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By definition, a classification model fractures its input space into a number of contiguous regions with different classes.

With a one- or two-dimensional input, these regions are easy to visualize; for example, (this)[https://i.imgur.com/cwWvwPe.png] figure shows the class predictions for a neural network trained on 2D toy data.

As far as this plot shows, there are five classification regions in total, the permeating orange one and the four distinct blue ones.

I've been wondering recently about roughly how many classification cells you'd find in a real neural network, with high-d input, many layers, and perhaps 10 to 100 classes.

Even if not, I'd be curious to hear your guesses, even if th…

LAMA (https://lamaai.io) is back again with couple of updates for you all.

You can find the video here, but as for the key highlights:This week, LAMA is hosting a paper presentation (paper presentations is the title when someone from our wider research group presents a paper they have not authored).

Dominika will be presenting Facebook AI's recent paper: Transformer is All You Need: Multimodal Multitask Learning with a Unified Transformer.

Dominika is a second year PhD student at Imperial College studying privacy preserving NLP.

His full talk can be found here, but as a summary:

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Hello Everyone,We (IEEE Soft Robotics Podcast) are going to have Fadel Adib, the founder director of signal kinetics group at MIT, and his work on seeing through walls, was named as one of the 50 ways MIT has transformed computer science over the past 50 years, if you have any questions for Fadel, you can send them here: https://docs.google.com/forms/d/e/1FAIpQLSfipx5418Ti4lZjNj9Pgc_kIb3Rm8YH295PoDkx3vO6feaYzQ/viewform?vc=0&c=0&w=1&flr=0&gxids=7628

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our thesis is about detecting whether a mosquito larva is aedes aegypti or non aedes aegypti and then count it real time using a camera, then we compare it to the actual count.

so far it has been a success, the system is able to differentiate aedes to non aedes larvae and we've had great results.

our only problem now is we don't have any idea on what type of statistical testing to use.

do you guys have any idea on what test to use?

a simple accuracy test is not accepted.

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Rather, I was pondering the fabulous rollercoaster of a ride of $GME as a timely example of something to apply linear regression to; one can apply a linear regression model to a given stock to predict its price in the future.

Linear regression just means that you are going to do something using a linear collection of parameters.

Linear Regression | Source WikipediaWe have our linear regression, which is most commonly used to describe a straight line like the picture above — but that would also be true for logistic and polynomial.

I doubt it can be called a linear regression anymore, but it is certainly a regression.

Evaluation of regression modelsNow that we have built a regression model, w…

Hyperparameters and scikit-learn Tunning MethodsIn a machine learning project, tuning the hyperparameters is one of the most critical steps.

Since the ML model cannot learn the hyperparameters from the data, it is our responsibility to tune them.

Random search (RandomizedSearchCV and HalvingRandomSearchCV)These methods also require us to set up a grid of hyperparameters.

These methods are possible because those models can fit data for a range of some hyperparameters' values almost as efficiently as fitting the estimator for a single value of the hyperparameters.

Since some models have quite a few hyperparameters, the grid search methods of scikit-learn will get very expensive pretty fast.

The tweet_id and text (tweet itself) is pushed to the Kinesis data stream for further processing.

Kinesis data stream is consumed and for each tweet, sentiment analysis is performed using AWS Comprehend ML as a service.

EMR loads staging and sentiment data from the buckets and merges them using tweet_id .

System ArchitectureThe project runs on AWS exposing some AWS services:S3: For storage of staging data, sentiment data, tweet transactional data, aggregated data EC2: To run ingest.py and consumer.py .

It is heavily dependent on the third-party API (API which is not a part of this system).

Photo by Leap Design on UnsplashJupyter Notebook & Spark on KubernetesJupyter notebook is a well-known web tool for running live code.

In this tutorial, we will bring up a Jupyter notebook in Kubernetes and run a Spark application in client mode.

Jupyter notebook image — used for Jupyter notebook and spark driver.

The YAML below contains all the required Kubernetes resources that we need for running Jupyter and Spark.

We will import this notebook from the application notebook in a second.

How to Import CSV Data into Quickbooks Using PythonIf you’ve dealt with importing data into Quickbooks Online, you know it’s a tedious process.

In this article, I’ll show you how to leverage hotglue’s target-quickbooks to import CSV data into Quickbooks.

Step 1: Format your SpreadsheetFirst, we have to put our Journal Entries spreadsheet in a format that the target-quickbooks package can understand.

target-quickbooks --config config.jsonIf any errors occur while sending the Journal Entries you will see the errors directly in your console.

A successful import should look something like this:target-quickbooks - INFO - Converting MAR21 REV_REC (2)...target-quickbooks - INFO - Loaded 1 journal …

With the spirit of learning by explaining, I decided to write a blog to explain the fixed effect regression model and its implementation in Python.

This blog will incorporate three parts:What is the fixed-effect model, and why we want to use it?

P(observing Y| what if I had not done X)So, a causal effect is a difference in outcome if we do a certain thing V.S.

You may also think, why can’t I use the estimator/coefficient of my variable of interest directly after training my model.

These variables are important in the sense that they are both correlated to our variable of interest (more likely to see the new feature) and correlated to our outcome variable (spend more).

Get Transparent about Your AI Ethics MethodologyPhoto by Anh Vy on UnsplashSo you’ve heard about AI ethics and its importance in building AI systems that are aligned with societal values.

Get ahead in the competition for limited AI talentAs outlined in multiple iterations of the Global AI Talent Report from ElementAI (now part of ServiceNow), AI talent continues to skew towards only some parts of the world.

In fact, my forthcoming book “Actionable AI Ethics” is helping to bridge that gap for AI practitioners!

As we saw from the discussion above, going transparent with your AI ethics methodology has many benefits:Get ahead in the competition for limited AI talentShowcase maturityImprove cust…

If you are an Apple user, you probably know about Apple Health.

In this article, we are going to download and explore our own health records.

From a data science standpoint, these comprehensive datasets constitute a golden opportunity for research and increasing our self-knowledge, that is, understanding better how we live.

If you are studying data science, there is another advantage: this is actual data from you.

Conversely, these health and lifelogging data sources can provide interesting, motivational and actionable examples.

In 2016, I was at the helm of a data team that was rapidly scaling.

Then, to make matters worse, our oldest data team member, someone who’d been with us for two years, told me that he wanted to quit.

That incident marked the start of the Assembly Line Project: an effort to make our data team as agile and resilient as possible.

In this article, I’ll share the principles and framework we use to organize our own data team, democratize our data, and make documentation a part of our daily workflow.

STEP 2: Build better behavior through measurable goalsAsk yourself, what does good documentation actually mean?

Context Theory II: Semantic FramesIn the previous articles of context theory series, first we discovered some linguistic concepts related to context.

As understood from the name, Semantic Frames are units of dialogue such that they capture all the necessary information to resolve the context.

Semantic Frame concept captures the concepts of slot filling, coreference resolution, context dependent intent classification, resolving error paths and classifying/controlling domain switch as well.

User: yes, mobile slotConfirmLet’s see the semantic frames on the action:Keeping the FlowFirst aim of the semantic framing is keeping the flow.

Our context encoder generates the domain jump probability wit…

MEASURING MARKETING CAMPAIGN SUCCESSCentral to the entire discipline of data science is the concept of translating business questions into measurable outcomes.

This is to say, of all the people who came into contact with your marketing campaign, and how many of those buying the product?

DAILY MARKETING REACH BY CHANNELA key aspect of marketing campaigns is to determine how many users are seeing the marketing assets each day.

DAILY HOUSE ADS CONVERSIONNow that you have confirmed that house ads conversion has been down since January 11.

Taken together, we focus on both issues and have shown how can measure the effectiveness of a marketing campaign.

OpinionThe Most Important Programming LessonUnsplash: Brett JordanIn the fall of 2012, I walked into my graduate advisor’s office and asked her which computer science class she recommended for me to enroll in.

I enrolled in an easier computer science course, Introduction to Computer Programming via the Web.

That feeling of constant anxiety and stress from my previous computer science course returned in full fashion.

Looking back, it is not hyperbole to say this was the most important lesson I have ever learned in computer science and technology.

The Most Important Debugging AdviceWhen it came to learning computer science and programming, I wish my professors emphasized the importance of lea…

Top-10 Research Papers in AIEach year scientists from around the world publish thousands of research papers in AI but only a few of them reach wide audiences and make a global impact in the world.

Below are the top-10 most impactful research papers published in top AI conferences during the last 5 years.

A simple but effective graph neural network that performs extremely well on the semi-supervised node classification task.

The presence of batch norm is one of the reasons deep neural networks achieve state-of-the-art results these days.

Faster convergence of Adam in deep learning neural networks.

Because many different combinations of velocity and angle result in a “score” from a singular distance, we have many options.

This means we must find the trajectory that has the slowest ball velocity.

In our script, we base the calculation of the trajectory on the motor speed itself, meaning the implementation of this optimization is very simple.

We used a simple for loop to run many paths, plot them, and output the motor speed and angle for each path.

To adjust our algorithm to record and output paths at which the ball scored at the peak of its trajectory, we could do one of many things.

The Realities of Socially Conscious Machine LearningPhoto by Jen Theodore on UnsplashWhen Dr. Latanya Sweeney Ph.D. first arrived at Harvard as a visiting professor, she had already published groundbreaking research in data anonymization and privacy.

An algorithm seemingly decided that Dr. Sweeney’s first name — Latanya — was more likely given to a person of color.

Making an informed decision to defend against discriminatory behaviors intersects the law, economics, personal values, and organizational values.

This piece draws on algorithmic bias research, federal guidance, legal precedent, and other research to help navigate this dilemma.

It aims to help practitioners (at all stages) develop…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dr. Timnit Gebru is one of those few.

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

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

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

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

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

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

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

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

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

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.

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…

📝 EditorialMicrosoft and Amazon have embarked on a frantic race for dominating the cloud machine learning (ML) ecosystem.

Azure Cognitive Search: A new service that enables semantic search capabilities as an API model.

Edge#70: deep dive into how LinkedIn uses typed features to accelerate machine learning experimentation at scale.

They claim that their platform is the first ML platform for data scientists that focuses on the feature engineering and feature serving experience.

Their mission is to become the open-source standard for data integration and data movement, and to commoditize it.

💡 Natural Language Understanding (NLU)In the AI context, Natural Language Processing (NLP) is the overarching umbrella that encompasses several disciplines that tackle the interaction between computer systems and human natural languages.

From that perspective, NLP includes several sub-disciplines such as discourse analysis, relationship extraction, natural language understanding (NLU) and a few other language analysis areas.

Natural Language Understanding (NLU) is a subset of NLP that focuses on reading comprehension and semantic analysis.

Closed-domain question-answering models focus on answering a limited set of questions about a specific topic or domain.

Also in Edge#22: the research beh…

The recent advent of machine learning (ML) workloads and the innovations in ML-hardware have brought us back to the era of tightly-coupled dependencies between ML models and hardware infrastructures – in this case GPUs.

The Many Challenges of Hardware Virtualization in ML WorkloadsThe dependencies between ML workloads and ML-hardware are very real.

In my opinion, this problem has three main dimensions:The increasing specialization of ML hardware.

The assignment and distribution of hardware resources in ML workloads.

Run:AI offers a scalable, flexible and extensible platform that can abstract the complexities of hardware topologies from ML workloads.

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 dissect Neural Architecture Search (NAS) in the context of automated machine learning (AutoML);we explain Microsoft’s Project Petridish, a new type of NAS algorithm that can produce neural networks for a given problem in an efficient way;

However, correlating the performance of ML models with the composition of training and test datasets is far from trivial.

Small inconsistencies and edge cases in datasets regularly alter the performance of ML models.

Most ML interpretability stacks are not great at sophisticated data exploration and most data labeling and exploration platforms lack ML interpretability techniques.

These two spaces are likely to collide in the near future, producing a new generation of platforms for the continuous improvement of ML models.

In modern ML pipelines, continuous dataset improvements and model interpretability should evolve together to enable better model performance.

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.

Mike Del Balso (MDB): I am the co-founder/CEO of Tecton, an enterprise feature store.

Feature stores enable this simplicity by introducing a new abstraction, the “feature view”.

Finally, feature stores orchestrate end-to-end feature dataflows on your infrastructure, eliminating engineering work on a per-feature level.

A feature store integrates with your data infrastructure to:Run data pipelines that transform raw data into feature values;Store and manage the feature data itself;Serve feature data consistently for training and inference purposes.

Data teams have spent the past few years centralizing their analytic data into data warehouses or data lakes.

In this issue:we continue to explore Blackbox Hyperparameter Optimization.

Let’s look at Bayesian Methods;we learn how Amazon uses AutoML for the entire lifecycle of ML models;

It’s been decades since chip manufacturers have paid so much attention to advancements in software architectures in order to design better specialized hardware.

And this race seems to be getting more diverse and specialized.

AI platform vendors like Google and Microsoft will continue making more inroads into AI-hardware, while chip vendors like Nvidia and Intel will deliver better AI software accelerators.

Its proprietary AI software platform works inside financial institutions’ software, analyzing customers’ financial data and behavior in real-time, delivering tips and suggestions to improve their longer-term financial health.

AI solutions startup Peak.AI raised a $21 million Series B roun…

💡 Emerging Machine Learning MethodsTraditional machine learning theory teaches us that the universe is divided into two forms of learning: supervised and unsupervised.

On the other end, the promise of unsupervised machine learning methods seems a bit distant.

As a form of representation learning, self-supervised learning is able to build knowledge without requiring large amounts of labeled data.

In Edge#26 we explained the concept of self-supervised learning; overviewed the self-supervised method for image classification proposed by Facebook; and explore Google’s SimCLR framework for advancing self-supervised learning.

In Edge#27: we presented the concept of contrastive learning; and explor…

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.

In this issue:we explain the concept of Hyperparameter Optimization: Grid Search vs. Random Search;we discuss how DeepMind uses AutoML to train Waymo’s self-driving cars;

From those areas, the most prominent example could be language pre-trained models, such as OpenAI’s GPT-3 or Google’s Switch Transformer.

However, it is undeniable that the capabilities of language pre-trained models can get out of control very quickly.

While today large language pre-trained models are under the control of somewhat reputable companies like OpenAI, Microsoft and Google, it is only a matter of months before those capabilities are recreated by other entities.

Widespread adoption of large language pre-trained models can result in chaos.

Language pre-trained models are amazing, but they are also going to push the ethical boundaries of the current generation of AI companies.

💡 Security and Privacy in ML ModelsIn this series of TheSequence Edge, we’ve covered different topics related to security in machine learning models.

Security and privacy are the aspects of machine learning solutions that are often ignored until they become a problem.

However, it is important to realize that, very often, introducing privacy methods creates friction in the learning process of machine learning models.

Those techniques require very unique architectures in order to enforce privacy without affecting the performance of the target machine learning model.

Edge#31: the concept of differential privacy; Apple’s research about differential privacy at scale, the TensorFlow Privacy frame…

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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…

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In this tutorial, you will discover the genetic algorithm optimization algorithm.

