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Converting numbers to datetimePandas has 2 built-in methods astype() and to_datetime() that can be used to convert numbers to datetime.

>>> df = pd.DataFrame({'date':['3/10/2015','3/11/2015','3/12/2015'],'value': [2, 3, 4]})>>> df.dtypes date objectvalue int64dtype: objectBoth to_datetime() and astype() can be used to convert strings to datetime.

>>> pd.to_datetime(df['date']) 0 2015-03-101 2015-03-112 2015-03-12Name: date, dtype: datetime64[ns] >>> df['date'].astype('datetime64') 0 2015-03-101 2015-03-112 2015-03-12Name: date, dtype: datetime64[ns]3.

df = pd.DataFrame({'year': [2015, 2016],'month': [2, 3],'day': [4, 5],'hour': [10,11]})To create a datetime column from a subset of columns>>…

Periodicity of Scripted Network AttacksIntroductionScripted network attacks refer to attacks automatically generated using automated scripts e.g DOS attacks and Probe attacks.

The frequency mining strategy is based on applying Fourier analysis to network traffic time series to test for periodicity.

Network monitors capture network traffic ‘signals’ from multiple communication channels in an attempt to pre-empt and mitigate cyber attacks before they happen.

Here, I will use examples of automated attacks on Friday to examine the frequency domain distribution of scripted attacks.

Network traffic is recorded in terms of individual network packets sent which consist of a header and a payload.

Announcing Feast 0.10Photo by Pietro Jeng on UnsplashA simpler feature storeToday, we’re announcing Feast 0.10, an important milestone towards our vision for a lightweight feature store.

With Feast 0.10, we’ve dramatically simplified the process of managing a feature store.

The field is used to uniquely identify a feature store, the is a source of truth for feature definitions, and the specifies the environment in which our feature store will run.

Stay tuned for more news, and we’d love for you to get started using Feast 0.10 today!

📢 Register for apply() — the ML data engineering conference, where we’ll demo Feast 0.10 and discuss future developments for AWS.

Figure 10 — Customer response frequency in training data (image by author)As the training data set has almost 43,000 samples, figure 10 shows that the response variable is significantly unbalanced.

Since this is the target variable that the prediction models will use, it would be a challenge to classify whether an individual will become a customer in the testing data.

For the testing data, 55% of actual customers were predicted correctly and 2% of the predicted customers were correctly classified.

For the testing data, 26% of actual customers were predicted correctly and 2% of predicted customers were correctly classified.

In the testing data, 10% of actual customers were predicted correctl…

Power BI — How to fit 200 million rows in less than 1GB!

Check how to pack 200 million rows in 18 MB of Power BI!

On top of that, applying aggregations within the Power BI data model will bring even more benefit, as you will see later.

Image by authorSince I’ve imported all the data in Power BI, I will check the metrics behind my data model.

The original request was to move a data model from SSAS cube to Power BI Pro workspace, without losing any of 200 million rows from the fact table!

MLOps vs DevOpsThis article goes through the similarities and differences between DevOps and MLOps as well as platforms that help enable MLOps Marco Susilo Just now·3 min readPhoto by HalGatewood.com on UnsplashAs the field of machine learning has matured in recent years, the need for integrating automatic continuous integration (CI), continuous delivery (CD) and continuous training (CT) to machine learning systems has increased.

The application of DevOps philosophy to a machine learning system has been termed MLOps.

The aim of MLOps is to fuse together the machine learning system development (ML) and machine learning system operation (Ops) together.

A machine learning system is similar but…

You go on LinkedIn and see how often people are getting new jobs and you immediately compare yourself to them.

You have no idea who this person is other than they secured a job while you are still looking.

Comparison is the thief of all joy, and it is especially true in the job application process.

Apply to 1 job a day?

If you are used to the job application, and you are used to consistently being rejected, keep going.

Notable Nodes: Identifying Influencers with Network AnalysisMy dog loves napping in his super-fuzzy dog bed.

Alteryx has network analysis capabilities that can help you identify these people and determine whether they’re a good fit for your needs.

Let’s take a closer look at the Network Analysis Tool and build our own workflow to identify potential Twitter influencers.

There’s a static image below, but you can also check out the interactive dashboard, which is available in Designer from the I output of the Network Analysis Tool.

In addition to exploring the interactive diagram, I can also use the numeric output from the Network Analysis Tool to examine my potential influencers more closely.

Some theoryUsually, the process of data analysis includes these five steps:Research objectives Gathering data Data preparation Data exploration Interpreting resultsSetting the purpose of the research means that we have to know, what question(s) need to be answered.

Gathering dataThe raw data for this research are taken from Google Fit Application which I installed on my phone in September 2019.

For Fit Application, Google provides a set of files for each day of the given period and the file with aggregated data.

We will not work with geographic data in this analysis, so we don’t need columns with latitude and longitude as well.

ConclusionSo we explored data from the Google Fit application f…

It's worth noting that the top five models are all either decision trees or random forests and linear regression models.

Linear regression assumes a linear problem.

3rd place — 2163 MSEThe third-place finisher built a linear regression model with some added features and used LassoCV.

Lasso regression is a type of linear regression that uses shrinkage.

This student's linear regression model with LassoCV produced an MSE of 2163 earning a third-place finish.

Advice for Aspiring Data ScientistsA few years ago I wrote a piece called “Advice for New Data Scientists,” based on my time on Airbnb’s Data Science Team.

So I’ve decided to try to synthesize my advice on breaking into your first data science role.

Most people think this basic training is enough to get a first job in data science.

If you take nothing else from this piece: build a portfolio, ideally in Github, that showcases your data science skills.

Others may discuss a data science challenge that needs to be solved — how would you solve it?

However, simple heuristics have found predictive success in domains including sports, medicine, finance and politics.

This isn’t to say, however, that 1/N should be the go-to, only that there are environments and circumstances in which simple heuristics can outperform complex strategies.

Why is it that simple heuristics can outperform more complex strategies?

Model accuracy (in fitting and predicting) vs complexity (the number of free parameters estimated in the model).

And in cases where simple strategies are just as successful as complex ones, it makes sense to save time (and money!).

5 Examples to Compare Python Pandas and R data.tablePhoto by Katka Pavlickova on UnsplashPython and R are the two predominant languages in the data science ecosystem.

Both of them offer a rich selection of libraries that expedite and improve data science workflow.

In this article, we will compare pandas and data.table, two popular data analysis and manipulation libraries for Python and R, respectively.

The examples we will cover are the common data analysis and manipulation operations.

I will be using Google Colab (for pandas) and RStudio (for data.table) as IDE.

Visual Introduction to Singular Value Decomposition (SVD)(image by author)In this article, you’ll learn about Singular value decomposition (SVD), which is a major topic of linear algebra, data science, and machine learning.

As eigendecomposition, the goal of singular value decomposition (SVD) is to decompose a matrix into simpler components: orthogonal and diagonal matrices.

To represent the unit circle and the basis vectors before the transformation, let’s use this function using the identity matrix:Figure 1: The unit circle and the basis vectors.

It will plot the unit circle and the basis vectors transformed by the matrix:Figure 2: Effect of the matrix A on the unit circle and the basis v…

What is “Artificial General Intelligence”?

The course was entitled CS 1501: Artificial General Intelligence, and was taught for three semesters at UVA to almost 150 students, even receiving a teaching award from the UVA CS faculty.

This new web series will introduce these questions by looking at a specific superset of AI: Artificial General Intelligence — or general-purpose AI.

Another “breed” of intelligence that is scientifically backed is Fluid Intelligence and Crystallized Intelligence.

We will do what all great people in modern history do when asked a question: we look at Wikipedia:Wikipedia: Artificial general intelligence (AGI) is the intelligence of a machine that can understand or …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dr. Timnit Gebru is one of those few.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What can we do to improve peer review?

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

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

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

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

What can we do to improve peer review?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nvidia remains the undisputed leader in AI-first chips, but its footprint in the AI industry is rapidly expanding beyond that domain.

During the GTC conference, Nvidia announced new products across a wide spectrum of AI hardware and software categories, which makes it extremely clear that the chip giant is adopting a holistic view of the AI ecosystem.

The trend of machine learning platforms optimized for Nvidia hardware architectures is not new but it's certainly expanding rapidly.

All things considered, Nvidia can be considered one of the companies with the most solid network effects in the entire AI space.

💬 Useful TweetMost promising AI companies, according to CBInsightsFollow us on Twit…

We’re excited to partner with Tecton on apply(), the first community conference for ML data engineering.

This is a free, online event where users and thought leaders will share their experiences and best practices on ML data engineering.

Speakers include practitioners from Google, Microsoft, LinkedIn, Netflix, DoorDash, Spotify, Pinterest, Snorkel, Fiddler, Provectus, Algorithmia, Confluent, Stitch Fix, and more.

Hien Luu and Arbaz Khan from DoorDash will share their journey at the session: Scaling Online ML Predictions to Meet DoorDash Logistics Engine and Marketplace Growth.

Aakash Sabharwal and Sheila Hu from Etsy unfold the way towards a unified real-time ML data pipeline.

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.

Today we announce 🧠 The Quiz winners: r…@virtusa.com and jaya.s@…com rece…

In this issue:we explain what few-shot learning is;we explore prototypical networks as one of the most popular few-shot learning architectures;

📝 EditorialMLOps is one of the most popular and overloaded terms in modern machine learning.

As a result, it becomes really confusing for organizations and data science teams trying to assemble MLOps capabilities in their ML pipelines.

So don’t feel bad if you are confused about MLOps😉The overcrowding of MLOps is a result of the tremendous levels of innovation in the machine learning space.

The second relevant group is end-to-end MLOps runtimes such as KubeFlow or MLFlow that manage many aspects of the lifecycle of machine learning solutions.

Finally, we have startups that are focusing on individual features of machine learning pipelines like training or monitoring.

AutoML recap is a collection of ten issues where we covered the evolution of the Automated Machine Learning (AutoML) space, the most relevant concepts, technologies, and research papers.

Get access to the full archive💡 Understanding AutoML and its Different DisciplinesThe fascination with AutoML is rooted in this idea of using machine learning to create better machine learning models.

In Edge#61, we provide an overview of the original AutoML paper and show how Amazon AutoGluon Brings Deep Learning to AutoML.

We also explore H2O AutoML and look into how DeepMind and Waymo use AutoML to train self-driving cars.

In this issue, we also speak about how Amazon uses AutoML for the entire lifecycle…

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.

Getting to know the experience gained by researchers, engineers and entrepreneurs doing real ML work can become a great source of insights and inspiration.

🛠 ML WorkArthur AI is trying to solve one of the most relevant challenges in the current state of ML.

When I was deploying ML systems at Capital One, there were no available solutions for this problem, and it kept me awake at night.

What best practices and techniques ML teams can follow to ensure fairness and minimize bias in ML models?

ML interpretability is a highly diverse problem that has sparked many interesting ideas to explain ML models’ behavior.

In this issue:we discuss what a Feature Store is;we tell the story of how Uber Michelangelo began the Feature Store movement;we explore the feature store market.

Despite the rise in popularity, the adoption of feature stores in real-world machine learning applications remains relatively low.

In addition to these three key building blocks, feature store platforms enable all sorts of complementary capabilities such as feature versioning, usage tracking, lifecycle monitoring, and many others.

Delivery Mode: On-premise and CloudEarly Adopters: Google, GoJek, Zulily, Agoda …AWS got into the feature store space with the launch of the SageMaker Feature Store.

The questions are the following:Which …

📝 EditorialWe typically associate fairness issues in machine learning (ML) models with large consumer tech startups like Facebook, Apple and Twitter.

While these examples are certainly visible and impactful, we should also understand that fairness is one of the hardest problems of modern ML.

To put some context to the monumental challenge that companies like Apple or Facebook face when it comes to ML fairness, let’s try to think about what we are doing to introduce fairness in our small-scale ML solutions.

I am going to guess that very little 😉 And this is not surprising because building ML fairness constructs is brutally hard.

Certainly, fairness mechanisms in ML models are not an exclusiv…

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.

Are you on Twitter?

Follow us there, we post our favorite math paradoxes, …

In this issue:we explain what N-Shot Learning is;we explore how OpenAI Uses One-Shot learning to teach AI agents to play;we introduce learn2learn, an open-source meta-learning framework.

Share💡 ML Concept of the Day: What is N-Shot Learning?

In the next few editions of TheSequence, we will discuss an emerging group of deep learning methods focused on build…

In the current state of the ML ecosystem dominated by supervised learning models, the mantra is to go big.

Bigger deep learning models tend to outperform smaller versions in most deep learning scenarios.

Model compression is one of the techniques that helps address those limitations.

As its name indicates, model compression tries to reduce the size of a given model without drastically sacrificing its performance.

So when it comes to large scale ML problems, definitely go big first but then compress.

What are the main challenges that organizations encounter when starting to build ML solutions in real world environments?

What are some of the things data science teams should consider when trying to adopt cutting edge ML research methods in practical applications?

Computer vision seems to dominate the headlines when comes to ML solutions in healthcare.

So, to answer your question, healthcare has it all: NLP, computer vision, recommendations, and a whole lot more.

In your experience, what are the top 3-5 practical components of modern ML solutions that most data science teams tend to overlook?

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IntroductionContinue reading on SyncedReview »

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

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

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

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

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

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

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

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

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

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

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

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

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

GShard: Scaling Giant Mo…

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

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 →

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Peer Re…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 was pretty lucky to get an industry lab research offer.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

markdown 375 2019 - 12 - 25 - neurips - 2019. markdown 402 2020 - 01 - 17 - berkeley - back - pay .

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

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

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

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

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

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

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

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

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

Google implemented the similar approach in their neural machi…

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

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

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

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

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…

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

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

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

The second term is what Barret a…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Inventing Ourselves: Responsibility and Diversity in Research.

