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I am searching for a toy data-set that will allow me to train a model that takes in as input a long text document (example pdfs) and marks spans (i.e sequence of words) IF they belong to predetermined (i.e.

known) list of topics.

Do you know of any data-sets like this ?

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Beyond the plethora of subjects/research/tools/news about AI and ML, I have no clue what the job life is like: - norms - career path - expectations - relationships - typical requests from employers - etc

​ Using the Gradio interface with the Crowd Counting model. You can try out this interface yourself in the link below! Today's model will be based on the paper at this arXiv link: Distribution Matching for Crowd Counting. Repo here and interface shown above here. Crowd counting is a popular research problem with applications in journalism, human traffic management, and surveillance. The paper linked above proposes a novel approach to crowd counting. These are the existing methods of crowd counting: 1) Detect-then-count method - This method detects every person in the image and then counts the number of individuals identified. This technique is not very accurate because it is very sensitive …

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6 NLP Techniques Every Data Scientist Should KnowPhoto by Sai Kiran Anagani on UnsplashNatural language processing is perhaps the most talked-about subfield of data science.

The advances in machine learning and artificial intelligence fields have driven the appearance and continuous interest in natural language processing.

The truth is, natural language processing is the reason I got into data science.

In this article, I will go through the 6 fundamental techniques of natural language processing that you should know if you are serious about getting into the field.

Despite the different tasks that natural language processing can execute, to get in the field and start building your own projec…

IntroductionData Science Interviews cover a wide range of topics, and interviewers frequently ask us to explain the most fundamental concepts.

My Data Science professional network has told me repeatedly that they do not expect job candidates to know every algorithm.

Statistics and Machine Learning are inseparable twins, and these are so many concepts used interchangeably in both domains.

In a sister chapter, I’ve elaborated on the most fundamental concepts in Machine Learning.

In case you haven’t had the chance, here it is:In today’s post, let’s shift gears towards Statistics and tackle the top 10 most common concepts in the Data Science Interviews.

The elements used to inform our audience about the content of our chart are almost the same.

We need a title, which is the text with the largest font, usually placed at the top of the chart.

My preferred method is to use .suptitle() for the title, .title() for the subtitle, and .text() for the caption.

Suptitle is a title for the figure — You can only have one per figure, so this method doesn’t work with subplots.

Centred title — Image by the authorI like using .suptitle() because the code feels a little more organized.

To solve this problem we need to create a decision variable for every single combination of route and driver.

Essentially if the model returns a value of 1 for a combination it implies that it's in the final solution.

I have assigned a negative value (-100) to any combination that has a difference in start time greater than 60 minutes.

PuLP managed to allocate work for 9 drivers in this example — with 5 of the drivers being allocated work that matches their preference.

Using linear programming can automate the process of allocating work whilst finding the most efficient way to plan your workforce and take into account worker preferences.

PYTORCH ECOSYSTEMAudio Classification with PyTorch’s Ecosystem ToolsAudio classification with torchaudio and Allegro TrainsAudio signals are all around us.

Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.

Using Allegro-Trains, torchaudio and torchvision for audio classificationPytorch’s ecosystem includes a variety of open source tools that can jump start our audio classification project and help us manage and support it.

As such, leveraging the PyTorch ecosystem open source tools can boost your audio classifica…

You can manually upload a CSV or JSON file with ad data directly to Google BigQuery from Google Cloud Storage, Google Drive, or your computer.

Upload data from other Google services such as Google Ads and Google Ad Manager.

To upload data from various Google services, you first need to configure the BigQuery Data Transfer Service.

So, let’s take a look at one of the most optimal ways to upload your ads data to Google BigQuery.

How to upload ad data to Google BigQuery with OWOX BI PipelineTo set up data collection, you must have BigQuery Data Editor and BigQuery User roles in the project in which you want to collect data.

Some statistical tests can be used, such as the percentage of agreement and the Kappa coefficient.

The authors used the following procedure to seek validity based on content:Literature review on the existing measures of prejudice and discrimination.

Statistical tests that are often used are Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Exploratory Structural Equation Modeling.

When we have tests that measure the same construct, we expect that they are closely related.

Tests have this type of validity if they are being used for the same reason they were built for.

Or put more succinctly: the paper’s thesis is that to advance AI we need to change towards learning on graphs.

The graphs are…directed (one-way edges),attributed (node-, edge-, and graph-level features are allowed),multigraphs (multiple edges can connect any two nodes, and self-edges are allowed).

The implementation of a Graph Network is essentially done using the modules.GraphNetwork class and constructs the core GN block.

This configuration can take three learnable sub-functions for edge, node and global and are calls to the Sonnet library and modules.

As for future work, we intend to explore how we can integrate Graph-Nets with Neo4j as well as exploring other GNN libraries such as ‘jrap…

How to Connect Azure Data Factory to an Azure SQL Database Using a Private EndpointPhoto by Hafidh Satyanto on UnsplashAzure Data Factory (ADF) is great for extracting data from multiple sources, the most obvious of which may be Azure SQL.

However, Azure SQL has a security option to deny public network access, which, if enabled, will prevent ADF from connecting without extra steps.

In this article, we’ll look at the steps required to set up a private endpoint and use it to connect to an Azure SQL database from Azure Data Factory.

“Exactly-once” Semantics across Multiple Kafka Instances Is PossiblePhoto Courtesy: Nathan Dumlao on unsplash“Exactly-once” semantics is a challenging problem in a distributed system.

Therefore, in the context of multiple instances, the natural question becomes:can we still have the same Exactly-once semantics as how we expect “transactional” or “exactly-once” data processing in one cluster?

A batch of data is consumed by a Kafka consumer from one cluster (called “source”) then immediately produced to another cluster (called “target”) by Kafka producer.

The consumer still has to live on the source cluster in order to pull the data from the source cluster, but the “source-of-truth” consumer …

Changing themesAfter installation, you can launch Jupyter Notebooks as normal and inspect the themes from within the notebook itself.

In order to switch themes you can use this command:!jt -t Let’s choose onedork theme.

From my personal experience, I have to restart Jupyter Notebook in order for the theme to change.

!jt -t onedork -T -N -klRestarting Jupyter Notebooks should give you the result similar to the screenshot below.

!jt -rNote that I have shown the commands being executed in the Jupyter notebook itself but you can use them without the exclamation mark in the terminal window as well.

IntroductionBack in the day on Windows there was this thing called DLL Hell.

For this article we’re going to go rogue and version-up (via pip) TensorFlow:pip install tensorflow-gpu==2.3.1Ignore any dependency errors you might see, and let’s move on to making our new conda env visible to Jupyter Notebooks!

conda install ipykernelWhat we’re installing above is an interactive Python kernel that supplies an execution backend to Jupyter Notebooks.

Fire up a Jupyter Notebook, go to Kernel -> Change kernel and you should see a selection for tf-warp-speed!

I can simply export my conda env to a yaml file and email that file (along with my *.ipynb file) to my friend.

Image by authorIf our input data is an image, LIME will generate several samples that are similar with our input image by turning on and off some of the super-pixels of the image.

Predict the class of each artificial data pointNext, LIME will predict the class of each of the artificial data point that has been generated using our trained model.

If your input data is an image, then the prediction of each perturbed image will be generated at this stage.

Calculate the weight of each artificial data pointThe third step is to calculate the weight of each artificial data to measure its importance.

If the input data is an image, then the cosine distance between each perturbed image and the origina…

If it takes you more than 3 minutes to understand your code, imagine how long it would take for your teammates to understand your code.

In this article, I will show you how to utilize the 6 practices mentioned above to write better Python functions.

It is because:The function is awfully longThe function tries to do multiple thingsThe code within the function is at multiple levels of abstractions.

It is better to write long names rather than write vague names.

Don’t try to be perfect when starting to write code.

Why Criticism of Kaggle Often Misses the PointPhoto by Markus Spiske on UnsplashOk, here we go, i’ll stick my head above the parapet.

Many competitions have been won by people with modest setups and Kaggle now hosts many competitions that require use of Kaggle notebooks with set limitations such as training time, levelling the playing field for competitors.

In addition, leaks and hacks are often called out by the community allowing organisers to make modifications to deal with these issues.

It’s the point about Kaggle not being reflective of working in data science however, that i want to discuss further and explain why I believe that criticism misses the point of Kaggle.

To do this we’ll b…

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

Part one of a three part deep dive into the curve neuron family.

How to tune hyperparameters for your machine learning model using Bayesian optimization.

An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'

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.

's Mythos of Model Interpretability, which I think is the best paper for understanding the different definitions of interpretability.

UtilityThe second area is to ensure that these interpretability approaches are actually providing value.

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

CreditsFeature image from https://github.com/adebayoj/sanity_checks_saliencyCitationFor attribution in academic contexts or books, please cite this work asOwen Shen, "Interpretability in ML: A Broad Overview", The Gradient, 2020.

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."

What can we do to improve peer review?

In a way, NLP peer review… prevents research on NLP peer review.

We need to regularly collect and regularly publish our peer review corpora, to develop mechanisms for running systematic experiments on peer review forms and policies, and for the subsequent implementation of the successful ones.

We need new roles for systematic development, testing, and implementation of peer review policies, as well as incentivizing good peer review work by increasing its prestige and visibility.

To cite our paper:@inproceedings{rogers-augenstein-2020-improve,title = "What Can We Do to Improve Peer Review in {NLP}?

What can we do to improve peer review?

In a way, NLP peer review… prevents research on NLP peer review.

We need to regularly collect and regularly publish our peer review corpora, to develop mechanisms for running systematic experiments on peer review forms and policies, and for the subsequent implementation of the successful ones.

We need new roles for systematic development, testing, and implementation of peer review policies, as well as incentivizing good peer review work by increasing its prestige and visibility.

To cite our paper:@inproceedings{rogers-augenstein-2020-improve,title = "What Can We Do to Improve Peer Review in {NLP}?

When applied to Computer Science problems, associative memories come in two high-level forms: autoassociative and heteroassociative memories.

Research MilestonesIn computer science, associative memories were first proposed by Karl Steinbuch (1961) in the form of the Lernmatrix.

Distributed Associative MemoriesAnother intriguing offshoot of AM research involves scaling via so-called distributed associative memories.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications.

CitationFor attribution in academic contexts or books, please cite this work asRobert Bates, "Don’t Forget About Associative Memories", The Gradient, 2020.

When applied to Computer Science problems, associative memories come in two high-level forms: autoassociative and heteroassociative memories.

Research MilestonesIn computer science, associative memories were first proposed by Karl Steinbuch (1961) in the form of the Lernmatrix.

Distributed Associative MemoriesAnother intriguing offshoot of AM research involves scaling via so-called distributed associative memories.

Theoretical Foundations for the Alpha-Beta Associative Memories: 10 Years of Derived Extensions, Models, and Applications.

CitationFor attribution in academic contexts or books, please cite this work asRobert Bates, "Don’t Forget About Associative Memories", The Gradient, 2020.

Why are some problems in medical image analysis harder than others for AI, and what can we do about them?

He coined the term computer-aided diagnosis (CAD) to refer to computer systems that help radiologists interpret and quantify abnormalities from medical images.

However, medical images often come in far higher dimensional form than natural images (e.g., ImageNet has images of only 224x224).

In general, the granularity of the label used during training, does not have to be the same as the granularity generated during inference.

CitationFor attribution in academic contexts or books, please cite this work asThijs Kooi, "Why skin lesions are peanuts and brain tumors a harder nut", The Gradie…

He coined the term computer-aided diagnosis (CAD) to refer to computer systems that help radiologists interpret and quantify abnormalities from medical images.

However, medical images often come in far higher dimensional form than natural images (e.g., ImageNet has images of only 224x224).

In general, the granularity of the label used during training, does not have to be the same as the granularity generated during inference.

Modality Paper Summary Data Labels Chest X-ray Rajpurkar, Pranav, et al.

CitationFor attribution in academic contexts or books, please cite this work asThijs Kooi, "Why skin lesions are peanuts and brain tumors a harder nut", The Gradient, 2020.

The Current State of Machine Learning EducationHaving taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow.

If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

The GapHaving analyzed both the current state of machine learning education as well as the skills needed to create important applied machine learning systems, we now comment on the gap between the two sides.

CitationFor attribution in academic contexts or books, please cite this work asJupinder Parmar, "The Gap: Where Machine Learning Ed…

The Current State of Machine Learning EducationHaving taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow.

If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

The GapHaving analyzed both the current state of machine learning education as well as the skills needed to create important applied machine learning systems, we now comment on the gap between the two sides.

CitationFor attribution in academic contexts or books, please cite this work asJupinder Parmar, "The Gap: Where Machine Learning Ed…

While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them.

In a new twist, these surveillance systems are starting to seep out of metropolitan police departments and into the suburbs.

Less easily spotted, amid the pandemonium, were the automated systems law enforcement rely on to coordinate their response to the protests, spy on them, identify them, and later locate them for arrest.

But police could be confident in the dragnet surveillance systems being built across the country to help them spy on and target protestors and rioters.

CitationFor attribution in academic contexts or…

In this issue:we discuss Amazon’s DeepAR Model;we explore Amazon research paper about multi-dimensional time-series forecasting;we dive deep into Sktime, which brings modern time-series forecasting to Scikit-Learn.

Enjoy the learning!

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📝 EditorialFeature stores have been steadily becoming one of the key building blocks in modern machine learning architectures.

These days, there is a growing number of venture-backed startups that are building feature store capabilities for machine learning solutions.

With that disproportionate growth, you can’t help but wonder if there is a little bubble in the feature store market.

Let’s keep in mind that platforms like AWS have already entered the feature store space with the release of the AWS Feature Store.

AI extension for human memory startup Human AI raised $3.2 million in a seed round.

Krishna Gade (KG): I am the Founder/CEO of Fiddler, an Explainable AI (XAI) Platform.

Can you describe the challenges with model explainability and monitoring?

🔻Is machine learning explainability a feature or a product?

What are some of your most ambitious ideas about machine learning explainability?

Can we get to the point of using machine learning to explain the behavior of other machine learning models?

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

Our goal is to keep you up to date with new developments in AI in a way that complements the concepts we are debating in other editions of our newsletter.

we present the new PyTorch Forecasting framework.

we explore Facebook’s AR-Net research that combines autoregressive models and neural networks;In this issue:✖ CloseThis site uses cookies.

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Most of the progress in transformers has been centered in the natural language understanding (NLU) space, but that’s rapidly changing.

Just this week, AI labs from Microsoft Research and OpenAI published new transformer architectures that are reaching important milestones in different areas.

BTW, we discussed transformers in the VERY FIRST ISSUE that was sent as TheSequence Edge.

Enterprise-focused visitor management software provider iLobby raised $100 million in a funding round.

AI-based robotic system for effective recycling AMP Robotics raised $55 million in a Series B funding round.

With Meena, Google tries to address some of these challenges by building an open-domain chatbot that can chat about almost anything.

Before building Meena, Google had to solve a non-trivial challenge that is often ignored in open-domain chatbot systems.

To address that challenge, Google started by introducing a new metric as the cornerstone of the Meena chatbot.

Additional Resources: Additional details can be found in the original Meena research paper and in this blog post from Google Research.

The question is the following:What neural network architecture Google used to build the Meena chatbot?

In this issue:we continue our series about time-series forecasting, this time explaining what ARIMA is;we discuss Google researchers’ blog post about some architectures to accelerate time series models using AutoML;

This newsletter is mostly oriented to developers and machine learning practitioners, focusing on practical applications of data science.

During 2020, I saw our data science teams regularly adopt several machine learning technologies to the point that, today, I find it very hard to envision any machine learning effort that doesn’t include those stacks.

MLOps: These days, it’s hard to envision building machine learning at scale without an MLOps stack.

From our vantage point, those three trends should play a role in most mainstream machine learning efforts in 2021.

Graphcore develops a specialized type of hardware – Intelligence Processing Units (IPUs) that hold the complete machine learning m…

This is the second Edge#Recap of some key topics covered in TheSequence Edge.

You can find Edge#Recap1 here.

In the first 50 issues, we have discussed very current topics in the machine learning and deep learning universe.

Before we start with a new set of topics next week, let’s recap some of the most important concepts complemented by the relevant res…

As we are approaching the end of 2020, we decided to provide a summary of some key topics covered in TheSequence.

In the first 50 issues, we have discussed very current topics in the machine learning and deep learning universe.

Automated Machine LearningAutoML and ideas for automating the creation of machine learning models are becoming a super hot topic.

Practical Machine LearningTheSequence Edge regularly discusses topics related to best practices and techniques to run machine learning models in production at scale.

Emerging Learning MethodsModern machine learning goes beyond supervised and unsupervised learning.

However, there is one prediction that we feel pretty confident making and that is that the frenzy of AI acquisitions by the large technology firms will continue in 2021.

The M&A engines in the AI space were incredibly active in 2020.

Let’s do a quick recap of relevant M&A transactions in the AI space in 2020:Apple: Acquired companies like Xnor.ai, Voysis, Camerai and Scout FM.

TheSequence is a summary of groundbreaking ML research papers, engaging explanations of ML concepts, exploration of new ML frameworks, and platforms.

5 minutes of your time, 3 times a week– you will steadily become knowledgeable about everything happening in the AI space.

🎄 Happy Holidays!

We wish you the very best🎄✖ CloseThis site uses cookies.

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we discuss how Uber uses neural networks to forecast during extreme events ;we provide an introduction to time-series forecasting models ;In this issue:✖ CloseThis site uses cookies.

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Those companies have created some of the top AI platform offerings in the market and have also been actively acquiring many early-stage AI startups so that they can increase their pool of data science talent.

All those factors have made it incredibly difficult for startups in the AI space to achieve meaningful traction.

After some struggle, some areas of the AI market are steadily showing a strong presence of well-capitalized startups.

AI explainability platform Truera raised $12 million in Series A.

TheSequence is a summary of groundbreaking ML research papers, engaging explanations of ML concepts, exploration of new ML frameworks, and platforms.