Tutorial OverviewThis tutorial is divided into four parts; they are:Genetic Algorithm Genetic Algorithm From Scratch Genetic Algorithm for OneMax Genetic Algorithm for Continuous Function OptimizationGenetic AlgorithmThe Genetic Algorithm is a stochastic global search optimization algorithm.

Now that we are familiar with the simple genetic algorithm procedure, let’s look at how we might implement it from scratch.

Genetic Algorithm From ScratchIn this section, we will develop an implementation of the genetic algorithm.

Genetic Algorithm for OneMaxIn this section, we will apply the genetic algorithm to a binary …

How to use the Differential Evolution optimization algorithm API in python.

Tutorial OverviewThis tutorial is divided into three parts; they are:Differential Evolution Differential Evolution API Differential Evolution Worked ExampleDifferential EvolutionDifferential Evolution, or DE for short, is a stochastic global search optimization algorithm.

Differential Evolution APIThe Differential Evolution global optimization algorithm is available in Python via the differential_evolution() SciPy function.

Differential Evolution Worked ExampleIn this section, we will look at an example of using the differential evolution algorithm on a challenging objective function.

How to use the Differential Evo…

# ackley multimodal function from numpy import arange from numpy import exp from numpy import sqrt from numpy import cos from numpy import e from numpy import pi from numpy import meshgrid from matplotlib import pyplot from mpl_toolkits.mplot3d import Axes3D # objective function def objective(x, y): return -20.0 * exp(-0.2 * sqrt(0.5 * (x**2 + y**2))) - exp(0.5 * (cos(2 * pi * x) + cos(2 * pi * y))) + e + 20 # define range for input r_min, r_max = -5.0, 5.0 # sample input range uniformly at 0.1 increments xaxis = arange(r_min, r_max, 0.1) yaxis = arange(r_min, r_max, 0.1) # create a mesh from the axis x, y = meshgrid(xaxis, yaxis) # compute targets results = objective(x, y) # create a surfa…

In this tutorial, you will discover how to perform a sensitivity analysis of dataset size vs. model performance.

Sensitivity analysis provides an approach to quantifying the relationship between model performance and dataset size for a given model and prediction problem.

# evaluate a model def evaluate_model(X, y): # define model evaluation procedure cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1) # define model model = DecisionTreeClassifier() # evaluate model scores = cross_val_score(model, X, y, scoring='accuracy', cv=cv, n_jobs=-1) # return summary stats return [scores.mean(), scores.std()] 1 2 3 4 5 6 7 8 9 10 # evaluate a model def evaluate_model ( X , y ) : # d…

There are no standard techniques for calculating a prediction interval for deep learning neural networks on regression predictive modeling problems.

In this tutorial, you will discover how to calculate a prediction interval for deep learning neural networks.

Calculating prediction intervals for nonlinear regression algorithms like neural networks is challenging compared to linear methods like linear regression where the prediction interval calculation is trivial.

... # make predictions with prediction interval newX = asarray([X_test[0, :]]) lower, mean, upper = predict_with_pi(ensemble, newX) print('Point prediction: %.3f' % mean) print('95%% prediction interval: [%.3f, %.3f]' % (lower, upp…

How to implement the simulated annealing algorithm from scratch in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:Simulated Annealing Implement Simulated Annealing Simulated Annealing Worked ExampleSimulated AnnealingSimulated Annealing is a stochastic global search optimization algorithm.

If the new point is better than the current point, then the current point is replaced with the new point.

Now that we are familiar with the simulated annealing algorithm, let’s look at how to implement it from scratch.

How to implement the simulated annealing algorithm from scratch in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:What Is the No Free Lunch Theorem?

… known as the “no free lunch” theorem, sets a limit on how good a learner can be.

Now that we have reviewed the implications of the no free lunch theorem for optimization, let’s review the implications for machine learning.

The no free lunch theorem for optimization and search is applied to machine learning, specifically supervised learning, which underlies classification and regression predictive modeling tasks.

Specifically, you learned:The no free lunch theorem suggests the performance of all optimization algorithms are identical, under some specific constraints.

Examples of stochastic optimization algorithms like simulated annealing and genetic algorithms.

Stochastic Optimization Algorithms Practical Considerations for Stochastic OptimizationWhat Is Stochastic Optimization?

Now that we have an idea of what stochastic optimization is, let’s look at some examples of stochastic optimization algorithms.

Some examples of stochastic optimization algorithms include:Iterated Local SearchStochastic Hill ClimbingStochastic Gradient DescentTabu SearchGreedy Randomized Adaptive Search ProcedureSome examples of stochastic optimization algorithms that are inspired by biological or physical processes include:Simulated AnnealingEvolution StrategiesGenetic Algorith…

shape [ 1 ] # define model model = Sequential ( ) model .

shape [ 1 ] # define model model = Sequential ( ) model .

# define model model = Sequential ( ) model .

# define model model = Sequential ( ) model .

shape [ 1 ] # define model model = Sequential ( ) model .

Nevertheless, it is possible to use alternate optimization algorithms to fit a regression model to a training dataset.

Tutorial OverviewThis tutorial is divided into three parts; they are:Optimize Regression Models Optimize a Linear Regression Model Optimize a Logistic Regression ModelOptimize Regression ModelsRegression models, like linear regression and logistic regression, are well-understood algorithms from the field of statistics.

Optimize a Linear Regression ModelThe linear regression model might be the simplest predictive model that learns from data.

We can tie all of this together and demonstrate our linear regression model for regression predictive modeling.

Optimize a Logistic Reg…

The local search optimization algorithms available in SciPy.

Tutorial OverviewThis tutorial is divided into three parts; they are:Optimization With SciPy Local Search With SciPy Global Search With SciPyOptimization With SciPyThe Python SciPy open-source library for scientific computing provides a suite of optimization techniques.

They are:Scalar Optimization : Optimization of a convex single variable function.

# minimize an objective function result = minimize ( objective , point )Additional information about the objective function can be provided if known, such as the bounds on the input variables, a function for computing the first derivative of the function (gradient or Jacobian matrix),…

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

MomentumMomentum is an extension to the gradient descent optimization algorithm, often referred to as gradient descent with momentum.

Gradient Descent OptimizationNext, we can apply the gradient descent algorithm to the problem.

... # define momentum momentum = 0.3 # perform the gradient descent sea…

In this tutorial, you will discover how to implement weight initialization techniques for deep learning neural networks.

Tutorial OverviewThis tutorial is divided into three parts; they are:Weight Initialization for Neural Networks Weight Initialization for Sigmoid and Tanh Xavier Weight Initialization Normalized Xavier Weight Initialization Weight Initialization for ReLU He Weight InitializationWeight Initialization for Neural NetworksWeight initialization is an important consideration in the design of a neural network model.

These modern weight initialization techniques are divided based on the type of activation function used in the nodes that are being initialized, such as “Sigmoid and …

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 .

markdown 1 , 461 2020 - 01 - 22 - mh - 2020. markdown 7 , 434 2020 - 02 - 27 - mh - 2020 - part2 .

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

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markdown 738 2020 - 01 - 22 - mh - 2020. markdown 322 2020 - 02 - 27 - mh - 2020 - part2 .

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.

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…

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

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

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

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

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

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

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

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

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

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

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

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

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

Inventing Ourselves: Responsibility and Diversity in Research.

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

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

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

Despite the imp…

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

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

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

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

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

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

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

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

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

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

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…

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

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

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

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

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

Can implicit regularization in deep learning be explained by norms?

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

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

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

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

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

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

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

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

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

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

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

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

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

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…

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

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

How the layers result in a final hidden state.

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

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

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

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

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

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

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

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.

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.

We introduce the first microarchitectural side channel attacks that leverage contention on the CPU ring interconnect.

Second, information that can be learned by an attacker through ring contention is noisy by nature and has coarse spatial granularity.

To address the first challenge, we perform a thorough reverse engineering of the sophisticated protocols that handle communication on the ring interconnect.

With this knowledge, we build a cross-core covert channel over the ring interconnect with a capacity of over 4 Mbps from a single thread, the largest to date for a cross-core channel not relying on shared memory.

To address the second challenge, we leverage the fine-grained temporal patter…

Formal verification of neural networks is critical for their safe adoption in real-world applications.

However, designing a verifier which can handle realistic networks in a precise manner remains an open and difficult challenge...

In this paper, we take a major step in addressing this challenge and present a new framework, called PRIMA, that computes precise convex approximations of arbitrary non-linear activations.

PRIMA is based on novel approximation algorithms that compute the convex hull of polytopes, leveraging concepts from computational geometry.

We evaluate the effectiveness of PRIMA on challenging neural networks with ReLU, Sigmoid, and Tanh activations.

Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited.

Using this decomposition, we prove that self-attention possesses a strong inductive bias towards "token uniformity".

Specifically, without skip connections or multi-layer perceptrons (MLPs), the output converges doubly exponentially to a rank-1 matrix.

On the other hand, skip connections and MLPs stop the output from degeneration.

Our experiments verify the identified convergence phenomena on different variants of standard transformer architectures.

Convolutional neural networks have enabled major progress in addressing pixel-level prediction tasks such as semantic segmentation, depth estimation, surface normal prediction, and so on, benefiting from their powerful capabilities in visual representation learning.

Typically, state-of-the-art models integrates attention mechanisms for improved deep feature representations...

Recently, some works have demonstrated the significance of learning and combining both spatial- and channel-wise attentions for deep feature refinement.

Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STruct…

We propose a two-stream convolutional network for audio recognition, that operates on time-frequency spectrogram inputs.

Following similar success in visual recognition, we learn Slow-Fast auditory streams with separable convolutions and multi-level lateral connections...

The Slow pathway has high channel capacity while the Fast pathway operates at a fine-grained temporal resolution.

We showcase the importance of our two-stream proposal on two diverse datasets: VGG-Sound and EPIC-KITCHENS-100, and achieve state-of-the-art results on both.

(read more)

One of the biggest bottlenecks in building accurate, high coverage neural open IE systems is the need for large labelled corpora.

The diversity of open domain corpora and the variety of natural language expressions further exacerbate this problem...

In this paper, we propose a syntactic and semantic-driven learning approach, which can learn neural open IE models without any human-labelled data by leveraging syntactic and semantic knowledge as noisier, higher-level supervisions.

Specifically, we first employ syntactic patterns as data labelling functions and pretrain a base model using the generated labels.

Then we propose a syntactic and semantic-driven reinforcement learning algorithm, whi…

These models are designed based on the assumption that past code change properties are similar to future ones...

In this work, we aim to investigate the effect of code change properties on JIT models over time.

We also study the impact of using recent data as well as all available data on the performance of JIT models.

Furthermore, we observed that the chronology of data in JIT defect prediction models can be discarded by considering all the available data.

On the other hand, the importance score of families of code change properties is found to oscillate over time.

We present the first edition of "VIPriors: Visual Inductive Priors for Data-Efficient Deep Learning" challenges.

We offer four data-impaired challenges, where models are trained from scratch, and we reduce the number of training samples to a fraction of the full set...

Furthermore, to encourage data efficient solutions, we prohibited the use of pre-trained models and other transfer learning techniques.

The majority of top ranking solutions make heavy use of data augmentation, model ensembling, and novel and efficient network architectures to achieve significant performance increases compared to the provided baselines.

(read more)

We introduce a method to determine if a certain capability helps to achieve an accurate model of given data.

We view labels as being generated from the inputs by a program composed of subroutines with different capabilities, and we posit that a subroutine is useful if and only if the minimal program that invokes it is shorter than the one that does not...

Since minimum program length is uncomputable, we instead estimate the labels' minimum description length (MDL) as a proxy, giving us a theoretically-grounded method for analyzing dataset characteristics.

We call the method Rissanen Data Analysis (RDA) after the father of MDL, and we showcase its applicability on a wide variety of settings …

One strategy for facilitating reading comprehension is to present information in a question-and-answer format.

We demo a system that integrates the tasks of question answering (QA) and question generation (QG) in order to produce Q&A items that convey the content of multi-paragraph documents... We report some experiments for QA and QG that yield improvements on both tasks, and assess how they interact to produce a list of Q&A items for a text.

The demo is accessible at qna.sdl.com.

(read more)

Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver diseases (CLD) which can progress to liver cancer.

The severity and treatment of NAFLD is determined by NAFLD Activity Scores (NAS)and liver fibrosis stage, which are usually obtained from liver biopsy...

Current methods to predict the fibrosis and NAS scores from noninvasive CT images rely heavily on either a large annotated dataset or transfer learning using pretrained networks.

However, the availability of a large annotated dataset cannot be always ensured andthere can be domain shifts when using transfer learning.

As the NAFLD causes changes in the liver texture, we also propose to use texture en…

Intensive research has been conducted on the verification and validation of deep neural networks (DNNs), aiming to understand if, and how, DNNs can be applied to safety critical applications.

However, existing verification and validation techniques are limited by their scalability, over both the size of the DNN and the size of the dataset...

In this paper, we propose a novel abstraction method which abstracts a DNN and a dataset into a Bayesian network (BN).

We make use of dimensionality reduction techniques to identify hidden features that have been learned by hidden layers of the DNN, and associate each hidden feature with a node of the BN.

We can also adapt existing structural coverage-g…

This article unveils a new relation between the Nishimori temperature parametrizing a distribution P and the Bethe free energy on random Erdos-Renyi graphs with edge weights distributed according to P. Estimating the Nishimori temperature being a task of major importance in Bayesian inference problems, as a practical corollary of this new relation, a numerical method is proposed to accurately estimate the Nishimori temperature from the eigenvalues of the Bethe Hessian matrix of the weighted graph.

The algorithm, in turn, is used to propose a new spectral method for node classification in weighted (possibly sparse) graphs...

The superiority of the method over competing state-of-the-art appro…

With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed.

Currently, almost all these models assume that the layer order is fixed and kept the same across data samples... We observe that different data samples actually favor different orders of the layers.

Based on this observation, in this work, we break the assumption of the fixed layer order in the Transformer and introduce instance-wise layer reordering into the model structure.

Our Instance-wise Ordered Transformer (IOT) can model variant functions by reordered layers, which enables…

Inductive link prediction -- where entities during training and inference stages can be different -- has been shown to be promising for completing continuously evolving knowledge graphs.

However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs.

TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs.

Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for ind…

We present a multilingual end-to-end Text-To-Speech framework that maps byte inputs to spectrograms, thus allowing arbitrary input scripts.

Besides strong results on 40+ languages, the framework demonstrates capabilities to adapt to various new languages under extreme low-resource and even few-shot scenarios of merely 40s transcribed recording without the need of lexicon, extra corpus, auxiliary models, or particular linguistic expertise, while retains satisfactory intelligibility and naturalness matching rich-resource models... Exhaustive comparative studies are performed to reveal the potential of the framework for low-resource application and the impact of various factors contributory to…

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns.

Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns.

One significant challenge faced by UAD methods is how to learn effective low-dimensional image representations to detect and localise subtle abnormalities, generally consisting of small lesions.