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

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

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

Despite the imp…

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

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

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

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

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

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

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

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

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

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

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…

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

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

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

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

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

When are Neural Networks more powerful than Neural Tangent Kernels?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Can implicit regularization in deep learning be explained by norms?

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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).

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

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

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

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

I think the really A…

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

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

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

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

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

Hashtag annotation for microblog posts has been recently formulated as a sequence generation problem to handle emerging hashtags that are unseen in the training set.

The state-of-the-art method leverages conversations initiated by posts to enrich contextual information for the short posts...

However, it is unrealistic to assume the existence of conversations before the hashtag annotation itself.

Therefore, we propose to leverage news articles published before the microblog post to generate hashtags following a Retriever-Generator framework.

Extensive experiments on English Twitter datasets demonstrate superior performance and significant advantages of leveraging news articles to generate ha…

Single image dehazing is a challenging ill-posed problem due to the severe information degeneration.

However, existing deep learning based dehazing methods only adopt clear images as positive samples to guide the training of dehazing network while negative information is unexploited...

CR ensures that the restored image is pulled to closer to the clear image and pushed to far away from the hazy image in the representation space.

Furthermore, considering trade-off between performance and memory storage, we develop a compact dehazing network based on autoencoder-like (AE) framework.

We term our dehazing network with autoencoder and contrastive regularization as AECR-Net.

In human vision objects and their parts can be visually recognized from purely spatial or purely temporal information but the mechanisms integrating space and time are poorly understood.

Here we show that human visual recognition of objects and actions can be achieved by efficiently combining spatial and motion cues in configurations where each source on its own is insufficient for recognition...

This analysis is obtained by identifying minimal videos: these are short and tiny video clips in which objects, parts, and actions can be reliably recognized, but any reduction in either space or time makes them unrecognizable.

State-of-the-art deep networks for dynamic visual recognition cannot re…

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds.

Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups...

In this work we provide a completely general algorithm for solving for the equivariant layers of matrix groups.

In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before, including O(1, 3), O(5), Sp(n), and the Rubik’s cube group.

We release our software library to enable researchers to construct equivariant layers fo…

Among recent advances in SISR, attention mechanisms are crucial for high performance SR models...

However, few works really discuss why attention works and how it works.

In this work, we attempt to quantify and visualize the static attention mechanisms and show that not all attention modules are equally beneficial.

We then propose attention in attention network (A$^2$N) for highly accurate image SR.

This allows attention modules to specialize to beneficial examples without otherwise penalties and thus greatly improve the capacity of the attention network with little parameter overhead.

The advance of image editing techniques allows users to create artistic works, but the manipulated regions may be incompatible with the background.

Localizing the inharmonious region is an appealing yet challenging task...

Realizing that this task requires effective aggregation of multi-scale contextual information and suppression of redundant information, we design novel Bi-directional Feature Integration (BFI) block and Global-context Guided Decoder (GGD) block to fuse multi-scale features in the encoder and decoder respectively.

We also employ Mask-guided Dual Attention (MDA) block between the encoder and decoder to suppress the redundant information.

Experiments on the image harmonizati…

Today's image prediction methods struggle to change the locations of objects in a scene, producing blurry images that average over the many positions they might occupy.

In this paper, we propose a simple change to existing image similarity metrics that makes them more robust to positional errors: we match the images using optical flow, then measure the visual similarity of corresponding pixels...

This change leads to crisper and more perceptually accurate predictions, and can be used with any image prediction network.

We apply our method to predicting future frames of a video, where it obtains strong performance with simple, off-the-shelf architectures.

(read more)

In this report, we introduce the technical details of our submission to the VIPriors object detection challenge.

Our solution is based on mmdetction of a strong baseline open-source detection toolbox... Firstly, we introduce an effective data augmentation method to address the lack of data problem, which contains bbox-jitter, grid-mask, and mix-up.

Secondly, we present a robust region of interest (ROI) extraction method to learn more significant ROI features via embedding global context features.

Thirdly, we propose a multi-model integration strategy to refinement the prediction box, which weighted boxes fusion (WBF).

Experimental results demonstrate that our approach can significantly impr…

In this work, we build upon the Active Inference (AIF) and Predictive Coding (PC) frameworks to propose a neural architecture comprising a generative model for sensory prediction, and a distinct generative model for motor trajectories.

We highlight how sequences of sensory predictions can act as rails guiding learning, control and online adaptation of motor trajectories... We furthermore inquire the effects of bidirectional interactions between the motor and the visual modules.

The architecture is tested on the control of a simulated robotic arm learning to reproduce handwritten letters.

(read more)

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

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

Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting...

a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light.

Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models.

Therefore, we propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention.

We experimentally validate the efficacy of our approach in urban settings involving complex scenarios using the CARLA urban driving simulator.

Mapping a single exposure low dynamic range (LDR) image into a high dynamic range (HDR) is considered among the most strenuous image to image translation tasks due to exposure-related missing information.

This study tackles the challenges of single-shot LDR to HDR mapping by proposing a novel two-stage deep network...

Notably, our proposed method aims to reconstruct an HDR image without knowing hardware information, including camera response function (CRF) and exposure settings.

The qualitative and quantitative comparisons demonstrate that the proposed method can outperform the existing LDR to HDR works with a marginal difference.

Apart from that, we collected an LDR image dataset incorpora…

Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning.

This problem is known as continual learning and has been particularly popular in the domain of computer vision, where several techniques to attack it have been developed.

This paper aims to assess to what extent such continual learning techniques can be applied to the HAR domain.

To this end, we propose a general framework to evaluate the performance of such techniques on various types of commonly used HAR datasets.

We then present a comprehensiv…

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".

The challenge addresses the growing need for reliable predictions within the boundaries of a shopping session, as customer intentions can be different depending on the occasion...

The need for efficient procedures for personalization is even clearer if we consider the e-commerce landscape more broadly: outside of giant digital retailers, the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers.

We release a new session-based dataset including more than 30M fine-gr…

Pixel binning is considered one of the most prominent solutions to tackle the hardware limitation of smartphone cameras.

In this paper, we tackle the challenges of joint demosaicing and denoising (JDD) on such an image sensor by introducing a novel learning-based method.

The proposed method leverages the depth and spatial attention in a deep network.

On top of that, we stretch the proposed image processing pipeline to comprehensively reconstruct and enhance the images captured with a smartphone camera, which uses pixel binning techniques.

The experimental results illustrate that the proposed method can outperform the existing methods by a noticeable margin in qualitative and quantitative co…

One popular state-of-the-art technique that specializes in continuously refining the model via failure identification is Learning Loss.

Our paper develops a foundation for Learning Loss which enables us to propose a novel modification we call LearningLoss++.

We show that gradients are crucial in interpreting how Learning Loss works, with rigorous analysis and comparison of the gradients between Learning Loss and LearningLoss++.

We validate LearningLoss++ for regression on the task of human pose estimation (using MPII and LSP datasets), as done in Learning Loss.

We show that LearningLoss++ outperforms in identifying scenarios where the model is likely to perform poorly, which on model refine…

In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks.

Besides overall speech quality, the model also predicts the four speech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness, and in this way gives more insight into the cause of a quality degradation.

Furthermore, new datasets with over 13,000 speech files were created for training and validation of the model.

Overall, NISQA was trained and evaluated on 81 datasets from different sources and showed to provide reliable predictions also for unknown speech samples.

The code, model weights, and datasets are open-sourced.

Domain adaptation enhances generalizability of a model across domains with domain shifts.

Most research effort has been spent on Unsupervised Domain Adaption (UDA) which trains a model jointly with labeled source data and unlabeled target data...

This paper studies how much it can help address domain shifts if we further have a few target samples (e.g., one sample per class) labeled.

This is the so-called semi-supervised domain adaptation (SSDA) problem and the few labeled target samples are termed as ``landmarks''.

Moreover, we apply consistency learning on unlabeled target images, by perturbing each image with light transformations and strong transformations.

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach.

The proposed solution integrates several state-of-the-art topic models and evaluation metrics...

These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration.

OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the be…

In this paper, we introduce a new benchmark dataset for the challenging writing in the air (WiTA) task -- an elaborate task bridging vision and NLP.

WiTA implements an intuitive and natural writing method with finger movement for human-computer interaction (HCI)... Our WiTA dataset will facilitate the development of data-driven WiTA systems which thus far have displayed unsatisfactory performance -- due to lack of dataset as well as traditional statistical models they have adopted.

Our dataset consists of five sub-datasets in two languages (Korean and English) and amounts to 209,926 video instances from 122 participants.

We capture finger movement for WiTA with RGB cameras to ensure wide ac…

The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations.

Current weakly-supervised counting methods adopt the CNN to regress a total count of the crowd by an image-to-count paradigm.

In this paper, we propose TransCrowd, which reformulates the weakly-supervised crowd counting problem from the perspective of sequence-to-count based on Transformer.

To the best of our knowledge, this is the first work to adopt a pure Transformer for crowd counting research.

Experiments on five benchmark datasets demonstrate that the proposed TransCrowd achieves superior performance compared with all the weakly-superv…

Although recent inpainting approaches have demonstrated significant improvements with deep neural networks, they still suffer from artifacts such as blunt structures and abrupt colors when filling in the missing regions.

To address these issues, we propose an external-internal inpainting scheme with a monochromic bottleneck that helps image inpainting models remove these artifacts...

In the external learning stage, we reconstruct missing structures and details in the monochromic space to reduce the learning dimension.

In the internal learning stage, we propose a novel internal color propagation method with progressive learning strategies for consistent color restoration.

Extensive experimen…

The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years.

In this paper, we set out to restore the misclassified bounding boxes, i.e., fake background, by proposing a re-check network.

The re-check network propagates previous tracklets to the current frame by exploring the relation between cross-frame temporal cues and current candidates using the modified cross-correlation layer.

The propagation results help to reload the "fake background" and eventually repair the broken tracklets.

By inserting the re-check network to a strong baseline tracker CSTrack (a variant of JDE), ou…

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds.

Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups...

In this work we provide a completely general algorithm for solving for the equivariant layers of matrix groups.

In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before, including $\mathrm{O}(1,3)$, $\mathrm{O}(5)$, $\mathrm{Sp}(n)$, and the Rubik's cube group.

We release our software library to enable researchers to…

Learning with labels noise has gained significant traction recently due to the sensitivity of deep neural networks under label noise under common loss functions.

Losses that are theoretically robust to label noise, however, often makes training difficult... Consequently, several recently proposed methods, such as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to learn a weighting function that downweights samples that are likely to have corrupted labels under the meta-learning framework.

However, obtaining such a set of clean samples is not always feasible in practice.

In this paper, we analytically show that one can easily train MW-Net without access to clean sampl…

In open set recognition, a classifier has to detect unknown classes that are not known at training time.

Recently proposed Capsule Networks have shown to outperform alternatives in many fields, particularly in image recognition, however they have not been fully applied yet to open-set recognition.

In capsule networks, scalar neurons are replaced by capsule vectors or matrices, whose entries represent different properties of objects.

In our proposal, during training, capsules features of the same known class are encouraged to match a pre-defined gaussian, one for each class.

We conducted several experiments and ablation of our model, obtaining state of the art results on different datasets i…

Given a single RGB panorama, the goal of 3D layout reconstruction is to estimate the room layout by predicting the corners, floor boundary, and ceiling boundary.

A common approach has been to use standard convolutional networks to predict the corners and boundaries, followed by post-processing to generate the 3D layout...

However, the space-varying distortions in panoramic images are not compatible with the translational equivariance property of standard convolutions, thus degrading performance.

The resulting network, which we call OmniLayout performs convolutions directly on the sphere surface, sampling according to inverse equirectangular projection and hence invariant to equirectangular …

The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.

The adversarial neural transfer (ANT) framework utilizes multiple loss terms that encourage the source-domain and the target-domain feature distributions to be similar while optimizing for domain-specific performance...

However, these objectives may be in conflict, which can lead to optimization difficulties and sometimes diminished transfer.

We propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics.

The proposed method outperforms transfer learning and meta-learning baselines.

Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data.

Recently, another type of method using semi-supervised learning (SSL) has been proposed, which augments these supervised robust methods to exploit (possibly) noisy samples more effectively.

Although supervised robust methods perform well across different data types, they have been shown to be inferior to the SSL methods on image classification tasks under label noise.

In this paper, we show that by initializing supervised robust methods using representations learned through contrastive learning leads to significantly improved performance under label …

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

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

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…

Multi-task data collection across multiple robots where different robots collect data for different tasks.

Large-Scale Multi-Task Data Collection SystemThe cornerstone for both MT-Opt and Actionable Models is the volume and quality of training data.

To that end, we create a scalable and intuitive multi-task success detector using data from all of the chosen tasks.

To further improve the performance, we focus data collection on underperforming tasks, rather than collecting data uniformly across tasks.

This post is based on two papers, "MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale" and "Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills,"…

There are a lot of great things happening at Google Cloud, and we’re delighted to share new product announcements, customer perspectives, interactive demos, and more through Google Cloud Summit series, a collection of digital events taking place over the coming months.

Join us to learn more about how Google Cloud is transforming businesses in various industries, including Financial Services, Manufacturing & Supply Chain, and Retail & Consumer Goods.

The summits kick off in May with the Google Data Cloud Summit (May 26 | Global) and Financial Services Summit (May 27 | Global & EMEA), with more to follow.

Hear from CEO Thomas Kurian and VP of Financial Services Solutions Derek White as they s…

This year, Stanford and Google are proud to announce a new version of the iGibson Challenge on Interactive and Social Navigation, one of the 10 active visual challenges affiliated with the Second Embodied AI Workshop at CVPR 2021.