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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 →

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: PointRend: Image Segmentation as Rendering (Facebook AI Research, 2020)

Natural- To Formal-Language Generation Using Tensor Product Representations (USA, 2019)

Linformer: Self-Attention with Linear Complexity (Facebook AI, 2020)

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (Johns Hopkins University, Google, 2020)

Training Generative Adversarial Networks with Limited Data (NVIDIA, 2020)

Multi-Modal Dense Video Captioning (Tampere University…

Недавно завершился «Deep Learning in Natural Language Processing», открытый образовательный курс по обработке естественного языка. По традиции кураторы курса — сотрудники проекта DeepPavlov, открытой библиотеки для разговорного искусственного интеллекта, которую разрабатывают в лаборатории нейронных систем и глубокого обучения МФТИ. Курс проводился при информационной поддержке сообщества Open Data Science. Если нужно больше деталей по формату курса, то вам сюда. Один из ключевых элементов «DL in NLP» — это возможность почувствовать себя исследователем и реализовать собственный проект. Периодически мы рассказываем на Medium о проектах, которые участники создают в рамках наших образовательных…

Здравствуйте, товарищи! На выходных проходил хакасборкатон — гонки на самоуправляемых моделях автомобилей на базе комплекта donkeycar при содействии Х5, FLESS и сообщества энтузиастов self-driving. Задача заключалась в следующем: сначала надо было собрать машинку из запчастей, затем ее обучить проходить трассу. Победитель определялся по самому быстрому прохождению 3 кругов. За наезд на конус — дисквалификация. Хотя подобная задача для машинного обучения не нова, но сложности могут поджидать на всем пути: от невозможности заставить нормально работать вайфай до нежелания обученной модели пилотировать железо по треку. И все это в жестких временных рамках! Когда мы собирались на это соревновани…

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: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (China, 2020)

TAPAS: Weakly Supervised Table Parsing via Pre-training (Google, 2020)

DeepFaceLab: A simple, flexible and extensible faceswapping framework (2020)

End-to-End Object Detection with Transformers (Facebook AI, 2020)

Language Models are Few-Shot Learners (OpenAI, 2020)

TabNet: Attentive Interpretable Tabular Learning (Google Cloud AI, 2020) Читать дальше →

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: Efficient Document Re-Ranking for Transformers by Precomputing Term Representations; EARL: Speedup Transformer-based Rankers with Pre-computed Representation (2020)

MakeItTalk: Speaker-Aware Talking Head Animation (Adobe, University of Massachusetts Amherst, Huya, 2020)

Jukebox: A Generative Model for Music (OpenAI, 2020)

Recipes for building an open-domain chatbot (Facebook AI Research, 2020)

One-Shot Object Detection without Fine-Tuning (HKUST, Hong Kong, Tencent, 2020)

f-BRS: Rethinki…

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: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization (Georgia Institute of Technology, Atlanta, USA, 2016)

X3D: Expanding Architectures for Efficient Video Recognition (Facebook AI Research, 2020)

Adaptive Attention Span in Transformers (Facebook AI Research, 2019)

ResNeSt: Split-Attention Networks (Amazon, 2020)

Weight Standardization (Johns Hopkins University, 2019)

Supervised Contrastive Learning (Google Research, MIT, 2020)

Improved Training Speed, Accurac…

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: TResNet: High Performance GPU-Dedicated Architecture (DAMO Academy, Alibaba Group, 2020)

Controllable Person Image Synthesis with Attribute-Decomposed GAN (China, 2020)

Learning to See Through Obstructions (Taiwan, USA, 2020)

Tracking Objects as Points (UT Austin, Intel Labs, 2020)

CookGAN: Meal Image Synthesis from Ingredients (USA, UK, 2020)

Designing Network Design Spaces (FAIR, 2020)

Gradient Centralization: A New Optimization Technique for Deep Neural Networks (Hong Kong, Alibaba, 2…

TLDR: кому перестановки делают больнее — меряем свёрткой графов.

Код: RolX и ванильная трёхслойная GCN на мотифах. Выгорание на рабочем месте повстречал ещё в начале своей карьеры — и с тех пор живо интересуюсь этим вопросом. Представьте обстановку. Большой проект внедрения SAP. Высокие ставки. Амбициозные сроки. Нагрузку каждый воспринимал по-своему. Кто-то сорвался и самоустранился от выполнения обязанностей, кто-то стал токсичнее, у меня самого в какой-то момент чувство юмора пропало. Ненадолго. Управление изменениями (дисциплина, направленная на снижение напряжения во время внедрения информационных систем) многим обязана медикам. Во-первых, сам феномен эмоционального выгорания впервые з…

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! Первая часть мартовской сборки обзоров опубликована ранее.

Статьи на сегодня: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (UC Berkeley, Google Research, UC San Diego, 2020)

Scene Text Recognition via Transformer (China, 2020)

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (Imperial College London, Google Research, 2019)

Lagrangian Neural Networks (Princeton, Oregon, Google, Flatiron, 2020)

Deformable Style Transfer (Chicago, USA, 2020)

Rethinking…

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: Fast Differentiable Sorting and Ranking (Google Brain, 2020)

MaxUp: A Simple Way to Improve Generalization of Neural Network Training (UT Austin, 2020)

Deep Nearest Neighbor Anomaly Detection (Jerusalem, Israel, 2020)

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Google, 2020)

SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training (RheinMain University, Germany, 2019)

High-Resolution Daytime Translation Without Domain Labels (Samsung AI Cen…

Источник: https://mlr3book.mlr-org.com/ Привет, Хабр! В этом сообщении мы рассмотрим самый продуманный на сегодняшний день подход к машинному обучению на языке R — пакет mlr3 и экосистему вокруг него. Данный подход основан на «нормальном» ООП с использованием R6-классов и на представлении всех операций с данными и моделями в виде графа вычислений. Это позволяет создавать упорядоченные и гибкие пайплайны для задач машинного обучения, но на первых порах может показаться сложным и запутанным. Ниже постараемся внести определенную ясность и замотивировать к использованию mlr3 в ваших проектах. Содержание: Немного истории и сравнение с конкурирующими решениями

Технические детали: R6-классы и паке…

How to calculate and report mean squared error, root mean squared error, and mean absolute error.

Tutorial OverviewThis tutorial is divided into three parts; they are:Regression Predictive Modeling Evaluating Regression Models Metrics for Regression Mean Squared Error Root Mean Squared Error Mean Absolute ErrorRegression Predictive ModelingPredictive modeling is the problem of developing a model using historical data to make a prediction on new data where we do not have the answer.

Evaluating Regression ModelsA common question by beginners to regression predictive modeling projects is:How do I calculate accuracy for my regression model?

Root Mean Squared ErrorThe Root Mean Squared Error, or…

ReLU Hidden Layer Activation FunctionThe rectified linear activation function, or ReLU activation function, is perhaps the most common function used for hidden layers.

Sigmoid Hidden Layer Activation FunctionThe sigmoid activation function is also called the logistic function.

Tanh Hidden Layer Activation FunctionThe hyperbolic tangent activation function is also referred to simply as the Tanh (also “tanh” and “TanH“) function.

Traditionally, the sigmoid activation function was the default activation function in the 1990s.

How to Choose an Output Activation FunctionYou must choose the activation function for your output layer based on the type of prediction problem that you are solving.

Tutorial OverviewThis tutorial is divided into three parts; they are:Visualization for Function Optimization Visualize 1D Function Optimization Test Function Sample Test Function Line Plot of Test Function Scatter Plot of Test Function Line Plot with Marked Optima Line Plot with Samples Visualize 2D Function Optimization Test Function Sample Test Function Contour Plot of Test Function Filled Contour Plot of Test Function Filled Contour Plot of Test Function with Samples Surface Plot of Test FunctionVisualization for Function OptimizationFunction optimization is a field of mathematics concerned with finding the inputs to a function that result in the optimal output for the function, typicall…

Tutorial OverviewThis tutorial is divided into three parts; they are:Gradient Descent Adam Optimization Algorithm Gradient Descent With Adam Two-Dimensional Test Problem Gradient Descent Optimization With Adam Visualization of AdamGradient DescentGradient descent is an optimization algorithm.

Adam Optimization AlgorithmAdaptive Movement Estimation algorithm, or Adam for short, is an extension to the gradient descent optimization algorithm.

Gradient Descent With AdamIn this section, we will explore how to implement the gradient descent optimization algorithm with Adam.

Gradient Descent Optimization With AdamWe can apply the gradient descent with Adam to the test problem.

# calculate gradient…

It is an important foundational topic required in machine learning as most machine learning algorithms are fit on historical data using an optimization algorithm.

Not all optimization algorithms are relevant to machine learning; instead, it is useful to focus on a small subset of algorithms.

Not all optimization problems in machine learning are well behaved, such as optimization used in AutoML and hyperparameter tuning.

Therefore, we will take a look at both books that cover classical optimization algorithms as well as books on alternate optimization algorithms.

Learn More:SummaryIn this post, you discovered books on optimization algorithms that are helpful to know for applied machine learn…

Univariate function optimization involves finding the input to a function that results in the optimal output from an objective function.

Tutorial OverviewThis tutorial is divided into three parts; they are:Univariate Function Optimization Convex Univariate Function Optimization Non-Convex Univariate Function OptimizationUnivariate Function OptimizationWe may need to find an optimal value of a function that takes a single parameter.

As such, Brent’s method for univariate function optimization is generally preferred over most other univariate function optimization algorithms given its efficiency.

# minimize the function result = minimize_scalar ( objective , method = 'brent' )Now that we know…

Nevertheless, working with modeling pipelines can be confusing to beginners as it requires a shift in perspective of the applied machine learning process.

Collectively, the operations required to address a predictive modeling problem can be considered an atomic unit called a modeling pipeline.

The Python scikit-learn machine learning library provides a machine learning modeling pipeline via the Pipeline class.

You can learn more about how to use this Pipeline API in this tutorial:Implications of a Modeling PipelineThe modeling pipeline is an important tool for machine learning practitioners.

It is also the philosophy behind modern AutoML (automatic machine learning) techniques that treat ap…

An example of this approach to semi-supervised learning is the label spreading algorithm for classification predictive modeling.

In this tutorial, you will discover how to apply the label spreading algorithm to a semi-supervised learning classification dataset.

After completing this tutorial, you will know:An intuition for how the label spreading semi-supervised learning algorithm works.

Tutorial OverviewThis tutorial is divided into three parts; they are:Label Spreading Algorithm Semi-Supervised Classification Dataset Label Spreading for Semi-Supervised LearningLabel Spreading AlgorithmLabel Spreading is a semi-supervised learning algorithm.

BooksPapersAPIsArticlesSummaryIn this tutorial, …

Tutorial OverviewThis tutorial is divided into three parts; they are:Multinomial Logistic Regression Evaluate Multinomial Logistic Regression Model Tune Penalty for Multinomial Logistic RegressionMultinomial Logistic RegressionLogistic regression is a classification algorithm.

Multinomial Logistic Regression: Modified version of logistic regression that predicts a multinomial probability (i.e.

Now that we are familiar with multinomial logistic regression, let’s look at how we might develop and evaluate multinomial logistic regression models in Python.

Evaluate Multinomial Logistic Regression ModelIn this section, we will develop and evaluate a multinomial logistic regression model using the…

An example of this approach to semi-supervised learning is the label propagation algorithm for classification predictive modeling.

In this tutorial, you will discover how to apply the label propagation algorithm to a semi-supervised learning classification dataset.

After completing this tutorial, you will know:An intuition for how the label propagation semi-supervised learning algorithm works.

Tutorial OverviewThis tutorial is divided into three parts; they are:Label Propagation Algorithm Semi-Supervised Classification Dataset Label Propagation for Semi-Supervised LearningLabel Propagation AlgorithmLabel Propagation is a semi-supervised learning algorithm.

BooksPapersAPIsArticlesSummaryIn t…

Gradient boosting ensembles that implement this technique and tailor the training algorithm around input variables under this transform are referred to as histogram-based gradient boosting ensembles.

Tutorial OverviewThis tutorial is divided into four parts; they are:Histogram Gradient Boosting Histogram Gradient Boosting With Scikit-Learn Histogram Gradient Boosting With XGBoost Histogram Gradient Boosting With LightGBMHistogram Gradient BoostingGradient boosting is an ensemble machine learning algorithm.

Two notable libraries that wrap up many modern efficiency techniques for training gradient boosting algorithms include the Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting …

In the case that there are few input features, all possible combinations of input features can be evaluated and the best subset found definitively.

Many Input Features: Stochastic optimization algorithm to find good subsets of features.

For example, with the five input features the sequence [True, True, True, True, True] would use all input features, and [True, False, False, False, False] would only use the first input feature as input.

Optimize Feature SubsetsWe can apply a stochastic optimization algorithm to the search space of subsets of input features.

... >80 f(240) = 0.918099 >81 f(236) = 0.918099 >82 f(238) = 0.918099 >83 f(236) = 0.918099 >84 f(239) = 0.918099 >85 f(240) = 0.918099…

There are perhaps hundreds of popular optimization algorithms, and perhaps tens of algorithms to choose from in popular scientific code libraries.

There are many different types of optimization algorithms that can be used for continuous function optimization problems, and perhaps just as many ways to group and summarize them.

First-Order AlgorithmsFirst-order optimization algorithms explicitly involve using the first derivative (gradient) to choose the direction to move in the search space.

Examples of second-order optimization algorithms for univariate objective functions include:Newton’s MethodSecant MethodSecond-order methods for multivariate objective functions are referred to as Quasi-…

Further, empirical results show a continued reduction in generalization error as the complexity of an ensemble learning model is incrementally increased.

In this tutorial, you will discover how to reconcile Occam’s Razor with ensemble machine learning.

By definition, ensemble machine learning algorithms are more complex than a single machine learning model, as they are composed of many individual machine learning models.

Yet ensemble machine learning algorithms are the dominant solution when predictive skill on new data is the most important concern, such as machine learning competitions.

Related TutorialsPapersBooksArticlesSummaryIn this tutorial, you discovered how to reconcile Occam’s Ra…

Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning.

After completing this tutorial, you will know:Meta-learning refers to machine learning algorithms that learn from the output of other machine learning algorithms.

Meta-learning in machine learning most commonly refers to machine learning algorithms that learn from the output of other machine learning algorithms.

If machine learning learns how to best use information in data to make predictions, then meta-learning or meta machine learning learns how to best use the predictions from machine learning algorithms…

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…

This is the third post in a 3-part blog series about monitoring machine learning models in production.

In a previous post we introduced the topic of monitoring machine learning models in production.

Before co-founding the company, Oren led ML activities at Intel and ran a machine learning consultancy helping organizations across industries like finance, marketing, and gaming build and deploy machine learning applications.

I had the chance to interview Oren about the challenges of monitoring machine learning in industry today.

Infrastructure challenges aside, what are your thoughts on using machine learning methods to monitor other machine learning models?

This is the second post in a multi-part blog series (find Part 1 here) on monitoring machine learning models in production.

Data science and operational teams require better solutions that are suited to addressing monitoring AI in production.

To overcome the ongoing temporal fluctuations of this highly dynamic ecosystem, the data science team collaborated with the engineering team to build a full orchestration flow in production.

This led to massive distribution shifts which affected model performance during the time period and made retraining the model useless.

During this entire time, the data science team didn’t see anything suspicious in their dashboards.

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 \(25\%\).

I Should Have Been More UncertainIt would be incredibly weird if I was never surprised by machine learning (ML) research.

(From JamesClear.com)Semi-Supervised and Unsupervised Learning are Getting BetterHistorically, unsupervised learning has been in this weird position where it is obviously the right way to do learning, and also a complete waste of time if you want something to work ASAP.

This success is a concrete example of the previous section (better unsupervised learning), and it’s a sign of the first section (better tooling).

When lots of fields use the same set of techniques, you get more knowledge sharing, and that drives better research.

The most likely problem I see with my story…

markdown 1 , 187 2019 - 12 - 25 - neurips - 2019. markdown 1 , 819 2020 - 01 - 17 - berkeley - back - pay .

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View CountsThese are the view counts from August 18, 2019 to today, for the posts I’ve written this year.

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I feel it’s worth having it public somewhere, even if no one cares about the Open AI LP debate anymore.

OpenAI LP is a for-profit company.

OpenAI Nonprofit is the nonprofit side.

It’s good for nonprofit AI companies to exist, and now there is one fewer.” There are enough for-profit AI startups and AI consulting gigs out there.

The key difference, is that it’s not that people don’t understand OpenAI’s argument, it’s that they don’t think OpenAI believes their own argument.

This post has several spoilers for the HBO mini-series Chernobyl.

Valery Legasov is the protagonist of the Chernobyl mini-series, and in real life he was the chief of the commission investigating Chernobyl.

Reality Doesn’t Care About PoliticsThe Chernobyl mini-series is ostensibly about the events of Chernobyl, but it’s really more about how people responded to Chernobyl.

In episode 4, “For the Happiness of All Mankind”, the Soviet Union explores using robots to clear debris off the Chernobyl reactor roof.

The central theme of the Chernobyl mini-series is truth, and the lies surrounding it.

Learning that return policy, however, is still an open problem for general domains.

Three papers came out on arXiv in the past week: Constrastive Unsupervised Reinforcement Learning (CURL), from Srinivas and Laskin et al, Image Augmentation is All You Need (DrQ) from Kostrikov and Yarats et al, and Reinforcement Learning with Augmented Data (RAD) from Laskin and Lee et al.

AI EconomistSalesforce put out a paper that uses reinforcement learning to design tax policy in a toy economic environment, and they argue their tax policies give better equality-productivity trade-offs, compared to the Saez framework.

Offline Reinforcement LearningSome colleagues from Google Brain and UC Berkeley have pu…

Contact tracing is the way we trace who infected people have been in contact with.

Vaccine development, test production, and contact tracing apps will all be done in parallel, but given the United States already has testing shortfalls, I expect contact tracing to finish first, meaning it’s the best hope for restarting the economy.

However, if you actually read the proposal for the contact tracing app, you find thatThe privacy loss is fairly minimal.

Furthermore, it’s a pretty small leap to assume that advertisers will also install the contact tracing app to their devices.

My feeling is that like before, these attacks could be executed on any contact tracing app.

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…

Neural Architecture Search (NAS) automates network architecture engineering.

By dissecting the methods for NAS into three components: search space, search algorithm and child model evolution strategy, this post reviews many interesting ideas for better, faster and more cost-efficient automatic neural architecture search.

Search space: The NAS search space defines a set of operations (e.g.

So far we have seen many interesting new ideas on automating the network architecture engineering through neural architecture search and many have achieved very impressive performance.

Liu et al (2020) delve into the question “Can we find high-quality neural architecture without human-annotated labels?” an…

2007) sketched an idea of using a forward dynamics prediction model to estimate learning progress and assigned intrinsic exploration reward accordingly.

And by definition we have \(p(s_f, \Omega \vert s_0) = p^J(s_f \vert s_0, \Omega) p^C(\Omega \vert s_0)\).

Combining them, we get mutual information \(I(\Omega; s_f \vert s_0)\) to maximize:\[\begin{aligned} I(\Omega; s_f \vert s_0) &= H(s_f \vert s_0) - H(s_f \vert s_0, \Omega) \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p(s_f, \Omega \vert s_0) \log \frac{p(s_f, \Omega \vert s_0)}{p^C(\Omega \vert s_0)} \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p^J(s_f \vert s_0, \Ome…

(2018) added a set of auxiliary losses to enable training a deep Transformer model on character-level language modeling which outperformed LSTMs.

Longer Attention Span (Transformer-XL)The vanilla Transformer has a fixed and limited attention span.

Image Transformer (Parmer, et al 2018) embraces a formulation of image generation similar to sequence modeling within the Transformer framework.

The top row illustrates the attention connectivity patterns in (a) Transformer, (b) Sparse Transformer with strided attention, and (c) Sparse Transformer with fixed attention.

2019)Cited as:@article{weng2020transformer, title = "The Transformer Family", author = "Weng, Lilian", journal = "lilianweng.githu…

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 …

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.

I’ve entitled this talk ‘Queering Machine Learning’, which is theme I want to explore with you today.

This is a queering of machine learning, and a powerful tool of self-refelection; an approach to machine learning research that is more critical and responsible; a tool available not only to queer researchers, but to everyone.