To address this challenge, we propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (…

Multi-robot systems can benefit from reinforcement learning (RL) algorithms that learn behaviours in a small number of trials, a property known as sample efficiency.

This research thus investigates the use of learned world models to improve sample efficiency... We present a novel multi-agent model-based RL algorithm: Multi-Agent Model-Based Policy Optimization (MAMBPO), utilizing the Centralized Learning for Decentralized Execution (CLDE) framework.

MAMBPO uses a learned world model to improve sample efficiency compared to model-free Multi-Agent Soft Actor-Critic (MASAC).

We demonstrate this on two simulated multi-robot tasks, where MAMBPO achieves a similar performance to MASAC, but requir…

The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles.

This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning...

The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform.

Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes.

(read more)

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction.

However, such a mechanism suffers from one drawback: only a few frequent words with sentiment polarities are tended to be taken into consideration for final sentiment decision while abundant infrequent sentiment words are ignored by models... To deal with this issue, we propose a progressive self-supervised attention learning approach for attentional ABSA models.

In this approach, we iteratively perform sentiment prediction on all training instances, and continually learn useful attention supervision information in …

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.

An approach to address this is to automatically create more diverse training environments by randomizing all the parameters of the simulator, a process called domain randomization (DR).

This minimax adversary can be trained to minimize the performance of the first RL agent by finding and exploiting weaknesses in its policy, e.g.

Using a purely adversarial objective is not well suited to generating training environments, either.

Once the protagonist learns to solve each environment, the adversary must move on to finding a slightly harder environment that the protagonist can’t solve.

Unlike minimax or domain randomization, the PAIRED adversary creates a curriculum of increasingly longer, but …

In order to truly serve our diverse communities, we recognized language translation services would be required to offer truly equitable services to everyone.

That’s when we started working closely with Google.org, SpringML, Google Cloud, and other partners with involvement from our Mayor and City CIO.

Thanks to the greater accuracy of translation supported by Google Cloud services, we were able to leverage the expertise of a small pool of community members to evaluate translations.

We’re also actively incorporating more language translation capabilities to better serve more people.

We are excited to continue working with SpringML, Google Cloud, and other partners to improve our city and the…

Practically every company relies on credit card transactions to fuel their business and facilitate the exchange of funds. In fact, many companies, especially those with digitally focused missions, now only accept credit card payments. This market saturation has attracted a wave of bad actors looking to use the credit card momentum to their advantage. Global losses from payment fraud has tripled in the last 10 years. According to Merchant Savvy, payment fraud is expected to continue increasing and is projected to cost $40.62 billion in 2027—25% higher than in 2020. Credit card fraud was ranked the number one type of identity theft fraud. In the UK alone, 7.5p per £100 spent on cards was lost…

Established in April 2002 and listed on the Hong Kong Stock Exchange in 2005, FIH Mobile is a leader in the worldwide mobile device industry.

FIH Mobile offers vertically integrated, end-to-end design, development and manufacturing services spanning handsets, mobile and wireless communication devices, and consumer electronics products.

In the original process, FIH Mobile relied on human eyes to spot flaws in the pre-production process before components and devices moved to the next stage.

With AutoML Vision, FIH Mobile can label data efficiently to maximize model performance.

To learn more about how you can use our vision products for visual inspection and other use cases, check out Google …

With the fraud detection Dataflow pipeline in place, the next step would be to set up fraud alerts.

Setting up alert-based fraud notifications using Pub/SubWe also want to make sure we can trigger some downstream action when fraud is detected.

For example, if a transaction is predicted to be fraud with a probability of greater than 70%, then it should send a Pub/Sub message.

Alternatively, if the transaction has a very high probability of fraud, the Pub/Sub message could also automatically trigger and alert an internal anti-fraud team to flag and freeze the transaction.

Of course, with a Dataflow and Pub/Sub pipeline, this can happen within seconds, so fraud can be prevented in real-time.

Gartner has named Google a Leader in its 2021 Magic Quadrant for Cloud AI Developer Services.

This flexibility is made possible by the deep integration and cohesion across the Cloud AI developer stack.

Purpose-built AI solutions for industry use casesTwo years ago, we spoke about the advent of a new era of Deployed AI.

To learn more about how to make AI work for you, download a complimentary copy of the Gartner 2021 Magic Quadrant for Cloud AI Developer Services report.

Disclaimer: Gartner, Magic Quadrant for Cloud AI Developer Services, 24 February 2021, Van Baker, Bern Elliot, Svetlana Sicular, Anthony Mullen, Erick Brethenoux.

CashApp opted to use Google Cloud AI and machine learning (ML) solutions and NVIDIA’s graphics processing units (GPUs) to handle the immense compute demands of its applied AI efforts.

Dessa has used Google Cloud’s AI Platform services which were configured and made available by Square’s Platform Infrastructure Engineering group to Square’s internal needs.

Google Cloud AI and NVIDIA were able to deliver a roughly 66% improvement to the processing time it takes to complete a core ML processing workflow.

NVIDIA developer support, GPUs, and AI Platform on Google Cloud have also improved the speed and quality of Cash App services to customers.

Further embedding AI into Cash AppBecause Cash App h…

Even though video might seem much more bandwidth hungry than audio, modern video codecs can reach lower bitrates than some high-quality speech codecs used today.

Combining low-bitrate video and speech codecs can deliver a high-quality video call experience even in low-bandwidth networks.

Yet historically, the lower the bitrate for an audio codec, the less intelligible and more robotic the voice signal becomes.

To solve this problem, we have created Lyra, a high-quality, very low-bitrate speech codec that makes voice communication available even on the slowest networks.

Lyra OverviewThe basic architecture of the Lyra codec is quite simple.

However, integrating virtual agents or bots with enterprise systems and processes can be difficult.

Chat and voice bots or virtual agents rely on enterprise data, systems, and business functions, accessed via APIs and integration frameworks.

To successfully facilitate this process, Google Cloud offers several solutions--such as Contact Center AI, Dialogflow and Apigee API management--that are helping enterprises to launch powerful virtual agents without being bogged down by system complexity.

Olive uses Dialogflow as its natural language platform, with APIs providing the information Olive needs to serve up useful customer interactions.

So, we leveraged Google Cloud’s Dialogflow and Apigee A…

In this post, we take a look at the technology behind this process, and demonstrate how Cinematic photos can turn a single 2D photo from the past into a more immersive 3D animation.

Camera 3D model courtesy of Rick Reitano.

We want the creation to include as much of the salient regions as possible when framing the virtual camera.

Now that you know how they are created, keep an eye open for automatically created Cinematic photos that may appear in your recent memories within the Google Photos app!

AcknowledgmentsCinematic Photos is the result of a collaboration between Google Research and Google Photos teams.

Python has solidified itself as one of the top languages for data scientists looking to prep, process, and analyze data for analytics and machine learning (ML) related use cases. However, base Python libraries are not designed for large-scale transformations, creating major obstacles for data scientists seeking to deploy their code in production environments. Increasingly, ML tasks must process massive amounts of data, requiring the processing to be distributed across multiple machines. Libraries like Dask and RAPIDS help data scientists manage that distributed processing in Python. Google Cloud’s AI Platform enables data scientists to easily provision extremely powerful virtual machines wi…

However, designing NNs that can generalize well is challenging because the research community's understanding of how a neural network generalizes is currently somewhat limited: What does the appropriate neural network look like for a given problem?

Techniques like neural architecture search (NAS), use algorithms, like reinforcement learning (RL), evolutionary algorithms, and combinatorial search, to build a neural network out of a given search space.

OverviewThe Model Search system consists of multiple trainers, a search algorithm, a transfer learning algorithm and a database to store the various evaluated models.

The search algorithms implemented in Model Search are adaptive, greedy and in…

For the past six years she’s been a VP at Google working on everything from site reliability engineering to bringing public Wi-Fi to India’s railroads.

Now, she’s taking on a new project: making sure Google develops artificial intelligence responsibly and that it has a positive impact.

To do this, Marian has created and will lead a new center of expertise on responsible AI within Google Research.

I sat down (virtually) with Marian to talk about her new role and her vision for responsible AI at Google.

You can watch parts of our conversation in the video above, or read on for a few key points she discussed.

In contrast, recent advances in deep RL have enabled model-based approaches to learn accurate world models from image inputs and use them for planning.

World models can learn from fewer interactions, facilitate generalization from offline data, enable forward-looking exploration, and allow reusing knowledge across multiple tasks.

We introduce a new metric that normalizes scores by the world record and clips them to not exceed the record.

ConclusionWe show how to learn a powerful world model to achieve human-level performance on the competitive Atari benchmark and outperform the top model-free agents.

We see world models that leverage large offline datasets, long-term memory, hierarchical pl…

One of the most fun ways to learn machine learning is by building projects for yourself.

In computer science, one great example is machine learning (ML).

It’s no surprise that the question I’m most frequently asked is, “Where should I start learning machine learning?”Typically I’ll recommend resources like Google’s Machine Learning Crash Course, the book Hands-On Machine Learning, or Andrew Ng’s classic Coursera course, which cover the fundamentals from theory to practice.

But if you’re like me and your favorite way to learn is by building, consider learning ML by building software for yourself.

Making with machine learning projectsWhat you’ll build: An archive that makes your home videos s…

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

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

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

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

Unsolved problemsThere are many prob…

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

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

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

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

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

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

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

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

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

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

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

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

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.

Tens of thousands of customers, such as Pepsi, Amway, Airbus, BBC, and Progressive Insurance are using Azure AI to deliver immersive customer experiences, identify new business opportunities, and drive impact.

Semantic search has significantly advanced the quality of Bing search results, and it has been a companywide effort: top applied scientists and engineers from Bing leverage the latest technology from Microsoft Research and Microsoft Azure.

Semantic search capabilities in Azure Cognitive SearchBelow are the features enabled by semantic search in Azure Cognitive Search.

By bringing semantic search to Azure Cognitive Search, we’re taking a major step toward democratizing advanced machine learning technologies for everyone.

We believe semantic search on Azure Cognitive Search offers the best combination of search relevance, developer experience, and cloud service capabilities available on the m…

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In today’s fast-paced world, there is often a need for instantaneous, real-time responses. Companies are finding the computing needed to support this 24/7 mentality generates staggering amounts of data, often from devices out in the physical world.

Based on this problem, we describe how to generate hard examples (HEXA), a family of augmented views whose pseudo-labels are difficult to predict.

Please check out our paper, “Self-supervised Pre-training with Hard Examples Improves Visual Representations,” which elaborates on the details presented in this blog post.

Specifically, we consider two DA schemes of generating hard examples: adversarial examples and cut-mixed examples.

We propose two types of hard examples: (c) adversarial examples that add perturbations on views and (d) cut-mixed examples that cut and paste patches between views.

This idea can be generalized to incorporate more types of hard examples, such as mix-up examples.

The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold.

The people and organizations involved: Kan Ren, Weiqing Liu, Dong Zhou, Jiang Bian, and Tie-Yan Liu from Microsoft Research Asia; Yuchen Fang, Weinan Zhang, and Yong Yu from Shanghai Jiao Tong UniversityAdditional resources and related work:Spotlight: Microsoft research newsletter Microsoft Research Newsletter Stay connected to the research community at Microsoft.

3) Target discrete tokens are synthesized into target speech using an inverter.

In the paper “UWSpeech: Speech to Speech Translation for Unwritten Languages,” researchers developed UWSpeech, a translation system for …

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If the ratio of the style reward to the task reward is too high, the style reward overwhelms the task reward and navigation performance suffers.

PBRS is a simple yet powerful technique where, at each step, we subtract the previous step’s style reward from the total reward (task reward plus style reward) of the current step.

Figure 1: Potential-based reward shaping (PBRS) makes integrating the style reward into the task reward easier, preventing the style reward from degrading task performance even at high style-reward-to-task-reward ratios.

The automatic reward ratio scheduler is effective in keeping the task reward above the specified task threshold.

Figure 2a shows the task reward with di…

In the paper “Denoised Smoothing: A Provable Defense for Pretrained Classifiers,” which we presented at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), we introduce denoised smoothing.

Via the simple addition of a pretrained denoiser, we can apply randomized smoothing to make existing pretrained classifiers provably robust against adversarial examples without custom training.

Randomized smoothing: An effective provable defense for classifiersAt the core of our approach is randomized smoothing, a promising and provable adversarial defense that has been shown to scale to large networks and datasets.

With our work on denoised smoothing, we make randomized smoothing…

A speller for 100-plus languages in MicrosoftDespite these challenges, we have recently launched our large-scale multilingual spelling correction models worldwide with high precision and high recall in 100-plus languages!

Traditionally, spelling correction solutions have leveraged noisy channel theory and made great improvements in building better statistical error models and language models.

At the core, spelling correction is about building an error model and a language model.

For large-scale language family–based multilingual spelling correction, we designed a spelling correction pretraining task to enrich standard Transformer-based models.

This pretraining task proves to be a first soli…

From improving clinical decision making to better managing the COVID-19 pandemic, the benefits of artificial intelligence (AI) applied to health and medicine are undeniable. 2021 is expected to be a year where health systems make unprecedented investments in AI to improve quality, reduce costs, and create more personalized experiences for patients and health consumers alike.

Spotlight: Webinar series Microsoft research webinars Lectures from Microsoft researchers with live Q&A and on-demand viewing.

Explore moreResearch to improve the gaming experienceProject PaidiaProject Paidia is a research project in close collaboration between Microsoft Research Cambridge and Ninja Theory.

The result is nuanced situation and player-aware emergent behavior that would be challenging or prohibitive to achieve using traditional Game AI.

Incubated over a decade in Microsoft Research, Project Triton ships in major game titles like Gears of War, Sea of Thieves, and Borderlands 3.

Explore moreTrueSkillThe TrueSkill ranking system is a skill based ranking system for Xbox Live devel…

Keeping up with the rapid pace of technology innovation today requires equal advances in the pace of development of new microelectronics.

Deep learning is advancing at lightning speed, and Alexander Amini ’17 and Ava Soleimany ’16 want to make sure they have your attention as they dive deep on the math behind the algorithms and the ways that deep learning is transforming daily life.

Last year, their blockbuster course, 6.S191 (Introduction to Deep Learning) opened with a fake video welcome from former President Barack Obama.

They co-developed 6.S191’s curriculum and have taught it during MIT’s Independent Activities Period (IAP) for four of the last five years.

They also explore deep learning’s myriad applications, and how students can evaluate a model’s predictions for accuracy and bias.

A fourth project proposed the use of …

As part of the efforts in Social and Ethical Responsibilities of Computing (SERC) within the MIT Stephen A. Schwarzman College of Computing, a new case studies series examines social, ethical, and policy challenges of present-day efforts in computing with the aim of facilitating the development of responsible “habits of mind and action” for those who create and deploy computing technologies.

Understanding and incorporating broader social context is becoming ever more critical,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

Each case study is brief, but includes accompanying notes and references to facilitate more in-depth exploration of a given topic.

The SERC c…

On March 1, MIT Solve launched its 2021 Global Challenges, with over $1.5 million in prize funding available to innovators worldwide.