In addition, this year’s interactive and social iGibson challenge explores interactive navigation and social navigation — how robots can learn to interact with people and objects in their environments — by combining the iGibson simulator, the Google Scanned Objects Dataset, and simulated pedestrians within realistic human environments.

New Features of the iGibson 2021 DatasetTo facilitate research into techniques that address these problems, the iGibson Challenge …

Because of its complexity, 3D animation is generally practiced by teams of skilled specialists and is inaccessible to almost everyone else, despite decades of advances in technology and tools.

Creating a walk cycle using Monster Mash.

Here you can see a few of the animated characters that have been created using Monster Mash.

The original hand-drawn outline used to create each 3D model is visible as an inset above each character.

All of the code for Monster Mash is available as open source, and you can watch our presentation and read our paper from SIGGRAPH Asia 2020 to learn more.

A potential reason for this is that many companies underestimate how hard it is to implement a virtual agent that can interact with a customer appropriately and naturally.

In more severe cases, inadequate attention to virtual agent design can harm your brand credibility or even cause a PR crisis.

Like an architect designing a house, they create blueprints for the virtual agent to use when interacting with customers.

They leverage professional human language skills to bring natural human speech patterns to the human to virtual agent interaction flows.

Read this to learn more about using Dialogflow CX to build virtual agents.

The lightweight wrapper can help organize your PyTorch code into modules, and it provides useful functions for common tasks.

One really nice feature of Lightning is being able to train on any hardware without changing your core model code.

In this blog post, we'll see how easy it is to start training models with Lightning on TPUs.

TPUs, or Tensor Processing Units, are specialized ML processors that can dramatically accelerate the time to train your model.

If you're new to TPUs, the blog post What makes TPUs fine-tuned for deep learning?

Case study backgroundGoogle partnered with several leading conservation organizations to build a project known as Wildlife Insights, which is a web app that enables people to upload, manage, and identify images of wildlife from camera traps.

The intention is for anyone in the world who wishes to protect wildlife populations and take inventory of their health, to do so in a non-invasive way.

The models built by the inter-organizational collaboration, presently classifies up to 732 species and includes region-based logic such as preventing a camera trap in Asia—for example—from classifying an African elephant (using geo-fencing).

These models have been in development for several years, and ar…

Editor’s note: Here we take a look at how Branch, a fintech startup, built their data platform with BigQuery and other Google Cloud solutions that democratized data for their analysts and scientists. As a startup in the fintech sector, Branch helps redefine the future of work by building innovative, simple-to-use tech solutions. We’re an employer payments platform, helping businesses provide faster pay and fee-free digital banking to their employees. As head of the Behavioral and Data Science team, I was tapped last year to build out Branch’s team and data platform. I brought my enthusiasm for Google Cloud and its easy-to-use solutions to the first day on the job. We chose Google Cloud for …

For anyone with a sweet tooth for confections and machine learning, I have some good news.

All of this was done through a baking ML model that they built with AutoML Tables, a no-code way to create machine learning models on tabular data.

Well, it wasn’t long before legendary confectionery manufacturer Mars Wrigley approached Sara and Cloud AI for a Maltesers + AI kitchen collaboration.

Sara trained a new ML model to generate recipes for cookies, cakes, scones, traybakes, and any “hy-bread” of these.

To break it down, Sara used a few tools to build and customize her model:

In March 2020 we introduced the Research Scholar Program, an effort focused on developing collaborations with new professors and encouraging the formation of long-term relationships with the academic community.

In November we opened the inaugural call for proposals for this program, which was received with enthusiastic interest from faculty who are working on cutting edge research across many research areas in computer science, including machine learning, human computer interaction, health research, systems and more.

Of the 86 award recipients, 43% identify as an historically marginalized group within technology.

Please see the full list of 2021 recipients on our web page, as well as in the…

Today, I want to outline why Google Cloud is uniquely positioned to be an excellent partner for Block.one and other distributed ledger technology (DLT) companies.

The EOSIO protocol, developed by Block.one and the basis for the EOS Public Blockchain, is an example of such a DLT.

This is where Google Cloud comes in.

Confidential Computing is available in nine Google Cloud regions and will continue to extend to a broader set of the regions to support customers like Block.one.

Confidential VMs followed by Confidential GKE Nodes are the first two products in Google Cloud’s Confidential Computing portfolio.

Google Cloud is offering no-cost training opportunities to help you gain the latest AI and machine learning skills. You’ll have a chance to learn more about the new Document AI along with Explainable AI, Looker, BigQuery ML, and Dialogflow CX. Document AIThe new Document AI (DocAI) platform, a unified console for document processing, became available for preview in November. Join me on April 22 to learn how to set up the Document AI Platform, process sample documents in an AI Platform notebook, and use the Procurement DocAI solution to intelligently process your unstructured data or "dark data" such as PDFs, images and handwritten forms to reduce the manual overhead of your document workflo…

Misinformation can have dramatic consequences on people’s lives — from finding reliable information on everything from elections to vaccinations — and the pandemic has only exacerbated the problem as accurate information can save lives.

To help fight the rise in minsformation, Full Fact, a nonprofit that provides tools and resources to fact checkers, turned to Google.org for help.

Today, ahead of International Fact Checking Day, we’re sharing the impact of this work.

Every day, millions of claims, like where to vote and COVID-19 vaccination rates, are made across a multitude of platforms and media.

It was becoming increasingly difficult for fact checkers to identify the most important claim…

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

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

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

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

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

discovered that the human brain possesses multimodal neurons.

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

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

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

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

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

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

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

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

Unsolved problemsThere are many prob…

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

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

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

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

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

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

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

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

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

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

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

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

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.

ZeRO-Infinity at a glance: ZeRO-Infinity is a novel deep learning (DL) training technology for scaling model training, from a single GPU to massive supercomputers with thousands of GPUs.

Despite the incredible capabilities of 3D parallelism for large model training, we are now arriving at the GPU memory wall.

Can we make large model training easier by eliminating this need for model refactoring?

Massive model training is no longer just a possibility for companies with access to massive supercomputers and heavy system expertise.

PyTorch lighting: We are happy to announce that PyTorch Lightning integrates DeepSpeed as a plugin for DL training optimizations: Accessing Multi-Billion Parameter M…

Unit, integration, and even stress testing don’t provide reasonable guarantees about the correctness of a concurrent program.

But the ways in which a concurrent program can behave are typically astronomical in number, making it challenging to efficiently find bugs.

We gave this a shot and designed QL, the first reinforcement learning–based CCT search strategy to the best of our knowledge.

QL: CCT meets Q-learningThe reinforcement learning (RL) problem (Figure 2) consists of an agent interacting with an environment about which it has no prior knowledge.

Figure 3: In QL, a new controlled concurrency testing (CCT) search strategy, the search strategy component in CCT is mapped to a reinforceme…

Microsoft Excel is one of the world’s most important software tools, relied upon users worldwide to create, understand, model, predict, and collaborate.

Not only has this allowed the Excel team to deliver innovation that would simply not have been possible otherwise, but it has also put research in a strategic role with material impact on the vision and resultant roadmap for Excel.

Simply put, Microsoft researchers now a core part of the Excel team helping create the product’s future.

David Gainer, Vice President of Product, OfficeNot only is Microsoft Excel the world’s most widely used spreadsheet, it could be argued that it is also the world’s most widely used programming language.

In fac…

As part of our paper “Initialization and Regularization of Factorized Neural Layers,” which we’re presenting at the International Conference on Learning Representations (ICLR 2021), we revisit the alternative compression approach of factorized neural layers.

We further demonstrate the usefulness of these schemes in two settings beyond model compression where factorized neural layers are applied.

It’s straightforward to apply standard deep network training algorithms such as stochastic gradient descent (SGD) to networks with factorized layers.

Thus, factorized neural layers serve as a strong, simple baseline regardless of whether we’re targeting memory savings or fast computation.

Figure 5: …

Recently, we’ve been making advances in applying network machine learning to inform new solutions across Microsoft 365 and Microsoft Viva.

For this study, we analyzed a subset of email interactions and meeting interactions in Microsoft Teams and Outlook.

A higher workgroup stability score means that communication patterns are more stable and more like the prior month.

Workgroup stability, which measures the change in workgroup membership over time, varies by the type of communication being used.

We see a large increase in Microsoft Teams usage beginning in March, as organizations adapted to many employees working from home.

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What do Cheetos have in common with plumbing pipes, pharmaceuticals, and windshield wiper blades?

They are extruded.

And if you extrude things, you know quality control is a big deal.

In this episode, Corey Sanders is joined by Kevin McCall, who shares how PepsiCo is using Microsoft Project Bonsai, a low-code AI development toolkit, to make perfect Cheetos.

[00:00] Intro[01:05] What is Project Bonsai[01:36] Who the AI toolkit is for and the problems it solves for industry[02:31] Why PepsiCo turned to AI to make perfect Cheetos[03:41] How we approach the problem - an AI toolkit non-AI experts can use[05:46] Project Bonsai demo - You don't have to know Reinforcement Learning to deploy an AI a…

Event camera simulation in Microsoft AirSim: an event camera produces +1/-1 spikes according to brightness changes instead of full frames.

In contrast to normal cameras, event cameras contain a 2D grid of pixels, with each pixel independently outputting a ‘spike’ only when it observes a change in illumination at that particular area.

Commercially available event cameras can comfortably output events at rates of around a billion events per second.

The event context network, which is a simple multi-layer perceptron, breaks up the event data into spatial and temporal parts.

Finally, we apply these representations in a reinforcement learning setting and show how event camera data can be used fo…

New Garage project Group Transcribe helps you transcribe and translate while advancing inclusive speech AIThere is healthy debate about the productivity of multi-tasking.

Now, you don’t have to choose between focus and productivity with our latest experiment, Group Transcribe, a Microsoft Garage project.

Group Transcribe also joins a strong research tradition at Microsoft, finding new ways to improve upon speech and language AI.

With confidence in the high-quality record of the conversation, users can skip note-taking and focus their attention on the conversation itself.

The Group Transcribe team collectively speaks over a dozen languages and dialects, and team members are passionate about …

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.

Microsoft today announced Azure Percept, its new hardware and software platform for bringing more of its Azure AI services to the edge.

Percept combines Microsoft’s Azure cloud tools for managing devices and creating AI models with hardware from Microsoft’s device partners.

To kickstart this, Microsoft also today launches a hardware development kit with an intelligent camera for vision use cases (dubbed Azure Percept Vision).

The kit features hardware-enabled AI modules for running models at the edge, but it can also be connected to the cloud.

In addition to Percept Vision, Microsoft is also launching Azure Percept Audio for audio-centric use cases.

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…

These automated features are powered by sophisticated language models that have learned to read and write computer code after absorbing thousands of examples.

But like other deep learning models trained on big datasets without explicit instructions, language models designed for code-processing have baked-in vulnerabilities.

Trained on GitHub and other program-sharing websites, code-processing models learn to generate programs just as other language models learn to write news stories or poetry.

Like the best language models, code-processing models have one crucial flaw: They’re experts on the statistical relationships among words and phrases, but only vaguely grasp their true meaning.

“That’…

Over the past decade, hospitals and other health care providers have put massive amounts of time and energy into adopting electronic health care records, turning hastily scribbled doctors' notes into durable sources of information.

Automated for the peopleCardea belongs to a field called automated machine learning, or AutoML.

Machine learning is increasingly common, used for everything from drug development to credit card fraud detection.

For instance, data scientists have built a number of machine learning tools for health care, but most of them aren't very accessible — even to experts.

Like all predictive apparatuses, machine learning models have strengths and weaknesses.

For years, physicians have relied on visual inspection to identify suspicious pigmented lesions (SPLs), which can be an indication of skin cancer.

An automated system detects, extracts, and analyzes all pigmented skin lesions observable in the wide-field image.

Extracted features are used to further assess pigmented lesions and to display results in a heatmap format.

The system utilized DCNNs to optimize the identification and classification of SPLs in wide-field images.

Using AI, the researchers trained the system using 20,388 wide-field images from 133 patients at the Hospital Gregorio Marañón in Madrid, as well as publicly available images.

The researchers have developed a robot that uses radio waves, which can pass through walls, to sense occluded objects.

The reader then emits an RF signal, which gets modulated by the tag and reflected back to the reader.

So, the problem gets very complicated.”The robot initiates the seek-and-pluck process by pinging the target object’s RF tag for a sense of its whereabouts.

With its two complementary senses, RF Grasp zeroes in on the target object.

Its RF sensing could even instantly verify an item’s identity without the need to manipulate the item, expose its barcode, then scan it.

MIT researchers have developed a screening platform that combines machine learning with high-throughput experimentation to identify self-assembling nanoparticles quickly.

The team then tested every combination of small-molecule drug and inactive ingredient, enabled by the Swanson Biotechnology Center, a suite of core facilities providing advanced technical services within the Koch Institute.

Now trained on 1,440 data points (with 94 nanoparticles already identified), the machine learning platform could be turned on a much bigger library of compounds.

Screening 788 small-molecule drugs against more than 2,600 inactive drug ingredients, the platform identified 38,464 potential self-assembling…

Materials called perovskites are widely heralded as a likely replacement for silicon as the material of choice for solar cells, but their greatest drawback is their tendency to degrade relatively rapidly.

Even under real-world conditions at full solar cell level, beyond just a small sample in a lab, this type of perovskite has performed three times better than the state-of-the-art formulations.

Perovskites are a broad class of materials characterized by the way atoms are arranged in their layered crystal lattice.

To confirm the results, the team went beyond making a tiny chip in the lab and incorporated the material into a working solar cell.

Buonassisi says the method the team developed co…

The potential of artificial intelligence to bring equity in health care has spurred significant research efforts.

Racial, gender, and socioeconomic disparities have traditionally afflicted health care systems in ways that are difficult to detect and quantify.