We can already see this same type of failure beginning to manifest in machine learning as well, with examples abound.

By organising in machine learning, and by queering machine learning, we build collective community and collective strength that makes it possible for belonging and loneliness and solitude to co-exist and strengthen each other for the benefit of our fiel…

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.

When assignment weights change in a ramp-up experiment, there are periods of constant assignment weights that we define as epochs.

In an OCE with constant assignment weights and a representative sample, this is an unbiased estimator.However, when there are changing assignment weights, then an unweighted average of data across the epochs can be a biased estimate.

Epoch: If assignment weights are changed at times $Z^*_1, ..., Z^*_J$ then the assignment weights are constant during $[Z^*_j, Z^*_{j+1})$.

An experimenter who changes assignment weights gets the same answer as the experimenter who doesn’t change assignment weights (modulo some rounding errors) so long as they use the adjusted estim…

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…

Training GANs - From Theory to PracticeGANs, originally discovered in the context of unsupervised learning, have had far reaching implications to science, engineering, and society.

However, training GANs remains challenging (in part) due to the lack of convergent algorithms for nonconvex-nonconcave min-max optimization.

In this post, we present a new first-order algorithm for min-max optimization which is particularly suited to GANs.

ConclusionIn this post we have shown how to develop a practical and convergent first-order algorithm for training GANs.

Our simulations show that a version of this algorithm can lead to more stable training of GANs.

Unlike minimization, where algorithms can always be shown to converge to some local minimum, there is no notion of a local equilibrium in min-max optimization that exists for general nonconvex-nonconcave functions.

Our greedy min-max equilibriumWe use the greedy max function to define a new second-order notion of local optimality for min-max optimization, which we refer to as a greedy min-max equilibrium.

This allows us to define a notion of greedy min-max equilibrium.

Greedy min-max equilibrium: $(x^{\star}, y^{\star})$ is an $\varepsilon$-greedy min-max equilibrium if\(\|abla_y f(x^\star,y^\star)\| \leq \varepsilon, \qquadabla^2_y f(x^\star,y^\star) \preceq \sqrt{\varepsilon},\)\(\|abla_x…

Exponential Learning Rate Schedules for Deep Learning (Part 1)This blog post concerns our ICLR20 paper on a surprising discovery about learning rate (LR), the most basic hyperparameter in deep learning.

These divergent approaches suggest that LR, the most basic and intuitive hyperparameter in deep learning, has not revealed all its mysteries yet.

SOTA performance with exponential LRAs mentioned, reaching state-of-the-art accuracy requires reducing the learning rate a few times.

Suppose the training has $K$ phases, and the learning rate is divided by some constant $C_I>1$ when entering phase $I$.

ConclusionWe hope that this bit of theory and supporting experiments have changed your outlook o…

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.

Let’s remove the aura of mystery around GPT3 and learn how it’s trained and how it works.

The dataset of 300 billion tokens of text is used to generate training examples for the model.

For example, these are three training examples generated from the one sentence at the top.

GPT3 actually generates output one token at a time (let’s assume a token is a word for now).

Please note: This is a description of how GPT-3 works and not a discussion of what is novel about it (which is mainly the ridiculously large scale).

Check out the first video in my new series introducing the general public to AI and machine learning.

My aim for this series is to help people integrate ML into their world-view away from all the hype and overpromises that plauge the topic.

This first video is a gentle visual introduction to Artificial Intelligence (AI) and some of its key commercial applications.

In this first video, we explain the simple trick that lies at the heart of the majority of AI/machine learning applications.

I rely on YouTube for a lot of my own learning in ML and various other topics.

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…

In this post I'd like share some of that agenda, in what I call the "no bullshit" approach to AI.

And since we don't see these things, we don't label datasets with them and hence these "symbols" never make it to AI, neither from the symbolic approach, nor machine learning approach.

Notably the stuff deep learning is mostly successfully used for these days is not mission critical.

The science wayThe scientific approach is really what this blog was all about, before it veered into making cynical posts about the general AI stupidity out there.

Failure of deep learning on delivering of many promises will likely lead to a similar winter.

Full loyalty to the charter is expected, to the point of even varying the compensation by the level of "faith" .

It is often better to invest resources in getting slightly better data, add one more sensor, than train some ridiculously huge deep learning model and expect miracles.

With honesty and integrity rarely found in Silicon Valley, he went in and said what many were whispering for a while - AI is not really "AI".

Deep learning in clinical applicationsThere was some buzz about deep learning replacing radiologists, nonsense initiated by Hinton and then promptly repeated by Andrew Ng.

The realization that deep learning is not going to cut it with respect to self driving cars and many oth…

The goal of this post is to nerd out over biochemistry and energy metabolism in the animal kingdom, and potentially inspire others on their own biohacking lite adventure.

It’s highly amusing to think that every single time you breathe out (in a fasted state) you are literally breathing out your fat carbon by carbon.

Energy deficit.

To validate the energy deficit math I spent 100 days around late 2019 very carefully tracking my daily energy input and output.

That said, focusing on fat, both approaches show me losing body fat at roughly the same rate, though they are off by an absolute offset.

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

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

Brain Computer Interface technologies are popular methods of communication between the human brain and external devices.

In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature.

BCI systems are composed of a wide range of components that perform signal pre-processing, feature extraction and decision making.

Firstly, we include aan additional pre-processing step of the signal: a differentiation of the EEG signal that makes it time-invariant.

Furthermore, we find that the pair Choquet/Sugeno integrals and overlap functions are the ones providing the best results.

Deep learning (DL) has demonstrated its powerful capabilities in the field of image inpainting, which could produce visually plausible results.

Meanwhile, the malicious use of advanced image inpainting tools (e.g.

The proposed GIID-Net consists of three sub-blocks: the enhancement block, the extraction block and the decision block.

The extraction block, automatically designed by Neural Architecture Search (NAS) algorithm, is targeted to extract features for the actual inpainting detection tasks.

In order to further optimize the extracted latent features, we integrate global and local attention modules in the decision block, where the global attention reduces the intra-class differences by m…

Anisotropic multi-slice Cardiac Magnetic Resonance (CMR) Images are conventionally acquired in patient-specific short-axis (SAX) orientation.

Recent research in deep learning-based methods mainly focused on SAX CMR images and they had proven to be very successful.

The network was trained on 122 registered 3D AX-SAX CMR volume pairs from a multi-centric patient cohort.

A mean 3D Dice of $0.86\pm{0.06}$ for the left ventricle, $0.65\pm{0.08}$ for the myocardium, and $0.77\pm{0.10}$ for the right ventricle could be achieved.

To conclude, our pre-trained task module has neither seen CMR images nor labels from the target domain, but is able to segment them after the domain gap is reduced.

Indeed, while many summary statistics can be used to encode network structures, their computational complexity can be highly variable.

For large networks, computation of summary statistics can quickly create a bottleneck, making the use of ABC difficult.

To reduce this computational burden and make the analysis of mechanistic network models more practical, we investigated two questions in a model choice framework.

Second, we performed selection using networks generated with a smaller number of nodes to reduce the time required for the selection step.

Our findings show that computationally inexpensive summary statistics can be efficiently selected with minimal impact on classification accura…

In the study of large random systems, researchers often need to simulate dynamics in the form of iterated matrix-vector multiplications interspersed with nonlinear operations.

This paper proposes a new algorithm, named Householder Dice (HD), for simulating such dynamics on several random matrix ensembles with translation-invariant properties.

Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts.

The memory and computation costs of the HD algorithm are $\mathcal{O}(nT)$ and $\mathcal{O}(nT^2)$, respectively, with $T$ being the number of iterations.

Numerical results demonstrate the promise of the new algorithm as a new…

Automatic pulmonary nodules classification is significant for early diagnosis of lung cancers.

To combat these challenges, in this work, we aim to build an efficient and (partially) explainable classification model.

Besides, we use the convolutional block attention module (CBAM) in the networks, which helps us understand the reasoning process.

Compared with previous state-of-the-art, our model shows highly comparable performance by using less than 1/40 parameters.

Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.

In this study, we propose the inclusion of attention mechanisms over a U-Net architecture: (i) Attention-gated U-Net (AGUNet) and (ii) Dual Attention U-Net (DAUNet), using a 3D MRI volume as input.

Attention has the potential to leverage the global context and identify features' relationships across the entire volume.

The validation studies were performed using a 5-fold cross validation over 600 T1-weighted MRI volumes from St. Olavs University Hospital, Trondheim, Norway.

Leveraging global context from a 3D MRI volume provided the best performances, even if the native volume resolution could not be processed directly.

A larger number of cases with meningiomas below 3ml might also be needed…

Existing machine comprehension datasets assume sentence-level input, lack of casual or motivational inferences, or could be answered with question-answer bias.

Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines.

Comparing to existing movie tag prediction tasks, tropes are more sophisticated as they can vary widely, from a moral concept to a series of circumstances, and embedded with motivations and cause-and-effects.

We introduce a new dataset, Tropes in Movie Synopses (TiMoS), with 5623 movie synopses and 95 different tropes collecting from a Wikipedia-style database, TVTropes.

Experimental result …

Artificial Neural Networks (ANNs) are widely used for approximating complex functions.

On the other hand, we observe that ANNs can be represented as graphs and their topological 'fingerprints' can be obtained using Persistent Homology (PH).

In this paper, we describe a proposal focused on designing more principled architecture search procedures.

To do this, different architectures for solving problems related to a heterogeneous set of datasets have been analyzed.

This approach based on topological analysis helps towards the goal of designing more principled architecture search procedures and having a better understanding of ANNs.

In this paper, we present a novel approach, Momentum$^2$ Teacher, for student-teacher based self-supervised learning.

The approach performs momentum update on both network weights and batch normalization (BN) statistics...

The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.

The Momentum$^2$ Teacher is simple and efficient.

It can achieve the state of the art results (74.5\%) under ImageNet linear evaluation protocol using small-batch size(\eg, 128), without requiring large-batch training on special hardware like TPU or inefficient across GPU operation (\eg, shuffling BN, synced BN).

To train a machine learning model is necessary to take numerous decisions about many options for each process involved, in the field of sequence generation and more specifically of music composition, the nature of the problem helps to narrow the options but at the same time, some other options appear for specific challenges.

This paper takes the framework proposed in a previous research that did not consider rhythm to make a series of design decisions, then, rhythm support is added to evaluate the performance of two RNN memory cells in the creation of monophonic music...

The model considers the handling of music transposition and the framework evaluates the quality of the generated pieces u…

Semi-supervised learning on graphs is a widely applicable problem in network science and machine learning.

Two standard algorithms -- label propagation and graph neural networks -- both operate by repeatedly passing information along edges, the former by passing labels and the latter by passing node features, modulated by neural networks...

Here, we develop a Markov random field model for the data generation process of node attributes, based on correlations of attributes on and between vertices, that motivates and unifies these algorithmic approaches.

We show that label propagation, a linearized graph convolutional network, and their combination can all be derived as conditional expectation…

In this paper, we consider the problem of leveraging textual descriptions to improve generalization of control policies to new scenarios.

EMMA is end-to-end differentiable and can learn a latent grounding of entities and dynamics from text to observations using environment rewards as the only source of supervision.

To empirically test our model, we design a new framework of 1320 games and collect text manuals with free-form natural language via crowd-sourcing.

We demonstrate that EMMA achieves successful zero-shot generalization to unseen games with new dynamics, obtaining significantly higher rewards compared to multiple baselines.

The grounding acquired by EMMA is also robust to noisy des…

Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history.

Several methods for question rewriting have recently been proposed, but they were compared under different retrieval pipelines... We bridge this gap by thoroughly evaluating those question rewriting methods on the TREC CAsT 2019 and 2020 datasets under the same retrieval pipeline.

We analyze the effect of different types of question rewriting methods on retrieval performance and show that by combining question rewriting methods of different types we can achieve state-of-the-art performance on both datasets.

(read more)

Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support.

If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden...

However, recent studies have shown that highly empathic conversations are rare in online mental health platforms.

In this paper, we work towards improving empathy in online mental health support conversations.

Our work has direct implications for facilitating empathic conversations on web-based platforms.

Histopathology image embedding is an active research area in computer vision.

However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level.

Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding.

We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept.

Our experimental results on a breast cancer histopathology dataset wit…

Large-scale model training has been a playing ground for a limited few requiring complex model refactoring and access to prohibitively expensive GPU clusters.

ZeRO-Offload changes the large model training landscape by making large model training accessible to nearly everyone...

ZeRO-Offload enables large model training by offloading data and compute to CPU.

Additionally, it can work together with model parallelism to train models with over 70 billion parameters on a single DGX-2 box, a 4.5x increase in model size compared to using model parallelism alone.

By combining compute and memory efficiency with ease-of-use, ZeRO-Offload democratizes large-scale model training making it accessible to…

Granger causality is a practical framework for analysing interactions in sequential data, applied in a wide range of domains...

In this paper, we propose a novel framework for inferring multivariate Granger causality under nonlinear dynamics based on an extension of self-explaining neural networks.

This framework is more interpretable than other neural-network-based techniques for inferring Granger causality, since in addition to relational inference, it also allows detecting signs of Granger-causal effects and inspecting their variability over time.

In comprehensive experiments on simulated data, we show that our framework performs on par with several powerful baseline methods at inferring…

Image-to-image translation with Deep Learning neural networks, particularly with Generative Adversarial Networks (GANs), is one of the most powerful methods for simulating astronomical images.

However, current work is limited to utilizing paired images with supervised translation, and there has been rare discussion on reconstructing noise background that encodes instrumental and observational effects...

Therefore, we aim to develop methods for using unpaired images and preserving noise characteristics in image translation.

By experimenting on multi-band galaxy images from the Sloan Digital Sky Survey (SDSS) and the Canada France Hawaii Telescope Legacy Survey (CFHT), we show that our method…

In frequency division duplexing (FDD) mode, it is necessary to send the channel state information (CSI) from user equipment to base station.

The downlink CSI is essential for the massive multiple-input multiple-output (MIMO) system to acquire the potential gain...

Recently, deep learning is widely adopted to massive MIMO CSI feedback task and proved to be effective compared with traditional compressed sensing methods.

In this paper, a novel network named ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation.

Moreover, valid approach to expand the network architecture in exchange of better performance is first discussed in CSI feedback t…

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…

What can I expect in the interview process?

Feryal: The interview process at DeepMind can vary depending on the particular role you’re applying for.

Phase two - technical interviewsThis part of the process involves several sessions - including one with a technical quiz that covers a large breadth of topics in computer science, statistics, mathematics and machine learning.

~30min] interviews with researchers and leads about your specific research background and interests.

Phase four - culture interviewTowards the end of the interview process, you will once again connect with the recruitment team to discuss DeepMind’s culture and mission.

The ‘dry’ form is relatively common among people over 65, and usually causes only mild sight loss.

Our contribution highlights the potential of using AI in preventative studies for diseases such as exAMD.

The Moorfields Eye Hospital AMD datasetWe used a dataset of anonymised retinal scans from Moorfields patients with exAMD in one eye, and at high-risk of developing exAMD in their other eye.

To address this, we worked with retinal experts to review all scans for each eye and specify the scan when exAMD was first evident.

In our previous work, now continuing in collaboration with Google Health, we developed a model capable of segmenting these eye scans into thirteen anatomical categories.

Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome.

We have all had experiences with specification gaming, even if not by this name.

In this post, we review possible causes for specification gaming, share examples of where this happens in practice, and argue for further work on principled approaches to overcoming specification problems.

In a Lego stacking task, the desired outcome was for a red block to end up on top of a blue block.

The agent was rewarded for the height of the bottom face of the red block when it is not touching the block.

The practical implications of modelling glassThe glass transition is a ubiquitous phenomenon which manifests in more than window (silica) glasses.

Understanding the glass transition may result in other applications of disordered materials, in fields as diverse as biorenewable polymers and food processing.

Our new work, published in Nature Physics, could help us gain an understanding of the structural changes that may occur near the glass transition.

Leveraging graph neural networks to model glassy dynamicsGlasses can be modelled as particles interacting via a short-range repulsive potential which essentially prevents particles from getting too close to each other.

We then trained a neural n…

Combining off-policy learning with memory is challenging because you need to know what you might remember when executing a different behaviour.

Within that strand, we distinguish two types of rewards: firstly, long-term novelty rewards encourage visiting many states throughout training, across many episodes.

Secondly, short-term novelty rewards encourage visiting many states over a short span of time (e.g., within a single episode of a game).

However, learning density models of high dimensional spaces is fraught with problems due to the curse of dimensionality.

For example, in Montezuma’s Revenge, unlike undirected exploration strategies, long-term novelty rewards allow the agent to surpass…

In “ToTTo: A Controlled Table-To-Text Generation Dataset”, we present an open domain table-to-text generation dataset created using a novel annotation process (via sentence revision) along with a controlled text generation task that can be used to assess model hallucination.

ToTTo (shorthand for “Table-To-Text”) consists of 121,000 training examples, along with 7,500 examples each for development and test.

Due to the accuracy of annotations, this dataset is suitable as a challenging benchmark for research in high precision text generation.

2019 19.2 29.2 13.9 25.8While automatic metrics can give some indication of performance, they are not currently sufficient for evaluating hallucination i…

The ability to recognize similarity in 3D pose using only 2D information will help vision systems better understand the world.

Trained with in-lab setting data, the model works on in-the-wild images out of the box, given a reasonably good 2D pose estimator (e.g., PersonLab, BlazePose, among others).

The same 2D pose can be projected from different 3D poses.

To accomplish this, we define the matching probability, i.e., the likelihood that different 2D poses were projected from the same, or similar 3D poses.

Therefore, we map a 2D pose through a probabilistic mapping to an embedding distribution, of which we use the variance to represent the uncertainty of the input 2D pose.

Using machine learning for road maintenanceThe City of Memphis struggled with a problem many cities have to face: the continuous degradation of paved roads and the formation, through usage and weather, of potholes.

As the SpringML team joined with Memphis to figure this out, they first looked at what sorts of data they could get access to.

Immediately the team had a treasure trove of data: every road covered by the mass transit system has daily recordings being captured.

The bus routes are well defined and each bus has GPS to help correlate the footage with precise locations.

At the end of the day they retrieved videos from each bus and uploaded them to on-prem storage —a fairly manual proc…

The machine learning system behind Lookout demonstrates that a powerful-but-compact machine learning model can accomplish this in real-time on a phone for nearly 2 million products.

To help better understand the behavior of language models, we developed the Language Interpretability Tool (LIT), a toolkit for better interpretability of language models, enabling interactive exploration and analysis of their decisions.

In collaboration with many other institutions, we also looked into memorization effects of language models, showing that training data extraction attacks are realistic threats on state-of-the-art large language models.

Machine Learning AlgorithmsWe continue to develop new machin…

In reality, it’s a more complicated combination of engineering, design and natural language processing at work, making it easier for many of us to use our smartphones.

But what happens when this voice technology isn’t available in our own language?

This is something Google India researcher Shachi Dave considers as part of her day-to-day work.