Finalists will be invited to attend Solve Challenge Finals on Sept. 19 in New York during U.N. General Assembly week.

The Solve community will convene at Virtual Solve at MIT on May 3-4 with 2020 Solver teams, Solve members, and partners to build partnerships and tackle global challenges in real-time.

As a marketplace for social impact innovation, Solve’s mission is to solve world challenges.

Organizations interested in joining the Solve community can learn more and apply for membership here.

Formed in 2018 and hosted by the Edgerton Center at MIT, MIT Driverless comprises 50 highly motivated engineers with diverse skill sets.

In October, a modified Dallara-15 Indy Lights race car programmed by MIT Driverless will hit the famed Indianapolis Motor Speedway at speeds of up to 120 miles per hour.

In this format, MIT Driverless and their competitors program and race a sleek electric vehicle dubbed the DEVBot 2.0.

Consider the connection between the Han Lab at MIT and MIT Driverless.

Han is a big fan of MIT Driverless, and he's been extremely helpful,” says Castillo.

In some cases, involving prostate cancer for example, the dogs had a 99 percent success rate in detecting the disease by sniffing patients’ urine samples.

Scientists have been hunting for ways of automating the amazing olfactory capabilities of the canine nose and brain, in a compact device.

These dogs can identify “cancers that don’t have any identical biomolecular signatures in common, nothing in the odorants,” Mershin says.

The miniaturized detection system, Mershin says, is actually 200 times more sensitive than a dog’s nose in terms of being able to detect and identify tiny traces of different molecules, as confirmed through controlled tests mandated by DARPA.

The research was supporte…

The importance of key words underlies a popular new tool for natural language processing (NLP) by computers: the attention mechanism.

When coded into a broader NLP algorithm, the attention mechanism homes in on key words rather than treating every word with equal importance.

So, MIT researchers have designed a combined software-hardware system, dubbed SpAtten, specialized to run the attention mechanism.

NLP models require a hefty load of computer power, thanks in part to the high memory demands of the attention mechanism.

The researchers developed a system called SpAtten to run the attention mechanism more efficiently.

So how can we better understand the problem of integrating autonomous vehicles into the transportation system?

At LIDS, Wu uses a type of machine learning called reinforcement learning to study how traffic systems behave, and how autonomous vehicles in those systems ought to behave to get the best possible outcomes.

And more importantly, reinforcement learning can shed new light toward understanding complex networked systems — which have long evaded classical control techniques.

Their advancements turned out to be a general improvement to most existing deep reinforcement learning methods.

But reinforcement learning techniques will need to be continually improved to keep up with the scale an…

“Deep Learning For Art, Aesthetics, and Creativity” was designed to accommodate exactly that sort of exploration.

In my research, I have been working on understanding and quantifying aesthetics and design, but after my PhD thesis, I got more involved with the intriguing notion of learning by creating,” he reports.

“The idea of the course is to help students understand how can we use AI for creativity, and how creativity can help us learn and develop better AI.

Whether Pokerbots students go on to reign on the competitive circuit or join a trading firm, their experience is sure to serve them well.

I think that’s valuable communications experience to get.”Not too bad for a byte-sized semester!

In a Nature Methods paper, the MIT researchers report a new AI-based software for reconstructing multiple structures and motions of the imaged protein — a major goal in the protein science community.

Unlike AI techniques that aim to predict protein structure from sequence data alone, protein structure can also be experimentally determined using cryo-EM, which produces hundreds of thousands, or even millions, of two-dimensional images of protein samples frozen in a thin layer of ice.

This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that …

Mirai was significantly more accurate than prior methods in predicting cancer risk and identifying high-risk groups across all three datasets.

“This, coupled with the higher instance of triple-negative breast cancer in this group, has resulted in increased breast cancer mortality.

With this information, the additive-hazard layer predicts a patient’s risk for each year over the next five years.

Beyond improving accuracy, additional research is required to determine how to adapt image-based risk models to different mammography devices with limited data.

The work was supported by grants from Susan G Komen, Breast Cancer Research Foundation, Quanta Computing, and the MIT Jameel Clinic.

MIT researchers have developed a type of neural network that learns on the job, not just during its training phase.

These flexible algorithms, dubbed “liquid” networks, change their underlying equations to continuously adapt to new data inputs.

There’s another advantage of the network’s flexibility, he adds: “It’s more interpretable.”Hasani says his liquid network skirts the inscrutability common to other neural networks.

That could help engineers understand and improve the liquid network’s performance.

“We have a provably more expressive neural network that is inspired by nature.

Last fall, the MIT Stephen A. Schwarzman College of Computing embarked on a project to design and construct a new building on Vassar Street in Cambridge, at the former site of Building 44.

The proposed project will establish a home for the MIT Schwarzman College of Computing, providing state-of-the-art space for computing research and education.

“The new building will serve as a hub for both disciplinary and interdisciplinary work in computing and collaboration at MIT.

It will also contain inviting, community-oriented spaces where we can bring a mix of people together,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

Collaborative research spaces will be spread th…

At “AI Education: Research and Practice,” an Open Learning Talks event in December, Breazeal shared her vision for educating students not only about how AI works, but how to design and use it themselves — an initiative she calls AI Literacy for All.

The AI Education project Breazeal is leading at MIT is a collaboration between MIT Open Learning and the Abdul Latif Jameel World Education Lab, the Media Lab, and the MIT Schwarzman College of Computing.

As head of the Personal Robots group and AI Education at MIT, Media Lab Professor Cynthia Breazeal is on a mission to help this generation of young people to grow up understanding the AI they use.

“It’s at this intersection of human psychology,…

The authors delve into the question of how work will change with AI and provide policy prescriptions that speak to different parts of society.

Thomas Malone is director of the MIT Center for Collective Intelligence and the Patrick J. McGovern Professor of Management in the MIT Sloan School of Management.

Rus: Despite recent and significant strides in the AI field, and great promise for the future, today’s AI systems are still quite limited in their ability to reason, make decisions, interact reliably with people and the physical world.

Another limitation of current AI systems is robustness.

For example, AI systems can be helpful by doing tasks such as interpreting medical X-rays, evaluating…

Self-Supervised Policy Adaptation during DeploymentOur method learns a task in a fixed, simulated environment and quickly adapts to new environments (e.g.

Assuming that gradients of the self-supervised objective are sufficiently correlated with those of the RL objective, any adaptation in the self-supervised task may also influence and correct errors in the perception and decision-making of the policy.

SAC+IDM is a Soft Actor-Critic (SAC) policy trained with an Inverse Dynamics Model (IDM), and SAC+IDM (PAD) is the same policy but with the addition of policy adaptation during deployment on the robot.

Policy adaptation is especially effective when the test environment differs from the traini…

The Successor Representation, $\gamma$-Models,and Infinite-Horizon PredictionThe Successor Representation, Gamma-Models, and Infinite-Horizon PredictionStandard single-step models have a horizon of one.

In order to amortize this long-horizon prediction, value functions are trained with either Monte Carlo estimates of expected cumulative reward or with dynamic programming.

In contrast, value functions amortize the work of long-horizon prediction at training, so a single-step prediction (and informally, a shorter "horizon") is sufficient during testing.

As opposed to incrementing one timestep into the future with every prediction, \(\gamma\)-model rollout steps have a negative binomial distri…

Does GPT-2 Know Your Phone Number?

Yet, OpenAI’s GPT-2 language model does know how to reach a certain Peter W --- (name redacted for privacy).

Maybe the model memorized credit card numbers, or maybe it memorized entire book passages, or even code snippets.

For example, we retain any sample on which GPT-2 assigns a much higher likelihood than a different language model (e.g., a smaller variant of GPT-2).

Does Training Language Models Infringe on Copyright?

Offline Reinforcement Learning: How Conservative Algorithms Can Enable New ApplicationsDeep reinforcement learning has made significant progress in the last few years, with success stories in robotic control, game playing and science problems.

As shown in the figure below, offline RL requires learning skills solely from previously collected datasets, without any active environment interaction.

COG: Learning Skills That Generalize via Offline RLCOG is an algorithmic framework for utilizing large, unlabeled datasets of diverse behavior to learn generalizable policies via offline RL.

Like supervised learning methods, offline RL algorithms can also “overfit” as a result of excessive trainin…

Learning State Abstractions for Long-Horizon PlanningMany tasks that we do on a regular basis, such as navigating a city, cooking a meal, or loading a dishwasher, require planning over extended periods of time.

Two-way consistency can be viewed as a generalization of value irrelevance to the goal-conditioned setting.

Furthermore, our main theorem tells us that we can merge nodes according to two-way consistency while preserving the graph’s quality.

Overall, we found that state aggregation with two-way consistency resulted in substantially more robust plans over the prior state-of-the-art.

How can two-way consistency be utilized beyond the scope of graphical-based planning methods?

EvolveGraph: Dynamic Neural Relational Reasoning for Interacting SystemsMulti-agent interacting systems are prevalent in the world, from purely physical systems to complicated social dynamic systems.

In this work, we took a step forward to handle these challenges and provided a generic framework for trajectory prediction with dynamic relational reasoning for multi-agent systems.

Dynamic Interaction Graph LearningIn many situations, the interaction patterns recognized from the past time steps are likely not static in the future.

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

Summary and Broader ApplicationsWe introduce …

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…

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…

With machine learning (ML) techniques, we introduce a scalable multimodal solution for event detection on sports video data.

Recent developments in deep learning show that event detection algorithms are performing well on sports data [1]; however, they’re dependent upon the quality and amount of data used in model development.

This post explains a deep learning-based approach developed by the Amazon Machine Learning Solutions Lab for sports event detection using Amazon SageMaker.

If the badminton class is found among two labels associated with a 1-second sample, vote for the audio model (get the label and probability from the audio model).

ConclusionThis post outlined a multimodal event det…

This task is made easier with the newly launched XGBoost training report feature.

For more information, see Debugger XGBoost Training Report Walkthrough.

ConclusionIn this post, we generated an XGBoost training report and profiler report using SageMaker Debugger.

We then walked through the XGBoost training report and identified a number of issues that we can alleviate with some hyperparameter tuning.

For more about SageMaker Debugger, see SageMaker Debugger XGBoost Training Report and SageMaker Debugger Profiling Report.

Amazon Polly, an AI generated text-to-speech service, enables you to automate and scale your interactive voice solutions, helping to improve productivity and reduce costs.

This post shows you how to convert Amazon Polly output to a common audio format like WAV.

Converting Amazon Polly file output to WAVOne of the challenges with legacy systems is that they may not support Amazon Polly file outputs like MP3.

The output of the Amazon Polly SynthesizeSpeech API call doesn’t support WAV, but some legacy IVRs obtain the audio output in WAV file format, which isn’t supported natively in Amazon Polly.

The following sample code which will help in such situations where audio is in WAV file format no…

Today, we’re launching Amazon SageMaker Reinforcement Learning Kubeflow Components supporting AWS RoboMaker, a cloud robotics service, for orchestrating robotics ML workflows.

Kubeflow Pipelines – Install Kubeflow Pipelines on your cluster.

SageMaker and AWS RoboMaker components prerequisites – For instructions on setting up IAM roles and permissions, see Amazon SageMaker Components for Kubeflow Pipelines.

– For instructions on setting up IAM roles and permissions, see Amazon SageMaker Components for Kubeflow Pipelines.

Components 3,4,5: Multiple AWS RoboMaker simulation jobs These components describe three AWS RoboMaker simulation jobs that use the simulation application created in the rob…

SageMaker Debugger offers the capability to debug ML models during training by identifying and detecting problems with the models in near-real time.

This feature can be used when training models within Kubeflow Pipelines through the SageMaker Training component.

For more information, see Amazon SageMaker Debugger – Debug Your Machine Learning Models.

Using SageMaker Debugger for Kubeflow Pipelines with XGBoostThis post demonstrates how adding additional parameters to configure the debugger component can allow us to easily find issues within a model.

Using SageMaker Debugger in your Kubeflow Pipelines lets you go beyond just looking at scalars like losses and accuracies during training.

You already know how to use Amazon Athena to transform data in Amazon S3 using simple SQL commands and the built-in functions in Athena.

Now you can also use Athena to translate and analyze text fields, thanks to Amazon Translate, Amazon Comprehend, and the power of Athena User Defined Functions (UDFs).

Athena is an interactive query service that makes it easy to analyze data stored in Amazon S3 using SQL.

Optimizing costIn addition to Athena query costs, the text analytics UDF incurs usage costs from Lambda and Amazon Comprehend and Amazon Translate.

ConclusionI have shown you how to install the sample text analytics UDF Lambda function for Athena, so that you can use simple SQL queries to…

Crawling your data with AWS GlueWe use AWS Glue to crawl through the JSON file to determine the schema of your data and create a metadata table in your AWS Glue Data Catalog.

Using AWS Glue to convert your files from CSV to JSONAfter your crawler finishes running, go to the Tables page on the AWS Glue console.

On the AWS Glue Dashboard, choose AWS Glue Studio.

AWS Glue Studio is an easy-to-use graphical interface for creating, running, and monitoring AWS Glue ETL jobs.

To learn more about Amazon Personalize scores, see Introducing recommendation scores in Amazon Personalize.

We worked with AWS Machine Learning (ML) Heroes and AWS ML Community Builders to bring to life projects and use cases that detect custom objects with Amazon Rekognition Custom Labels.

Amazon Rekognition Custom Labels allows you to detect custom labeled objects and scenes with zero Jupyter notebook experience.

AWS ML Heroes and AWS ML Community BuildersClassify LEGO bricks with Amazon Rekognition Custom Labels by Mike Chambers.

Using Amazon SageMaker and Amazon Rekognition Custom Labels to automate detection by Luca Bianchi.

Learn how to detect clean and dirty HVACs using Amazon Rekognition Custom Labels and Amazon SageMaker from AWS ML Hero Luca Bianchi.

You can package your code and dependencies as a container image using tools such as the Docker CLI.

For more information about handlers for Lambda, see AWS Lambda function handler in Python.

The base images are preloaded with language runtimes and other components required to run a container image on Lambda.

Creating the Lambda function with the TensorFlow codeTo create your Lambda function, complete the following steps:On the Lambda console, choose Functions.

You can bring your custom models and deploy them on Lambda using up to 10 GB for the container image size.

Set up an Amazon A2I human loop to review and modify the Amazon Textract response.

Use the Amazon Textract Parser Library to process the responseWe will now import the Amazon Textract Response Parser library to parse and extract what we need from Amazon Textract’s response.

client = boto3.client( service_name='textract', region_name= 'us-east-1', endpoint_url='https://textract.us-east-1.amazonaws.com', ) with open(documentName, 'rb') as file: img_test = file.read() bytes_test = bytearray(img_test) print('Image loaded', documentName) # process using image bytes response = client.analyze_document(Document={'Bytes': bytes_test}, FeatureTypes=['TABLES','FORMS'])You can use the Amazon Textract R…

Talkdesk broadens contact center machine learning capabilities with AWS Contact Center Intelligence.

