpsKOGC9f/view?usp=sharing 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

Lean Data Science track https://ods.ai/tracks/lean-ds-df2021 Третий митап трека в рамках DataFest. Обсуждаем конфликт исследователя и бизнеса и учимся строить продажи в B2B для технологического продукта. 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

Data Collection track https://ods.ai/tracks/data-collection-df2021 Спикер: Александр Мамаев, Mail.ru 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

Data Collection track https://ods.ai/tracks/data-collection-df2021 Спикер: Сергей Нестерук, Аспирант Сколковского инстутута науки и технологий 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

Data Collection track https://ods.ai/tracks/data-collection-df2021 Спикер: Роман Куцев, CEO & Founder TrainingData.ru Как собрать датасет для аутентификации человека по лицу через Толоку. • Биометрическая идентификация человека и виды хакерских атак • Часть 1. Собираем 10 000 spoofing attack за 10 дней и 300$ • Часть 2. Заставляем крутить головой 25 000 человек • Часть 3. Собираем еще 11 000 spoofing attack • Часть 4. Интеграция сканера лица в Толоку • Часть 5. Планы на будущее 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:…

Data Fest Online 2021

Code Mining track https://ods.ai/tracks/code-mining-df2021 Доклады прошлых лет на ODS.ai:

https://ods.ai/tracks/code-mining-df2020 Страница проекта CodeMining с описанием общей движухи:

https://ods.ai/projects/code_mining 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 in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021

Телеграм-канал https://t.me/mlinmarketing Спикер: Алексей Чернобровов, Data science consultant at chernobrovov.com Из доклада вы узнаете, что такое модели пользовательского взаимодействия в том числе и Customer Journey Map. Затем мы поговорим о том, как автоматизировать Customer Journey Map с помощью современных методов Data Science. Обсудим, когда это делать и когда не нужно. И закончим рассмотрением кейсов. Презентация: https://drive.google.com/file/d/1SloSh2a8tHI7ylj4PCofDQ6FS_KCeCnB/view?usp=sharing Посмотреть эфир и список треков и организаторов: https://datafest.ru/2021/

Зарегистрироваться на фест…

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:

Christian Gruber

Matthijs Ruijgrok

Christopher

Anthony Eufemio

José Hamilton

Zachariah Richard Fournier

Vladimir Duchenchuk

Noah Healy

JMakes

Mike Schmidt

PeepPhysics

Anders Fjeldvær

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…

Charles Hoskinson is the founder of Cardano, co-founder of Ethereum, a mathematician, and a farmer.

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(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…

Daniel Schmachtenberger is a philosopher interested understanding the rise and fall of societies and individuals.

Please support this podcast by checking out our sponsors:– Ground News: https://ground.news/lex– NetSuite: http://netsuite.com/lex to get free product tour– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Daniel’s Website: https://civilizationemerging.com/The Consilience Project: https://consilienceproject.org/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lw…

Jordan Ellenberg is a mathematician and author of Shape and How Not to Be Wrong.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(06:44) – Mathematical thinking(10:21) – Geometry(14:58) – Symmetry(25:29) – Math and science in the Soviet Union(33:09) – Topology(47:57) – Do we live in many more than 4 dimensions?

(52:28) – How many holes does a straw have(1:01:53) – 3Blue1Brown(1:07:40) – Will AI ever win a Fields Medal?

(1:16:05) – Fermat’s last theorem(1:33:23) – Reality cannot be explained simply(1:39:08) – Prime numbers(2:00:37) – John Conway’s Game of Life(2:12:29) – Group theory(2:15:45) – Gauge theory(2:23:47) – Grigori Pere…

David Sinclair is a geneticist at Harvard and author of Lifespan.

Please support this podcast by checking out our sponsors:– Onnit: https://lexfridman.com/onnit to get up to 10% off– Clear: https://clearme.com/lexpod and use code LexPod to get 2 months free– National Instruments (NI): https://www.ni.com/perspectives– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Linode: https://linode.com/lex to get $100 free creditEPISODE LINKS:David’s Twitter: https://twitter.com/davidasinclairDavid’s Website: https://lifespanbook.comPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8…

Vitalik Buterin is the co-founder of Ethereum.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Indeed: https://indeed.com/lex to get $75 credit– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Vitalik’s Twitter: https://twitter.com/VitalikButerinVitalik’s Blog: https://vitalik.ca/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS:…

Frank Wilczek is a Nobel Prize winning physicist at MIT.