While English is the most widely spoken language globally, it ranks third as the most widely spoken native language (behind Mandarin and Spanish)—just ahead of Hindi, Bengali and a number of other languages that are official in India.

Shachi, who is a founding member of the Google India Research team, works on natural language understanding, a field of…

Scaling a business that sorts through millions of documents daily, across a global operation, is a tall order. Workday, with more than 3,400 core Workday Financial Management and Workday HCM customers, offers the Workday Expenses solution to provide a frictionless expense reporting experience for their customers. Here's how they did it using Google Cloud's Procurement DocAI.Solving for multi-language support for international expansionWorkday provides enterprise cloud applications for finance and human resources. Their in-house expense receipt parsing functionality needed to scale for international customers in multiple languages.Workday leverages Google Cloud’s Procurement DocAI machine le…

It’s winter, it’s the holidays and it’s quarantine-times: It’s the perfect recipe for doing a ton of baking.

In fact, U.S. search interest in "baking" spiked in both November and December 2020.

Plus, we used our ML model to come up with two completely new baking recipes: a cakie (cake-cookie hybrid) and a breakie (bread-cookie hybrid).

We started off by collecting hundreds of cookie, cake and bread recipes.

Our model was able to accurately tag breads, cookies and cakes, but could also identify recipes it deemed “hybrids” — something that’s, say, 50% cake and 50% bread, or something that’s 50% cake and 50% cookie.

Historically, one of the biggest challenges in the data science field is that many models don't make it past the experimental stage. As the field has matured, we've seen MLOps processes and tooling emerge that have increased project velocity and reproducibility. While we've got a ways to go, more models than ever before are crossing the finish line into production.That leads to the next question for data scientists: how will my model scale in production? In this blog post, we will discuss how to use a managed prediction service, Google Cloud’s AI Platform Prediction, to address the challenges of scaling inference workloads.Inference WorkloadsIn a machine learning project, there are two prim…

We demonstrate 33%-60% speedup on three graph optimization tasks compared to TensorFlow default optimization.

Overview of GO: An end-to-end graph policy network that combines graph embedding and sequential attention.

Multi-task policy network that extends GO’s policy network with additional recurrent attention layers for each task and residual connections.

This approach, called GO-one, consistently outperforms expert manual placement (HP), TensorFlow METIS placement, and Hierarchical Device Placement (HDP) — the current state-of-the-art reinforcement learning-based device placement.

Co-optimizing placement, scheduling, and fusion (pl+sch+fu):Optimizing simultaneously for placement, scheduli…

Have you ever wondered what, fundamentally, scientifically, makes a piece of cake different from a slice of bread or a cookie?

But now this important, controversial question finally has an answer, thanks to explainable machine learning.

In this post, we’ll show you how to build an explainable machine learning model that analyzes baking recipes, and we’ll even use it to come up with our own, new recipes--no data science expertise required.

This project idea comes from Sara Robinson, who works on AI for Google Cloud.

In April, she started a storm of pandemic baking, and like any good machine-learning-practitioner-baker, soon turned her modeling skills to baking.

As such, training data extraction attacks are realistic threats on state-of-the-art large language models.

While this work focuses on GPT-2 specifically, the results apply to understanding what privacy threats are possible on large language models generally.

The Training Data Extraction AttackBy design, language models make it very easy to generate a large amount of output data.

LessonsWhile we demonstrate these attacks on GPT-2 specifically, they show potential flaws in all large generative language models.

Language models continue to demonstrate great utility and flexibility—yet, like all innovations, they can also pose risks.

Over the next six months, more than 600 sensor cameras will be deployed in bushfire-affected areas across Australia, monitoring and evaluating the surviving wildlife populations.

Using Wildlife Insights, a platform powered by Google’s Artificial Intelligence technology, researchers across the country will upload and share sensor camera photos to give a clearer picture of how Australian wildlife is coping after the devastating bushfires in the past year.

For many Aussies, the horror of last summer’s fires is still very raw and real.

Up to 19 million hectares were burned (more than 73,000 square miles), with 12.6 million hectares primarily forest and bushland.

A staggering three billion anima…

In Portrait Mode photographs, Portrait Light provides more dramatic lighting to accompany the shallow depth-of-field effect already applied, resulting in a studio-quality look.

These innovations enable Portrait Light to help create attractive lighting at any moment for every portrait — all on your mobile device.

On existing images from your Google Photos library, try it where faces are slightly underexposed, where Portrait Light can illuminate and highlight your subject.

We see Portrait Light as the first step on the journey towards creative post-capture lighting controls for mobile cameras, powered by machine learning.

AcknowledgementsPortrait Light is the result of a collaboration between…

Over the past three years, Google Cloud has worked closely with Cisco to deliver a number of customer-focused solutions in areas such as hybrid cloud, multicloud, work transformation, and contact center integrations.

By leveraging Google Cloud capabilities in ML, natural language understanding, and speech recognition and synthesis, this joint solution from Google Cloud and Cisco helps customers get answers to questions quickly, through natural and efficient conversations.

CCAI also supports contact center agents, helping them address questions and problems with easy access to documents and information.

Cisco’s Contact Center AI APIs connect the Google Cloud Dialogflow service to the state’s…

MediaPipe Holistic is being released as part of MediaPipe and is available on-device for mobile (Android, iOS) and desktop.

Pipeline and QualityThe MediaPipe Holistic pipeline integrates separate models for pose, face and hand components, each of which are optimized for their particular domain.

First, MediaPipe Holistic estimates the human pose with BlazePose’s pose detector and subsequent keypoint model.

MediaPipe Holistic pipeline overview.

ConclusionWe hope the release of MediaPipe Holistic will inspire the research and development community members to build new unique applications.

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.

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.

Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.

The OpenAI Scholars program provides stipends and mentorship to individuals from underrepresented groups to study deep learning and open-source a project.

Demo Day introductions by Sam Altman and Greg BrockmanLearn more about our Scholars program.

I joined the Scholars program in order to learn from the brilliant folks at OpenAI and to immerse myself in AI research.

The OpenAI Scholars program was this magical opportunity to get started by learning from the very best minds in the field.

However, the same broad class of models has not been successful in producing strong features for image classification.

From language GPT to image GPTIn language, unsupervised learning algorithms that rely on word prediction (like GPT-2 and BERT) have been extremely successful, achieving top performance on a wide array of language tasks.

Because masked language models like BERT have outperformed generative models on most language tasks, we also evaluate the performance of BERT on our image models.

LimitationsWhile we have shown that iGPT is capable of learning powerful image features, there are still significant limitations to our approach.

Notably, we achieved our results by directly applyi…

We’re releasing an API for accessing new AI models developed by OpenAI.

Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task.

Your browser does not support videoGiven any text prompt, the API will return a text completion, attempting to match the pattern you gave it.

We've designed the API to be both simple for anyone to use but also flexible enough to make machine learning teams more productive.

Today the API runs models with weights from the GPT-3 family with many speed and throughput improvements.

We’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.

Procgen CompetitionSign up for ProcgenThe Procgen Competition focuses on improving sample efficiency and generalization in reinforcement learning.

Since all content is procedurally generated, each Procgen environment intrinsically requires agents to generalize to never-before-seen situations.

Moreover, we designed Procgen environments to be fast and simple to use.

One well-known way to reduce the environment sample complexity is to leverage human priors and demonstrations of the desired behavior.

Other measures of AI progressIn addition to efficiency, many other measures shed light on overall algorithmic progress in AI.

Shufflenet achieved AlexNet-level performance with an 18x inference efficiency increase in 5 years (15-month doubling time), which suggests that training efficiency and inference efficiency might improve at similar rates.

This efficiency analysis suggests that policymakers could develop accurate intuitions about the cost of deploying AI capabilities—and how these costs are going to alter over time—by more closely assessing the rate of improvements in efficiency for AI systems.

Our results suggest that for AI tasks with high levels of investment (researcher time and/o…

Curated samples Provided with genre, artist, and lyrics as input, Jukebox outputs a new music sample produced from scratch.

We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.

Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.

To better understand future implications for the music community, we shared Jukebox with an initial set of 10 musicians from various genres to discuss their feedback on this work.

While Jukebox is an interesting research result, these musicians did not find …

Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?

Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.

Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.

Organizations developing AI and funding bodies should support research into the interpretabili…

We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability.

Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.

This is the goal of the OpenAI Microscope.

Microscope systematically visualizes every neuron in several commonly studied vision models, and makes all of those neurons linkable.

Our initial release includes nine frequently studied vision models, along with several visualization techniques we’ve found particularly u…

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Moreover, can we perform ensemble learning over the models after knowledge distillation to further improve test accuracy?

Figure 2: Knowledge distillation and self-distillation also give performance boosts in deep learning.

This figure compares ensemble and knowledge distillation in deep learning versus that in a linear model over random feature mappings.

Knowledge distillation: Forcing an individual model to learn multiple viewsIn this new work, we continue to show how knowledge distillation works.

Figure 6: Knowledge distillation has learned most of the view features from the ensemble, and so ensemble learning on models after knowledge distillation offers no more performance boost.

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The CNN-based object detection model trained on the Visual Genome (VG) dataset is the most popular choice before our work.

The CNN-based object detection model trained on the Visual Genome (VG) dataset is the most popular choice before our work.

Here, we introduce recent Microsoft work on improving the image encoding module.

Figure 2: Detections from a classical object detection model trained on Open Images (left) and our object-attribute detection model trained on four public object detection datasets (right).

Our newly developed image encoding model can benefit a wide range of VL tasks, as illustrated by examples in this paper.

There are some wild stats about Microsoft Flight Simulator.

In the sim, all 117 million lakes in the world are rendered in their appropriate places.

Developers pushed 2.5 petabytes of Bing Maps satellite photo data through Azure’s machine learning systems to construct the sim’s world.

In a chat with Engadget at CES 2021, Flight Simulator head Jorg Neumann said developers basically build the planet every 72 hours, procedurally planting somewhere in the realm of 2 trillion trees and creating 2 billion buildings in that timeframe alone.

Flight Simulator also pipes in real-time weather and actual flight paths, allowing players to soar through active natural disasters or follow their own flights…

The new feature, dubbed Dynamic View, will be rolling out worldwide on Teams for the desktop in March.

SEE: Top 100+ tips for telecommuters and managers (free PDF) (TechRepublic)"Dynamic view automatically optimizes shared content and video participants in Teams meetings.

Microsoft has posted a short video on LinkedIn to demonstrate the new feature dynamically adjusting shared content and key speakers.

Another new Teams meetings feature coming in February is custom layouts.

Microsoft developed Together Mode to alleviate the tedium of endless video meetings for people working from home during the global pandemic.

Since its release in 2019, top research teams around the world have been developing large-scale pretrained language models (PLMs) that have driven striking performance improvement on the SuperGLUE benchmark.

Microsoft recently updated the DeBERTa model by training a larger version that consists of 48 Transformer layers with 1.5 billion parameters.

The significant performance boost makes the single DeBERTa model surpass the human performance on SuperGLUE for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE benchmark rankings, outperforming the human baseline by a decent margin (90.3 versus 89.8).

Microsoft will release …

Since the start of the pandemic, Microsoft Healthcare Bot has been at the leading edge of helping organizations be more agile with the patient engagement.

Machine learning and deep learning have become an important part of many applications we use every day.

Fortunately, there are several courses that provide a high-level overview of machine learning and deep learning without going too deep into math and coding.

Linear regression machine learning with ExcelLinear regression is a simple machine learning algorithm that has many uses for analyzing data and predicting outcomes.

Other machine learning algorithms with ExcelBeyond regression models, you can use Excel for other machine learning algorithms.

Deep learning and natural language processing with ExcelLearn Data Mining Through Excel shows that Excel can even express advanced machine learnin…

Microsoft Word (and Outlook) in Windows 10 is getting a fancy new grammar checker, known as the ‘Microsoft Editor’, which uses artificial intelligence and machine learning to better spot grammar and typing mistakes.

While the Windows 10 version of Word already checks your grammar and highlights errors, the new tool goes even further, by using AI to check your writing and make advanced suggestions based on clarity, formality, inclusiveness and more.

It will also highlight sensitive geopolitical references, and overall, it could make your writing a lot better.

Using the EditorAs Windows Latest explains, the new Microsoft Editor will highlight errors as usual, but you can also select a word, t…

Microsoft Seeing AI app received a major update today in Apple App Store.

This Version 4.0 update brings support for LiDAR sensor available in the latest iPhone 12 Pro and iPhone 12 Pro Max.

With this support, people can explore an unfamiliar space in 3D, using spatial audio.

What’s new in Microsoft Seeing AI:The new World channel, available on devices with a LiDAR scanner running iOS 14, enables you to explore an unfamiliar space in 3D, using spatial audio.

When wearing headphones, you will hear objects around you announced from their location in the room.

We present the details of this research in our paper “Unadversarial Examples: Designing Objects for Robust Vision.”Why design objects for neural networks?

Designing robust objects for visionOur starting point in designing robust objects for vision is the observation that modern vision models suffer from a severe input sensitivity that can, in particular, be exploited to generate so-called adversarial examples: imperceptible perturbations of the input of a vision model that break it.

Figure 2: An example of an unadversarial patch (left) placed on a toy jet and an unadversarial texture (right) implicit in the design of a jet.

Unadversarial patch: To train an unadversarial patch, at each itera…

This year has felt like a turning point, a cliff, the end of one world and the beginning of another.

So we took a whack at distilling a year’s worth of science memories into a handful of digital files.

Most modern storage media — like hard drives or flash memory — is built to be cheap, not durable.

But there’s one up-and-coming storage medium that could keep our files safe for millennia: synthetic DNA.

Over the past few weeks, we worked with a team of scientists and engineers to encode our digital mementos into custom strands of DNA.

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The field of artificial intelligence is moving at a staggering clip, with breakthroughs emerging in labs across MIT.

To get a clearer picture faster, engineers are experimenting with physics-informed deep learning algorithms to translate these sensor distress signals.

To try and solve ARC’s 20 or so pattern-completion tasks, Kantamneni created a script to generate similar examples to train the deep learning model.

In a UROP project this fall in Roger Levy’s lab in BCS, sophomore Pranali Vani ran a set of sentence-processing experiments online that were developed by an earlier UROP student.

Vani and her advisor, Ethan Wilcox, a PhD student at Harvard University, got similar results when they…

James DiCarlo, the Peter de Florez Professor of Neuroscience, has been appointed to the role of director of the MIT Quest for Intelligence.

MIT Quest was launched in 2018 to discover the basis of natural intelligence, create new foundations for machine intelligence, and deliver new tools and technologies for humanity.

As director, DiCarlo will forge new collaborations with researchers within MIT and beyond to accelerate progress in understanding intelligence and developing the next generation of intelligence tools.

As department head, DiCarlo oversaw significant progress in the department’s scientific and educational endeavors.

A search committee will convene early this year to recommend ca…

One reason it’s so difficult to produce effective vaccines against some viruses, including influenza and HIV, is that these viruses mutate very rapidly.

“Viral escape is a big problem,” says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory.

For these mutations to promote viral escape, they must help the virus change the shape of its surface proteins so that antibodies can no longer bind to them.

Therefore, a mutation that enables viral escape must maintain the grammaticality of the sequence but change the protein’s structure in a useful way.

For influenza, the model revealed that…

The MIT.nano Immersion Lab, MIT’s first open-access facility for augmented and virtual reality (AR/VR) and interacting with data, is now open and available to MIT students, faculty, researchers, and external users.

Like MIT.nano’s fabrication and characterization facilities, the Immersion Lab is open to researchers from any department, lab, and center at MIT.

Undergraduate students can use the Immersion Lab through sponsored Undergraduate Research Opportunities Program (UROP) projects.

All of these fields have something to contribute to the problems we are tackling in the Immersion Lab,” says Anthony.

Such broad faculty engagement ensures that the Immersion Lab engages in projects across ma…

If the fuel rods that drive reactions there are ideally placed, they burn less fuel and require less maintenance.

Through decades of trial and error, nuclear engineers have learned to design better layouts to extend the life of pricey fuel rods.

The AI system can also find optimal solutions faster than a human, and quickly modify designs in a safe, simulated environment.

“This technology can be applied to any nuclear reactor in the world,” says the study’s senior author, Koroush Shirvan, an assistant professor in MIT’s Department of Nuclear Science and Engineering.

Deep reinforcement learning combines deep neural networks, which excel at picking out patterns in reams of data, with reinforce…

In the age of big data, “time series are collected all over the place, from satellites to turbines,” says Kalyan Veeramachaneni.

The group has developed a new, deep-learning-based method of flagging anomalies in time series data.

TadGAN outperformed ARIMA in anomaly detection for eight of the 11 datasets.

Alnegheimish emphasized that their goal was not only to develop a top-notch anomaly detection algorithm, but also to make it widely useable.

Plus, they developed a benchmarking system for users to compare the performance of different anomaly detection models.

Just as the toddler could infer the man’s goal merely from his failure, machines that infer our goals need to account for our mistaken actions and plans.

“Otherwise, AI systems might wrongly infer that, since we failed to achieve our higher-order goals, those goals weren't desired after all.

By detecting these potential failures in advance, the team hopes the model could be used by machines to better offer assistance.

While to date the researchers have explored inference only in relatively small planning problems over fixed sets of goals, through future work they plan to explore richer hierarchies of human goals and plans.

“Though this work represents only a small initial step, my hope is t…

As Covid-19 infections soar across the U.S., some states are tightening restrictions and reinstituting quarantine measures to slow the virus’ spread.

This value is what the researchers label as “quarantine strength,” which reflects how effective a region is in quarantining an infected individual.

The model can process data over time to see how a region’s quarantine strength evolves.

“The quarantine strength in our model changes a day or two after policies are instituted, among all countries.

“Now that we can give a measure of quarantine strength that matches reality, we can say, ‘What if we kept everything constant?

And it is an Indigenous question: ‘Is it wise?’”In November, 10 Indigenous media scholars and artists convened at MIT — virtually — for the inaugural Indigenous Digital Delegation.

In a week-long series of gatherings, the delegation met with over 60 MIT scientists, staff, fellows, and students.

The theme of the gathering was “Indigenous Knowledge, Artificial Intelligence, and Digital Worlds.”Delegates met with MIT scholars to discuss diverse domains, from the decolonization of space, to re-imagining Indigenous architecture, to the role of community-based governance in the genetic modification of invasive species.

This first Indigenous Delegation of its kind at MIT was originally scheduled a…

Computer vision models known as convolutional neural networks can be trained to recognize objects nearly as accurately as humans do.

Convolutional neural networks are often used in artificial intelligence applications such as self-driving cars, automated assembly lines, and medical diagnostics.

Today’s leading computer vision systems are already loosely guided by our current knowledge of the brain’s visual processing.

“Adversarial attacks are a big, open problem for the practical deployment of deep neural networks.

Better defenseCurrently, the best defense against adversarial attacks is a computationally expensive process of training models to recognize the altered images.

In a new paper, Frankle and colleagues discovered such subnetworks lurking within BERT, a state-of-the-art neural network approach to natural language processing (NLP).

You’ve probably interacted with a BERT network today.

Users can then fine-tune BERT’s neural network to a particular task, like building a customer-service chatbot.