Although the core job of the contact center hasn’t changed for decades (deliver great service to customers), AI helps us do it better.

Now, we’re teaming with AWS Contact Center Intelligence (AWS CCI) solutions.

In 2021, Talkdesk will expose all the Amazon Transcribe and Amazon Transcribe Medical features to its clients through an easy-to-use, non-technical interface.

He oversees strategy and execution of products that use machine learning techniques to increase operational efficiency in the contact center.

The output of SageMaker Data Wrangler is data transformation code that works with SageMaker Processing, SageMaker Pipelines, SageMaker Feature Store, or with Pandas in a plain Python script.

The output of SageMaker Data Wrangler is data transformation code that works with SageMaker Processing, SageMaker Pipelines, SageMaker Feature Store, or with Pandas in a plain Python script.

Detecting bias – With SageMaker Clarify, in the data prep or training phases, we can detect pre-training (data bias) and post-training bias (model bias).

To build this pipeline, we will prepare some data (customers and claims) by ingesting the data into SageMaker Data Wrangler and apply various transformations in Sa…

Amazon Fraud Detector is a fully managed service that uses ML and more than 20 years of fraud detection expertise from Amazon to identify potential fraudulent activity so you can catch more online fraud faster.

Solution walkthroughIn this post, we set up Amazon Fraud Detector using the AWS Management Console, and set up Amazon A2I using an Amazon SageMaker notebook.

Set up an Amazon A2I human loop with Amazon Fraud Detector.

Setting up an Amazon A2I human loop with Amazon Fraud DetectorIn this section, we show you to configure an Amazon A2I custom task type with Amazon Fraud Detector using the accompanying Jupyter notebook.

For instructions, see the following:ConclusionThis post demonstrate…

In this post, we use Zopa’s fraud detection system for loans to showcase how Amazon SageMaker Clarify can explain your ML models and improve your operational efficiency.

Zopa trains its fraud detection model on SageMaker and can use SageMaker Clarify to view a feature attributions plot in SageMaker Experiments after the model has been trained.

Zopa uses SageMaker MMS model serving stack in a similar BYOC fashion to register the models for the SageMaker Clarify processing job.

With SageMaker Clarify, Zopa can now produce model explanations more quickly and seamlessly.

To learn more about SageMaker Clarify, see What Is Fairness and Model Explainability for Machine Learning Predictions?

Solution overviewWe first show you how to set up GluonTS on SageMaker using the MXNet estimator, then train multiple models using SageMaker Experiments, use SageMaker Debugger to mitigate suboptimal training, evaluate model performance, and finally generate time series forecasts.

When you select an algorithm, you can configure the hyperparameters to control the learning process during model training.

The Amazon SageMaker Python SDK MXNet estimators and models and the SageMaker open-source MXNet container make writing a MXNet script and running it in SageMaker easier.

Creating the MXNet estimatorYou can run MXNet training scripts on SageMaker by creating an MXNet estimator.

Debugger automati…

While these devices can provide ultra-low latency response, they can be difficult to program, expensive, and tend to be inflexible if the signal processing goal changes.

NVIDIA offers a plethora of C/CUDA accelerated libraries targeting common signal processing operations.

Online signal processing — memory handling and FFT benchmarkingWhile offline signal processing on highly sampled signals may be valuable as a pre-processing step in a signal processing pipeline, many applications, particularly within the SDR domain, depend on frequent, relatively small copies from the radio buffer to the CPU or accelerator before further processing is invoked.

This cost is only paid once and can be ‘pre-p…

And the technology — computer vision for smart sprayers — is now being licensed and deployed in pilot tests by CCI, an agricultural equipment company.

The efforts promise to help farmers combat what’s known as “citrus greening,” the disease brought on by bacteria from the Asian citrus psyllid insect hitting farms worldwide.

The agricultural equipment supplier has seen farmers lose one-third of the orchard acreage in Florida from the onslaught of citrus greening.

The team’s image recognition models are run on the Jetson AI platform in the field for inference.

Like many, UF and CCI are developing applications for deployment on the NVIDIA Jetson edge AI platform.

A cluster or a pod is simply a set of computers linked by high-speed networks into a single unit.

Pods vs. Clusters: A War of WordsWhile computer architects called these systems clusters, some networking specialists preferred the term pod.

The term pod gained traction in the early days of cloud computing.

More recently, the Kubernetes group adopted the term pod.

They define a software pod as “a single container or a small number of containers that are tightly coupled and that share resources.”Industries like aerospace and consumer electronics adopted the term pod, too, perhaps to give their concepts an organic warmth.

Check out this month’s list of all the exciting new titles and classic games coming to GeForce NOW in March.

First, let’s get into what’s coming today.

Here’s what’s new to GFN starting today:Loop Hero (day-and-date release on Steam) Equal parts roguelike, deck-builder and auto battler, Loop Hero challenges you to think strategically as you explore each randomly generated loop path and fight to defeat The Lich.

PC Gamer gave this indie high praise, saying, “don’t sleep on this brilliant roguelike.” Disgaea PC (Steam) The turn-based strategy RPG classic lets you amass your evil hordes and become the new Overlord.

And that’s not all — check out even more games coming to GFN in March:Door Kick…

NVIDIA NGCThe NVIDIA NGC catalog is the hub of GPU-optimized AI/ML software that can be deployed across on-premises, cloud, edge, and hybrid environments.

This post provides step-by-step instructions to build a question answering (QA) service using NGC and NVIDIA A100-powered GCP instances.

When it’s fine-tuned and given a question and the context, the fine-tuned BERT model should be able to return an answer, highlighted in color.

For this post, you use the Stanford Question Answering Dataset (SQuAD) to train the QA model.

You sent requests with the preprocessed question and context to the deployed BERT QA service and received the answer back.

In this post, we will provide a gentle introduction to the RAPIDS ecosystem and showcase the most common functionality of RAPIDS cuDF, the GPU-based pandas DataFrame counterpart.

It is an ETL workhorse allowing building data pipelines to process data and derive new features.

Being part of the ecosystem, all the other parts of RAPIDS build on top of cuDF making the cuDF DataFrame the common building block.

Check the sample code below that presents how familiar cuDF API is to anyone using pandas.

In addition, cuDF supports saving the data stored in a DataFrame into multiple formats and file systems.

Over several installments, We’ll be building a blueprint for predicting customer churn — that is, identifying customers who are likely to cancel subscriptions.

In this installment, we’ll level-set by introducing a typical end-to-end machine learning workflow and the overall architecture for our churn prediction solution.

Machine learning workflows and the customer churn problemTen years ago, a “data scientist” was a practitioner whose role combined domain expertise with elements of analytics, applied statistics, machine learning, software engineering, and even infrastructure operations and was responsible for data storytelling and end-to-end machine learning solutions.

Synthesizing data at …

Since NVIDIA announced construction of the U.K.’s most powerful AI supercomputer — Cambridge-1 — Marc Hamilton, vice president of solutions architecture and engineering, has been (remotely) overseeing its building across the pond.

Hamilton points to the concentration of leading healthcare companies in the U.K. as a primary reason for NVIDIA’s decision to build Cambridge-1.

Hamilton promises to provide the latest updates on Cambridge-1 at GTC 2021.

The Massachusetts General Hospital Center for Clinical Data Science — led by Mark Michalski — promises to accelerate that, using AI technologies to spot patterns that can improve the detection, diagnosis and treatment of diseases.

One of the pilla…

Few research centers take a wider lens to environmental science than the NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS).

Since the 1990s, the service, part of the United Kingdom’s Natural Environment Research Council and overseen by the National Centre for Earth Observation (NCEO), has made the Earth observation data collected by hundreds of satellites freely available to researchers.

Earth Under ObservationMore than 10TB of Earth observation data is collected daily by sensors on more than 150 satellites orbiting the planet.

“MAGEO offers an excellent opportunity to accelerate artificial intelligence and environmental intelligence research,” said Stephen Goult, a dat…

Nick Bild, a Florida-based software engineer, has created an application that can signal to batters whether pitches are going to be balls or strikes.

It relies on the NVIDIA Jetson edge AI platform for split-second inference, which triggers the lights.

The first Jetson project he created was called DOOM Air.

His Favorite Jetson ProjectsBild likes many of his Jetson projects.

But Tipper is Bild’s favorite Jetson project of all.

Cloud gaming uses powerful, industrial-strength GPUs inside secure data centers to stream your favorite games over the internet to you.

Cloud gaming streams the latest games from powerful GPUs in remote data centers to nearly any device.

So, cloud gaming servers need to process information and render frames in real time.

Combined with advanced GeForce PC gaming technologies, GeForce NOW delivers high-end PC gaming to passionate gamers.

Growing Cloud Gaming Around the WorldWith the ambition to deliver quality cloud gaming to all gamers, NVIDIA works with partners around the world including telecommunications and service providers to put GeForce NOW servers to work in their own data centers, …

Water, Water, EverywhereIt’s an industry that was kicked off by the 1972 U.S. Clean Water Act, a landmark not just in the United States, but globally.

The challenge: while almost every industry creates wastewater, wastewater expertise isn’t exactly ubiquitous.

The solution, Arndt realized, was to use deep learning to train an AI that could yield instantaneous results.

To do this, last year Arndt reached out on social media to colleagues throughout the wastewater industry to send him samples.

After all, “no one wants to have to wait a week to know if it’s safe to take a sip of water,” Arndt says.

For more information about the random forests algorithm, see An Implementation and Explanation of the Random Forest in Python (Toward Data Science) or Lesson 1: Introduction to Random Forests (Fast.ai).

Random forestsThe main idea behind random forests is to learn multiple independent decision trees and use a consensus method to predict the unknown samples.

Decision tree algorithmBecause random forests are a collection of decision trees, they need to start with an efficient algorithm to build a single tree.

Building decision trees: putting it all togetherBuilding individual decision trees is where the heavy lifting of the random forest is done.

From these examples, you can see a 20x — 45x s…

Models for meaningful interactions and quality content are powered by state-of-the-art ML, such as multitask learning on neural networks, embeddings, and offline learning systems.

We are sharing new details of how we designed an ML-powered News Feed ranking system.

Building a ranking algorithmTo understand how this works, let’s start with a hypothetical person logging in to Facebook: We’ll call him Juan.

On the other hand, perhaps Juan has previously engaged more with video content than photos, so the like prediction for Wei’s cocker spaniel photo might be lower.

Approximating the ideal ranking function in a scalable ranking systemNow that we know the theory behind ranking (as exemplified t…

This is why we need to make sure our server hardware is reliable and that we can manage server hardware failures at our scale with as little disruption to our services as possible.

And we automate root cause analysis for hardware and system failures at scale to get to the bottom of issues quickly.

How we handle hardware remediationWe periodically run a tool called MachineChecker on each server to detect hardware and connectivity failures.

If the issue requires manual repair from a technician, the system creates a ticket in our repair ticketing system.

We have deployed this analyzer widely inside Facebook for the RCA on hardware component failure rate, unexpected server reboots, and software…

What’s New:PPL Bench is an open source benchmark framework for evaluating probabilistic programming languages (PPLs) used for statistical modeling.

PPL Bench does this by using predictive log likelihood as a standard measurement.

PPL Bench also reports other common metrics used to evaluate statistical models, including effective sample size, R-hat, and inference time.

We hope that community contributions will help grow and diversify PPL Bench and encourage wider industrial deployments of PPLs.

Read the full paper:PPL Bench: Evaluation framework for probabilistic programming languagesGet it on GitHub:PPL Bench

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

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

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

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

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 …

Data labeling tools come very much in handy because they can automate the labeling process, which is particularly tedious.

It offers data labeling for every possible data type: text, images, video, audio, time series, multi-domain data types, etc.

Sloth is an open-source data labeling tool mainly built for labeling image and video data for computer vision research.

LabelBox is a popular data labeling tool that offers an iterate workflow process for accurate data labeling and creating optimized datasets.

The tool offers a command center to control and perform data labeling, data management, and data analysis tasks.

Overall, ML model monitoring is necessary for making your model successful.

One of the easiest ways to ensure things work smoothly is to use ML model monitoring tools.

Here are the best tools that’ll help you do ML model monitoring as best as possible.

Hence, it’s best if you’re looking for an easy ML model monitoring performance solution.

Currently, 6 reports are available:Data Drift: detects changes in feature distribution Numerical Target Drift: detects changes in the numerical target and feature behavior Categorical Target Drift: detects changes in categorical target and feature behavior Regression Model Performance: analyzes the performance of a regression model and model errors Classi…

In this article, you’ll dive into:what transfer learning is,how to implement transfer learning (in Keras),transfer learning for image classification,transfer learning for natural language processingWell then, let’s start learning!

Transfer learning is about leveraging feature representations from a pre-trained model, so you don’t have to train a new model from scratch.

Transfer learning in 6 stepsYou can implement transfer learning in these six general steps.

Since this is text data, it has to be converted into numerical form because that’s what the deep learning model expects.

This is not specific to transfer learning in text classification, but to machine learning models in general.

The pygad.torchga module (torchga is short for Torch Genetic Algorithm) helps us formulate the PyTorch model training problem the way PyGAD expects it.

Now, let’s go over the steps needed to train a PyTorch model using PyGAD.

Train PyTorch models using PyGADTo train a PyTorch model using PyGAD, we need to go through these steps:Classification or Regression?

def callback_generation (ga_instance) : print( "Generation = {generation}" .format(generation=ga_instance.generations_completed)) print( "Fitness = {fitness}" .format(fitness=ga_instance.best_solution()[ 1 ]))The next step is creating an instance of the pygad.GA class, responsible for running the genetic algorithm to train the PyTorch mo…

Digging into ASR and TTS architecturesQuartznetAs their paper states, Jasper is an end-to-end neural acoustic model for automatic speech recognition.

postprocess(inference_output): Save the wav audio file to a directory under the container file system.

create_output_manifest(file_path): saves the final result into a json file with the input audio file path, the audio duration, the audio sampling rate and the corresponding transcribed text.

ASRViewController will send an HTTP Post request with the audio file you’ve recorded, and receive the transcribed text.

Conversational AI is getting closer to seamlessly discussing intelligent systems, without even noticing any substantial difference with…

Contents:Genetic algorithm quick overviewHow mutation worksRandom mutation python exampleConstant mutation probabilityConstant mutation probability Python exampleAdaptive mutationAdaptive mutation Python exampleFor more informationConclusionGenetic algorithm quick overviewThe genetic algorithm is a population-based evolutionary algorithm, where a group of solutions works together to find the optimal parameters for a problem.

So, let’s review the mutation operation, and whether high or low mutation probability is better.

Now we’ll move on to adaptive mutation, which adapts mutation probability according to the fitness/quality of the solution.

We’ve discussed the genetic algorithm and adaptiv…

Here’s your list of the best go-to resources about MLOps—books, articles, podcasts, and more.

Introducing MLOps from O’ReillyIntroducing MLOps: How to Scale Machine Learning in the Enterprise is a book written by Mark Treveil and the Dataiku Team (collective authors).