Please support this podcast by checking out our sponsors:– The Information: https://theinformation.com/lex to get 75% off first month– NetSuite: http://netsuite.com/lex to get free product tour– 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:Frank’s Twitter: https://twitter.com/FrankWilczekFrank’s Website: https://www.frankawilczek.com/Fundamentals: Ten Keys to Reality (book): https://amzn.to/3vLPyQBPODCAST INFO:Podcast website:…

Bryan Johnson is the founder and CEO of Kernel, OS Fund, and previously Braintree.

Please support this podcast by checking out our sponsors:– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– NetSuite: http://netsuite.com/lex to get free product tour– Grammarly: https://grammarly.com/lex to get 20% off premium– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Bryan’s Twitter: https://twitter.com/bryan_johnsonBryan’s Website: https://www.bryanjohnson.co/Kernel’s Twitter: https://twitter.com/KernelCoKernel’s Website: https://www.kernel.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcas…

Sam Harris is an author, podcaster, and philosopher.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:18) – Where do thoughts come from?

(14:18) – Consciousness(31:50) – Psychedelics(41:14) – Nature of reality(58:09) – Free will(1:56:55) – Ego(2:05:59) – Joe Rogan(2:08:59) – How will human civilization destroy itself?

(2:16:27) – AI(2:37:10) – Jordan Peterson(2:45:12) – UFOs(2:53:02) – Brazilian Jiu Jitsu(3:02:47) – Love(3:13:50) – Meaning of life

Katherine de Kleer is a professor of Planetary Science and Astronomy at Caltech.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(07:07) – Pluto(12:14) – Kuiper belt(16:12) – How to study planets and moons(19:54) – Volcanoes on Io – moon of Jupiter(32:25) – Is there life in the oceans of Europa?

(41:46) – How unlikely is life on Earth?

(52:15) – Life on Venus(54:30) – Mars(1:01:17) – What is interesting about Earth as a planet?

Po-Shen Loh is a mathematician at CMU and coach of the USA International Math Olympiad team.

Please support this podcast by checking out our sponsors:– The Jordan Harbinger Show: https://jordanharbinger.com/lex/– Onnit: https://lexfridman.com/onnit– BetterHelp: https://betterhelp.com/lex to get 10% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– LMNT: https://drinkLMNT.com/lex to get free sample packEPISODE LINKS:Po’s Twitter: https://twitter.com/poshenlohPo’s Website: https://www.poshenloh.com/Daily Challenges: https://daily.poshenloh.com/NOVID: https://www.novid.org/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https:…

John Danaher is a coach, scholar, and educator of jiu jitsu, submission grappling, judo, MMA, and the martial arts.

Please support this podcast by checking out our sponsors:– Onnit: https://lexfridman.com/onnit– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Indeed: https://indeed.com/lex to get $75 credit– Linode: https://linode.com/lex to get $100 free creditEPISODE LINKS:John’s Instagram: https://www.instagram.com/danaherjohnPODCAST 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/lexfridman…

Sergey Nazarov is the CEO of Chainlink, a decentralized oracle network that provides data to smart contracts.

Please support this podcast by checking out our sponsors:– Wine Access: https://wineaccess.com/lex to get 20% off first order– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Indeed: https://indeed.com/lex to get $75 credit– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Sergey’s Twitter: https://twitter.com/SergeyNazarovChainlink Website: https://chain.link/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.c…

Jeremi Suri is a historian at UT Austin.

Please support this podcast by checking out our sponsors:– LMNT: https://drinkLMNT.com/lex to get free sample pack– Munk Pack: https://munkpack.com and use code LEX to get 20% off– Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first order– 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:Jeremi’s Twitter: https://twitter.com/JeremiSuriJeremi’s Website: http://jeremisuri.netThis is Democracy Podcast: http://jeremisuri.net/archives/1798The Impossible Presidency (book): https://amzn.to/2QKC5JpPODCAST …

Georges St-Pierre is a martial artist.