Stultz: Many factors contribute to economic disparities in health care systems.

Q: What are the policy implications for government agencies and the industry of more equitable AI for health care?

Christia: The use of AI in health care is now a reality and for government agencies and the industry to reap the benefits of a more equitable AI for health care, they need to create an AI ecosystem.

What if the model makes mistakes with very high confidence?

“An especially alarming situation is when the model is making mistakes, but with very high confidence.

Bayes-TrEx can also help with understanding model behaviors in novel situations.

“We want to make human-AI interaction safer by giving humans more insight into their AI collaborators,” says MIT CSAIL PhD student Serena Booth, co-lead author with Zhou.

Along with Booth, Zhou, and Shah, MIT CSAIL postdoc Nadia Figueroa Fernandez has contributed work on the RoCUS tool.

MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings.

That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.

Creating soft robots that complete real-world tasks has been a long-running challenge in robotics.

Soft robots are not so tractable.

“The main problem with soft robots is that they are infinitely dimensional,” says Spielberg.

From swallowing pills to injecting insulin, patients frequently administer their own medication.

The researchers say the system, which can be installed in a home, could alert patients and caregivers to medication errors and potentially reduce unnecessary hospital visits.

Finally, the system alerts the patient or their health care provider when it detects an error in the patient’s self-administration.

The team developed a neural network to key in on patterns indicating the use of an inhaler or insulin pen.

Every proper medicine administration follows a similar sequence — picking up the insulin pen, priming it, injecting, etc.

Now it’s 10 million particles per minute, says Jorg Scholvin, assistant director of user services for Fab.nano.

The final tour in the series, led by MIT.nano Assistant Director for Infrastructure Nick Menounos, took virtual attendees on a walkthrough of the non-public spaces that keep MIT.nano running.

Following this series, Scholvin led a separate class on thin-film deposition, lithography, and etching processes at the micro- and nanoscale.

“Having experience using tools including a 360 camera, Photoshop/Premiere Pro, and a VR headset has empowered me to pursue 360 projects on my own in the future,” said one participant.

Zanforlin used a Ricoh Theta V 360 camera and a monopod, along with e…

The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.

The second category is less computationally expensive, but it yields only an approximation of the binding free energy.

Exact and efficientDeepBAR computes binding free energy exactly, but it requires just a fraction of the calculations demanded by previous methods.

The “BAR” in DeepBAR stands for “Bennett acceptance ratio,” a decades-old algorithm used in exact calculations of binding free energy.

“This research is an example of combining traditional computational chemistry methods, developed over decades, with the latest developments in machine learning,” says Ding.

Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain.

The result is an AI platform that holds a number of advantages over traditional neural network-based systems.

While other systems use meticulously tuned, fixed algorithms, users can interact with Nara Logics’ platform, changing variables and goals to further explore their data.

Perhaps most importantly, Nara Logics’ platform can provide the reasons behind every recommendation it makes — a key driver of adoption in sectors like health care.

Eggers became convinced Nara Logics’ AI engine offered a superior way to help businesses.

“People previously thought that with existing consumer-grade hardware, it was impossible to do real-time 3D holography computations,” says Liang Shi, the study’s lead author and a PhD student in MIT’s Department of Electrical Engineering and Computer Science (EECS).

Training a neural network typically requires a large, high-quality dataset, which didn’t previously exist for 3D holograms.

This advance paves the way for real-time 3D holography.

The research “shows that true 3D holographic displays are practical with only moderate computational requirements,” says Joel Kollin, a principal optical architect at Microsoft who was not involved with the research.

This technology could prove faster …

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

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

With that data, the agent creates a structured learning tool – a dynamics model – to reason about the world.

Automated Machine Learning (AutoML) is a field dedicated to the study of using machine learning algorithms to tune our machine learning tools.

Thi…

Pretrained Transformers as Universal Computation EnginesTransformers have been successfully applied to a wide variety of modalities: natural language, vision, protein modeling, music, robotics, and more.

This enables the models to utilize generalizable high-level embeddings trained on a large dataset to avoid overfitting to a small task-relevant dataset.

To illustrate this, we take a pretrained transformer language model and finetune it on various classification tasks: numerical computation, vision, and protein fold prediction.

Furthermore, we find the language-pretrained frozen transformers converge faster than the randomly initialized frozen transformers, typically by a factor of 1-4x, in…

Our analysis provides a theoretically-justified explanation for the empirical robustness of MaxEnt RL, and proves that MaxEnt RL is itself a robust RL algorithm.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

Standard RL MaxEnt RL Trained and evaluated without the obstacle: Trained without the obstacle, but evaluated with the obstacle:TheoryWe now formally describe the technical results from the paper.

Standard RL MaxEnt RL Evaluation on adversarial perturbationsMaxEnt RL is robust to adversarial perturbations of the hole (where the robot inserts the peg).

ConclusionIn summary, our paper sho…

Maximum Entropy RL (Provably) Solves Some Robust RL ProblemsNearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment.

In a recent paper, we prove that every MaxEnt RL problem corresponds to maximizing a lower bound on a robust RL problem.

In the rest of this post, we’ll provide some intuition into why MaxEnt RL should be robust and what sort of perturbations MaxEnt RL is robust to.

ConclusionIn summary, this paper shows that a commonly-used type of RL algorithm, MaxEnt RL, is already solving a robust RL problem.

We do not claim that MaxEnt RL will outperform purpose-designed robust RL algorithms.

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

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

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

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

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

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

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

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

Does GPT-2 Know Your Phone Number?

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

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

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

Does Training Language Models Infringe on Copyright?

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

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

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

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

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

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

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

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

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

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

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

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

Summary and Broader ApplicationsWe introduce EvolveGraph, a generic trajectory prediction framework with dynamic relational reasoning, which can handle evolving inte…

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

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

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

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

Summary and Broader ApplicationsWe introduce …

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

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

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

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

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

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

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

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

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.

You can now control the Amazon Virtual Private Cloud (Amazon VPC) and encryption settings for your Amazon Comprehend APIs using AWS Identity and Access Management (IAM) condition keys, and encrypt your Amazon Comprehend custom models using customer managed keys (CMK) via AWS Key Management Service (AWS KMS).

This policy applies these rules for all Amazon Comprehend APIs that start new asynchronous jobs, create custom classifiers, and create custom entity recognizers.

When you enable classifier encryption, Amazon Comprehend encrypts the data in the storage volume while your job is being processed.

Model encryption with a CMKAlong with encrypting your training data, you can now encrypt your c…

Today, we’re pleased to launch Machine Learning Essentials for Business and Technical Decision Makers—a series of three free, on-demand, digital-training courses from AWS Training and Certification.

These courses are intended to empower business leaders and technical decision makers with the foundational knowledge needed to begin shaping a machine learning (ML) strategy for their organization, even if they have no prior ML experience.

With the new Machine Learning Essentials for Business and Technical Decision Makers course, we’re making a portion of the AWS Machine Learning Embark curriculum available globally as free, self-paced, digital-training courses.

The AWS Machine Learning Embark p…

The team surpassed our expectations, developing a 3D pose estimation pipeline using 2D videos captured with mobile phones in just two weeks.

Recent advances in computer vision and deep learning have enabled scientists to explore pose estimation in a 3D space, where the Z-axis provides additional insights compared to 2D pose estimation.

However, building a 3D pose estimation model from scratch is challenging because it requires imaging data along with 3D labels.

3D pose estimationWe employed a state-of-the-art 3D pose estimation algorithm encompassing a camera distance-aware top-down method for multi-person per RGB frame referred to as 3DMPPE (Moon et al.).

We used two metrics commonly used …

Finally, we see the savings that we achieved by running our training job on Spot Instances using Managed Spot Training in SageMaker.

Managed Spot Training uses EC2 Spot Instances to run training jobs instead of On-Demand Instances.

Managed Spot Training is available in all training configurations:All instance types supported by SageMakerAll models: built-in algorithms, built-in frameworks, and custom modelsAll configurations: single instance training and distributed trainingInterruptions and checkpointingThere’s an important difference when working with Managed Spot Training.

TensorFlow image classification model with Managed Spot TrainingTo demonstrate Managed Spot Training and checkpointi…

HawkEye 360 partnered with the Amazon ML Solutions Lab to build machine learning (ML) capabilities into our analytics.

Knowing which characteristics are indicative of a suspicious vessel isn’t immediately clear.

The following image demonstrates some of the existing pattern finding behavior that has been built into Mission Space.

We can see that a Mission Analyst identified a specific rendezvous (highlighted in magenta) and Mission Space automatically identified other related rendezvous (in purple).

He is responsible for the conception, creation, and productization of all HawkEye space innovations.

Choose Create notebook instance.

For Notebook instance name, enter a name for your notebook instance.

client.publish(topic=iot_topic, payload='Loading model...') model = awscam.Model(model_path, {'GPU': 1})Then you run the model frame-per-frame over the images from the camera.

For more details about connecting an AWS DeepLens device to a Raspberry Pi device, see Building a trash sorter with AWS DeepLens.

For a more detailed walkthrough of this tutorial and other tutorials, samples, and project ideas with AWS DeepLens, see AWS DeepLens Recipes.

We need to enable cross-account permissions for Data Wrangler in Account B to access the data tables located in Account A’s data lake via Lake Formation permissions.

With this architecture, data scientists and engineers outside the data lake account can access data from the lake and create data transformations via Data Wrangler.

Data lake setup using Lake FormationTo get started, create a central data lake in Account A.

Lake Formation permissions enable fine-grained access control for data in your data lake.

ConclusionIn this post, we demonstrated how to enable cross-account access for Data Wrangler using Lake Formation and AWS RAM.

We’re working with NVIDIA to bring an Arm processor-based, NVIDIA GPU accelerated Amazon Elastic Compute Cloud (Amazon EC2) instance to the cloud in the second half of 2021.

In 2018, AWS was the first major cloud provider to offer Arm-based instances in the cloud with EC2 A1 instances powered by AWS Graviton processors.

In 2020, AWS released AWS-designed, Arm-based Graviton2 processors, delivering a major leap in performance and capabilities over first-generation AWS Graviton processors.

AWS Graviton2 processors deliver seven times more performance, four times more compute cores, five times faster memory, and caches twice as large over first-generation AWS Graviton processors.

To learn more…

Set up an Amazon A2I private human loop and review the predictions from Amazon Lookout for Equipment.

Create the Amazon Lookout for Equipment datasetWe use Amazon Lookout for Equipment Create Dataset APIs to create a dataset and provide the component map we created in the previous step as an input.

For more details, refer to the section Set up Amazon A2I to review predictions from Amazon Lookout for Equipment in this post.

With Amazon Lookout for Equipment and Amazon A2I, you can set up a continuous prediction, review, train, and feedback loop to audit predictions and improve the accuracy of your models.

Visit the webpages to learn more about Amazon Lookout for Equipment and Amazon Augmente…

The ML Solutions Lab team used the existing data collected by KAES equipment in the field for an in-depth acoustic data exploration.

Anomaly detection with Amazon Lookout for EquipmentTo implement these solutions, the ML Solutions Lab team used Amazon Lookout for Equipment, a new service that helps to enable predictive maintenance.

Amazon Lookout for Equipment uses AI to learn the normal operating patterns of industrial equipment and alert users to abnormal equipment behavior.

Amazon Lookout for Equipment analyzes the data from industrial equipment sensors to automatically train a specific ML model for that equipment with no ML expertise required.

After sufficient data is ingested into the …

With the 2021 AWS DeepRacer League Virtual Circuit now underway, developers have five times more opportunities to win physical prizes, such as exclusive AWS DeepRacer merchandise, AWS DeepRacer Evo devices, and even an expenses paid trip to AWS re:Invent 2021 to compete in the AWS DeepRacer Championship Cup.

Digital rewards: Collect them all and showcase your collectionDigital rewards are unique cars, paint jobs, and body kits that are stored in a new section of the AWS DeepRacer console: your racer profile.

The next time you log in and access your racer profile, you’ll see the celebration to commemorate your achievement.

Customize your racer profile and avatarWhile new digital rewards allo…

Detecting industrial equipment issues at an early stage and using that data to inform proper maintenance can give your company a significant increase in operational efficiency.

Customers see value in detecting abnormal behavior in industrial equipment to improve maintenance lifecycles.

Amazon Lookout for Equipment automates these traditional data science steps to open up more opportunities for a broader set of equipment than ever before.

Combining TensorIoT and Amazon Lookout for Equipment has never been easierTo delve deeper into how to visualize near real-time insights gained from Amazon Lookout for Equipment, let’s explore the process.

With Amazon Lookout for Equipment and TensorIoT solu…

Object detection, which is one type of CV task, has many applications in various fields like medicine, retail, or agriculture.

Object detection models allow you to implement these diverse use cases and automate your in-store operations.

In this post, we discuss Detectron2, an object detection and segmentation framework released by Facebook AI Research (FAIR), and its implementation on Amazon SageMaker to solve a dense object detection task for retail.

Researchers use this dataset to test object detection algorithms on dense scenes.

You can reuse the code associated with this post on your own data labeled for object detection with Ground Truth.

On June 2, 2021, don’t miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit.

Machine learning is one of the most disruptive technologies we will encounter in our generation.

This Summit, which is open to all and free to attend, brings together industry luminaries, AWS customers, and leading ML experts to share the latest in machine learning.

Hear from ML leaders from across AWS, Amazon, and the industry, including Swami Sivasubramanian, VP of AI and Machine Learning, AWS; Bratin Saha, VP of Machine Learning, AWS; and Yoelle Maarek, VP of Research, Alexa Shopping, who will share a keynote on how we’re applying …

Vingroup, Vietnam’s largest conglomerate, is installing the most powerful AI supercomputer in the region.