It introduces the key concepts of MLOps, shows how to maintain and improve ML models over time, and tackles the challenges of MLOps.

This is An awesome list of references for MLOps – Machine Learning Operations from ml-ops.orgIt’s a list of links to numerous resources, beginning with books, articles, to communities, and many, many more.

The Stanford MLSys Seminar Series is, as the name suggests, a series of seminars focused on…

This function can take many forms, but one common metric that pops up in many tasks is the Wasserstein distance (WD).

Distance metricsWikipedia tells us that “Wasserstein distance […] is a distance function defined between probability distributions on a given metric space M”.

A distance function is something we use to measure the distance between two or more objects.

Which is how this method for textual similarity became known as the Word Mover’s distance (WMD).

If you tried to measure the similarity between two sentences or documents, you might have used something like cosine similarity.

We’ll start with an example from my research work, then I’ll explain this phenomenon, and talk about how to deal with it.

Deep Exploration caught my eye.

These algorithms derive from the idea of Deep Exploration and ensemble/bootstrap networks.

I got caught in the trap of the fractal nature of creative work!

When you do creative work, each step deeper into the topic doesn’t narrow down the scope of your research.

In this article, we’ll discuss:What is feature engineeringTypes of problem in feature engineeringOpen source tools for feature engineeringComparison of feature engineering toolsFeature engineering examplesLet’s start with a couple of examples.

Types of problems in feature engineeringBefore going into tools for feature engineering, we’ll look at some of the operations that we can perform.

This can also be a recursive process where, after feature selection, we train the model, calculate the accuracy score, and then do feature selection again.

ComparisonTo finish, let’s compare these libraries so you can see which will fit your work:Tools/Measures Support for Type of Databases Feature Engineer…

In this article, we’ll explore two libraries: Pytorch Lighting and Pytorch Ignite, which offer flexibility and structure for your deep learning code.

Lightning provides structure to pytorch functions where they’re arranged in a manner to prevent errors during model training, which usually happens when the model is scaled up.

When not to use PyTorch LightningIf you don’t know Pytorch, then learn Pytorch first and then use Lightning.

The same metric can be used to find the accuracy, loss, etc.

When to use PyTorch IgniteHigh-level library with great interface, with additional property to customise the Ignite API according to requirements.

If, like me, you’re a deep learning engineer working with TensorFlow/Keras, then you should consider using Keras Tuner.

READ ALSOHyperparameter Tuning in Python: a Complete Guide 2021In this article, I’ll tell you how I like to implement Keras Tuner in deep learning projects.

To find the best model architecture via hyperparameters tuning, we need to select a metric for model evaluation.

Keras Tuner implementationHigh-level overview of available tunersHow can we get the most out of our model using Keras Tuner?

Model that has been better optimized for a particular problem domain using hyperparameters tuning led our service to a more stable and accurate long-run performance.

These are:experiment trackingmachine learning model managementMLOps (machine learning operations)At the end of the article, you will know the differences between the three, as well as the various parts of each.

Model monitoringOnce a machine learning model is in use, it has to be monitored.

MLOps vs experiment trackingTo develop any machine learning model, you need to try a lot of features, parameters and datasets.

Final RemarksIn this article, you have seen that MLOps is the entire ecosystem concerned with bringing your machine learning model to production.

Specifically, we’ve covered:what MLOps is,parts of MLOps,what model management is,various parts of model management,what experiment tr…

Tools for model serving in machine learning can provide you with solutions to many of the data engineers and devops concerns.

Let’s take a look at the best tools that can help you in model serving!

It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.

It is a scalable policy-based cloud-native machine learning model server for easily deploying and managing ML models.

Full auditability through model input-output request (logging integration with Elasticsearch)To wrap it upThere are plenty of tools for machine learning model serving to choose from.

#Shorts #shorts #openai In the paper Multimodal Neurons in Artificial Neural Networks OpenAI suggests that CLIP can be attacked adversarially by putting textual labels onto pictures. They demonstrated this with an apple labeled as an iPod. I reproduce that experiment and suggest a simple, but effective fix. Yes, this is a joke ;) Original Video: https://youtu.be/Z_kWZpgEZ7w OpenAI does a huge investigation into the inner workings of their recent CLIP model via faceted feature visualization and finds amazing things: Some neurons in the last layer respond to distinct concepts across multiple modalities, meaning they fire for photographs, drawings, and signs depicting the same concept, even wh…

#openai #clip #microscope OpenAI does a huge investigation into the inner workings of their recent CLIP model via faceted feature visualization and finds amazing things: Some neurons in the last layer respond to distinct concepts across multiple modalities, meaning they fire for photographs, drawings, and signs depicting the same concept, even when the images are vastly distinct. Through manual examination, they identify and investigate neurons corresponding to persons, geographical regions, religions, emotions, and much more. In this video, I go through the publication and then I present my own findings from digging around in the OpenAI Microscope. OUTLINE:

0:00 - Intro & Overview

3:35 - O…

#glom #hinton #capsules Geoffrey Hinton describes GLOM, a Computer Vision model that combines transformers, neural fields, contrastive learning, capsule networks, denoising autoencoders and RNNs. GLOM decomposes an image into a parse tree of objects and their parts. However, unlike previous systems, the parse tree is constructed dynamically and differently for each input, without changing the underlying neural network. This is done by a multi-step consensus algorithm that runs over different levels of abstraction at each location of an image simultaneously. GLOM is just an idea for now but suggests a radically new approach to AI visual scene understanding. OUTLINE:

0:00 - Intro & Overview

3…

#fastweights #deeplearning #transformers Transformers are dominating Deep Learning, but their quadratic memory and compute requirements make them expensive to train and hard to use. Many papers have attempted to linearize the core module: the attention mechanism, using kernels - for example, the Performer. However, such methods are either not satisfactory or have other downsides, such as a reliance on random features. This paper establishes an intrinsic connection between linearized (kernel) attention and the much older Fast Weight Memory Systems, in part popularized by Jürgen Schmidhuber in the 90s. It shows the fundamental limitations of these algorithms and suggests new update rules and …

#deberta #bert #huggingface DeBERTa by Microsoft is the next iteration of BERT-style Self-Attention Transformer models, surpassing RoBERTa in State-of-the-art in multiple NLP tasks. DeBERTa brings two key improvements: First, they treat content and position information separately in a new form of disentangled attention mechanism. Second, they resort to relative positional encodings throughout the base of the transformer, and provide absolute positional encodings only at the very end. The resulting model is both more accurate on downstream tasks and needs less pretraining steps to reach good accuracy. Models are also available in Huggingface and on Github. OUTLINE:

0:00 - Intro & Overview

2:…

#dreamer #deeprl #reinforcementlearning Model-Based Reinforcement Learning has been lagging behind Model-Free RL on Atari, especially among single-GPU algorithms. This collaboration between Google AI, DeepMind, and the University of Toronto (UofT) pushes world models to the next level. The main contribution is a learned latent state consisting of one discrete part and one stochastic part, whereby the stochastic part is a set of 32 categorical variables, each with 32 possible values. The world model can freely decide how it wants to use these variables to represent the input, but is tasked with the prediction of future observations and rewards. This procedure gives rise to an informative lat…

#transformer #gan #machinelearning Generative Adversarial Networks (GANs) hold the state-of-the-art when it comes to image generation. However, while the rest of computer vision is slowly taken over by transformers or other attention-based architectures, all working GANs to date contain some form of convolutional layers. This paper changes that and builds TransGAN, the first GAN where both the generator and the discriminator are transformers. The discriminator is taken over from ViT (an image is worth 16x16 words), and the generator uses pixelshuffle to successfully up-sample the generated resolution. Three tricks make training work: Data augmentations using DiffAug, an auxiliary superresol…

#nfnets #deepmind #machinelearning Batch Normalization is a core component of modern deep learning. It enables training at higher batch sizes, prevents mean shift, provides implicit regularization, and allows networks to reach higher performance than without. However, BatchNorm also has disadvantages, such as its dependence on batch size and its computational overhead, especially in distributed settings. Normalizer-Free Networks, developed at Google DeepMind, are a class of CNNs that achieve state-of-the-art classification accuracy on ImageNet without batch normalization. This is achieved by using adaptive gradient clipping (AGC), combined with a number of improvements in general network ar…

#transformer #nystromer #nystromformer The Nyströmformer (or Nystromformer, Nyströmer, Nystromer), is a new drop-in replacement for approximating the Self-Attention matrix in Transformers with linear memory and time requirements. Most importantly, it uses the Nystrom-Method to subselect (or segment mean) queries and keys as so-called landmarks and uses those to reconstruct the inherently low-rank attention matrix. This is relevant for many areas of Machine Learning, especially Natural Language processing, where it enables longer sequences of text to be processed at once. OUTLINE:

0:00 - Intro & Overview

2:30 - The Quadratic Memory Bottleneck in Self-Attention

7:20 - The Softmax Operation in…

#deeplearning #kernels #neuralnetworks Full Title: Every Model Learned by Gradient Descent Is Approximately a Kernel Machine Deep Neural Networks are often said to discover useful representations of the data. However, this paper challenges this prevailing view and suggest that rather than representing the data, deep neural networks store superpositions of the training data in their weights and act as kernel machines at inference time. This is a theoretical paper with a main theorem and an understandable proof and the result leads to many interesting implications for the field. OUTLINE:

0:00 - Intro & Outline

4:50 - What is a Kernel Machine?

10:25 - Kernel Machines vs Gradient Descent

12:40 …

#ai #science #transformers Autoregressive Transformers have taken over the world of Language Modeling (GPT-3). However, in order to train them, people use causal masking and sample parallelism, which means computation only happens in a feedforward manner. This results in higher layer information, which would be available, to not be used in the lower layers of subsequent tokens, and leads to a loss in the computational capabilities of the overall model. Feedback Transformers trade-off training speed for access to these representations and demonstrate remarkable improvements in complex reasoning and long-range dependency tasks. OUTLINE:

0:00 - Intro & Overview

1:55 - Problems of Autoregressiv…

#ai #research #blockchain Big Tech is currently dominating the pursuit of ever more capable AI. This happens behind closed doors and results in a monopoly of power. SingularityNET is an open, decentralized network where anyone can offer and consume AI services, and where AI agents can interlink with each other to provide ever more sophisticated AI, with the goal to create a singularity that's beneficial for humanity. This video takes a look at the basics behind SingularityNET and some of its core components. OUTLINE:

0:00 - Intro & Overview

2:55 - Document Summarization Example Workflow

5:50 - Why AI needs a Marketplace?

9:20 - A network of APIs

12:30 - AI Evaluators & Matchmakers

15:00 - M…

#ai #technology #switchtransformer Scale is the next frontier for AI. Google Brain uses sparsity and hard routing to massively increase a model's parameters, while keeping the FLOPs per forward pass constant. The Switch Transformer compares favorably to its dense counterparts in terms of speed and sample efficiency and breaks the next magic number: One Trillion Parameters. OUTLINE:

0:00 - Intro & Overview

4:30 - Performance Gains from Scale

8:30 - Switch Transformer Architecture

17:00 - Model-, Data- and Expert-Parallelism

25:30 - Experimental Results

29:00 - Stabilizing Training

32:20 - Distillation into Dense Models

33:30 - Final Comments Paper: https://arxiv.org/abs/2101.03961

Codebase T…

#memes #science #ai Part 2 of Antonio and me examining the latest and greatest 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/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out t…

#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…

This video walks through an implementation of Reptile in Keras using the Omniglot dataset. I was really inspired by this example, I think the Omniglot challenge of dynamically being able to recombine characters to form new alphabets is an incredibly interesting problem, connecting Human and Artificial Intelligence. I hope you found this example interesting as well, please check out the rest of the Keras Code Example playlist! Content Links:

Few-shot learning with reptile: https://keras.io/examples/vision/reptile/

On First-Order Meta Learning: https://arxiv.org/pdf/1803.02999.pdf

MAML: https://arxiv.org/pdf/1703.03400.pdf

Generative Teaching Networks: https://arxiv.org/pdf/1912.07768.pdf

Tea…

This video walks through the Keras Code Example implementation of Point Cloud Classification. I had a tough time understanding what the TNET blocks are motivated by, but if interested the paper link is below. I hope this tutorial still provided a decent enough example of what point clouds are and how to load them into a Keras workspace. Thanks for watching, please check out the rest of the Keras Code Example playlist! Content Links:

Point Cloud Classification - Keras Code Examples: https://keras.io/examples/vision/pointnet/

PointNet (Paper): https://arxiv.org/pdf/1612.00593.pdf

ModelNet (Dataset Project Page): https://modelnet.cs.princeton.edu/

Point Clouds (Wikipedia): https://en.wikipedia…

Thank you so much to everyone who has shown support and interest in bringing back the AI Weekly Update series. Here is a preview for the return, I hope these quick overviews are useful to those looking to get ahead of it and find some interesting reading over the Weekend! Content Links:

Multimodal Neurons: https://openai.com/blog/multimodal-neurons/

Wikipedia Image-Text Dataset: https://arxiv.org/pdf/2103.01913.pdf

Self-Supervised Learning: The Dark Matter of Intelligence: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence

Principles for Tackling Distribution Shift: https://www.youtube.com/watch?v=QKBh6TmvBaw

Do Transformer Modifications Transfer? https://…

This video explains a technique for domain agnostic data augmentation accepted into the ICLR 2021 conference. I am really excited about this technique to bring the success of data augmentation in Computer Vision to more applications! Content Links:

Paper: https://openreview.net/forum?id=XjYgR6gbCEc

Robert Luxemburg's video on StyleGAN2 interpolation: https://www.youtube.com/watch?v=6E1_dgYlifc

Population-Based Augmentation: https://arxiv.org/abs/1905.05393

Cutout: https://arxiv.org/pdf/1708.04552.pdf

Easy Data Augmentation: https://arxiv.org/pdf/1901.11196.pdf

TensorFlow Data Augmentation: https://www.tensorflow.org/tutorials/images/data_augmentation

StyleGAN: https://arxiv.org/abs/1912.049…

This video explains how to implement Metric Learning for Image Similarity Search in Keras! This is one of the most exciting areas of Deep Learning especially in applications such as Contrastive Self-Supervised Learning. You may also be interested in checking out a video I made taking apart the Retrieval-Augmented Generation model for NLP. The embeddings that come out of Deep Neural Networks / distributed representations, are one of the most interesting and useful products of Deep Learning. I hope you find this walkthrough useful, I think the key takeaway is understanding how to write a custom DataLoader and Training Step for this kind of learning task. Content Links:

Original Keras Code Exa…

This Keras Code Examples show you how to implement Knowledge Distillation! Knowledge Distillation has lead to new advances in compression, training state of the art models, and stabilizing Transformers for Computer Vision. All you need to do to build on this is swap out the Teacher and Student architectures. I think the example of how to overwrite keras.Model and integrate two loss functions controlled with an alpha hyperparameter weighting is very useful as well. Content Links

Knowledge Distillation (Keras Code Examples): https://keras.io/examples/vision/knowledge_distillation/