Luckily, “the lottery ticket hypothesis seems to be a solution.”To cut computing costs, Chen and colleagues sought to pinpoint a smaller model concealed within BERT.

They experimented by iteratively pruning parameters from the full BERT network, then comparing the new subnetwork’s performance to that of the original BERT model.

When our table is shaky, we quickly find that we can put a stack of paper under the table leg to stabilize it.

But while these actions seem so natural to us, they are believed to be a hallmark of great intelligence — only a few other species use objects in novel ways to solve their problems, and none can do so as flexibly as people.

Solving the puzzles in this game requires reasoning about a number of physical principles, including launching, blocking, or supporting objects.

They built a model that instantiated these principles, called the “Sample, Simulate, Update,” or “SSUP,” model, and had it play the same game as people.

They found that SSUP solved each puzzle at similar rates and in si…

In today’s world of fast fashion, retailers sell only a fraction of their inventory, and consumers keep their clothes for about half as long as they did 15 years ago.

According to Singh, Armoire has grown 300 to 500 percent a year since its founding in 2016.

In fact, when Singh started Armoire, classmates used it as a case study for marketing and analytics research projects.

Singh credits Armoire’s leadership team with creating a welcoming work environment, noting there’s been very little turnover in Armoire’s warehouses.

“We don’t get 95 percent of our inventory rented because I’m so good at picking out clothes,” Singh says.

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…

Our answer to this is to follow eigenvectors of the Hessian (‘ridges’) with negative eigenvalues from a saddle, in what we call Ridge Rider (RR).

As you see in the diagram, when we take a step along the ridge (in red) we reach a new point.

The full pictureIn the next diagram we show the full Ridge Rider algorithm.

Ridge Rider for Out of Distribution GeneralizationWe tested RR on the colored MNIST dataset, from [2].

Ridge Rider for Zero-Shot Co-ordinationFinally, we consider the zero-shot co-ordination problem.

Our answer to this is to follow eigenvectors of the Hessian (‘ridges’) with negative eigenvalues from a saddle, in what we call Ridge Rider (RR).

As you see in the diagram, when we take a step along the ridge (in red) we reach a new point.

The full pictureIn the next diagram we show the full Ridge Rider algorithm.

Ridge Rider for Out of Distribution GeneralizationWe tested RR on the colored MNIST dataset, from [2].

Ridge Rider for Zero-Shot Co-ordinationFinally, we consider the zero-shot co-ordination problem.

In this post, I will survey these works as well as other prominent frameworks for handling distribution shift.

ERM methods assume that there is no distribution shift, so the test distribution exactly matches the training distribution.

To move beyond ERM and learn models that generalize in the face of distribution shift, we must introduce additional assumptions.

If there is distribution shift, observing multiple test points can be useful either to infer the test distribution or otherwise adapt the model to this new distribution, even in the absence of labels.

Combining Training and Test AssumptionsPrior frameworks for distribution shift have assumed either training groups or test batches, bu…

Adapting on the Fly to Test Time Distribution ShiftImagine that you are building the next generation machine learning model for handwriting transcription.

In this post, I will survey these works as well as other prominent frameworks for handling distribution shift.

ERM methods assume that there is no distribution shift, so the test distribution exactly matches the training distribution.

To move beyond ERM and learn models that generalize in the face of distribution shift, we must introduce additional assumptions.

If there is distribution shift, observing multiple test points can be useful either to infer the test distribution or otherwise adapt the model to this new distribution, even in th…

Reinforcement learning is supervised learning on optimized dataThe two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.

In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

The table below compares the supervised learning perspective to the optimization and dynamic programming perspectives:Â Optimization Perspective Dynamic Programming Perspective Supervised Learning Perspective What are we optimizi…

Reinforcement learning is supervised learning on optimized dataThe two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.

In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

It turns out that finding “good data” is much easier in the multi-task setting, or settings that can be converted to a different problem for which obtaining “good data” is easy.

The table below compares the supervised learning pe…

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningThis post is cross-listed on the CMU ML blog.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$.

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Future directionsPlan2Explore demonstrates that effective behavior can be learned through self-supervised exploration only.

We recently announced Amazon SageMaker Pipelines, the first purpose-built, easy-to-use continuous integration and continuous delivery (CI/CD) service for machine learning (ML).

SageMaker Pipelines is a native workflow orchestration tool for building ML pipelines that take advantage of direct Amazon SageMaker integration.

You can use SageMaker Pipelines independently to create automated workflows; however, when used in combination with SageMaker projects, the additional CI/CD capabilities are provided automatically.

The following screenshot shows how the three components of SageMaker Pipelines can work together in an example SageMaker project.

MLOps template for building, training, and deplo…

Amazon SageMaker Ground Truth simplifies and accelerates this task.

Creating a Ground Truth labeling workforceGround Truth offers three options for defining workforces that complete the labeling: Amazon Mechanical Turk, vendor-specific workforces, and private workforces.

Create a private workforce with the following steps:On the Amazon SageMaker console, under Ground Truth, choose Labeling workforces.

Configuring a custom labeling jobIn this section, we create a labeling job and use this job to explain the details and data flow of a custom labeling job.

On the Amazon SageMaker console, under Ground Truth, choose Labeling jobs.

In this post, we walk you through the steps to build machine learning (ML) models in Amazon SageMaker with data stored in Amazon HealthLake using two example predictive disease models we trained on sample data using the MIMIC-III dataset.

As part of the transformation, Amazon HealthLake tags and indexes unstructured data using specialized ML models.

For an example implementation, see the section Connecting Athena with HealthLake in the post Population health applications with Amazon HealthLake – Part 1: Analytics and monitoring using Amazon QuickSight.

Amazon HealthLake augmented modelOur Amazon HealthLake augmented model had an accuracy of 89.1%.

ConclusionIn this post, we demonstrated how…

End-users or your data scientists can orchestrate these steps using AWS Lambda functions and AWS Step Functions.

You can orchestrate an Amazon Personalize workflow using the AWS Step Functions Data Science SDK for Python using an Amazon SageMaker Jupyter notebook.

The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish ML models using Amazon SageMaker Jupyter notebooks and Step Functions.

Setting up Step Function Execution roleYou need a Step Functions execution role so that you can create and invoke workflows in Step Functions.

lambda_state_schema = LambdaStep( state_id="create schema", parameters={ "…

The solution includes the following high-level steps:Reduce the problem to a single vessel voyage (when the vessel departs from one given port and gets to another).

From a given checkpoint, predict the ETA in days for the vessel to reach the destination port (inside a given vessel voyage).

This view is for one particular vessel, which shows all the checkpoints that belong to one given vessel voyage.

Some examples include weather conditions from the geolocation of the vessel, port conditions, accidents, extraordinary events, seasonality, and holidays in the port countries.

It’s a measurement of how strong or important a given feature from the dataset is for the prediction itself.

In this post, we show how using the AUC as the model evaluation metric for highly imbalanced data allows Autopilot to generate high-quality models.

In the preceding AUC ROC plot, Autopilot models provide high AUC when optimizing both objective metrics.

As we can see from the plot, Autopilot models provide good precision and recall, because the graph is heavily skewed toward the top-right corner.

Autopilot outputsIn addition to handling the heavy lifting of building and training the models, Autopilot provides visibility into the steps taken to build the models by generating two notebooks: CandidateDefinitionNotebook and DataExplorationNotebook .

For more information about tuning, training, a…

This post demonstrates how to use the Amazon SageMaker reinforcement learning starter kit for the NeurIPS 2020 – Procgen competition hosted on AIcrowd.

It also helps you reduce the time and effort required to build your sample-efficient reinforcement learning solutions using homogenous and heteregeneous scaling.

SageMaker supports distributed reinforcement learning in a single SageMaker ML instance with just a few lines of configuration by using the Ray RLlib library.

The single instance training with ml.p3.8x converges in 20 minutes, helping you iterate faster to meet the competition deadline.

Her current research focus is deep reinforcement learning (RL) for smart automation and robotics.

We’re excited to announce the global availability of AWS Contact Center Intelligence (AWS CCI) solutions powered by AWS AI Services and made available through the AWS Partner Network.

AWS CCI solutions use a combination of AWS AI-powered services for text-to-speech, translation, intelligent search, conversational AI, transcription, and language comprehension capabilities.

AWS CCI provides a simple and fast route to deploy AWS ML solutions no matter which contact center provider you use.

AWS CCI customer success storiesMultiple customers already benefit from an improved customer experience and reduced operational costs as a result of using AWS CCI solutions through AWS Partners.

And, AWS CCI…

Amazon SageMaker Studio notebooks provide a full-featured integrated development environment (IDE) for flexible machine learning (ML) experimentation and development.

Creating VPC-only SageMaker Studio resourcesAll SageMaker Studio resources reside within a domain, with a maximum of one domain per Region in an AWS account.

For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC and Securing Amazon SageMaker Studio connectivity using a private VPC.

Installing a Python package onto the SageMaker Studio notebookTo start using the PyPI server from the SageMaker Studio notebook, complete the following steps:On the SageMaker Studio Control Panel, choose Open Studio next…

We signed off last year at AWS re:Invent on the artificial intelligence (AI) and machine learning (ML) track with the first ever machine learning keynote and over 50 AI/ML focused technical sessions covering industries, use cases, applications, and more.

You can access all the content for the AI/ML track on the AWS re:Invent website.

Join AWS and Coinbase to learn how to detect fraud faster using sample datasets and architectures, and help save millions of dollars for your organization.

We look forward to seeing you on the artificial intelligence and machine learning track.

About the AuthorShyam Srinivasan is on the AWS Machine Learning marketing team.

AWS account setupBayer Crop Science organizes its cloud resources into a large number of application-, team-, and project-specific accounts.

KubeFlow pipeline setupOur ML pipeline (see the following diagram) uses Amazon SageMaker Components for KubeFlow Pipelines to standardize the integration with SageMaker training and deployment services.

This is because all pipelines run in the same namespace and run using the generic KubeFlow Pipeline Runner IAM role from the Kubeflow AWS account.

Each pipeline in Bayer’s Kubeflow environment has a dedicated IAM role associated with it that has a trust relationship with the Kubeflow Pipeline Runner IAM role.

Step 5: Training the modelThe SageMaker Trai…

If you want to implement one of these built-in task types, along with a default labeling GUI, creating a labeling job requires no customization steps.

For more details on custom workflows, see Creating Custom Labeling Workflows and Creating custom labeling jobs with AWS Lambda and Amazon SageMaker Ground Truth.

What if you wanted to implement a custom GUI, but implement the built-in preprocessing and postprocessing logic that the built-in task types provide?

Creating the custom labeling jobAfter you upload the files to Amazon S3, you can create your labeling job.

ConclusionIn this post, I demonstrated how to implement a custom labeling GUI with built-in preprocessing and postprocessing logi…

Amazon Kendra supports search filtering based on user access tokens that are provided by your search application, as well as document access control lists (ACLs) collected by the Amazon Kendra connectors.

Reviewing Amazon Kendra configuration and starting the data source syncIn the following steps, we configure Amazon Kendra to enable secure token access and start the data source sync to begin crawling and indexing documents.

You can also cross-verify by checking on the respective management consoles for Amazon Kendra, Amazon Amplify, and the Amazon Cognito user pool and identity pool.

ConclusionIn this post, we demonstrated how you can create a secure search application using Amazon Kendra…

Sharing data and computing in the cloud allows data users to focus on data analysis rather than data access.

In this post, we demonstrate how to extract buildings and roads from two large-scale geospatial datasets: SpaceNet satellite images and USGS 3DEP LiDAR data.

Data registrationFor this post, we select the Las Vegas AOI where both SpaceNet satellite images and USGS LiDAR data are available.

We reproduce a winning algorithm and evaluate its performance with both RGB images and LiDAR data.

Training dataIn the Las Vegas AOI, SpaceNet data is tiled to size 200m x 200m.

In this DRIVE Labs episode, NVIDIA experts demonstrate how DRIVE IX perceives driver attention, activity, emotion, behaviour, posture, speech, gesture and mood with a variety of detection capabilities.

Finally, ActivityNet tracks driver activity such as phone usage, hands on/off the wheel and driver attention to road events.

DRIVE IX can also detect whether the driver is properly sitting in their seat to focus on road events.

Taking in data from the base face-detect and fiducial-point networks, DRIVE IX can classify a driver’s state as happy, surprised, neutral, disgusted or angry.

A Customizable SolutionVehicle manufacturers can leverage the driver monitoring capabilities in DRIVE IX to de…

Toorians built the vehicle to show a classic car can be recycled into an electric ride that taps NVIDIA Jetson AI for safety, security and vehicle management features.

He hopes the car blazes a path for others to explore electric vehicle conversions that pack AI.

But for him the big payoff is repurposing the older gasoline vehicle to electric to avoid environmental waste and exhaust pollutants.

“Electric cars help keep our air and environment clean and are the way toward a more sustainable future in transportation,” he said.

DIY makers and businesses alike turn to NVIDIA Jetson for edge AI.

Flying, driving, gaming, racing… amid the first-ever virtual Consumer Electronics Show this week, NVIDIA-powered technologies spilled out in all directions.

In automotive, Chinese automakers SAIC and NIO announced they’ll use NVIDIA DRIVE in future vehicles.

In gaming, NVIDIA on Tuesday led off a slew of gaming announcements by revealing the affordable new RTX 3060 GPU and detailing the arrival of more than 70 30-series GPUs for gamers and creatives.

“More than ever, gaming has become an integral part of our lives.”Hitting the RoadIn automotive, two Chinese automakers announced they’ll be relying on NVIDIA DRIVE technologies.

Just as CES was starting electric car startup NIO announced a sup…

SAIC, the largest automaker in China, joined forces with etail giant Alibaba to unveil a new premium EV brand, dubbed IM, or “intelligence in motion.” The long-range electric vehicles will feature AI capabilities powered by the high-performance, energy-efficient NVIDIA DRIVE Orin compute platform.

By centralizing and unifying the compute architecture, IM vehicles will be able to receive advanced software features as they’re developed.

Premium Vehicles Inside and OutDeveloping a top-of-the-line premium electric brand requires best-in-class in-vehicle compute.

The software-defined IM vehicles don’t just improve like mobile devices, they also work seamlessly with such technology.

As a new, con…

NVIDIA is the second-best place to work in the U.S. according to a ranking released today by Glassdoor.

The site’s Best Places to Work in 2021 list rates the 100 best U.S. companies with more than 1,000 employees, based on how their own employees rate career opportunities, company culture and senior management.

Right behind in NVIDIA on the list are In-N-Out Burger, HubSpot and McKinsey & Company.

“This year’s winning employers have proven, according to employees, that even during extraordinary times, they’ll rise to the challenge to support their people,” said Christian Sutherland-Wong, Glassdoor chief executive officer.

Among other recent recognitions of NVIDIA’s efforts to take care of o…

The latest consoles and the rest of the gaming ecosystem are now onboard: Ray tracing is the new standard.

There are already over 30 titles with ray tracing and many more on the way.

And turning on ray tracing to get the best visuals makes Watch Dogs: Legion virtually unplayable on a GTX 1060.

With Reflex and our Game Ready Drivers, over 100 million GeForce gamers are instantly more competitive.

Once the holy grail of computer graphics, ray tracing is now the standard.

Bringing more gaming capabilities to millions more gamers, NVIDIA on Tuesday announced more than 70 new laptops will feature GeForce RTX 30 Series Laptop GPUs and unveiled the NVIDIA GeForce RTX 3060 graphics card for desktops.

last year, NVIDIA launched its second generation of RTX, the GeForce RTX 30 Series GPUs.

And over the past four months, NVIDIA has launched four NVIDIA Ampere architecture-powered graphics cards, from the ultimate BFGPU — the GeForce RTX 3090 priced at $1,499 — to the GeForce RTX 3060 Ti at $399.

Resizable BAR will also be supported on GeForce RTX 30 Series graphics cards for desktops, starting with the GeForce RTX 3060.

AvailabilityManufacturers worldwide, starting …

The latest NVIDIA Studio laptops, powered by new NVIDIA GeForce RTX 30 Series Laptop GPUs, are empowering the next generation of creativity.

NVIDIA Studio laptops with new RTX 30 Series Laptop GPUs offer improved performance.

The New 30 Series Studio LaptopsNew Studio laptops with GeForce RTX 3060, 3070 and 3080 Laptop GPUs will start rolling out later this month.

New GeForce RTX 30 Series NVIDIA Studio laptops from ASUS, Gigabyte, MSI and Razer will begin rolling out later this month.

Learn more about NVIDIA Studio hardware and software for creators on the NVIDIA Studio website.

How AI Can Drive Down Drug Discovery CostsThe typical drug discovery process takes about a decade, costs $2 billion and suffers a 90 percent failure rate during clinical development.

But the rise of digital data in healthcare in recent years presents an opportunity to improve those statistics with AI.

Healthcare Ecosystem Rallies Around AIShe noted that amid the COVID-19 pandemic, momentum around AI for healthcare has accelerated, with startups estimated to have raised well over $5 billion in 2020.

And over 20,000 AI healthcare papers were submitted last year to PubMed, showing exponential growth over the past decade.

Everything we’ve learned is applicable for every future drug discovery pr…

It’s not too different from cryo-electron microscopy, or cryo-EM, which scientists use to study the structure of tiny molecules frozen in vitreous ice.

Correcting the motion across frames is a computationally demanding task — but can be done in seconds on NVIDIA GPUs.

To speed the development of new applications, Zheng, who’s used NVIDIA GPUs for his research for a decade, uses a workstation powered by two NVIDIA Tensor Core GPUs.

NVIDIA GPUs can fully automate the reconstruction process, taking a half hour on a single GPU, he says.

Main image shows a cryo-EM density map for the enzyme beta-galactosidase, showing the gradual increase in quality of the cryo-EM structures from low to high res…

The journey to making the upcoming film Gods of Mars changed course dramatically once real-time rendering entered the picture.

But they switched gears once they experienced the power of real-time NVIDIA RTX graphics and Unreal Engine.

The live-action elements of the film are supported by LED walls with real-time rendered graphics created from Unreal Engine.

Hyoguchi doesn’t need to spend thousands of dollars for render farms, or wait weeks for one shot to complete rendering.

Learn more about Gods of Mars and NVIDIA RTX.

Electric vehicle upstarts have gained a foothold in the industry and are using NVIDIA DRIVE to keep that momentum going.

Along with more efficient powertrains, these fleets are also introducing new and intelligent features to daily commutes with NVIDIA DRIVE.

NIO Unveils a Supercharged Compute PlatformLast week, NIO announced a supercomputer to power its automated and autonomous driving features, with NVIDIA DRIVE Orin at its core.

Li Auto Powers AheadIn September, standout EV maker Li Auto said it would develop its next generation of electric vehicles using NVIDIA DRIVE AGX Orin.

Li Auto plans to continue this momentum with its upcoming models, packed with even more intelligent features en…

Chinese electric automaker NIO will use NVIDIA DRIVE for advanced automated driving technology in its future fleets, marking the genesis of truly intelligent and personalized NIO vehicles.