The NVIDIA DGX SuperPOD will power VinAI Research, Vingroup’s machine-learning lab, in global initiatives that span autonomous vehicles, healthcare and consumer services.

He believes the DGX SuperPOD can accelerate by at least 10x the AI work of the NVIDIA DGX A100 system VinAI currently uses, letting engineers update their models every 24 hours.

Developing World-Class TalentWith a DGX SuperPOD in place, Hung hopes to attract and develop more world-class AI talent in Vietnam.

It’s the kind of world-class work that two years ago Vingroup’s chairman and Vietnam’s first billionaire, Pham N…

An application programmer can use the standard USD API to query a scene and alter it at will.

Virtual Worlds Where AI, Robots and Autonomous Vehicles Can LearnCapabilities like these aren’t just critical for humans.

Nothing should go out into the real world until it’s thoroughly tested in simulation, he said.

“We want all of our mistakes to happen in the virtual world, and we based that entire virtual world on USD,” Kass said.

The first steps, however, have already been taken — thanks to USD — making it easier to exchange data about shared 3D worlds.

The offerings range from software development kits for conversational AI and ray tracing, to hands-on courses from the NVIDIA Deep Learning Institute.

DLI recently launched public workshops for its popular instructor-led courses, increasing accessibility to individual developers, data scientists, researchers and students.

To extend training further, DLI is releasing a new book, “Learning Deep Learning,” that provides a complete guide to deep learning theory and practical applications.

TensorRT , for high-performance deep learning inference, includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.

, for h…

Over the past couple of years, NVIDIA and NASA have been working closely on accelerating data science workflows using RAPIDS, and integrating these GPU-accelerated libraries with scientific use cases.

This approach is particularly useful to detect and quantify air pollution anomalies, i.e., patterns in air quality observations that cannot be explained by the model.

The model data is then combined with the surface observation data to build an XGBoost bias-correction model that relates the model NO 2 prediction to the observations.

Given the growing number of available air quality observations, fast data processing becomes ever more critical for such an application.

NASA Model Reveals How Muc…

University of Pennsylvania researchers have used convolutional neural networks to catalog the morphology of 27 million galaxies, giving astronomers a massive dataset for studying the evolution of the universe.

“Galaxy morphology is one of the key aspects of galaxy evolution,” said study author Helena Domínguez Sánchez, former postdoc at Penn.

So images from the Dark Energy Survey, which contains more images of distant galaxies than previous studies, “show us what galaxies looked like more than 6 billion years ago,” said Mariangela Bernardi, professor in the Department of Physics and Astronomy at Penn.

They then created a synthetic dataset that simulated how the images would look if they dep…

The rollout of 5G for edge AI services promises to fuel a magic carpet ride into the future for everything from autonomous vehicles, to supply chains and education.

It will also radically improve AI services, such as online gaming, those provided by AVs, and robots used for logistics.

In addition, AI on 5G could help deliver services like online learning and micro banking to remote regions of underdeveloped parts of the world today.

Executives from Verizon, Wind River, Mavenir, Google and NVIDIA shared their views on the wide-ranging impact 5G will have on edge AI services.

NVIDIA has been investing in GPU and DPU platforms for accelerated compute to support the ecosystem of edge AI applica…

Mercedes-Benz is calling on its long heritage of luxury to accelerate electric vehicle technology with the new EQS sedan.

The focal point of the revolutionary vehicle is the MBUX Hyperscreen, a truly intuitive and personalized AI cockpit, powered by NVIDIA.

The cabin is further transformed by the MBUX Hyperscreen — a single, 55-inch surface extending from the cockpit to the passenger seat.

“The EQS is high tech in a true luxury shell,” said Ola Källenius, chairman of the Mercedes-Benz Board of Management.

“The MBUX Hyperscreen surprises with intelligence in many flavors,” said Sajjad Khan, executive vice president at Mercedes-Benz.

NVIDIA Research is revving up a new deep learning engine that creates 3D object models from standard 2D images — and can bring iconic cars like the Knight Rider’s AI-powered KITT to life — in NVIDIA Omniverse.

These multi-view images were plugged into a rendering framework for inverse graphics, the process of inferring 3D mesh models from 2D images.

They then used NVIDIA Omniverse Kit and NVIDIA PhysX tools to convert the predicted texture into high-quality materials that give KITT a more realistic look and feel, and placed it in a dynamic driving sequence.

The final model, trained on 55,000 car images generated by the GAN, outperformed an inverse graphics network trained on the popular Pas…

The CUDA 11.3 release of the CUDA C++ compiler toolchain incorporates new features aimed at improving developer productivity and code performance.

As a result, developers were unable to upgrade to the latest NVRTC library without upgrading the entire CUDA toolkit.

In the event of such an incompatibility between the CUDA Driver and the newer NVRTC library, you have two options:Install a more recent CUDA driver that is compatible with the CUDA toolkit containing the NVRTC library being used.

In CUDA 11.3, CUDA C++ introduces support for using the memory allocator alloca in device code as a preview feature.

Comparing alloca and malloc usage and performanceThe performance benefits of allocating…

Access to programming Tensor Cores in CUDA C became available in the CUDA 9.0 release for Volta GPUs through the WMMA (Warp Matrix Multiply and Accumulate) API, which was extended in CUDA 11.0 to support Ampere GPUs.

CUDA Fortran Tensor Core data precision and WMMA tile sizes.

CUDA Fortran wmma moduleThe use of Tensor Cores through the WMMA API in CUDA Fortran requires the wmma module as well as the cuf_macros.CUF macro file.

This becomes important when you use shared memory for the input arrays or more specifically, when you pad shared memory arrays.

The size of the shared memory tile is chosen largely to keep the indexing for loading A into shared memory as simple and efficient as possibl…

A demo at GTC21 this week showcased how NVIDIA RTX Direct Illumination (RTXDI) technology is paving the way for realistic lighting in graphics.

Traditionally, creating realistic lighting required complex baking solutions and was limited to a small number of “hero” lights.

Hit the Light Spots in RTX Technology ShowcaseThe RTX Technology Showcase features discrete ray-tracing capabilities, so users can choose to turn on specific technologies and immediately view their effects within the attic scene.

Watch the RTX Technology Showcase in action:Developers can download the demo to discover the latest and greatest in ray-tracing innovations with RTX Technology Showcase.

Watch a replay of the GTC …

Powell, NVIDIA’s vice president of healthcare, presented an overview of AI innovation in medicine that highlighted advances in drug discovery, medical imaging, genomics and intelligent medical instruments.

Watch replays of these talks — part of a packed lineup of more than 100 healthcare sessions among 1,600 on-demand sessions — by registering free for GTC through April 23.

Accelerating the AI Healthcare EraIt’s not enough to just have abundant data to create an effective deep learning model for medicine, however.

The NVIDIA Clara Discovery suite of AI libraries harnesses transformer models, popular in natural language processing, to parse biomedical deta.

For more, subscribe to NVIDIA heal…

This GFN Thursday — when GeForce NOW members can learn what new games and updates are streaming from the cloud — we’re adding 15 games to the service, with new content, including NVIDIA RTX and DLSS in a number of games.

Plus, we have a GeForce NOW Reward for Spellbreak from our friends at Proletariat.

This week, we’re offering the Noble Oasis outfit, a rare outfit from the game Spellbreak that’s exclusive to GeForce NOW members.

Updates to Your CatalogGeForce NOW members are getting updates to a few games this week in the form of new expansions or RTX support.

Meanwhile, three games are adding RTX support with real-time, ray-traced graphics and/or NVIDIA DLSS.

People upload hundreds of millions of videos to Facebook every day.

From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient.

A relatively small percentage (roughly one-third) of all videos on Facebook generate the majority of overall watch time.

The impact of the new video encoding modelIn addition to improving viewer experience with newly uploaded videos, the new model can identify older videos on Facebook that should have been encoded with more advanced encodings and route more computing resources to them.

The improved compression has also allowed people on Facebook with limited data plans, such as those i…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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…

You’ll learn how you can run a LightGBM experiment using these tools.

params = { 'boosting_type' : 'gbdt' , 'objective' : 'regression' , 'num_leaves' : 40 , 'learning_rate' : 0.09 , 'feature_fraction' : 0.8 } exp = neptune.create_experiment(name= 'LightGBM-training' ,params=param)You’re now set to train the LightGBM model.

For instance, let’s look at how you can log the mean absolute error, mean squared error and the root mean squared error.

Weights and BiasesWeights and Biases is a platform for experiment tracking, model, dataset versioning, and managing ML model building metadata.

from sacred import Experiment ex = Experiment( 'lightgbm' ,interactive= True )Next, define the experiment con…

There’s no universal recipe for everyone to learn machine learning.

Massive Open Online Courses (MOOCs)The machine learning crash course from Google is an example of a MOOC (massive open online course).

One of my favourite sites to learn machine learning is Udacity.

Machine Learning concepts and topics to learnYou now should have a good understanding HOW you can learn machine learning.

You love working with images and videos, so you’d like to learn machine learning and link it to your digital passion.

Most of the time, as data scientists, we think that our core value is our ability to figure out a machine learning algorithm that solves a task.

What we need is to engineer an ETL process that transforms our data according to what we pretend to do with it.

credit cards): Number of Open loans (installment like car loan or mortgage) and lines of credit (e.g.

Commonly, most financial industry data contains missing values, or values that don’t make sense for a particular characteristic.

I hope this tutorial was useful to you, as I’ve designed it to fully cover different aspects of real data science use-cases.

In this article, our main objective is to underline one of the million-dollar questions in training neural networks: how long do you train a neural network?

Figure 6: Early stopping [4]Monitoring the experiment using NeptuneMonitoring the training process, and other experiments, can be done easily with Neptune.

Now, let’s introduce early stopping in our code:valid_loss_array = np.array(valid_losses) min_valid_loss = np.min(valid_loss_array) if (test_cost > min_valid_loss): patience_counter += 1 else : patience_counter = 0 if (patience_counter > patience): print( "Early stopping called at {} epochs" .format(epoch+ 1 )) breakWe’ll use patience as one of the hyperparameters to trigger early st…

Implemented as a dual-database, Feature Stores are designed to serve data both in real-time and to be processed in batches:Online feature stores serve online applications with data at a low-latency.

Feature Store vs Data Lake vs Data WarehouseAt an abstract level, Feature Stores offer a subset of the functionalities of a Data Lake.

Benefits of a Feature Store in ML pipelinesThe output from Feature Stores is implementation-agnostic.

Hopsworks also supports the creation of more than one feature store, because one feature store should not necessarily be accessible to all parts of an enterprise.

Some of the examples of feature stores are Uber’s Michelangelo, Google’s Feast, Hopsworks’ Feature S…

What are Machine Learning orchestration tools?

Machine learning orchestration tools are used to automate and manage workflows and pipeline infrastructure, with a simple, collaborative interface.

Functionality:Open–sourceFocus on: manage real-life data science projectsLightweightCoulerThe only workflow orchestration tool for managing other workflow orchestration tools.

It supports an end-to-end machine learning workflow storing every model, experiment, and artifact automatically.

LightweightDagsterDagster has a rich UI to perform workflow orchestration for machine learning, analytics, and ETL (Extract, Transform, Load).

Require more hands-on machine learning talent that can operate across the entire machine learning lifecycle – from data engineering, algorithmic and model development to deploying and monitoring machine learning models in production instead of specialized talent to focus individually on the various aspects of the machine learning lifecycle.

A better understanding of the skills and abilities of diverse machine learning teams is essential for hiring managers and teams to build out a complete and productive machine learning team.

Profiles in a Machine Learning teamModern machine learning teams are truly diverse.

The Machine Learning LifecycleAs in Figure 1, the machine learning lifecycle has f…

A chatbot platform is a service where developers, data scientists, and machine learning engineers can create and maintain chatbots.

Like Dialogflow, Lex has its own set of terminologies such as intents, slots, fulfilments, and more.

With the toolkit, third-party applications can send user input to the Watson Assistant service, which can interact with the vendor’s back-end systems.

Companies such as DB Dialog and DB Steel, BBank of Scotland, Staples, Workday all use IBM Watson Assistant as their conversational AI platform.

I hope this article gave you some ideas on which platform to use for building your chatbots.

We’ll be running different analytics and ML processes to see how well Neptune can support you in daily work.

This way, we could efficiently manage the portfolio and dissect the different levels of value each group actually offers.

K-Means for Recency:kmeans = KMeans(n_clusters= 4 ) kmeans.fit(customers[[ 'Recency' ]]) customers[ 'RecencyCluster' ] = kmeans.predict(customers[[ 'Recency' ]])Let’s log the Recency Distribution and the predicted clusters.

Organizing ML development in NeptuneIn this section we’ll take advantage of one excellent feature that Neptune offers, which is ML integrations.

CHECK ALSO📌 How to Organize Your XGBoost Machine Learning (ML) Model Development Process – Best Pra…

How to choose the right data annotation tool?

The criteria for choosing the right data annotation tool are as follows:Efficiency,Functionality,Formatting,Application,Price.

So, depending on the problem you’re working on, you should have an annotation tool that provides all the functionality you need.

If you work with sensitive data, consider privacy issues: uploading your data to a 3rd-party web app increases the risk of a data breach.

Functionally, it’s not limited to a single data annotation process.

Gradient boosted decision trees have proven to outperform other models.

In this article, we’ll see what gradient boosted decision trees are all about.

Regression with the Scikit-learn gradient boosting estimatorThe Scikit-learn gradient boosting estimator can be implemented for regression using `GradientBoostingRegressor`.

Final thoughtsIn this article, we explored how to implement gradient boosting decision trees in your machine learning problems.

Specifically, we’ve covered:what is gradient boosting,how gradient boosting works,various types of gradient boosting algorithms,how to use gradient boosting algorithms for regression and classification problems,the advantages of gradient boosting…

– (Deep Learning Book, Ian Goodfellow; 2014)These two factors correspond to the two central challenges in machine learning: underfitting and overfitting.