DistilBERT: https://arxiv.org/pdf/1910.01108.pdf

Self-Training with Noisy Student: https://arxiv.org/pdf/1911.042…

Sorry everyone, I didn't have the interest to take this apart completely. Uploading for completeness of the Keras Code Examples. Admittedly hit the trapezoidal rule thing and just called it quits. I hope this overview is still useful for those scrolling through the Keras Examples and curious what each title entails. When using this in my own experiments, I plan to just copy and paste the code Content Links:

Keras Code Example - Model interpretability with Integrated Gradients: https://keras.io/examples/vision/integrated_gradients/

Visualizing the Impact of Feature Attribution Baselines: https://distill.pub/2020/attribution-baselines/ Please check out the rest of the Keras Code Example Playl…

This video walks through the Keras Code Example implementation of Vision Transformers!! I see this as a huge opportunity for graduate students and researchers because this architecture has a serious room for improvement. I predict that Attention will outperform CNN models like ResNets, EfficientNets, etc. it will just take the discovery of complimentary priors, e.g. custom data augmentations or pre-training tasks. I hope you find this video useful, please check out the rest of the Keras Code Examples playlist! Content Links:

Keras Code Exampes - Vision Transformers: https://keras.io/examples/vision/image_classification_with_vision_transformer/

Google AI Blog Visualization: https://ai.google…

This tutorial will walkthrough Determined AI's CIFAR-10 example. This will show you the basics of how to define a hyperparameter search configuration and how to integrate that with different data loaders, model definitions, loss functions, callbacks, and so on. From this tutorial you should have a sense of the Determined API and the Experiment Tracking visualization and Hyperparameter search features! Determined AI Examples: https://github.com/determined-ai/determined/tree/master/examples

How to setup EC2 Runtime for Determined: https://www.youtube.com/watch?v=htObOwwnhQk&t=61s ****minor errata on the video**** the type annotations in Determined's function definitions are just that; annotat…

I'm really excited to present this video on Determined AI! Determined has been teaching me how to use their platform and showing me what they are building. I am so excited about this with advancing my Deep Learning experimentation skills and I hope you all find value out of this as well. More particularly, I think the Determined team is really onto something with their Hyperparameter Optimization tools. The remainder of this series will have a similar objective as the Keras Code Examples series. I highly recommend checking out some of the content links below to learn more about Determined. Content Links:

This is the video that got me up and running with my first Determined Experiment: https…

This video walks through an example of fine-tuning EfficientNet for Image Classification. There is a really interesting result in the example of showing the efficacy of freezing only BatchNormalization while fine-tuning the rest of the network. I really enjoyed going through this, it is an excellent introduction to Transfer Learning! Content Links:

Image classification via fine-tuning with EfficientNet: https://keras.io/examples/vision/image_classification_efficientnet_fine_tuning/

EfficientNet Paper: https://arxiv.org/pdf/1905.11946.pdf

TensorFlow EfficientNet Repo: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

Keras Applications: https://www.tensorflow.org/api…

This video walks through an example that shows you how to see which region of an image most influences predictions and gradients when applying Deep Neural Networks for Image Classification. I hope this is a useful introduction for anyone looking to add some Interpretability to their Computer Vision models! Content Links:

Keras Code Example: https://keras.io/examples/vision/grad_cam/

tf.GradientTape: https://www.tensorflow.org/api_docs/python/tf/GradientTape

Keras Applications: https://www.tensorflow.org/api_docs/python/tf/keras/applications/xception

Xception: https://keras.io/api/applications/xception/ Thanks for watching! Please check out the Keras Code Examples Playlist!

This video walks through a basic example of predicting the next frame in a sequence of video data. This has really exciting applications in Model-Based RL, however it still has a long way to go. Hopefully this introduction helps you get started with it if interested in this research direction! Content Links:

Keras Code Examples - Next-frame prediction with Conv-LSTM: https://keras.io/examples/vision/conv_lstm/

ConvLSTM2D Layer - https://keras.io/api/layers/recurrent_layers/conv_lstm2d/

Colah - Understanding LSTMs- http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Cool Robot Videos

https://bair.berkeley.edu/blog/2020/05/05/fabrics/

https://openai.com/blog/solving-rubiks-cube/ Thanks …

This video walks through a CNN+RNN Captcha reader. The key to this is the CTC loss, the article below goes into a deeper dive than the video. Content Links:

Keras Code Examples OCR for reading Captchas: https://keras.io/examples/vision/captcha_ocr/

TDS Connectionist Temporal Classification Loss: https://towardsdatascience.com/intuitively-understanding-connectionist-temporal-classification-3797e43a86c Thanks for watching! Please check out the rest of the Keras Code Examples playlist!

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…

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "A Level-Set Method for Magnetic Substance Simulation" is available here:

https://binwangbfa.github.io/publication/sig20_ferrofluid/SIG20_FerroFluid.pdf

https://starryuniv.cn/

http://vcl.pku.edu.cn/publication/2020/magnetism

https://starryuniv.cn/publication/a-level-set-method-for-magnetic-substance-simulation/

Some links may be down, trying to add several of them to make sure you find one that works! ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "MATch: Differentiable Material Graphs for Procedural Material Capture" is available here:

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https://dcgi.fel.cvut.cz/home/sykorad/monster_mash Web demo - make sure to click "Help" and read the instructions:

http://monstermash.zone/# More on Flow by Mihály Csíkszentmihályi - it is immensely important to master this!

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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/instacolorization/reports/Overview-Instance-Aware-Image-Colorization---VmlldzoyOTk3MDI 📝 The paper "Time-Travel Rephotography" is available here:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "High-performance brain-to-text communication via imagined handwriting" 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/in-between/reports/-Overview-Robust-Motion-In-betweening---Vmlldzo0MzkzMzA 📝 The paper "Robust Motion In-betweening" 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/yolo-drive/reports/Bounding-Boxes-for-Object-Detection--Vmlldzo4Nzg4MQ 📝 The paper "Robust Eulerian-On-Lagrangian Rods" 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 report on this paper is available here: https://wandb.ai/wandb/xfields/reports/-Overview-X-Fields-Implicit-Neural-View-Light-and-Time-Image-Interpolation--Vmlldzo0MTY0MzM 📝 The paper "X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation" is available here:

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The blog post on "DALL·E: Creating Images from Text" is available here:

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ML Training 2019 Artur Kuzin tells about his participation in the competition Driven Data Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth. He took second place. In this video, you will find out: - Overview of a training procedure on Imagenet1k from scratch

- Implementation Details of Hacks & Tricks

- The specialty of working with JPEG pictures and resize in different frameworks Presentation - https://gh.mltrainings.ru/presentations/Kuzin_DrivenDataHakuna.pdf

ML Training 2019 Yury Bolkonsky and Andrei Dukhounik tell about their participation in Kaggle Understanding Clouds from Satellite Images. The team got a silver medal. In this video you will find out:

- Thresholding is an evil, believe in your classification models

- Why you should always use modern best practices

- Why it is not recommended to use postprocessing without local validation Presentation - https://gh.mltrainings.ru/presentations/Bolkonsky_KaggleUnderstandingClouds.pdf

ML Training 2019 Yury Bolkonsky tells about his participation in Kaggle ASHRAE - Great Energy Predictor III. His team won a gold medal. In this video you will find out:

- How to create timestamp features

- Do you need to use a leak if it is noisy?

- Leak validation for the best solution

ML Training 2019 Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out:

- How AutoML competition differs from most common Kaggle-alike and why you should try them

- Features engineering approach for time-series tasks when you have no idea about domain

- Why validation split should emulate train-test split

- Why you should always check the code of top participants and how small bugs might drop your score Presentation - https://gh.mltrainings.ru/presentations/Vorotyntsev_CodalabAutoML.pdf

ML Training 2019 Adel Valiullin tells about his participation in the competition Kaggle Peking University/Baidu - Autonomous Driving. He won a silver medal. In this video, you will find out: - Overview of the Autonomous Vehicles problem

- Dataset description and exploration: images with 6D pose information, taken from the roof of a car, 3D models of cars and input data analysis - Problems with mAP metric and dataset in this challenge

- The implementation of CenterNet Neural Network for 6D car pose estimation

- Score boosters and other better and high scored approaches

ML Training 2019 Ilya Kibardin tells about his participation in 2 competitions: Topcoder SpaceNet 5 Challenge & Signate The 3rd Tellus Satellite Challenge. He took fourth and second places. In this video you will find out:

- Spacenet 5 challenge at Topcoder, dataset and metric description

- Overview of a UNet pipeline for road graph extraction from satellite images

- The same pipeline applied to ice segmentation at Signate

- Hacks & Tricks for better performance Presentation - https://gh.mltrainings.ru/presentations/Kibardin_Spacenet5Tellus_v2.pdf

Data Fest Online 2020

Uncertainty Estimation in ML track https://ods.ai/tracks/uncertainty-estimation-in-ml-df2020 Speaker: Bruno Mlodozeniec, University of Cambridge In this video we discuss how ensembles of models can be effectively emulated using a single “Prior Network” model via a technique called Ensemble Distribution Detection. This enables a single model to efficiently retain both the ensemble’s predictive performance and uncertainty measures at low computational and memory cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

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

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Huawei's DaVinci AI compute architecture. Dmitrii Khizbullin, Overview of DaVinci compute architecture for Deep Learning training and inference, design choices for hardware and software layers. Register and get access to the tracks: https://ods.ai/events/datafest2020

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

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speaker: Evgenii Zheltonozhskii, Technion, Israel Institute of Technology Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

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

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

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Difference between them and GPU. IVA TPU.

Mike Ivanov, AI Architect, IVA Technologies Register and get access to the tracks: https://ods.ai/events/datafest2020

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

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 ShiftCNN: Generalized Low-Precision Architecture for Inference of CNNs

DNN Feature Map Compression using Learned Representation over GF(2) E2X: A Framework to Interpret and Correct DNN Object Detector Prediction Denis Gudovskiy, Senior Deep Learning Engineer at Panasonic USA 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 Speakers: Francesco Paci, GreenWaves Technologies, Maxim Zemlyanikin, Anastasiya Reshetova 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 Simon Thye Andersen, RISC-V Based Neural Network Processor, ANN in FPGAs Register and get access to the tracks: https://ods.ai/events/datafest2020

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Data Fest Online 2020

Open Data Science Open Source track https://ods.ai/tracks/open-sourse-df2020 Project links: https://github.com/albumentations-team/albumentations Register and get access to the tracks: https://ods.ai/events/datafest2020

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Neural networks are prone to learning shortcuts – they often model simple correlations, ignoring more complex ones that potentially generalize better.

For example, a real-world dataset will typically depict cows on green pastures in most images. The most straightforward correlation a classifier can learn to predict the label ”cow” is hence the connection to a green, grass-textured background. One central concept in causality states that a causal generative process is composed of autonomous modules that do not influence each other. Each of those modules controls a single factor of variation (FoV), in our example background and image of the animal itself.

We want to be able to produce counter…

Исследование среды, как известно, является одной из фундаментальных проблем в области обучения с подкреплением. Одним из способов решения данной задачи является использования набора из нескольких агентов с различными политиками. Такой подход позволяет получать данные о разных возможных поведениях агента. Большой сложностью при использовании данного метода является необходимость поддерживать достаточное количество различных политик одновременно с оптимизацией получаемой ими суммарной награды. В рамках данного семинара мы рассмотрим проблемы, возникающие при использовании такого подхода, а также обсудим то, как к их решению подходят авторы статьи Effective Diversity in Population Based Reinfo…

Зависимости проекта — это очень специфичный и любопытный источник информации. С одной стороны, по сравнению с самим кодом проекта они представляются очень маленькими, с другой стороны, они несут в себе чрезвычайно много инофрмации о проекте — подчас одного взгляда на них достаточно, чтобы сказать, о чём проект. С этой точки зрения зависимости можно рассматривать как "скелет" проекта — и, следовательно, на их основании можно сравнивать проекты и делать о них выводы. В нашей работе мы решили исследовать создание эмбеддингов зависимостей и создали прототип рекомендательной системы на их основании. Мы собрали датасет из 7,132 фалов requirements.txt, представляющих собой списки зависимостей прое…

Оптимизационные алгоритмы играют важную роль во всех разделах машинного обучения. В зависимости от дополнительных свойств, которыми обладает целевая функция, строятся алгоритмы, учитывающие данную специфику. Например, относительно несложно оптимизировать выпуклую непрерывную функцию. Однако на практике часто приходится иметь дело с непрерывными функциями, не являющимися выпуклыми. Один из алгоритмов, позволяющих оптимизировать такие функции – CEM (Cross Entropy Method) – упоминался еще в статьях девяностых годов. Тем не менее, с различными модификациями он используется до сих пор. Существенный недостаток этого алгоритма состоит в том, что если целевая функция зависит от параметра, то решени…

На сегодняшний день все большую популярность набирает Self-Supervised Learning – обучение на неразмеченных данных, позволяющее получить скрытые представления каких-либо объектов. Авторы статьи, о которой пойдет речь, применили данный подход при обучении модели на основе трансформера для задачи распознавания речи. Обучение предобученной на неразмеченных данных модели лишь на 10 минутах размеченных аудио позволило достичь 4.8/8.2 WER на тесте clean/other датасета LibriSpeech! На семинаре поговорим про архитектуру и обучение модели. Также, затронем возможные улучшения текущего подхода и обсудим, насколько широко могут быть применены выученные представления аудио. Докладчик: Александра Филимохи…

Обучение с подкреплением идеологически ассоциируется с задачами обучения роботов. Однако, как известно процесс обучения в meta-RL алгоритмах с сенсорным входом (например используя изображение с камеры робота) занимает продолжительное время для достижения достаточного количество симуляций в реальном мире. Техника MELD - meta-RL c динамическим латентным слоем позволяет быстро получать новые модели поведения робота на основе предыдущих результатов обучения и накопленной информации в структуре латентного слоя. На семинаре я расскажу, как авторы статьи, используя MELD сумели научить реального робота вставлять Ethernet кабель расположенный в новом месте, используя моделирование WindowX и всего 8 …

Современные модели обработки естественного языка преимещественно основаны на глубоких нейронных сетях, которые работают по принципу "черного ящика". Поэтому в последнее время вырос интерес к интерпретируемым NLP системам, которые могут раскрыть, как и почему они пришли к тем или иным результатам. В связи с этим возникла проблема оценивания и сравнения таких моделей, отслеживание прогресса в данном направлении. На семинаре мы рассмотрим ERASER benchmark -- инструмент, который позволяет оценивать качество интерпертируемых NLP моделей. Обсудим его структуру, наборы данных и метрики, которые лежат в основе данного инструмента, а также результаты работы некоторых современных моделей. Докладчик: …

Моделям, работающим с графами знаний, для обучения требуются как положительные, так и негативные примеры. При этом в самих графах знаний негативные примеры отсутствуют, а потому их поиск становится нетривиальной задачей. Стандартной практикой является равномерное негативное сэмплирование: для каждого позитивного примера (существующего ребра в графе знаний) негативные примеры отбираются путем замены одной из вершин ребра на случайную из равномерного распределения. У такого подхода есть свои недостатки: он занимает много времени, влечет большое количество ложноотрицательных примеров, часто генерирует некачественные примеры, ведет к проблеме исчезающих градиентов. На семинаре мы подробнее обсу…