“The cooperation between NIO and NVIDIA will accelerate the development of autonomous driving on smart vehicles,” said NIO CEO William Li.

And now, with NVIDIA DRIVE powering automated driving features in its future vehicles, NIO is set to redefine mobility with continuous improvement and personalization.

The new NIO Adam supercomputer is one of the most powerful platforms to run in a vehicle.

With high-performance compute at its core, Adam is a major achievement in the creation of automotive intelligence…

The AI cockpit has reached galactic proportions with the new Mercedes-Benz MBUX Hyperscreen.

Dubbed the MBUX Hyperscreen, the system is powered by NVIDIA technology and shows how AI can create a truly intuitive and personalized experience for both the driver and passengers.

“The MBUX Hyperscreen reinvents how we interact with the car,” said Sajjad Khan, executive vice president at Mercedes-Benz.

“Zero Layer” User InterfaceThe driving principle behind the MBUX Hyperscreen is that of the “zero layer” — every necessary driving feature is delivered with a single touch.

The MBUX Hyperscreen will debut with the all-electric Mercedes-Benz EQS, combining electric and artificial intelligence.

Canadian high schooler Ana DuCristea has a clever solution for the quarantine slump.

Using the Nano and her background on Python, DuCristea spent her after-school hours creating an app that does just that.

With the app, users can message a bot on Discord requesting a reminder for a specific task, date and time.

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 and TuneIn.

Make the AI Podcast BetterHave a few minutes to spare?

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…

What the research is:We’ve built a data classification system that uses multiple data signals, a scalable system architecture, and machine learning to detect semantic types within Facebook at scale.

This is important in situations where it’s necessary to detect where an organization’s data is stored in many different formats across various data stores.

In these cases, a classification system enables organizations to automatically enforce privacy- and security-related policies, such as access control policies.

Why it matters:Organizations generally have a well-defined set of privacy policies aimed at ensuring that people’s privacy is respected.

Read the full paper:Secure and scalable data cl…

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…

Instead of programming only a single desktop or laptop, users can leverage this system to program the whole computer cluster.

Fiber allows users to write programs that run on a computer cluster without needing to dive into the details of the computer cluster.

This overall architecture is summarized in Figure 2, below:Job-backed processesFiber introduces a new concept called job-backed processes (also called a Fiber processes).

When starting a new Fiber process, Fiber creates a new job with the proper Fiber back end on the current computer cluster.

Our hypothesis was that Fiber should perform similarly to multiprocessing because neither Fiber nor multiprocessing rely on complex scheduling me…

Unfortunately, adding support for a new deep learning framework across an entire machine learning stack is resource and time-intensive.

Using multiple deep learning frameworksDeep learning (DL) is advancing very quickly and different DL frameworks are effective at different tasks.

Over the last year, we have deployed hundreds of Neuropod models across Uber ATG, Uber AI, and the core Uber business.

Deep learning with NeuropodLet’s take a look at the overall deep learning process when using Neuropod to see how it helps make experimentation, deployment, and iteration easier.

Next stepsNeuropod has allowed Uber to quickly build and deploy new deep learning models, but that’s just the start.

The “spikes” at intersections result from the SDV crossing the same intersection multiple times as part of a “grid-coverage” driving pattern.

Data mining the scenario “pedestrian crossing the street”While the SDV perception system is designed to detect pedestrians, only a subset of pedestrians actually cross the street.

Analyzing the “pedestrian crossing the street” scenarioThe scenario of a pedestrian crossing the street has many relevant measurements, including the pedestrian crossing speed, road width, distance walked, crossing duration, distance walked on crosswalk, and traffic light state(s) at the time of crossing.

Let’s start by analyzing just one measurement: the pedestrian crossing…

For instance, in a social network we may use link prediction to power a friendship recommendation system, or in the case of biological network data, we might use link prediction to infer possible relationships between drugs, proteins, and diseases.

In principle, it can be combined with a wide variety of link prediction approaches based on GNNs, but we adopted a specific GNN, variational graph autoencoders (VGAEs), as our base link prediction framework9.

Experiment setupTo test how Meta-Graph might work in a real-world setting, we designed three novel benchmarks for few-shot link prediction.

In this few-shot link prediction setting, there are train/val/test splits at both the edge level and …

We’ll treat working memory capacity as the length of the longest list of random digits that the participant can memorize.

InferenceWe use Bayesian inference to incorporate our new observation into an estimate of the participant’s working memory capacity.

It models the probability of correctly remembering the list of digits of different lengths for people with different working memory capacities, as shown in Figure 1, below:We also need a sense of what working memory capacities are plausible.

Computing the optimal designOur score for experimental designs, EIG, is notoriously difficult to estimate.

In our paper, we showed that this method can be remarkably accurate on a range of different exp…

Last year we introduced the Paired Open-Ended Trailblazer (POET) to explore the idea of open-ended algorithms.

ANNECS: A new way to measure progress in open-ended systemsQuantifying the performance of open-ended algorithms has remained elusive for the field.

Compare those from Original POET in Figure 4a to those produced by Enhanced POET in Figure 4b, below.

If this piques your interest, be sure to check out videos of example Enhanced POET agents on the Uber AI YouTube channel.

Towards that end, we are not only releasing a paper with full technical details, but also have open sourced the code for Enhanced POET.

We’ll cover:Criteria for choosing a model management toolModel management tools: Neptune, Amazon SageMaker, Azure Machine Learning, Domino Data Science Platform, Google Cloud AI Platform, Metaflow, MLflowREAD MOREMachine Learning Model Management in 2021 and Beyond – Everything That You Need to KnowML model management toolsThere are plenty of ML tools for model management.

Let’s see how Azure Machine Learning fits the criteria:Data, notebook, model, code, and environment versioning and experiment tracking:Azure Machine Learning has everything you might need from a good model management tool.

Model management is all about developing a good machine learning model.

Kind of like project managem…

That means if a hyperparameter is nudged or there’s a change in training data then it can affect the model’s performance in many ways.

This means you’ve to jot down every change in hyperparameter and training data to be able to reproduce your work.

Neptune is a complete tool that helps individuals and teams to track their experiments smoothly.

Below is a similar experiment but tracked using the Neptune-AI tool:Setting up Neptune experiment in PytorchThe process of setup is trivial.

pip install neptune-client Step 2 Connect to the tool by adding a snippet to your training code.

I will also show you how you can add experiment management to your current workflow in just a few steps.

CHECK ALSONeptune’s integration with lightGBM – docsHow ML model development with LightGBM looks like todayObtaining the datasetAny model development process will kick off with obtaining the dataset.

This package will enable us to log our metrics to Neptune while training the LightGBM model.

The parameters tab shows the parameters used to train the LightGBM model.

ConclusionHopefully, this has shown you how easy it is to add experiment tracking and model versioning to your LightGBM training scripts using Neptune.

For a successful deep learning project, you need a lot of iterations, a lot of time, and a lot of effort.

Model deployment Project maintenanceLifecycleBecause deep learning projects are so iterative, we have to be very careful to organize the project in a way that reduces any tension and complexity.

– Cassie Kozyrkov, Chief Decision Scientist at GoogleWhen it comes to deep learning, trade-offs between speed and accuracy should be taken into account.

Mock out your deep learning model and iterate (if required) on the user experience, keeping in mind the targeted audience and type of model shipped to them.

Neptune offers sharing of your projects with your teammates | SourceThis concludes the d…

(CI) is no longer only about testing and validating code and components, but also testing and validating data, data schemas, and models.

SourceMLOps vs Experiment Tracking vs ML Model ManagementWe’ve defined what MLOps is, what about experiment tracking and ML model management?

CharacteristicsManual, script-driven, and interactive process: every step is manual, including data analysis, data preparation, model training, and validation.

every step is manual, including data analysis, data preparation, model training, and validation.

It can take even longer to build a data pipeline that can produce value for your organization.

If you’re already considering which tool is the right one for you, today we’ll compare Neptune, Tensorboard, and MLflow.

Here’s what you’ll find in this article:A quick overview of MLflow, Tensorboard, Neptune, and what they do;A detailed chart comparing the features of MLflow, Tensorboard, Neptune;When Neptune is a better alternative than MLflow and Tensorboard;How Neptune integrates with MLflow and Tensorboard.

Quick overview of MLflow, Tensorboard, and NeptuneAlthough you can use all three tools to solve similar problems, the differences can be really important depending on your use case.

Detailed chart comparing the features of MLflow, Tensorboard, NeptuneNeptune’s flexibility when it c…

Read this article with interesting insights to get a sense of what the machine learning world needs to achieve even more.

» Why robotics and language need each other by Matthew Hutson on The Week | January 3We already know machine learning and language have a lot in common (and it’s not only about the programming language 😉 ).

» 10 Underappreciated Python Packages for Machine Learning Practitioners by Vinay Uday Prabhu on KDnuggetsHere’s a more technical piece in which you’ll find 10 underappreciated Python packages covering neural architecture design, calibration, UI creation and dissemination.

» Best Machine Learning and Artificial Intelligence Books by Stella Sebastian on Reconshell | Ja…

TensorFlow Serving is a robust, high-performance system for serving machine learning models.

🧩 See our integration with MLflowRELATED ARTICLEBest Tools to Manage Machine Learning ProjectsIt helps to simplify the process of automating ML model tracking.

👉 See the best MLflow alternativesSourceThe main objective of Kubeflow is to maintain machine learning systems.

BentoML pros:Supports high-performance model serving, model management, model packaging, and a unified model format.

Works only with PyTorch ModelsConclusionThe creation and deployment of high-performance and scalable machine learning models are challenging tasks.

There are tools that can help developers organize their machine learning model development process.

CHECK ALSONeptune’s integration with XGBoost – documentationHow ML model development with XGBoost typically looks likeGet the dataset readyBefore we can train any model, we need a dataset.

It contains a callback that will let us log our metrics, the model, and feature importances to Neptune while training the XGBoost model.

The parameters tab shows the parameters used to train the XGBoost model.

Collaborate on ML experiments with your teamYou can share any of your Neptune experiments by inviting your teams to collaborate.

ModelDB is an open-source system to version machine learning models including their ingredients: code, data, config, and environment and to track ML metadata across the model lifecycle.

There are other options that can help you keep track of all your data, and manage ML models.

To help you better manage your ML models, here’s a list of the 10 best alternatives to ModelDB.

It facilitates the scaling of machine learning models by making run orchestration and deployments of machine learning workflows easier.

It works wherever you run your code with any machine learning library, and for any machine learning task.

SourceWhat are the Data Science and Machine Learning use cases in E-CommerceAs a Data Scientist working in the retail space, all your primary goals revolve around “Customers”.

RELATED ARTICLES👉 Most Used Tools, Frameworks, and Libraries in Machine Learning Industry (Roundup)👉 The Best Tools, Libraries, Frameworks and Methodologies that Machine Learning Teams Actually Use – Things We Learned from 41 ML Startups [ROUNDUP]Low code tools to implement Machine Learning in your e-commerce use cases (cost intensive)1.

BigQueryMLBigQueryML is one of Google’s most sophisticated low-code Machine Learning approaches to build complex Machine Learning models.

With a strong Data Science team, you can buil…

SourceTensorFlow is a popular open-source library created by the Google Brain team to develop and train both machine learning and deep learning models.

Every Minecraft-lover wouldTensorFlow is a powerful library for numerical computations, particularly for large scale machine learning and deep learning projects.

SourcePyTorch, developed by Facebook, is an open-source framework based on Torch (an open-source machine learning package designed in Lua) to build and train machine learning models.

Distributed fitness function implementation for the WatchmakerSourceApache SINGA is an open-source machine learning library that provides a flexible architecture for scalable distributed training.

Sourc…

Google Colab is a great platform for deep learning enthusiasts, and it can also be used to test basic machine learning models, gain experience, and develop an intuition about deep learning aspects such as hyperparameter tuning, preprocessing data, model complexity, overfitting and more.

IntroductionColaboratory by Google (Google Colab in short) is a Jupyter notebook based runtime environment which allows you to run code entirely on the cloud.

Google Colab supports both GPU and TPU instances, which makes it a perfect tool for deep learning and data analytics enthusiasts because of computational limitations on local machines.

Mounting a driveGoogle Colab allows you to import data from your Go…

We always say that visualization is the key to data understanding so, here are a few highlights of the year 2020.

I checked which articles were most visited and read and prepared a list of the top posts in various categories.

GuidesThis is a complete guide on Keras loss functions.

In this article, Alfrick Opidi talks about popular loss functions in PyTorch, and about building custom loss functions.

You’ll find out what loss functions are, how to add PyTorch loss functions, which loss functions are available in PyTorch, and how to create a custom loss function in PyTorch.

#ai #openai #technology Paper Title: Learning Transferable Visual Models From Natural Language Supervision

CLIP trains on 400 million images scraped from the web, along with text descriptions to learn a model that can connect the two modalities. The core idea is a contrastive objective combined with a large batch size. The resulting model can be turned into arbitrary zero-shot classifiers for new image & text tasks. OUTLINE:

0:00 - Introduction

3:15 - Overview

4:40 - Connecting Images & Text

9:00 - Building Zero-Shot Classifiers

14:40 - CLIP Contrastive Training Objective

22:25 - Encoder Choices

25:00 - Zero-Shot CLIP vs Linear ResNet-50

31:50 - Zero-Shot vs Few-Shot

35:35 - Scaling Propert…

#openai #science #gpt3 OpenAI's newest model, DALL·E, shows absolutely amazing abilities in generating high-quality images from arbitrary text descriptions. Like GPT-3, the range of applications and the diversity of outputs is astonishing, given that this is a single model, trained on a purely autoregressive task. This model is a significant step towards the combination of text and images in future AI applications. OUTLINE:

0:00 - Introduction

2:45 - Overview

4:20 - Dataset

5:35 - Comparison to GPT-3

7:00 - Model Architecture

13:20 - VQ-VAE

21:00 - Combining VQ-VAE with GPT-3

27:30 - Pre-Training with Relaxation

32:15 - Experimental Results

33:00 - My Hypothesis about DALL·E's inner working…

#ai #privacy #tech This paper demonstrates a method to extract verbatim pieces of the training data from a trained language model. Moreover, some of the extracted pieces only appear a handful of times in the dataset. This points to serious security and privacy implications for models like GPT-3. The authors discuss the risks and propose mitigation strategies. OUTLINE:

0:00 - Intro & Overview

9:15 - Personal Data Example

12:30 - Eidetic Memorization & Language Models

19:50 - Adversary's Objective & Outlier Data

24:45 - Ethical Hedging

26:55 - Two-Step Method Overview

28:20 - Perplexity Baseline

30:30 - Improvement via Perplexity Ratios

37:25 - Weights for Patterns & Weights for Memorization

…

#memes #science #ai Antonio and I critique the creme de la creme of Deep Learning memes. Music:

Sunshower - LATASHÁ

Papov - Yung Logos

Sunny Days - Anno Domini Beats

Trinity - Jeremy Blake More memes:

facebook.com/convolutionalmemes Links:

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/ If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completel…

#ai #technology #poker This paper does for Poker what AlphaZero has done for Chess & Go. The combination of Self-Play Reinforcement Learning and Tree Search has had tremendous success in perfect-information games, but transferring such techniques to imperfect information games is a hard problem. Not only does ReBeL solve this problem, but it provably converges to a Nash Equilibrium and delivers a superhuman Heads Up No-Limit Hold'em bot with very little domain knowledge. OUTLINE:

0:00 - Intro & Overview

3:20 - Rock, Paper, and Double Scissor

10:00 - AlphaZero Tree Search

18:30 - Notation Setup: Infostates & Nash Equilibria

31:45 - One Card Poker: Introducing Belief Representations

45:00 - S…

#ai #technology #poker Daniel Negreanu posted a set of very interesting No-Limit Hold'em situations on Twitter. I try to analyze them from the perspective of a poker bot. See how such bots think about the game and approximate Nash equilibria. https://twitter.com/RealKidPoker/status/1337887509397741568

https://twitter.com/RealKidPoker/status/1337899147337244673

https://twitter.com/RealKidPoker/status/1337904860721606656 Links:

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

BiliBili: https://space.bilibili.com/1824646584

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

Pa…

#deepmind #biology #ai This is Biology's AlexNet moment! DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there. OUTLINE:

0:00 - Intro & Overview

3:10 - Proteins & Protein Folding

14:20 - AlphaFold 1 Overview

18:20 - Optimizing a differentiable geometric model at inference

25:40 - Learning the Spatial Graph Distance Matrix

31:20 - Multiple Sequence Alignment of Evolutionarily Similar Sequences

39:40 - Distance Matr…

#ai #biology #neuroscience Backpropagation is the workhorse of modern deep learning and a core component of most frameworks, but it has long been known that it is not biologically plausible, driving a divide between neuroscience and machine learning. This paper shows that Predictive Coding, a much more biologically plausible algorithm, can approximate Backpropagation for any computation graph, which they verify experimentally by building and training CNNs and LSTMs using Predictive Coding. This suggests that the brain and deep neural networks could be much more similar than previously believed. OUTLINE:

0:00 - Intro & Overview

3:00 - Backpropagation & Biology

7:40 - Experimental Results

8:4…

#ai #research #engineering Numerical solvers for Partial Differential Equations are notoriously slow. They need to evolve their state by tiny steps in order to stay accurate, and they need to repeat this for each new problem. Neural Fourier Operators, the architecture proposed in this paper, can evolve a PDE in time by a single forward pass, and do so for an entire family of PDEs, as long as the training set covers them well. By performing crucial operations only in Fourier Space, this new architecture is also independent of the discretization or sampling of the underlying signal and has the potential to speed up many scientific applications. OUTLINE:

0:00 - Intro & Overview

6:15 - Navier S…

#ai #tech #news This soccer camera is operated by an AI to track the ball. However, the AI has an interesting failure mode and repeatedly mixes up the ball with the bald head of a referee. This raises some interesting questions about the role of ethics in AI research. Footage from SPFL Championship : ICTFC 1 v 1 AYR : 24/10/2020 Links:

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/ If you want to support me, th…

#ai #research #machinelearning Deep Learning models are often overparameterized and have many degrees of freedom, which leads to many local minima that all perform equally well on the test set. But it turns out that even though they all generalize in-distribution, the performance of these models can be drastically different when tested out-of-distribution. Notably, in many cases, a good model can actually be found among all these candidates, but it seems impossible to select it. This paper describes this problem, which it calls underspecification, and gives several theoretical and practical examples. OUTLINE:

0:00 - Into & Overview

2:00 - Underspecification of ML Pipelines

11:15 - Stress Te…

#ai #research #nlp Knowledge Graphs are structured databases that capture real-world entities and their relations to each other. KGs are usually built by human experts, which costs considerable amounts of time and money. This paper hypothesizes that language models, which have increased their performance dramatically in the last few years, contain enough knowledge to use them to construct a knowledge graph from a given corpus, without any fine-tuning of the language model itself. The resulting system can uncover new, unknown relations and outperforms all baselines in automated KG construction, even trained ones! OUTLINE:

0:00 - Intro & Overview

1:40 - TabNine Promotion

4:20 - Title Misnomer…

#ai #research #attention Transformers have huge memory and compute requirements because they construct an Attention matrix, which grows quadratically in the size of the input. The Performer is a model that uses random positive orthogonal features to construct an unbiased estimator to the Attention matrix and obtains an arbitrarily good approximation in linear time! The method generalizes beyond attention and opens the door to the next generation of deep learning architectures. OUTLINE:

0:00 - Intro & Outline

6:15 - Quadratic Bottleneck in Attention Mechanisms

10:00 - Decomposing the Attention Matrix

15:30 - Approximating the Softmax Kernel

24:45 - Different Choices, Different Kernels

28:00 …

#ai #research #attention Transformers, having already captured NLP, have recently started to take over the field of Computer Vision. So far, the size of images as input has been challenging, as the Transformers' Attention Mechanism's memory requirements grows quadratic in its input size. LambdaNetworks offer a way around this requirement and capture long-range interactions without the need to build expensive attention maps. They reach a new state-of-the-art in ImageNet and compare favorably to both Transformers and CNNs in terms of efficiency. OUTLINE:

0:00 - Introduction & Overview

6:25 - Attention Mechanism Memory Requirements

9:30 - Lambda Layers vs Attention Layers

17:10 - How Lambda La…

I hope the Keras code series isn't off putting to people working with PyTorch! These videos will be interleaved throughout the Keras Code Examples to showcase similarities between the frameworks and show you how to leverage these Keras examples to improve your PyTorch skills if that's your framework of choice. Please let me know any other examples where you would like a translation from TensorFlow to PyTorch or vice versa! (Sorry no Jax haha) Links:

PyTorch custom dataloader: https://pytorch.org/tutorials/beginner/data_loading_tutorial.html

3D image classification: https://keras.io/examples/vision/3D_image_classification/

image classification from scratch: https://keras.io/examples/vision/i…

This video shows you how to use 3D Convolutions to process Viral Pneumonia detection from CT Scans! 3D Image Classification: https://keras.io/examples/vision/3D_image_classification/

Keras Examples: https://keras.io/examples Thanks for watching! Please subscribe and check out the Keras Code Examples playlist!