Model basicsA machine learning algorithm, or deep learning algorithm, is a mathematical model that uses mathematical concepts to recognize or learn a certain type of pattern or correlation from a dataset.

They can play an important role for building a good machine learning model, or even a deep learning model that yields good results.

Fold 1 Accuracy Comparison on model 1 : 0.7664370565884211 0.7801300087611103 Accuracy Comparison on model 2 : 0.8485031490397249 0.8586081582213081 Accuracy Comparison on model 3 : 0.8950440772346971 0.90073…

The 4 types of metadata to store during training:So, storing metadata lets you compare results during experiments and reproduce them.

: The algorithm used to train the model is a classic piece of model metadata.

Examples of context:Source codePrograming languages + versionDependencies + packagesHost info including system packages, CPU and OS information, environment variablesThe selection criteria for ML metadata storeGiven a variety of options for ML Metadata Store, it can be hard to know which to pick.

The dashboard is a visual interface to the metadata database, so you can see all your experiment metadata, models, and datasets in one place.

The Kubeflow UI lets you view logged artifacts …

It introduced a new method to train neural networks, where weights and activations are binarized at train time, and then used to compute the gradients.

GPUs consume huge amounts of power, making it difficult for neural networks to be trained on low-power devices.

In this article, we’ll see how Binarized Neural Networks work.

How Binarized Neural Networks workBefore we dig any deeper, let’s see how BNNs work.

You can use LCE(Larq Compute Engine) with Tensorflow Lite Java to train and infer neural networks on Android, consuming less power.

In a paper “Exploring Randomly Wired Neural Networks for Image Recognition” from Facebook AI Research, authors investigate connectivity patterns through the lens of randomly wired neural networks.

The architecture of randomly wired networksLet’s take a look at the foundational concepts of the architecture of randomly wired networks.

The table below shows a summary of randomly wired neural networks, referred to as RandWire.

Randomly wired neural networks performanceExperiments in these networks are performed on the ImageNet 1000-class classification task.

Final thoughtsWe’ve explored randomly wired neural networks inspired by random graph models from graph theory.

#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE:

0:00 - Intro & Overview

4:50 - View Synthesis Task Description

5:50 - The fundamental difference to classic Deep Learning

7:00 - NeRF Core Concept

15:30 - Training the NeRF from sparse views

20:50 - Radiance Field Volume …

#minecraft #neuralnetwork #backpropagation I built an analog neural network in vanilla Minecraft without any mods or command blocks. The network uses Redstone wire power strengths to carry the signal through one hidden layer, including nonlinearities, and then do automatic backpropagation and even weight updates. OUTLINE:

0:00 - Intro & Overview

1:50 - Redstone Components Explained

5:00 - Analog Multiplication in Redstone

7:00 - Gradient Descent for Square Root Computation

9:35 - Neural Network Demonstration

10:45 - Network Schema Explained

18:35 - The Network Learns a Datapoint

20:20 - Outro & Conclusion I built this during a series of live streams and want to thank everyone who helped me …

#dreamcoder #programsynthesis #symbolicreasoning Classic Machine Learning struggles with few-shot generalization for tasks where humans can easily generalize from just a handful of examples, for example sorting a list of numbers. Humans do this by coming up with a short program, or algorithm, that explains the few data points in a compact way. DreamCoder emulates this by using neural guided search over a language of primitives, a library, that it builds up over time. By doing this, it can iteratively construct more and more complex programs by building on its own abstractions and therefore solve more and more difficult tasks in a few-shot manner by generating very short programs that solve …

In the recurring debate about bias in Machine Learning models, there is a growing argument saying that "the problem is not in the data", often citing the influence of various choices like loss functions or network architecture. In this video, we take a look at PAIR's AI Explorables through the lens of whether or not the bias problem is a data problem. OUTLINE:

0:00 - Intro & Overview

1:45 - Recap: Bias in ML

4:25 - AI Explorables

5:40 - Measuring Fairness Explorable

11:00 - Hidden Bias Explorable

16:10 - Measuring Diversity Explorable

23:00 - Conclusion & Comments AI Explorables: https://pair.withgoogle.com/explorables/ Links:

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

We build a Deep Neural Network in Minecraft

No Command Blocks

No Mods Multiplier inspiration from here: https://www.youtube.com/watch?v=Wc29p6mgRMo

World Save: https://polybox.ethz.ch/index.php/s/YnKl1hkoct6coQy Links:

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

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

Twitter: https://twitter.com/ykilcher

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

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

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

Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the bes…

We build a Deep Neural Network in Minecraft

No Command Blocks

No Mods Multiplier inspiration from here: https://www.youtube.com/watch?v=Wc29p6mgRMo

World Save: https://polybox.ethz.ch/index.php/s/YnKl1hkoct6coQy Links:

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

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

Twitter: https://twitter.com/ykilcher

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

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

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

Parler: https://parler.com/profile/YannicKilcher

LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/

BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the bes…

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We build a Deep Neural Network in Minecraft Links:

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

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

Twitter: https://twitter.com/ykilcher

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#machinelearning #phd #howto This video is advice for new PhD students in the field of Machine Learning in 2021 and after. The field has shifted dramatically in the last few years and navigating grad school can be very hard, especially when you're as clueless as I was when I started. The video is a personal recount of my mistakes and what I've learned from them. If you already have several published papers and know what to do, this video is not for you. However, if you are not even sure where to start, how to select a topic, or what goes in a paper, you might benefit from this video, because that's exactly how I felt. Main Takeaways:

- Select niche topics rather than hype topics

- Write pap…

#genderbias #algorithmicfairness #debiasing A brief look into gender stereotypes in Google Translate. The origin is a Tweet containing a Hungarian text. Hungarian is a gender-neutral language, so translating gender pronouns is ambiguous. Turns out that Google Translate assigns very stereotypical pronouns. In this video, we'll have a look at the origins and possible solutions to this problem. OUTLINE:

0:00 - Intro

1:10 - Digging Deeper

2:30 - How does Machine Translation work?

3:50 - Training Data Problems

4:40 - Learning Algorithm Problems

5:45 - Argmax Output Problems

6:45 - Pragmatics

7:50 - More on Google Translate

9:40 - Social Engineering

11:15 - Conclusion Songs:

Like That - Anno Domi…

#perceiver #deepmind #transformer Inspired by the fact that biological creatures attend to multiple modalities at the same time, DeepMind releases its new Perceiver model. Based on the Transformer architecture, the Perceiver makes no assumptions on the modality of the input data and also solves the long-standing quadratic bottleneck problem. This is achieved by having a latent low-dimensional Transformer, where the input data is fed multiple times via cross-attention. The Perceiver's weights can also be shared across layers, making it very similar to an RNN. Perceivers achieve competitive performance on ImageNet and state-of-the-art on other modalities, all while making no architectural adj…

#universalcomputation #pretrainedtransformers #finetuning Large-scale pre-training and subsequent fine-tuning is a common recipe for success with transformer models in machine learning. However, most such transfer learning is done when a model is pre-trained on the same or a very similar modality to the final task to be solved. This paper demonstrates that transformers can be fine-tuned to completely different modalities, such as from language to vision. Moreover, they demonstrate that this can be done by freezing all attention layers, tuning less than .1% of all parameters. The paper further claims that language modeling is a superior pre-training task for such cross-domain transfer. The p…

#selfsupervisedlearning #yannlecun #facebookai Deep Learning systems can achieve remarkable, even super-human performance through supervised learning on large, labeled datasets. However, there are two problems: First, collecting ever more labeled data is expensive in both time and money. Second, these deep neural networks will be high performers on their task, but cannot easily generalize to other, related tasks, or they need large amounts of data to do so. In this blog post, Yann LeCun and Ishan Misra of Facebook AI Research (FAIR) describe the current state of Self-Supervised Learning (SSL) and argue that it is the next step in the development of AI that uses fewer labels and can transfer…

#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…

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

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

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

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

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

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

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

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

CodeTrans: https://ar…

Dienste anbieten und betreiben, z.

Personalisierte Inhalte und Werbeanzeigen können ebenfalls darauf basieren, darüber hinaus aber auch auf Aktivitäten wie Suchanfragen bei Google und Videos, die Sie sich bei YouTube ansehen.

Zu personalisierten Inhalten und Werbeanzeigen gehören beispielsweise Dinge wie relevantere Ergebnisse und Empfehlungen, eine individuelle YouTube-Startseite und Werbung, die auf Ihre Interessen zugeschnitten ist.

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Thank you for watching! Please Subscribe! Content Links:

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

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

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

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

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

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

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

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

Visual PT w/ Contrastive D…

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

Thanks for watching and please subscribe! Content Links:

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

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

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

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

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

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

Thanks for watching! Please Subscribe! Content Links:

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

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

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

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

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

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

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

Thank you for watching! Please Subscribe! Content Links:

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

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

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

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

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

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

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

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

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

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

Keras…

This video explains the Keras Example of a Convolutional Autoencoder for Image Denoising. This is a relatively simple example in the Keras Playlist, I hope beginners find this useful for getting starter with Deep Learning and exciting ideas like Generative Modeling, Data Compression, or Image Denoising/Super-resolution. Thanks for watching, please check out the rest of the Keras Code Examples playlist! Content Links:

Keras Code Example: https://keras.io/examples/vision/autoencoder/

Building Autoencoders in Keras: https://blog.keras.io/building-autoencoders-in-keras.html Chapters

0:00 Welcome to Keras Code Examples!

0:44 Motivation - Denoising Autoencoders

4:00 Helper Functions

9:07 Model Co…

This video previews the content for the next AI Weekly Update for Monday March 15th, 2021! Thanks for watching and please subscribe! Content Links:

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

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

Barlow Twins: https://arxiv.org/pdf/2103.03230v1.pdf

VISSL: https://vissl.ai/tutorials/Large_Scale_Training

HuggingFace Reads: https://huggingface.co/blog/long-range-transformers

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

Igor Mordatch's Twitter Thread: https://twitter.com/IMordatch/status/1369688157558431749

Attention is NOT all you need: https://arxiv.org/pdf/2103.03404.pdf

A new lens on un…

This video explains Negative Data Augmentation, a strategy for using label-corrupting, rather than label-preserving transformations in Deep Learning. The authors test this framework for training GANs and for Contrastive Learning such as CPC and MoCo. I think this is a really exciting direction for Data Augmentation and overcoming the challenge of learning from limited labeled data, I hope you find this video useful! Content Links:

Negative Data Aug (Paper): https://arxiv.org/pdf/2102.05113.pdf

Self-Supervised Learning: The Dark Matter of Intelligence: https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence

Learning the difference that makes a difference: https:…

This video explains an interesting new paper for applying Data Augmentation to NLP. I thought the interpretation of Data Augmentation as constructing local neighborhoods was very interesting and like the way the MoCo loss compliments this intuition. I hope you found this video useful and it inspires your interest in Data Augmentation! Content Links:

Paper: https://arxiv.org/pdf/2010.08670.pdf

On the Measure of Intelligence: https://arxiv.org/pdf/1911.01547.pdf

EDA: https://arxiv.org/pdf/1901.11196.pdf

AutoAugment: https://arxiv.org/pdf/1805.09501.pdf

MoCo: https://arxiv.org/pdf/1911.05722.pdf Chapters

0:00 Beginning

1:10 Research Questions

1:50 High-Level Idea

3:30 Data Aug for Text

4:57 St…

Thank you for watching! Please Subscribe! Content Links:

Multimodal neurons (OpenAI): https://openai.com/blog/multimodal-neurons/

Multimodal neurons (Distil): https://distill.pub/2021/multimodal-neurons/

DeepDream (Wikipedia): https://en.wikipedia.org/wiki/DeepDream

CLIP (OpenAI): https://openai.com/blog/clip/

CLIP (Keras Code Examples): https://keras.io/examples/nlp/nl_image_search/

OpenAI Microscope: https://microscope.openai.com/models

Yannic Kilcher's Explanation of Multimodal Neurons: https://www.youtube.com/watch?v=Z_kWZpgEZ7w&t=622s

Wikipedia-based Image-Text Pairs: https://arxiv.org/pdf/2103.01913.pdf

WIT Dataset (GitHub repo): https://github.com/google-research-datasets/wit

Self-su…

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://…

General exponentials, Love, Schrödinger, and more.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/thanks ------------------

The Romeo-Juliet example is based on this essay by Steven Strogatz:

http://www.stevenstrogatz.com/essays/loves-me-loves-me-not-do-the-math The book shown at the start is Vladimir Arnold's (excellent) textbook on ordinary differential equations.

https://amzn.to/3dtXSwj Need a review of ordinary powers of e?

https://youtu.be/m2MIpDrF7Es Or of linear algebra?

https://youtu.be/kYB8IZa5AuE Timetable

0:00 - Definition

6:40 - Dynamics of love

13:17 - General equation

20:03 - On general rotations

22:11 - Visualizing with flow ------------------

C…

Bayes factors, aka Likelihood Ratios*, offer a very clear view of how medical test probabilities work.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/bayes-factor-thanks The book by my friend Matt Cook about paradoxes mentioned at then end:

https://amzn.to/3aBrEzg On the topic, I can't help also mentioning another paradox book I'm rather fond of by Bunch:

https://amzn.to/3mBDSKE *As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same term in this setting, where Bayes rule is used on the probability of an event with only two possible outcomes (you either have the disease or you don't), they do take…

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

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

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

A discovery-oriented introduction to error correction codes.

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

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

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

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

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

Bestätigung erforderlichDurch diesen Extraschritt kann YouTube bestätigen, dass du ein echter Mensch bist.

Du kannst dich stattdessen auch anmelden.