Please support this podcast by checking out our sponsors:– Allform: https://allform.com/lex to get 20% off– 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– Theragun: https://theragun.com/lex to get 30 day trial– The Information: https://theinformation.com/lex to get 75% off first monthEPISODE LINKS:GSP’s Twitter: https://twitter.com/GeorgesStPierreGSP’s Instagram: https://www.instagram.com/georgesstpierre/GSP’s Facebook: https://www.facebook.com/georgesstpierreGSP’s Website: https://www.gspofficial.comPODCAST INFO:Podcast website: https://lex…

Michael Malice is an anarchist.

Yaron Brook is an objectivist.

Both are podcasters and authors.

On some podcast players you should be able to click the timestamp to jump to that time.

(4:23:12) – Back to the island

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…

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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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…

Fairness Aware Outlier DetectionToday on the show we have Shubhranshu Shekar, a Ph.

D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.

https://shubhranshu-shekhar.github.io/

Life May Be RareToday on the show Dr. Anders Sanburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare@anderssandberg

Social NetworksMayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro SzekleySocial MediaLinkedInTwitter

The QAnon ConspiracyQAnon is a conspiracy theory born in the underbelly of the internet.

Max Aliapoulios joins us to discuss the paper The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information.

This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.

This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be.

In a few weeks, we’re going to ask everyone to vote for their favorite theme for our next season.

Benchmarking Computer Vision on Edge vs CloudKarthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.

Social Media

Goodhart’s Law in Reinforcement LearningHal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart’s law and Reinforcement Learning.

Also mentioned was The Book of Why by Judea Pearl

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

Sidney Arcidiacono joins us to discuss her studies and work at Make School and her interest in utilizing AI for healthcare, as well as her tips and strategies for becoming a successful early-career data scientist.

In this episode, I discuss the amazing benefits of implementing peer-driven learning in your professional life.

Additional materials: www.superdatascience.com/476

David Langer joins us to discuss his work as a data analytics educator and his beliefs in the use of Excel, SQL and R in analytics work.

In this episode you will learn:• Intro to Dave on Data [6:50]• 20% analytics tha…

In this episode, I discuss the architecture of a “machine learning house”, representing the skills and learnings you can use as foundations to build your data science career.

Additional materials: www.superdatascience.c…

Anima Anandkumar joins us to discuss her work as a researcher in machine learning at NVIDIA and a professor at CalTech, and how they often go hand-in-hand and inform each other.

In this episode you will learn:• Anima’s…

In this episode, I share a note I received from a student who expressed his thoughts on the learning that never stops as he goes through his data science career.

Additional materials: www.superdatascience.com/472

Kirill Eremenko returns to the SDS podcast as a guest to debunk common myths you may believe about getting a data science job.

In this episode you will learn:• What has Kirill been up to?

[3:48]• The genesis of the 99…

In this episode, I follow up on the popular book recommendation portion of the podcast with my own list of favorite books.

Additional materials: www.superdatascience.com/470

Konrad Körding joins us to discuss his work in educating the next generation in deep learning and his views on the importance of causality in deep learning research.

In this episode you will learn:• Konrad’s academic b…

In this episode, I tackle another historical topic: the history of data.

Additional materials: www.superdatascience.com/468

Noah Gift joins us to discuss how he believes data science urgency and the end of hierarchies will change the world for the better.

In this episode you will learn:• Catch up with Noah [2:50]• Educational options to pu…

We use cookies for various purposes including analytics and personalized marketing.

By continuing to use the service, you agree to our use of cookies as described in the Cookie Policy

Konrad Kopczynski joins us to discuss how data, tracking, analytics, and key performance indicators can help your professional and personal development.

In this episode you will learn:• What does Konrad do [3:40]• Too…

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

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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.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

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/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in …

March 10, 2021 podcastIn plain English, concurrent and parallel are synonyms.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix 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…

🎙 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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