This video will show you how to use a U-Net style ConvNet to map from a 160x160xRGB image of a PET into the same 160x160 dimensional annotation map of each pixel in the image. This involves segmenting the pet from its border and the background. Image segmentation with a U-Net-like architecture: https://keras.io/examples/vision/oxford_pets_image_segmentation/

Keras Code Examples: https://keras.io/examples/ Thanks for watching! Please subscribe and check out the rest of the Keras Code Examples playlist!

This example shows you how to train a very simple convolutional neural network on the famous MNIST dataset! Simple MNIST convnet: https://keras.io/examples/vision/mnist_convnet/

Keras Code Examples: https://keras.io/examples/ Thanks for watching! Please subscribe and check out the Keras Code Examples video playlist!

This example shows you how to train an Image classifier with your own custom dataset! Image Classification from Scratch: https://keras.io/examples/vision/image_classification_from_scratch/

Keras Code Examples: https://keras.io/examples/ Thank you for watching! Please subscribe and check out the Keras Examples video playlist!

Hey everyone, my new year's resolution is to increase my time spent coding! I hope you share the same goals and enjoy this series! Keras Code Examples: https://keras.io/examples/ Thanks for watching! Please Subscribe and check out the Video Series!

This video explains how CLIP from OpenAI transforms Image Classification into a Text-Image similarity matching task. This is done with Contrastive Training and Zero-Shot Pattern-Exploiting Training. Thanks for watching! Paper Links:

Clip (Blog Post): https://openai.com/blog/clip/

VirTex: https://arxiv.org/pdf/2006.06666.pdf

ConVIRT: https://arxiv.org/pdf/2010.00747.pdf

Pattern-Exploiting Training: https://arxiv.org/pdf/2001.07676.pdf

Vision Transformer (Blog Post, Nice Animation): https://ai.googleblog.com/2020/12/transformers-for-image-recognition-at.html Thanks for watching! Please Subscribe!

This video is a quick explanation of the DALL-E model from OpenAI. I hope this makes it clear how text and images are unified as tensors in Deep Neural Networks! Links:

DALL-E: https://openai.com/blog/dall-e/

The Illustrated Transformer: http://jalammar.github.io/illustrated-transformer/

Attention is all you Need: https://arxiv.org/abs/1706.03762

Thank you for watching! Please Subscribe! Content Links:

Data-Efficient Image Transformers: https://ai.facebook.com/blog/data-efficient-image-transformers-a-promising-new-technique-for-image-classification/

Pre-Training a Language Model without Human Language: https://arxiv.org/pdf/2012.11995.pdf

Evaluating Agents without Rewards: https://arxiv.org/pdf/2012.11538.pdf

Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning: https://www.biorxiv.org/content/10.1101/2020.12.21.423785v1.full.pdf

Few-Shot Text Generation with Pattern-Exploiting Training: https://arxiv.org/pdf/2012.11926.pdf

When BERT plays the Lottery, All Tickets are Winning: https://the…

Hey everyone, maybe this will be a fun video series to compliment the AI weekly update videos. I think it's interesting to guess at what a paper is about and why it's worth reading based on the title and abstract. With so much information overload, I think curation is an important skill to develop. I've usually selected everything for the update video by Thursday so I can study it over Friday to Sunday. Please share any early thoughts or understandings you have about this content and let me know what I've missed. Thank you for watching, see you on Monday! Content Links:

Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning: https://www.biorxiv.org…

Hey everyone, hope you are having a great week!

I ran into a wall with my PhD experiments because I was trying to organize a large project in Jupyter notebooks. I hope this video helps show you how OOP can help organize large projects. PyTorch-Cifar (example in video): https://github.com/kuangliu/pytorch-cifar Thanks for watching! Please Subscribe!

Thank you for watching! Please Subscribe! Content Links:

Model-Based Deep RL for Robotic Systems: https://slideslive.com/38938089/modelbased-deep-reinforcement-learning-for-robotic-systems

Transformer Protein Language Models are Unsupervised Structure Learners: https://www.biorxiv.org/content/10.1101/2020.12.15.422761v1.full.pdf

WILDS Benchmark: https://wilds.stanford.edu/

Spatio-Temporal Reasoning: https://arxiv.org/pdf/2012.08508.pdf

Meta-Policy Gradients: A Survey: https://roberttlange.github.io/posts/2020/12/meta-policy-gradients/

Semi-Supervised Reward Learning: https://arxiv.org/pdf/2012.06899.pdf

Model-Based Approach towards identifying the Brain's Learning Algorithms: http://ai.stan…

Thank you for watching! Please Subscribe! Paper / Content Links:

Abstraction & Reasoning in Modern AI Systems: https://slideslive.com/38935790/abstraction-reasoning-in-ai-systems-modern-perspectives

Neurosymbolic AI: The 3rd Wave: https://arxiv.org/pdf/2012.05876.pdf

On the Binding Problem in ANNs: https://arxiv.org/abs/2012.05208

GPU Accelerated Exhaustive Search for Optimal Ensemble of Black-Box Optimization Algorithms: https://arxiv.org/pdf/2012.04201.pdf

What makes for good views for Contrastive Learning: https://arxiv.org/abs/2005.10243

CASTing Your Model: https://blog.einstein.ai/casting-your-model-learning-to-localize-improves-self-supervised-representations/

Distilling Knowledge fro…

Thank you for watching! Please Subscribe! Paper Links:

AlphaFold2: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology

What Google DeepMind Really Achieved: https://www.blopig.com/blog/2020/12/casp14-what-google-deepminds-alphafold-2-really-achieved-and-what-it-means-for-protein-folding-biology-and-bioinformatics/

MolBERT: https://arxiv.org/pdf/2011.13230.pdf

SMILES: https://pubs.acs.org/doi/pdf/10.1021/ci00057a005

Scaling Down Deep Learning (Paper): https://arxiv.org/pdf/2011.14439.pdf

Scaling Down Deep Learning (Blog Post): https://greydanus.github.io/2020/12/01/scaling-down/

Yoshua Bengio Guest Talk - Causal ML: https://www.youtube.com/watch…

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…

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

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Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

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Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

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A fun puzzle stemming from repeated exponentiation.

Full playlist: https://www.youtube.com/playlist?list=PLZHQObOWTQDP5CVelJJ1bNDouqrAhVPev

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/ldm-thanks Notes by Ngân Vũ:

https://twitter.com/ThuyNganVu/status/1261014161464516608?s=20 Play along on Desmos:

https://www.desmos.com/calculator/nul32eaaa9 Related videos.

Calculus series:

https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr In particular look at:

https://youtu.be/CfW845LNObM Numberphile on Grahm's constant:

https://youtu.be/XTeJ64KD5cg ------------------

Video timeline (thanks to user "noonesperfect")

0:36 Question 1

1:13 Answer 1

1:29 …

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Surface-Only Ferrofluids" is available here:

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https://openai.com/blog/dall-e/ Tweet sources:

- Code completion: https://twitter.com/gdm3000/status/1151469462614368256

- Website layout: https://twitter.com/sharifshameem/status/1283322990625607681

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Immersive Light Field Video with a Layered Mesh Representation" 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/stacey/xray/reports/X-Ray-Illumination--Vmlldzo4MzA5MQ 📝 The paper "Deformable Neural Radiance Fields" 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/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows" 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/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "Monolith: A Monolithic Pressure-Viscosity-Contact Solver for Strong Two-Way Rigid-Rigid Rigid-Fluid Coupling" is available here:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Differentiable Vector Graphics Rasterization for Editing and Learning" is available here:

- https://people.csail.mit.edu/tzumao/diffvg/

- https://people.csail.mit.edu/tzumao/diffvg/supplementary_webpage/ The mentioned Mona Lisa genetic algorithm 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 "The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies" 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/One-Shot-3D-Photography/reports/Paper-Summary-One-Shot-3D-Photography--VmlldzozNjE2MjQ 📝 The paper "One Shot 3D Photography" 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/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "Complementary Dynamics" 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/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly#System-4 📝 The paper "An Adaptive Variational Finite Difference Framework for Efficient Symmetric Octree Viscosity" is available here:

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Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Miku…

❤️ 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/wandb/getting-started/reports/Debug-Compare-Reproduce-Machine-Learning-Models--VmlldzoyNzY5MDk?utm_source=karoly 📝 The paper "Learning Quadrupedal Locomotion over Challenging Terrain " is available here:

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Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child,…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report on this exact paper is available here: https://wandb.ai/wandb/retiming-video/reports/Retiming-Instances-in-a-Video--VmlldzozMzUwNTk 📝 The paper "Layered Neural Rendering for Retiming People in Video" is available here:

https://retiming.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Jace O'Brien, Javier Bustamante, J…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report on this paper is available here: https://wandb.ai/wandb/skyAR/reports/The-Sky-Is-In-Our-Grasp---VmlldzozMjY0NDI 📝 The paper "Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos" is available here:

https://jiupinjia.github.io/skyar/ ☀️The mentioned free light transport course is available here:

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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

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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

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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

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DataFest Online 2020

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

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Difference between them and GPU. IVA TPU.

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 ShiftCNN: Generalized Low-Precision Architecture for Inference of CNNs

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Data Fest Online 2020

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Data Fest Online 2020 https://fest.ai/2020/

ML in Healthcare track https://ods.ai/tracks/ml-in-healthcare-df2020 Speaker: Stepan Kudin, Adalisk Register and get access to the tracks: https://ods.ai/events/datafest2020

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В современном машинном обучении модели, основанные на деревьях, являются самыми популярными нелинейными моделями, использующими табличные данные. Эти модели широко применяются в таких областях как медицина, финансы, маркетинг и др., где важна не только точность модели, но также и её интерпретируемость, то есть возможность понять, как модель использует те или иные признаки в предсказании. В медицине закономерности, выявленные с помощью модели, могут быть более ценными, чем сами предсказания. Однако, несмотря на важность задачи, существующие методы локальной интерпретации имеют либо существенно ограниченную область применимости, либо большую вычислительную сложность. В статье авторы представл…

С постоянно растущим числом научных статей, исследователям становится все труднее исследовать область, с которой они не очень хорошо знакомы. Это сильно ограничивает возможности для междисциплинарных исследований. Традиционное введение в область может происходить в форме обзорной статьи, однако не всегда они существуют. Мы поговорим про вычислительные методы анализа научных публикаций, изучим основные методы библиометрии, и их применение в сервисе для анализа биомедицинских статей PubTrends. Также рассмотрим метод автоматического создания обзорной статьи для научной области с помощью глубокого обучения, представленный на конференции ICMLA2020 - Automatic generation of reviews of scientific …

Anomaly detection является важной задачей со множеством применений. Например, возможность находить необычные события по истории банковских операций или истории логинов может привести к значительной экономии денег. Однако anomaly detection сложна задача, так как представляет из себя задачу классификации в условиях с сильно ограниченным (часто вообще пустым) множеством негативных примеров. На семинаре рассмотрим два новых подхода к anomaly detection. В одном аномалии ищут, классифицируя преобразования, примененные к данным, а во втором пользуются предположением о том, что нормальные данные лежат не неком многообразии малой размерности. Докладчик: Фарид Багиров. Слайды: https://drive.google.co…

Метод опорных векторов (SVM) - набор схожих алгоритмов обучения с учителем, использующихся для задач классификации и регрессионного анализа. В последнее время SVM широко используют в разработке новых лекарств. Алгоритмическая основа SVM-моделирования сложна, модели имеют "черный ящик" (подобный другим подходам ML, таким как нейронные сети), и поэтому их трудно интерпретировать в рамках химических терминов. Однако основной причиной растущей популярности SVM, несмотря на ограниченную интуитивную доступность, является эффективность в классификации активных веществ, прогнозировании их химических и биологических свойств, а также моделировании нелинейных взаимосвязей. На семинаре мы разберем теор…

Построение хороших рекомендательных систем для таких областей как мода является сложной задачей из-за высокого уровня субъективности и трудности определения семантических свойств объектов(например, стилей одежды). Добавление визуальных представлений объектов в оптимизируемую функцию улучшает точность рекомендательной системы. Обычно используются представления из предобученных сетей. Однако не всегда выделенные в них характеристики подходят для решения задач в других областях. На семинаре будет рассмотрена модель рекомендации одежды, в которой визуальные представления объектов обучаются непосредственно для указанной задачи. Полученные представления используются для построения персонализирова…

В обучении с подкреплением агенты (а вместе с ними и мы) зачастую сталкиваются с классической дилеммой “exploration vs. exploitation”. Всегда есть выбор: использовать уже выученную успешную стратегию или исследовать среду, в надежде найти стратегию, которая потенциально, но не гарантированно, принесет больше выгоды. Дилемма особенно актуальна в задачах реального мира, где количество возможных действий огромно, а функция награды разреженная. Агенту важно уметь находить наиболее выгодные для исследования стратегии, т.к. от этого зависит скорость сходимости, но полный перебор невозможен. Поэтому, эффективное исследование среды является одной из важнейших проблем обучения с подкреплением. Автор…

Большинство лиц, осуществляющих уход за людьми с деменцией (CPWD), испытывают высокий уровень стресса из-за необходимости оказания помощи, особенно при устранении непредсказуемых поведенческих и психологических симптомов деменции. Из-за таких сложных обязанностей лица, осуществляющие уход, подвержены плохому качеству сна, что пагубно сказывается на их общем состоянии здоровья. Следовательно, мониторинг качества сна лиц, осуществляющих уход, может дать важную оценку стресса. Большинство современных исследований сна основаны на полисомнографии, которая является дорогостоящей и потенциально нарушает распорядок ухода за больными. Для решения этих проблем предлагается система прогнозирования кач…

Рефакторинг в IntelliJ IDEA — очень важный и полезный инструмент, который облегчает жизнь программистам. К сожалению, не всегда у программиста есть возможность сходу придумать хорошее название для той или иной именной сущности. Хочется автоматизировать этот процесс и с помощью машинного обучения предлагать пользователю консистентные названия для различных идентификаторов. На семинаре будет рассказано о проекте Suggesting Identifier Names, которым я занимался в качестве НИР в ВШЭ этой весной и на летней стажировке в JB. За это время удалось создать плагин для IntelliJ IDEA, который предлагает консистентные имена переменных с помощью n-gram модели. Разберем, почему была выбрана именно эта мод…

На данный момент Transformer-based архитектуры стали стандартом де-факто во всевозможных задачах NLP. Однако, применение трансформеров для других типов данных остается довольно ограниченным, либо вовсе отсутствует на практике. Например, для классификации изображений используют механизм внимания (основа любого трансформера) в сочетании со сверточными нейронными сетями, сохраняя при это общую структуру прежней. Авторы данной статьи показывают, что в CNN нет никакой необходимости, поскольку использование чистого трансформера приводит к state-of-the-art решению и, более того, к меньшим вычислительным и временным затратам. На семинаре мы рассмотрим transformer-based архитектуру для классификации…

Многие задачи реального мира естественно моделировать как системы, состоящие из множества агентов, скооперированных друг с другом. Подобные задачи полезно решать методами мультиагентного обучения в подкреплением, т.к. они обладают многими преимуществами по сравнению с методами для обучения одного агента. Однако данные модели содержат некоторые проблемы. Например, для такой популярной модели как independent Q–learning невозможно эффективно использовать подход experience replay memory для глубоких нейронных сетей, который помогает стабилизировать обучение сети. Авторы статьи "Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning" предлагают два метода, которые могут помочь…

Обработка естественного языка (NLP) – одно из наиболее активно развивающихся направлений машинного обучения. Одним из его применений в сфере биомедицины является классификация медицинских текстов по ICD-лейблам (International Classification of Diseases). Присваивание лейблов вручную – сложная задача, требующая больших временных затрат и повышенного внимания. В то же время она является особенно важной для поддержания баз данных, а также создания единого стандартизованного языка для обмена медицинскими текстами по всему миру. Авторы статьи «Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes» (2020) ставят своей целью решить эту задачу посредством алгоритмов машинного об…

Зачастую большинство RL-алгоритмов обучается в специально созданной для них виртуальной среде. Однако, при таком подходе возникают немалые сложности при попытке использования этих же самых агентов в реальном мире. Данная проблема носит название The Reality Gap. В ходе семинара будет небольшое введение в рассматриваемую область, а также проведен обзор некоторых ключевых работ в данной тематике. Помимо этого будет затронута тема создания беспилотной системы, способной ездить в реальном мире, но обученной целиком в симуляторе. Рассматриваемые статьи: * Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World * Using Simulation and Domain Adaptation to Improv…

Object Detection является одной из задач компьютерного зрения. На данный момент известно много моделей достаточно точно решающих данную задачу. Однако большинство из них не оптимальны с точки зрения используемых ресурсов и времени, что зачастую препятствует их применению. На семинаре будет рассмотрена современная модель детектирования объектов EfficientDet. Данная модель интересна тем, что в ней была использована weighted bi-directional feature pyramid network (BiFPN), которая позволяет эффективно обрабатывать карты признаков разного масштаба. Также рассмотрим Compound Scaling – способ масштабирования всех составляющих модели с помощью увеличения глубины, ширины и разрешения нейросетей. Бол…

*не записались первые несколько предложений доклада, надеемся, это не сильно помешает общему восприятию. Семантическая сегментация применяется к медицинским визуализациям (например МРТ) для определения точного местоположения и формы структур тела и имеет важное значение для обнаружения аномалий и их успешного лечения. С задачей семантической сегментации достаточно хорошо справляются глубокие сверточные сети, однако для их обучения необходима большая база размеченных данных. Это ограничение особенно важно при сегментации медицинских изображений, для которых разметка требует много времени. Авторы статьи “Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning”…

Зачастую в задаче генерации музыки используются лишь различные модификации сверточных или рекуррентных сетей. Однако зачастую музыка при таком подходе получается довольно однообразной и может звучать ненатурально. Может ли помочь RL решить эту проблему? Как вообще сформулировать задачу генерации музыки, чтобы использовать в ней RL? В ходе семинара будут рассмотрены статьи Bach2Bach: Generating Music Using A Deep Reinforcement Learning Approach, RL-Duet: Online Music Accompaniment Generation using Deep Reinforcement Learning. Также будут рассмотрены статьи, где обучение агента RL проходит в качестве дополнительного шага после обучения обычной рекуррентной сети. Такой подход может пригодиться…

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…

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https://www.lesswrong.com/ For discussion and updates

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- Twitch: https://www.twitch.tv/primerjustin Made possible by support through Patreon:

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Store: https://store.dftba.com/collections/primer More on voting theory:

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- The best single resource I found: https://www.lesswrong.com/posts/D6trAzh6DApKPhbv4/a-voting-theory-primer-for-rationalists Organizations that advocate for voting reform:

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Avi Loeb is an astrophysicist at Harvard.