❤️ 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/NSFF/reports/Overview-Neural-Scene-Flow-Fields-NSFF-for-Space-Time-View-Synthesis-of-Dynamic-Scenes--Vmlldzo1NzA1ODI 📝 The paper "OmniPhotos: Casual 360° VR Photography" is available here:

https://richardt.name/publications/omniphotos/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javie…

❤️ 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/gudgud96/big-sleep-test/reports/Image-Generation-Based-on-Abstract-Concepts-using-CLIP-BigGAN--Vmlldzo1MjA2MTE 📝 The paper "Multimodal Neurons in Artificial Neural Networks" is available here:

https://openai.com/blog/multimodal-neurons/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bus…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "FuSta - Hybrid Neural Fusion for Full-frame Video Stabilization" is available here:

- Paper https://alex04072000.github.io/FuSta/

- Code: https://github.com/alex04072000/FuSta - Colab: https://colab.research.google.com/drive/1l-fUzyM38KJMZyKMBWw_vu7ZUyDwgdYH?usp=sharing 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Serban, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Haris Husic, Ivo Galic, Jace O'Brien, Javier Bustamante, J…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "A Model for Soap Film Dynamics with Evolving Thickness" 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/latentspace/published-work/The-Science-of-Debugging-with-W-B-Reports--Vmlldzo4OTI3Ng 📝 The paper "Playing hard exploration games by watching YouTube" is available here:

Paper: https://papers.nips.cc/paper/7557-playing-hard-exploration-games-by-watching-youtube.pdf

Gameplay videos: https://www.youtube.com/playlist?list=PLZuOGGtntKlaOoq_8wk5aKgE_u_Qcpqhu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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Paper: https://arxiv.org/abs/2006.06676

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https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-learning-and-synthesis/ Unofficial StyleGAN2-ADA trained on corgis (+ colab notebook):

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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/mathisfederico/wandb_features/reports/Visualizing-Confusion-Matrices-With-W-B--VmlldzoxMzE5ODk 📝 The paper "GANSpace: Discovering Interpretable GAN Controls" is available here:

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https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/ ☀️ The free course on writing light simulations 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

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Huawei's DaVinci AI compute architecture. Dmitrii Khizbullin, Overview of DaVinci compute architecture for Deep Learning training and inference, design choices for hardware and software layers. Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speaker: Evgenii Zheltonozhskii, Technion, Israel Institute of Technology Register and get access to the tracks: https://ods.ai/events/datafest2020

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DataFest Online 2020

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

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

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DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 ShiftCNN: Generalized Low-Precision Architecture for Inference of CNNs

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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Разработанные для работы с последовательностями трансформеры показывают state-of-the-art результаты в различных задачах. Применение трансформеров в задачах компьютерного зрения вместо привычных сверточных нейронных сетей позволяет избавиться от предположений о локальности взаимодействий внутри изображения. Однако в таком случае требуется учить все взаимодействия, что может быть недостижимо с вычислительной точки зрения для длинных последовательностей - например, изображений с высоким разрешением. На семинаре мы рассмотрим модель VQGAN для генерации изображений с высоким разрешением, которая объединяет в себе и сверточные сети, и трансформер. С помощью сверточных сетей модель учит объекты, к…

Важным этапом синтеза наночастиц является проверка, что полученные частицы имеют необходимую форму и размер, поскольку именно эти параметры определяют их функцию. Обычно исследователи пользуются сложными и времязатратными методами, такими как трансмиссионная электронная микроскопия (ТЭМ). В рассматриваемой работе предложен более простой метод: использование машинного обучения для получения формы и размера наночастиц из их излучаемого спектра, так как, во-первых, именно морфология частицы определяет её оптические свойства, а, значит, влияет на спектр излучения, а, во-вторых, экспериментальное получение спектра проще, чем трудоемкие измерения с помощью ТЭМ. На семинаре мы подробнее обсудим да…

Современные актор-критик методы основаны на двух составляющих: акторе, который определяет политику агента, и критике, который вычисляет значение value-функции для предложенной актором политики. Несмотря на успешное решение многих задач обучения с подкреплением, актор-критик и его модификации часто сталкиваются с проблемой неэффективного исследования среды. Авторы статьи “Adversarially Guided Actor-Critic” предлагают бороться с задачей балансирования между exploration и exploitation с помощью добавления в модель еще одной нейронной сети -- оппонента (the adversary), задача которого предсказывать действия актора путем минимизации KL-дивергенции между распределениями действий. В то же время в …

Тема копирования и наличия клонов в коде является достаточно хорошо исследованной. Литература показывает, что большой процент современного кода состоит из клонов и что часто присутствие клонов внутри проекта влечет за собой негативные последствия: код с ошибками может распространяться в клонированном коде, а информация о клонах в коде помогает разработчикам более эффективно поддерживать проект. Одним из способов борьбы с клонами в коде является рефакторинг «Извлечение метода»: необходимый фрагмент кода извлекается в отдельный метод, а его клоны заменяются на вызовы данного метода. В то же время, несмотря на то, что такая возможность часто есть, это требует от разработчика отдельных усилий: …

Многие приложения для молекулярных вычислений и обработки информации опираются на идентификацию отдельных молекул в сложных реагирующих смесях. Хотя идентификация молекул внутри таких сложных смесей может в определенных ситуациях выполняться с помощью одного спектроскопического инструмента, такого как масс-спектрометрия или ядерный магнитный резонанс (NMR), существует множество ситуаций, в которых эта задача требует мультимодальных измерений с использованием нескольких различных спектроскопических инструментов. Чтобы помочь химикам в выборе инструментов измерения (модальностей) для исследования таких решений, в статье используют методы глубокого обучения для получения масс-спектров, NMR и и…

В последнее время исследуется применение трансформеров в различных задачах, связанных с обработкой изображений. В отличии от обработки естественного языка, где трансформеры стали стандартным решением для многих задач, применение их к изображениям лимитировано. Большинство моделей просто используют механизм attention совместно со сверхточными сетями, сохраняя уже известные архитектуры. Однако в прошлом году вышла работа, описывающая первую архитектуру для распознавания объектов, основывающуюся только на трансформерах (ViT). И хотя эта модель показывает сравнимые результаты со state-of-the-art сетями, она обладает некоторыми недостатками, такими как большой размер модели и необходимость предо…

Ключевую роль в оценке исследования в рекомендательных системах, как и во многих других областях, играет сравнение с уже хорошо изученными и проверенными подходами. Но зачастую в исследованиях не уделяют достаточного внимания оптимальной настройке простых методов, поэтому результаты исследования могут сравниваться с неоптимальными результатами работы более простых методов на конкретной задаче. В следствии чего работоспособность и результативность работы нового метода может стоять под вопросом. Настройка старых методов и последующая оценка их работы на поставленной задаче требует ответственного подхода, что может занимать много усилий и времени. Такой подход необходим для получения оптимальн…

Комбинации лекарственных препаратов имеют широкое применение в терапии, т.к. совместный полезный эффект их взаимодействия может быть выше, чем у отдельных средств. Кроме того, комбинации лекарств могут снижать вероятность возникновения побочных эффектов, поскольку компоненты имеют меньшие дозировки, что способствует снижению токсичности. В недавних исследованиях в данной области, методы машинного обучения применялись для выявления синергических комбинаций для борьбы с раковыми заболеваниями. Но описанные в них методы неприменимы к новым заболеваниям, т.к. данные об эффективности комбинаций весьма ограничены. В условиях текущей пандемии нахождение успешной комбинации существующих молекул име…

Один из способов ускорения алгоритмов обучения с подкреплением - изменение функции награды (Reward shaping). В данном направлении хорошо проработана теория: ещё в 1999 году был разработан метод потенциалов, дающий необходимое и достаточное условие на то, что новая функция награды никак не изменит оптимальную политику для среды. У этого подхода есть серьёзный недостаток - в задачах, возникающих на практике, поиск функций-потенциалов по сложности эквивалентен самому обучению агента. Поэтому активно развивается направление обучения с подкреплением, связанное с аппроксимацией функций-потенциалов. На семинаре мы рассмотрим алгоритм аппроксимации потенциалов с помощью графовых свёрточных сетей (G…

Письменность Кандзи это навык, с которым знакомятся студенты, начинающие изучать японский язык. Овладеть этим навыком непросто: трудностью является огромное количество символов, их отличная от латиницы структура и визуальная схожесть для новичка. Обычно на занятиях преподаватели контролируют процесс написания иероглифов учащимися. Авторы статьи представляют Kanji Workbook, интеллектуальную систему обучения, которая имитирует обратную связь с преподавателем. На семинаре мы обсудим: - как происходит препроцессинг данных в Kanji Workbook;

- метрики оценивания навыка письма учащегося, а также алгоритмы, позволяющие их сосчитать;

- интерфейс Kanji Workbook;

- результаты экспериментов, проведенны…

Органический синтез – процесс получения органических соединений (продуктов) при помощи химических реакций с исходными молекулами-реактантами. Синтез предполагает проведение серии реакций с некоторым набором химических соединений для получения молекулы-продукта с желаемыми свойствами. При этом для получения одной и той же молекулы можно использовать разные наборы реакций и реактантов, которые будут отличаться по скорости реакции, цене реагентов, количеству производимого продукта реакции. Для известной молекулы-продукта хотелось бы найти лучший способ синтеза. Процесс, обратный синтезу, т.е. процесс разложения молекулы на исходные молекулы-реактанты называется ретросинтезом. В машинном обучен…

Во многих сложных средах агенту трудно самостоятельно выучить оптимальную политику. Индивидуальное обучение часто может быть неэффективным, дорогостоящим или даже небезопасным (автопилоты, роботы). Для решения этой проблемы можно воспользоваться Imitation Learning, который делится на Behavioral Cloning и Inverse Reinforcement Learning (IRL). Данный подход заключается в обучении агента с помощью набора демонстраций эксперта. IRL направлен на изучение функции вознаграждения, при которой политика эксперта является оптимальной. В последнее время для изучения функции вознаграждения используется состязательное обучение. Такой подход называется Adversarial Imitation Learning (AIL). На семинаре мы …

В последнее время всё чаще можно встретить работы, посвящённые применению машинного обучения к исходному коду. В таких задачах исследователи строят модели, например, для суммаризации кода или поиска дубликатов. При этом надо помнить, что модели машинного обучения очень зависят от формата входных данных. Поэтому не удивительно, что ведётся много работы в поисках оптимальных представлений кода. На текущий момент одно из самых устоявшихся представлений это абстрактные синтаксические деревья (AST) и производные из них структуры данных. Однако абстрактные синтаксические деревья давно используются в различных областях SE, например, в средах разработки (IDE). IDE предоставляют API для манипуляции …

На сегодняшний день одним из многообещающих подходов, который позволит значительно расширить область применения обучения с подкреплением является целенаправленное иерархическое обучение. Данный подход уже показал себя во многих задачах с долгосрочным присвоением кредита и редкими вознаграждениями. Однако, при большой размерности пространства целей, возникают трудности как с генерацией подцелей на высоком уровне так и с обучением политики на низком уровне, вследствие чего рассматриваемый подход может оказаться неэффективным. Авторы статьи, о которой пойдет речь, показали, что эта проблема может быть успешно решена путем ограничения пространства действий высокого уровня от всего пространства …

Моделирование и генерация молекулярных структур имеет большое значение в изучении лекарств и иммунологии. Графы представляют собой полезную математическую абстракцию для выражения и анализа таких структур.

В последнее время появился ряд работ по изучению генеративных моделей графов по данным. Одни из популярных таких моделей: GraphVAE и junction tree variational autoencoders. Принцип работы данных моделей заключается в следующем: закодировать граф в пространство скрытых состояний, а затем декодировать сэмплы скрытых переменных из априорного распределения. Есть и другой подход: генерировать граф последовательно, по одной вершине (autoregressive approach).