Jupyter-ноутбуки (Jupyter notebooks) — среда программирования для группы языков (Julia, Python, R и других), которая получила широкое распространение в последние годы. На данный момент на Github насчитывается более 10 миллионов. .ipynb файлов. Благодаря своей интерактивной природе, ноутбуки чаще всего используются для обучения, прототипирования и выполнения исследовательских задач. На этом семинаре мы будем говорить о том, что делает формат ноутбука востребованным. Мы рассмотрим, какие проблемы возникают у пользователей и какие еще задачи (помимо пользовательских) можно решать с помощью данных из ноутбуков. Подробнее всего мы остановимся на проблеме упорядочивания ноутбука и нашем прототипе…

Задача генерации текста является ключевой в обработке естественного языка. Стандартный подход обучения максимизации правдоподобия с последующим декодингом наиболее вероятной последовательности выдаёт однообразные тексты с повторениями. Для решения этой проблемы были предложены некоторые методы сэмплирования, такие как top-k и nucleus sampling. На семинаре мы рассмотрим новый способ обучения генеративной модели — unlikelihood training, который понижает вероятность повторения слов. При данном способе сгенерированные последовательности содержат меньшее количество повторений, более разнообразны, при этом они сохраняют perplexity при использовании жадного алгоритма сэмплирования или beam search.…

С помощью обучения с подкреплением успешно решается ряд задач в машинном обучении, особенно в игровой индустрии и робототехнике. Однако, обучение с подкреплением полагается на существование функции-награды, с определением которой в отдельных случая возникают трудности – это препятствует использованию алгоритмов на практике. Имитационное обучение или обучение через подражание стремится к решению задач в средах, где определить награду явным образом непросто. Стратегия парадигмы заключается в изучении политики через фиксированное число демонстраций поведения экспертом. На семинаре будут рассмотрены основные аспекты имитационного обучения, а также один из алгоритмов обучения через подражание: P…

До недавнего времени использование LSTM было стандартной практикой для обработки последовательностей. При машинном переводе из одного языка на другой данная модель может запоминать контекст от нескольких десятков до нескольких сотен слов. С появлением Transformer качество машинного перевода сильно улучшилось. Произошло это в том числе и потому, что данная модель позволяет расширить контекст до нескольких тысяч слов. Однако дальнейшее увеличение контекста требует наличие большого количества памяти и вычислений. На семинаре рассмотрим модель Reformer. Данная модель эффективно расходует память и использует Хеширование для быстрого вычисления Attention, что позволяет использовать Reformer для к…

Спортивное программирование — популярный вид деятельности, сочетающий образовательные цели с тренировкой навыков практического программирования, пусть и в весьма специфичном ключе. Как показывает практика, участие в подобного рода активностях не только повышает мотивацию учеников к изучению программирования, но и помогает проходить технические интервью при приёме на работу в IT компании. Одной из самых популярных платформ для тренировок и подготовки к олимпиадам является CodeForces. Для упрощения поиска задач на интересующую пользователя тематику на этом сайте была придумана система тегов (Динамическое программирование, Математика, Строки и т.д.). Однако расставляются такие теги вручную пол…

Одной из важных проблем современного обучения с подкрепление при решении сложных задач является необходимость большого количества взаимодействия с окружением. Одним из решений данной проблемы является использование transfer learning, который позволяет передать опыт, полученный на предыдущих задачах, для обучения новым, более сложным задачам. При использовании данного подхода возникает вопрос о том, как правильно выбирать задачи. Основная цель curriculum learning (CL) — построить план постепенно усложняющихся задач, обучение по которому позволило бы решить исходную задачу быстрее и/или эффективнее, чем при попытке научиться решать ее сразу. Этот процесс можно сравнить с развитием людей: обуч…

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

Yandex Research — это исследовательская группа внутри Яндекса, которая занимается фундаментальными проблемами в важнейших областях computer science и искусственного интеллекта, таких как компьютерное зрение, Natural Language Processing, речевые технологии, краудсорсинг, поиск и рекомендации. В рамках митапа исследователи из Yandex Research и научной лаборатории Яндекса на Факультете Компьютерных Наук НИУ ВШЭ рассказали об интересных задачах, которыми они занимаются, а ещё о том, как стать частью команды. Участники митапа и программа: • Андрей Малинин. Неопределенность в структурных предсказаниях; • Станислав Морозов. Big GANs Are Watching You: о сегментации объектов без учителя с помощью го…

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В этом видео победители AIJ 2020 расскажут о своих решениях задачи по распознаванию почерка Петра I ✍🏻 Подробное описание задачи и лидерборд на странице соревнования https://ods.ai/tracks/aij2020/competitions/aij-petr 🥇 1 место: команда OCRV

Детальное описание решения в слаке ODS https://opendatascience.slack.com/archives/C2LJA6VP0/p1606079747484300 🥈 2 место: Владислав Крамаренко

Код решения https://storage.yandexcloud.net/datasouls-ods/submissions/e4b4ce84-dcac-4c84-bdf2-1bbd02fcb4ad/6e73c568/OCR-transformer.zip 🥉 3 место: Magic City

Код решения https://github.com/ArefievMC/sberbank_petr/blob/main/final_petr.ipynb Дополнительно:

- Рассказ о задаче от организаторов https://www.youtube.com/…

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Спикер: Алексей Григорьев, Lead Data Scientist at OLX Group, Founder at DataTalks.Club Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

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Спикеры: Денис Воротынцев, Data Scientist at Unity, Юрий Болконский, Kaggle Grandmaster Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

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Plush blobs: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning A good place for learning more about how to be less wrong:

https://www.lesswrong.com/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

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- Facebook: facebook.com/primerlearning Streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made possible by support through Patreon:

Christian Gruber

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Support these videos on Patreon: https://www.patreon.com/primerlearning

Store: https://store.dftba.com/collections/primer More on voting theory:

- Interactive by Nicky Case: https://ncase.me/ballot/

- The best single resource I found: https://www.lesswrong.com/posts/D6trAzh6DApKPhbv4/a-voting-theory-primer-for-rationalists Organizations that advocate for voting reform:

- Team Approval: https://electionscience.org/

- Team Instant Runoff: https://www.fairvote.org/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

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Josh Barnett is an MMA fighter, catch wrestler, and a scholar of violence.

Please support this podcast by checking out our sponsors:– Munk Pack: https://munkpack.com and use code LEX to get 20% off– LMNT: https://drinkLMNT.com/lex to get free sample pack– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Rev: https://rev.ai/lex to get 7-day free trialEPISODE LINKS:Josh’s Website: https://www.joshbarnett.com/Josh’s Twitter: https://twitter.com/JoshLBarnettJosh’s Instagram: https://www.instagram.com/joshlbarnettJosh’s Facebook: https://www.facebook.com/JoshBarnettOfficialJosh’s Wikipedia: https://en.wikipedia.org/wiki/Josh_BarnettJosh’s YouTube: https://www.…

Andrew Huberman is a neuroscientist at Stanford.

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Eric Weinstein is a mathematical physicist and podcaster.

Please support this podcast by checking out our sponsors:– Indeed: https://indeed.com/fridman to get $75 credit– Theragun: https://theragun.com/lex to get 30 day trial– Wine Access: https://wineaccess.com/lex to get 20% off first order– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:Eric’s Twitter: https://twitter.com/EricRWeinstein​Eric’s YouTube: https://www.youtube.com/ericweinsteinphdThe Portal podcast: https://podcasts.apple.com/us/podcast/the-portal/id1469999563PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2n…

Jim Keller is a legendary microprocessor engineer, previously at AMD, Apple, Tesla, Intel, and now Tenstorrent.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Brooklinen: https://brooklinen.com and use code LEX to get $25 off + free shipping– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Belcampo: https://belcampo.com/lex and use code LEX to get 20% off first orderEPISODE LINKS:Jim’s Twitter: https://twitter.com/jimkxaJim’s Wiki: https://en.wikipedia.org/wiki/Jim_Keller_(engineer)Tenstorrent: https://www.tenstorrent.com/PODCAST INFO:Podcast website: …

Jason Calacanis is an angel investor, entrepreneur, and co-host of All-In Podcast and This Week in Startups.

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

(00:00) – Introduction(06:52) – WallStreetBets and Robinhood(17:58) – How does the WallStreetBets saga end?

(21:09) – Capitalism(26:30) – Ideas vs Execution(27:38) – Learning to learn(33:06) – Risk-aversion(39:07) – Robinhood(47:33) – Parler and AWS(49:59) – Social networks(56:33) – Leadership(1:01:21) – Work-life balance(1:09:44) – Great leaders lead by example(1:17:30) – Advice for startup founders(1:21:57) – Clubhouse(1:22:42) – When will we fully re-open the economy(1:33:20) – Augmented realit…

Brendan Eich is the creator of JavaScript and co-founder of Mozilla and Brave.

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Richard Craib is the founder of Numerai, a crowd-sourced, AI-run stock trading system.

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(00:00) – Introduction(08:12) – WallStreetBets and GameStop saga(22:25) – Evil shorting and chill shorting(24:31) – Hedge funds(30:04) – Vlad(37:00) – Numerai(1:04:16) – Futre of AI in stock trading(1:09:55) – Numerai data(1:13:37) – Is stock trading gambling or investing?

(1:17:32) – What is money?

(1:20:49) – Cryptocurrency(1:24:06) – Dogecoin(1:28:36) – Advice for startups(1:44:27) – Book recommendations(1:46:29) – Advice for young people(1:50:30) – Meaning of life

Zev Weinstein is a young man who thinks deeply about the world.

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Natalya Bailey is a rocket propulsion engineer from MIT and now CTO of Accion Systems.

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Tim Dillon is a comedian and podcaster.

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(00:00) – Introduction(06:37) – Will AGI suffer from depression?

(11:14) – Love is an escape from the muck of life(17:14) – What questions would you ask an alien?

(25:58) – How to pivot careers to computer science(33:06) – What will robots look like in the future?

(35:54) – Disagreement with Einstein about happiness(41:50) – How I pick podcast guests(51:26) – How to stay optimistic about the future(59:09) – Major topics I changed my mind on(1:06:39) – Benefits of keto diet(1:16:02) – Darkest time in my life

Max Tegmark is a physicist and AI researcher at MIT.

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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?

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

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Video Anomaly DetectionYuqi Ouyang, in his second year of PhD study at the University of Warwick in England, joins us today to discuss his work Video Anomaly Detection by Estimating Likelihood of Representations.

Fault Tolerant Distributed Gradient DescentNirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.

Conference Details:https://georgetown.zoom.us/meeting/register/tJ0sc-2grDwjEtfnLI0zPnN-GwkDvJdaOxXF

Decentralized Information GatheringMikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements.

Follow Mikko: @mikko_lauri

Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper “Taming the Contention in Consensus-based Distributed Systems.” Works Mentioned “Taming the Contention in Consensus-based Distributed Systems” by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindranhttps://www.ssrg.ece.vt.edu/papers/tdsc20-author-version.pdf “Fast Paxos” by Leslie Lamport https://link.springer.com/article/10.1007/s00446-006-0005-x

Maartje der Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper "What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization."

Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work “Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives". WORKS MENTIONED: Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives by Brian Brubach, Aravind Srinivasan, and Shawn Zhao

Even Cooperative Chess is HardAsside from victory questions like “can black force a checkmate on white in 5 moves?” many novel questions can be asked about a game of chess.

Some questions are trivial (e.g.

“How many pieces does white have?")

while more computationally challenging questions can contribute interesting results in computational complexity theory.

In this episode, Josh Brunner joins us to discuss his recent paper Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard.

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.

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. …

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Ayodele Odubela joins us to discuss fairness in AI and how we can work towards a more equitable and transparent world of data science and machine learning.

In this episode you will learn:• Comet ML [3:22]• What is a d…

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Michael Segala joins us to discuss how machine learning can provide creative and novel solutions to longstanding problems in both the private and public sectors.

In this episode you will learn:• SFL Scientific [4:20]•…

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Sinan Ozdemir joins us to share his work in conversational AI and what it takes to keep chatbots up to date and functional in an ever-changing world.

In this episode you will learn:• Kylie.ai under Directly [4:51]• Si…

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Jeff Wald joins us to discuss his book and the research he has done into the data and trends around the job market, the decline of the 9-5 office job, and more.

In this episode you will learn:• The Birthday Rules [3:51…

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Kate Strachnyi joins us to discuss her work in data visualization education from conferences to published books as well as her tips for visualization best practices.

In this episode you will learn:• What does Kate do (…

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Deblina Bhattacharjee joins us to talk about her amazing work in computer vision and give advice for getting into and excelling in the field.

In this episode you will learn:• Deblina’s master’s program work [4:03]• De…

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Claudia Perlich joins us to discuss her work at one of the world’s largest hedge funds and how she got to work there, as well as her history of winning data science competitions.

In this episode you will learn:• Life a…

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March 2, 2021 podcastThis is one of the most dynamic and fascinating topics: API technologies for machine learning.

In this episode I speak about three must-know technologies to place your model behind an API.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love ThoughtWorks Technology Podcast.

It’s a podcast for techies by techies.

Their team of experienced technologists take a deep dive into a tech topic that’s piqued their interest — it could be how machine learning is being used in astrophysics or maybe how to succeed at c…

February 24, 2021 podcastIn this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love Thoug…

February 15, 2021 podcastThe financial system is changing.

It is becoming more efficient and integrated with many more services making our life more… digital.

Is the old banking system doomed to fail?

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will…

Our ServicesAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

AI/ML Fintech Healthcare/RWE Predictive maintenanceWe provide solutions in:

February 1, 2021 podcastIn this podcast I get inspired by Paul Done‘s presentation about The Six Principles for Building Robust Yet Flexible Shared Data Applications, and show how powerful of a language Rust is while still maintaining the flexibility of less strict languages.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Art…

January 25, 2021 podcastIn this episode I explain the basics of computer architecture and introduce some features of the Apple M1Is it good for Machine Learning tasks?

January 18, 2021 podcastIn this episode I speak with Daniel McKenna about Rust, machine learning and artificial intelligence.

You can find Daniel fromDon’t forget to come join me in our Discord channel speaking about all things data science.

Subscribe to the official Newsletter and never miss an episode

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.

Follow me on Twitch during my live coding sessions usually in Rust and PythonOur Sponsors

🎙 Пилотный выпуск нашего подкаста! В гостях у нас Евгений Кашин – ML-инженер из Snap Inc. С Женей мы успели обсудить кучу вещей: Из Воронежа в Лондон 🇬🇧

Snapchat и маски 😷

Будущие прорывы в DS и AR 🚀

Вопросы от русской GPT-3 🤖 … и многое другое 😉

Домашнее задание: отгадать, какие именно вопросы были сгенерированы нейросетью 🦾😄

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