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(14:23) – Consciousness(19:01) – Sending digital copies of humans to space(23:38) – Oumuamua(45:42) – Alien space junk(49:41) – What do aliens look like?

(1:06:58) – Drake equation(1:08:00) – Industrial polution from aliens(1:19:52) – UFO sightings(1:27:48) – How long will human civilization last?

(1:30:28) – Radio signal from Proxima Centauri(1:33:49) – Breakthrough Starshot project(1:36:49) – Space race(1:42:00) – Human space exploration(1:47:15) – Social media is a threat to society(1:52:04) – Are humans ready for discovering an alien civilization?

Dmitry Korkin is a professor of bioinformatics and computational biology at WPI.

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(1:10:35) – AlphaFold 2(1:32:13) – Will AI revolutionize art and music?

(1:39:12) – Multi-protein folding(1:43:39) – Will AlphaFold 2 result in a Nobel Prize?

(1:46:10) – Will AI be used to engineer deadly viruses?

Dan Gable is one of the greatest Olympic athletes and wrestling coaches of all time.

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Dan Kokotov is VP of Engineering at Rev.ai, an automatic speech recognition company.

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Diana Walsh Pasulka is a professor of philosophy and religion at UNCW and author of American Cosmic: UFOs, Religion, and Technology.

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(18:59) – Donald Hoffman(22:57) – Immanuel Kant’s Critique of Pure Reason(26:27) – Ayn Rand(33:25) – How do religions start?

(48:38) – Religion is an evolutionary advantage(53:59) – Religion used in propaganda(58:32) – What did Nietzsche mean by “God is Dead”?

(1:03:59) – American Cosmic(1:07:45) – What do aliens look like?

Charles Isbell is the Dean of the College of Computing at Georgia Tech.

Michael Littman is a computer scientist at Brown University.

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(00:00) – Introduction(07:51) – Is machine learning just statistics?

(12:14) – NeurIPS vs ICML(14:30) – Data is more important than algorithm(20:14) – The role of hardship in education(28:57) – How Charles and Michael met(33:30) – Key to success: never be satisfied(36:47) – Bell Labs(48:15) – Teaching machine learning(58:25) – Westworld and Ex Machina(1:06:24) – Simulation(1:13:14) – The college experience in the times of COVID(1:41:52) – Advice for young people(1:48:44) – …

Dmitri Dolgov is the CTO of Waymo, an autonomous vehicle company.

Please support this podcast by checking out our sponsors:– Tryolabs: https://tryolabs.com/lex– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– BetterHelp: https://betterhelp.com/lex to get 10% off– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Waymo’s Twitter: https://twitter.com/waymoWaymo’s Website: https://waymo.comPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube Clips: https://youtu…

Michael Mina is an immunologist, epidemiologist, and physician at Harvard.

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

(00:00) – Introduction(07:28) – Interacting between viruses and bacteria(11:42) – Deadlier viruses(15:13) – Will COVID-19 mutate?

(16:47) – Rapid testing(34:11) – PCR vs rapid antigen tests(43:55) – Medical industrial complex(47:47) – Lex takes COVID test(54:32) – FDA and cheap tests(57:17) – Explanation of Elon Musk’s positive COVID tests(1:04:25) – Role of testing during vaccine deployment(1:07:54) – Public health policy(1:17:34) – A weather system for viruses(1:34:26) – Can a virus kill all humans?

(1:40:05) – Engineering a dea…

Matthew W. Johnson is a professor and psychedelics researcher at Johns Hopkins.

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

(1:29:46) – What is the most dangerous drug?

(1:32:20) – Does drug prohibition work?

(1:36:14) – Cocaine and sex(1:43:15) – Risky sexual decisions(1:54:12) – Psilocybin helping people quit smoking(2:00:30) – Young Jamie(2:22:38) – Participating in a study(2:29:57) – Psychedelics and the human mind(2:37:20) – The future of psychedelics(2:40:01) – Neuralink(2:49:33) – Consciousness(3:02:15) – Panpsychism(3:12:20) – Aliens and DMT(3:22:24) – Mortality(3:32:12) – Meaning of life

Michael Littman is a computer scientist at Brown University.

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(00:00) – Introduction(07:43) – Robot and Frank(10:02) – Music(13:13) – Starring in a TurboTax commercial(23:26) – Existential risks of AI(41:48) – Reinforcement learning(1:07:36) – AlphaGo and David Silver(1:17:15) – Will neural networks achieve AGI?

(1:29:42) – Bitter Lesson(1:42:32) – Does driving require a theory of mind?

(1:51:58) – Book Recommendations(1:57:20) – Meaning of life

John Clarke is a BJJ black belt and MMA coach.

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

(00:00) – Introduction(08:25) – The great American road trip(25:54) – Martial arts and philosophy(28:55) – Real vs fake success on Instagram(39:40) – The brutal honesty of Mike Tyson(44:26) – Breaking your opponent in wrestling(52:32) – Genghis Khan(1:03:38) – It’s okay to change your mind(1:08:15) – Why do politicians become inauthentic(1:14:52) – Greatness requires sacrifice(1:17:36) – Whiplash(1:25:44) – Relationships(1:31:21) – Greatest fighters of all time(1:39:02) – Greatest fight of all time(1:53:25) – Khabib Nurmagomedov(1:55:13) – Can Conor McGregor…

Manolis Kellis is a computational biologist at MIT.

Please support this podcast by checking out our sponsors:– Grammarly: https://grammarly.com/lex to get 20% off premium– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Manolis Website: http://web.mit.edu/manoli/Manolis Twitter: https://twitter.com/manoliskellisManolis YouTube: https://www.youtube.com/channel/UCkKlJ5LHrE3C7fgbnPA5DGAPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episo…

Erik Brynjolfsson is an economist at Stanford.

Please support this podcast by checking out our sponsors:– Vincero: https://vincerowatches.com/lex to get up to 25% off + free shipping– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Cash App: https://cash.app/ and use code LexPodcast to get $10EPISODE LINKS:Erik’s Twitter: https://twitter.com/erikbrynErik’s Website: https://www.brynjolfsson.com/The Second Machine Age (book): https://amzn.to/33f1Pk2Machine, Platform, Crowd (book): https://amzn.to/3miJZ76PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podc…

Dynamic random-access memory – or DRAM – is the most popular form of volatile computer memory in the world but it’s particularly susceptible to Rowhammer, an adversarial attack that can cause data loss and security exploits in everything from smart phones to the cloud.

Today, Dr. Stefan Saroiu, a Senior Principal Researcher in MSR’s Mobility and Networking group, explains why DRAM remains vulnerable to Rowhammer attacks today, even after several years of mitigation efforts, and then tells us how a new approach involving bespoke extensibility mechanisms for DRAM might finally hammer Rowhammer in the fight to keep data safe and secure.

Many computer science researchers set their sights on building general AI technologies that could impact hundreds of millions – or even billions – of people.

But Dr. Danielle Bragg, a senior researcher at MSR’s New England lab, has a slightly smaller and more specific population in mind: the some seventy million people worldwide who use sign languages as their primary means of communication.

Today, Dr. Bragg gives us an insightful overview of the field and talks about the unique challenges and opportunities of building systems that expand access to information in line with the needs and desires of the deaf and signing community.

https://www.microsoft.com/research

MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world.

One of those researchers is Dr. Akshay Krishnamurthy and today, he explains how his work on feedback-driven data collection and provably efficient reinforcement learning algorithms is helping to move the RL needle in the real-world direction.

https://www.microsoft.com/research

Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI.

Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.

This episode originally aired in August, 2018.

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft.

But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got…

Dr. Devon Hjelm is a senior researcher at the Microsoft Research lab in Montreal, and today, he joins me to dive deep into his research on Deep InfoMax, a novel self-supervised learning approach to training AI models – and getting good representations – without human annotation.

He also tells us how an interest in neural networks, first human and then machine, led to an inspiring career in deep learning research.

https://www.microsoft.com/research

This episode originally aired in June, 2019You may not know who Dr. Andrew Fitzgibbon is, but if you’ve watched a TV show or movie in the last two decades, you’ve probably seen some of his work.

An expert in 3D computer vision and graphics, and head of the new All Data AI group at Microsoft Research Cambridge, Dr. Fitzgibbon was instrumental in the development of Boujou, an Emmy Award-winning 3D camera tracker that lets filmmakers place virtual props, like the floating candles in Hogwarts School for Witchcraft and Wizardry, into live-action footage.

But that was just his warm-up act.

On today’s podcast, Dr. Fitzgibbon tells us what he’s been working on since the Emmys in 2002, including bod…

This episode originally aired in April, 2018Emotions are fundamental to human interaction, but in a world where humans are increasingly interacting with AI systems, Dr. Mary Czerwinski, Principal Researcher and Research Manager of the Visualization and Interaction for Business and Entertainment group at Microsoft Research, believes emotions may be fundamental to our interactions with machines as well.

And through her team’s work in affective computing, the quest to bring Artificial Emotional Intelligence – or AEI – to our computers may be closer than we think.

Today, Dr. Czerwinski tells us how a cognitive psychologist found her way into the research division of the world’s largest software…

This episode originally aired in April, 2019.

We hear a lot these days about “AI for good” and the efforts of many companies to harness the power of artificial intelligence to solve some of our biggest environmental challenges.

It’s rare, however, that you find a company willing to bring its environmental bona fides all the way to the C Suite.

Well, meet Dr. Lucas Joppa.

A former environmental and computer science researcher at MSR who was tapped in 2017 to become the company’s first Chief Environmental Scientist, Dr. Joppa is now the Chief Environmental Officer at Microsoft, another first, and is responsible for managing the company’s overall environmental sustainability efforts from opera…

This episode originally aired in December, 2017On today’s episode, neuroscientist and virtual reality researcher, Dr. Mar Gonzalez Franco, talks about her work in VR, explains how avatars can help increase our empathy and reduce our biases via role play, and addresses the misconceptions that exist between the immersive experiences of virtual reality and psychedelic drugs.

Forty years ago, database research was an “exotic” field and, because of its business data processing reputation, was not considered intellectually interesting in academic circles.

But that didn’t deter Dr. Philip Bernstein, now a Distinguished Scientist in MSR’s Data Management, Exploration and Mining group, and a pioneer in the field.

Today, Dr. Bernstein talks about his pioneering work in databases over the years and tells us all about Project Orleans, a distributed systems programming framework that makes life easier for programmers who aren’t distributed systems experts.

He also talks about the future of database systems in a cloud scale world, and reveals where he finds his research s…

Brad Smith is the President of Microsoft and leads a team of more than 1400 employees in 56 countries.

He plays a key role in spearheading the company’s work on critical issues involving the intersection of technology and society.

In his spare time, he’s also an author!

He also gave us a peek inside the life of a person the New York Times has described a “de facto ambassador for the technology industry at large” – himself!

https://www.microsoft.com/research

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Consecutive Votes in PaxosEil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos.

Adrián Martín joins us to discuss Convolutional Neural Networks Deceived by Visual Illusions.

Earthquake Detection with Crowd-Sourced DataOmkar Ranadive and Suzan van der Lee join us to discuss the recent paper Applying Machine Learning to Crowd-sourced Data from Earthquake Detective.

Byzantine Fault Tolerant ConsensusByzantine fault tolerance (BFT) is a desirable property in a distributed computing environment.

BFT means the system can survive the loss of nodes and nodes becoming unreliable.

There are many different protocols for achieving BFT, though not all options can scale to large network sizes.

Ted Yin joins us to explain BFT, survey the wide variety of protocols, and share details about HotStuff.

Alpha FoldKyle shared some initial reactions to the announcement about Alpha Fold 2’s celebrated performance in the CASP14 prediction.

By many accounts, this exciting result means protein folding is now a solved problem.

Arrow’s Impossibility TheoremAbove all everyone wants voting to be fair.

Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system.

For example, unanimity - if everyone picks candidate A, then A should win!

Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria.

This episode is a discussion about the structure of the proof and some of it’s implications.

Face Mask Sentiment AnalysisAs the COVID-19 parndemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask wearing via Twitter.

What does this data tell us about public opinion?

How does it vary by demographic?

Meil Yeung, Jonathan Lai, and Jiebo Luo join us this week to discuss their recent paper.

Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.

Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper “On the Robustness of Winners: Counting Briberies in Elections.” Links Mentioned: https://www.akt.tu-berlin.de/menue/team/boehmer_niclas/ Works Mentioned: “On the Robustness of Winners: Counting Briberies in Elections.” by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier Thanks to our sponsors: Springboard School of Data: Springboard is a comprehensive end-to-end online data career program. Create a portfolio of projects to spring your career into action. Learn more about how you can be one of twenty $…

Clement Fung joins us to discuss sybil attacks on federated learning.

Differential Privacy at the US CensusSimson Garfinkel joins us to discuss using differential privacy at the US Census Bureau.

Some of the discussion resolves around the topics in the paper Randomness Concerns When Deploying Differential Privacy.

Heidi Howard joins us to discuss distributed consensus with Paxos.

ACID ComplianceLinhda joins us to discuss the topic of ACID Compliance.

The National Popular Vote Interstate CompactPatrick Rosenstiel joins us to discuss the The National Popular Vote.

Defending the p-valueYudi Pawitan joins us to discuss his paper Defending the P-value.

All good things must come to an end, including this podcast.

This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!

), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week.

Thanks, best wishes, and good night!

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be…

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.Relevant Links:Stanford Open Policing ProjectProject ZeroTodd’s LinkedIn PageTodd’s email: hendricks.ta@gmail.com

This is a re-release of an episode that originally ran in October 2019.If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this…

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another.

Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis.

The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

Relevant links:

This is a re-release of an episode that first ran on January 29, 2017.This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal …

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fai…

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

This is a re-release of an episode that originally aired on April 1, 2018If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net.

This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Relevant links:

This is a re-release of an episode that was originally released on February 26, 2017.

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group?

The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Relevant links:

The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal information. That problem has motivated the study of differential privacy, a set of techniques and definitions for keeping personal information private when datasets are released or used for study. Differential privacy is getting a big boost this year, as it’s being implemented across the 2020 US Census as a way of protecting the privacy of census respondents while still opening up the dataset for research and policy use. …

What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning?

Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case.

This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems.

It’s not a free lunch, but for those (like us!)

who love crossover topics, causal trees are a smart approach from one field hopping the fence to another.

You may not realize it consciously, but beautiful visualizations have rules.

The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues.

It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code.

This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.

Relevant links:

It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaussian processes introduce a nonparameteric option where you can fit over all the possible types of functions, using the data points in your datasets as constraints on the results that you get (the idea being that, no matter what the “true” underlying function is, it produced the data points you’re trying to fit). What this means is a very flexible, but depending on your parameters not-too-flexible, way to fit complex datasets.The math underlying GPs ge…

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Erica Greene joins us to discuss her work as a machine learning manager at Etsy, how they tackle problem-solving, how they implement ML scaling, and more.

In this episode you will learn:• Erica’s role at Etsy and probl…

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December 31, 2020 podcastLet’s finish this year with an amazing episode about scaling ML with clusters and GPUs.

Kind of as a continuation of Episode 112 I have a terrific conversation with Aaron Richter from Saturn Cloud about, well, making ML faster and scaling it to massive infrastructure.

Aaron can be reached on his website https://rikturr.com and Twitter @rikturrOur SponsorSaturn Cloud is a data science and machine learning platform for scalable Python analytics.

Users can jump into cloud-based Jupyter and Dask to scale Python for big data using the libraries they know and love, while leveraging Docker and Kubernetes so that work is reproducible, shareable, and ready for production.

Tr…

December 19, 2020 podcastWhat is data ethics?

In this episode I have an interesting chat with Denny Wong from FaqBot and Muna.

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.

December 8, 2020 podcastOur LinksCome join me in our Discord channel speaking about all things data science.

Subscribe to the official Newsletter and never miss an episodeFollow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorsProtonMail offers a simple and trusted solution to protect your internet connection and access blocked or restricted websites.

All of ProtonMail and ProtonVPN’s apps are open source and have been inspected by cybersecurity experts, and Proton is based in Switzerland, home to some of the world’s strongest privacy lawsoffers a simple and trusted solution to protect your internet connection and access blocked or restricted websites.

All o…

December 4, 2020 podcastIn this episode Adam Leon Smith, CTO of DragonFly and expert in data regulations explains some of the consequences of Schrems II and data transfers from EU to US.

For very interesting references and a practical example, subscribe to our Newsletter

December 1, 2020 podcastOur LinksCome join me in our Discord channel speaking about all things data science.

Subscribe to the official Newsletter and never miss an episodeFollow me on Twitch during my live coding sessions usually in Rust and PythonOur Sponsors

November 24, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonSubscribe to the official Newsletter and never miss an episodeOur Sponsors

November 17, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur 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.

ReferencesDataset distillation (official paper)GitHub repo

November 11, 2020 podcastCome join me in our Discord channel speaking about all things data science.

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November 7, 2020 podcastCome join me in our Discord channel speaking about all things data science.

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November 3, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorsProtonMail is a secure and private email provider that protects yourmessages with end-to-end encryption and zero-access encryption so that besides you, noone can access them.

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Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logis…

October 30, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur SponsorsThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.comApps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

A…

October 23, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by monday.comThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com

October 18, 2020 podcastLet’s talk about federated learning.

Why is it important?

Why large organizations are not ready yet?

Come join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

October 12, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comMonday.com bring teams together so you can plan, manage and track everything your team is working on in one centralized placeThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

🎙 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 😉

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