В статье представлена Energy based m…

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

В этом году мы решили помочь тем, кто готовится к поступлению в Школу анализа данных, и поделиться решениями нескольких заданий из вариантов письменного экзамена, демонстрирующими полезные приёмы. Каждую неделю мы будем публиковать здесь разбор одной из задач, которые были на письменном экзамене в ШАД в 2019 году. Условия задач и текстовые разборы вы найдёте на сайте: https://yandexdataschool.ru/stepbystep

Yandex Research — это исследовательская группа внутри Яндекса, которая занимается фундаментальными проблемами в важнейших областях computer science и искусственного интеллекта, таких как компьютерное зрение, Natural Language Processing, речевые технологии, краудсорсинг, поиск и рекомендации. В рамках митапа исследователи из Yandex Research и научной лаборатории Яндекса на Факультете Компьютерных Наук НИУ ВШЭ рассказали об интересных задачах, которыми они занимаются, а ещё о том, как стать частью команды. Участники митапа и программа: • Андрей Малинин. Неопределенность в структурных предсказаниях; • Станислав Морозов. Big GANs Are Watching You: о сегментации объектов без учителя с помощью го…

В этом видео победители соревнования NoFloodWithAI от Сбербанка и МЧС по предсказанию наводнений на реке Амур расскажут о своих решениях, о проблемах подобных задач и нетривиальных подходах к прогнозированию. 03:25 1 место - Дмитрий Раевский

Презентация https://storage.yandexcloud.net/datasouls-ods/ML_trainings_presentations/Dmitry_Raevsky.pdf

Ссылка на репозиторий https://github.com/RaevskyDN/aij2020-amur-noflood-public

Ссылка на описание решения https://opendatascience.slack.com/archives/C2LJA6VP0/p1606908626129600 13:07 2 место - Александр Мамаев

Презентация https://storage.yandexcloud.net/datasouls-ods/ML_trainings_presentations/Alexander_Mamaev.pdf

Ссылка на репозиторий https://github.…

ODS 6th Birthday

Панельная дискуссия, март 2021 Спикеры:

Артур Кузин, Kaggle Grandmaster

Станислав Семенов, Kaggle Grandmaster

Михаил Трофимов, Kaggle Grandmaster

Евгений Нижибицкий, ML Engineer Понравилось это видео? Подключайтесь к прямому эфиру в наш День Рождения 13 марта - будет очень интересно! https://ods.ai/events/birthday6 Вступить в сообщество: https://ods.ai/

Соцсети Дата Фест с актуальными анонсами: https://t.me/datafest

https://vk.com/datafest

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/…

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Спикер: Асхат Уразбаев, Founder of ScrumTrek, Agile Coach Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Спикер: Алексей Смирнов, CEO at Profiscope.io Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Спикер: Алексей Григорьев, Lead Data Scientist at OLX Group, Founder at DataTalks.Club Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Спикер: Дмитрий Колодезев, Director of Promsoft Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Спикеры: Денис Воротынцев, Data Scientist at Unity, Юрий Болконский, Kaggle Grandmaster Посмотреть эфир Ёлки: https://ods.ai/events/elka2020

Треки сообщества: https://ods.ai/tracks Наши соцсети:

Telegram Open Data Science: https://t.me/ods_ru

Telegram Data Fest: https://t.me/datafest

Brilliant: http://www.brilliant.org/primer Papers:

- https://www.researchgate.net/publication/41910312_Altruism_Spite_and_Greenbeards

- https://www.reed.edu/biology/professors/srenn/pages/teaching/2007_syllabus/2007_readings/a13_Keller_1998.pdf For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning Sometimes streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made with Unity

https://github.com/Helpsypoo/PrimerUnity Music by Mathieu Keith. For business inquiries: mathieu.keith@gmail.com Several other inputs into the graphics are from public domain contributions to blendswap.com Plush blo…

Plush blobs: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning A good place for learning more about how to be less wrong:

https://www.lesswrong.com/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself working on these monstrosities:

- Twitch: https://www.twitch.tv/primerjustin Made possible by support through Patreon:

Christian Gruber

Matthijs Ruijgrok

Christopher

Anthony Eufemio

José Hamilton

Zachariah Richard Fournier

Vladimir Duchenchuk

Noah Healy

JMakes

Mike Schmidt

PeepPhysics

Anders Fjeldvær

Ghost G…

Check out Brilliant: http://www.brilliant.org/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning

Store: https://store.dftba.com/collections/primer More on voting theory:

- Interactive by Nicky Case: https://ncase.me/ballot/

- The best single resource I found: https://www.lesswrong.com/posts/D6trAzh6DApKPhbv4/a-voting-theory-primer-for-rationalists Organizations that advocate for voting reform:

- Team Approval: https://electionscience.org/

- Team Instant Runoff: https://www.fairvote.org/ For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning

- Twitter: @primerlearning

- Facebook: facebook.com/primerlearning Streaming myself wor…

Risto Miikkulainen is a computer scientist at UT Austin.

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

(00:00) – Introduction(06:51) – If we re-ran Earth over 1 million times(10:08) – Would aliens detect humans?

(12:46) – Evolution of intelligent life(16:31) – Fear of death(22:47) – Hyenas(26:12) – Language(29:43) – The magic of programming(35:43) – Neuralink(43:15) – Surprising discoveries by AI(46:49) – How evolutionary computation works(58:12) – Learning to walk(1:01:25) – Robots and a theory of mind(1:10:29) – Neuroevolution(1:20:47) – Tesla Autopilot(1:24:11) – Language and vision(1:29:53) – Aliens communicating with humans(1:35:29) – Would AI …

Robert Breedlove is a decentralized finance entrepreneur, philosopher, and podcaster.

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

(00:00) – Introduction(08:46) – Sovereignty(15:50) – Territorial imperative(20:28) – Property(26:33) – Anarchism(29:11) – Inflation is theft(33:35) – Volatility is truth(38:14) – Taleb and Bitcoin(42:22) – Life is information propagating through flesh(53:21) – Intelligence(57:34) – Space and time(1:04:16) – Pragmatic truth(1:15:14) – Creative destruction(1:19:03) – Capitalism vs Communism(1:31:46) – Jordan Peterson on religion(1:36:03) – Inflation(1:39:54) – What is money?

(2:04:36) – Bitcoin vs other cryptocurrencies(2…

Yannis Pappas is a comedian and podcaster.

Please support this podcast by checking out our sponsors:– 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 premium– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Yannis’s Twitter: https://twitter.com/yannispappasLong Days Podcast: https://www.youtube.com/channel/UCywn6iboO1P8U7fotfllocwStand Up Special: https://www.youtube.com/watch?v=R156F0uXhzkPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8…

Tyler Cowen is an economist, writer, and podcaster.

Please support this podcast by checking out our sponsors:– Linode: https://linode.com/lex to get $100 free credit– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Public Goods: https://publicgoods.com/lex and use code LEX to get $15 offEPISODE LINKS:Tyler’s Twitter: https://twitter.com/tylercowenTyler’s Blog: https://marginalrevolution.com/Conversations with Tyler (Podcast): https://conversationswithtyler.com/Big Business (Book): https://amzn.to/2OBPbaKTyler’s Wiki: https://en.wikipedia.org/wiki/Tyler_CowenPODCAST INFO…

Nic Carter is a financial researcher, investor, writer, and podcaster on topics of decentralized finance.

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

(00:00) – Introduction(13:05) – Can humans fully understand reality?

(15:34) – The dollar system(21:44) – Bitcoin(23:19) – Opendime(27:22) – Core values of Bitcoin(35:51) – Who is Satoshi Nakamoto?

(41:39) – How Bitcoin works(50:02) – Bitcoin blocksize wars(1:02:27) – Layered scaling of Bitcoin(1:07:25) – Lightning network(1:10:25) – Schnorr/Taproot update to Bitcoin(1:15:17) – Criticisms of Bitcoin(1:25:04) – Bitcoin failure modes(1:33:07) – Bitcoin vs Ethereum(1:37:03) – Vitalik Buterin(1:39:56) – …

Ryan Schiller is the creator of Librex, an anonymous discussion feed for college communities.

Please support this podcast by checking out our sponsors:– Allform: https://allform.com/lex to get 20% off– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– BetterHelp: https://betterhelp.com/lex to get 10% off– Brave: https://brave.com/lexEPISODE LINKS:Librex App: https://librexapp.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube Clips: https://youtube.com/lexclipsSUPPORT & CONNECT:– Chec…

Anthony Pompliano is an entrepreneur, investor, writer, and podcaster on topics of decentralized finance.

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

(00:00) – Introduction(08:54) – Army(16:35) – Iraq(24:14) – Will there always be war?

(31:27) – Bitcoin maximalism(39:56) – Money is a belief system(42:27) – Bitcoin(46:03) – Censorship(50:35) – Bitcoin as main currency(59:27) – Scarcity creates value(1:01:08) – Money is time(1:09:34) – Eric Weinstein vs Bitcoin Community(1:23:23) – Ray Dalio(1:40:31) – Bitclout(1:43:43) – How to get Bitcoin(1:54:28) – Investing(2:05:05) – Volatility(2:18:08) – Philosophy of the meme(2:28:03) – Dogecoin(2:37:33) – NF…

Ronald Sullivan is a law professor at Harvard and previously a lawyer for Harvey Weinstein and Aaron Hernandez.

Please support this podcast by checking out our sponsors:– Brooklinen: https://brooklinen.com and use code LEX to get $25 off + free shipping– Wine Access: https://wineaccess.com/lex to get 20% off first order– Munk Pack: https://munkpack.com and use code LEX to get 20% off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:Ronald’s Twitter: https://twitter.com/profronsullivanRonald’s Website: https://hls.harvard.edu/faculty/directory/10870/SullivanRonald’s Wikipedia: https://en.wikipedia.org/wiki/Ronald_S._Sullivan_Jr.

Ronald’s NY Times Artic…

Ryan Hall is a martial artist, BJJ black belt, and MMA fighter undefeated in the UFC.

Please support this podcast by checking out our sponsors:– Indeed: https://indeed.com/fridman to get $75 credit– Audible: https://audible.com/lex to get $9.95 a month for 6 months– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– LMNT: https://drinkLMNT.com/lex to get free sample packEPISODE LINKS:Ryan’s Twitter: https://twitter.com/ryanhall5050​Ryan’s Website: http://www.ryanhallmma.com/​Ryan’s School: https://www.5050bjj.com​Ryan’s Online Courses: https://ryanhallonline.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwq…

Silvio Micali is a computer scientist at MIT, Turing award winner, and founder of Algorand.

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Saagar Enjeti is a DC-based political correspondent and podcaster.

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Cal Newport is a computer scientist who also writes about productivity.

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

(00:00) – Introduction(08:24) – Deep work(13:10) – Focus(18:52) – Time blocking(25:47) – Deadlines(35:22) – Do less, do better, know why(38:04) – Clubhouse(52:07) – Burnout(58:34) – Boredom(1:06:19) – Quit social media for 30 days(1:16:13) – Social media(1:41:21) – How email destroyed our productivity at work(1:51:07) – How we fix email(1:58:09) – Over-optimization(2:02:23) – When to use email and when not to(2:10:06) – Podcasting(2:14:42) – Alan Turing proving the impossible(2:18:41) – Fragility of math in the face of randomness(2:2…

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.

Please support this podcast by checking out our sponsors:– MasterClass: https://masterclass.com/lex to get 15% off– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Andrew’s YouTube: https://www.youtube.com/channel/UC2D2CMWXMOVWx7giW1n3LIgHuberman Lab Podcast: https://hubermanlab.libsyn.com/Andrew’s Instagram: https://www.instagram.com/hubermanlab​Andrew’s Wikipedia: https://en.wikipedia.org/wiki/Andrew_…​Andrew’s Website: http://www.hubermanlab.com/PODCAST INFO:Podcast…

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…

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.

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

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

https://shubhranshu-shekhar.github.io/

Life May Be RareToday on the show Dr. Anders Sanburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare@anderssandberg

Social NetworksMayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro SzekleySocial MediaLinkedInTwitter

The QAnon ConspiracyQAnon is a conspiracy theory born in the underbelly of the internet.

Max Aliapoulios joins us to discuss the paper The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information.

This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity.

This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be.

In a few weeks, we’re going to ask everyone to vote for their favorite theme for our next season.

Benchmarking Computer Vision on Edge vs CloudKarthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.

Social Media

Goodhart’s Law in Reinforcement LearningHal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart’s law and Reinforcement Learning.

Also mentioned was The Book of Why by Judea Pearl

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.

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:

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Sam Hinton joins us to discuss his work since assisting COVID-19 data pipelines, now working in renewable energy and applications of ML and MLOps for the industry.

In this episode you will learn:• Catching up with Sam …

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Vince Petaccio joins us to discuss how he sees data science, ML, and AI making positive impacts in the fight against climate change.

In this episode you will learn:• Where in the world is Vince?

[2:08]• Vince’s intere…

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Harpreet Sahota joins us to discuss his data science mentorship work outside his day job and how you can land your dream job.

In this episode you will learn:• Harpreet’s current life and location [2:25]• Data Communit…

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Horace Wu joins us to discuss his work on Syntheia, a unique product that helps sift through massive amounts of legal data to augment the capacities and function of law firms.

In this episode you will learn:• Horace’s …

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Stephen Welch joins to go over his year-end 2020 list of 10 important questions and pain points that machine learning can improve.

In this episode you will learn:• Welch Labs on YouTube [4:54]• What Stephen’s been up …

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Dan Shiebler joins us to discuss his category theory Ph.D. program, his full-time job at Twitter, and how the two crossover and combine in his overall data work.

In this episode you will learn:• Dan’s neuroscience unde…

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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…

April 13, 2021 podcastIs there a gap between life science and data science?

What’s the situation when it comes to interdisciplinary research?

In this episode I am with Laura Harris, Director of Training for the Institute of Cyber-Enabled Research (ICER) at Michigan State University (MSU), and we try to answer some of those questions.

You can contact Laura at training@msu.edu or on LinkedIn

April 11, 2021 podcastIn this episode I am with George Hosu from Cerebralab and we speak about how dangerous it is not to pay for the services you use, and as a consequence how dangerous it is letting an algorithm decide what you like or not.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceIf building software is your passion, you’ll love ThoughtW…

April 8, 2021 podcastIn this episode I speak with Ritchie Vink, the author of Polars, a crate that is the fastest dataframe library at date of speaking 🙂 If you want to participate to an amazing Rust open source project, this is your change to collaborate to the official repository in the references.

Our SponsorAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

Referenceshttps://github.com/ritch…

March 31, 2021 podcastDo you want to know the latest in big data analytics frameworks?

Have you ever heard of Apache Arrow?

In this episode I speak with Andy Grove one of the main authors of Apache Arrow and Ballista compute engine.

Andy explains some challenges while he was designing the Arrow and Ballista memory models and he describes some amazing solutions.

It’s a podcast for techies by techies.

March 19, 2021 podcastPandas is the de-facto standard for data loading and manipulation.

Python is the de-facto programming language for such operations.

Rust is the underdog.

In this episode I am showing you why that is no longer the case.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

Our SponsorsThis episode is supported by Chapman’s Schmid College of Science and Technology, where master’s and PhD students join in cutting-edge research as they prepare to take the next big leap in their professional journey.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in …

March 10, 2021 podcastIn plain English, concurrent and parallel are synonyms.

In this episode I summarize the ways to parallelize on different architectures and operating systems.

Rock-star data scientists must know how concurrency works and when to use it IMHO.

To learn more about the innovative tools and collaborative approach that distinguish the Chapman program in Computational and Data Sciences, visit chapman.edu/datascienceAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and…

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

🎙 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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