I don't have powerful GPU to work on ImageNet dataset. I want to work on some classes of ImageNet in PyTorch. submitted by /u/SAbdusSamad [link] [comments]

So I am training an LSTM model with a daily rainfall dataset to see whether it can predict the next-day rainfall. Now the problem with my dataset is that it has missing values. Data for many consecutive days are missing. How should I handle missing data? Should I drop out of the missing records or fill out the missing data by analyzing the values around its nearby days? And for 1-day forecasting, would dropping out missing records lead to my model predicting wrong results? Also, I have seen many youtube tutorials taking multiple data as input to forecast the next day. For example, they would train the LSTM model by taking 5 hours of rainfall to predict the rainfall in the next hour. Since I…

For the general continuous case of RN --> RM data with unknown structure, deep ReLU MLPs have been the go-to model for the last 10 years or so. Before that it was SVMs and GMMs. But recently transformer-type models such as DeepMind's Perceivers have had some good results while basically plug-n-play. These models are more expressive than MLPs, but I understand they can be harder to train. What do you think, are they ready for primetime or are more breakthroughs needed? submitted by /u/svantana [link] [comments]

Hi everyone, If you have a small dataset, maybe hundreds and you expect your feature vectorizer can represent your input text effectively to do classification task which here is Intent Detection, a subset of Text Classification Task. Whether or not BERT in specific or contextual word embeddings, in general, would be a preferable choice in this case? Can anyone give me advice or navigate me toward a more sensible solution? ​ Note: I used to try with some common techniques NLP literature such as TF-IDF, BoW, word2vec, ... But none of them make me happy. submitted by /u/hosjiu [link] [comments]

Firstly, here's a demo video of the original software generating midi drum patterns based on input from another midi or audio file: https://youtu.be/eYUaYzfZUCo To further explain what is happening: A Google research team had several professional drummers come in to play on electronic drum kits that turn their performances into midi files. They then trained a neural network with Tensorflow on over 14 hours of drum midis played by professional drummers. This is what creates such profound results as seen in the above video. And I have used this myself and found that if you repeatedly give it the same input, it will give nearly the same output, only with very slight variations, as I would expe…

Source Code | Demo Video For an ML app, I needed to collect data from postgres, mongodb, Google sheets, Google analytics, Google ads, etc. It was painful to do so and took me couple of months with lots of bugs. To solve this problem, I created a customer data platform to build customer data pipelines that connect whole customer data stack and then makes them smarter by triggering enrichment and activation in customer tools based on analysis in data warehouses such as aws redshift, snowflake and more. Working on a major upgrade for the project, and hence seeking your feedback on how I can we improve it? Shoot me your questions, suggestions, what use cases comes to your mind for this project,…

Object-NeRF NeRF models have come a long way since the initial “explosion” last year. Yet one of the things they still can’t quite handle is scene compositionality, meaning that the model is not aware of the distinct objects that make up the scene. Object NeRF aims to tackle this issue using a dual-branch model that separately encodes the global context of the scene and each object in it. This approach not only reaches competitive levels of quality with current SOTA methods on static scenes but also enables object-level editing. For example, adding or moving furniture in a real-world scene. Check out the full paper summary on Casual GAN Papers (Reading time ~5 minutes). Subscribe to my chan…

Hello! I am working on a text classifier but at the moment im quite lost on what to do. The classes form a tree with three levels, for example: class A (level 1), class A.1 (level 2, subclass of A) and class A.1.a (level 3, subclass of A.1). I have a lot of texts but without labels, also its hard to create a set of labeled data because of the big number of classes. I've looked at word embeddings, clustering algorithms, topic modeling, keyword extraction algorithms and other approaches but im not sure whats would be a good way to proceed. Also im not sure how to evaluate the success of the classifier, maybe i could build a small dataset but i wanted to know if there was any other option At t…

I'm trying to predict when and what kind of problem a machine will most likely have and a have a lot of entry, but the frequency of each error ranges quite a bit, so I'm not sure whether those with lower frequency should be grouped together. I have about 175k entries and 12 types of errors, with the most frequent being at 48k times and the least being ~350, but most ranging from 8k-25k. Is it enough? I've thought of grouping the 5 less common because they have under 2.5k but I'm not sure if it will make any difference. I've read a bit about 10x the number of parameters and 'the curse of dimentionality', but I'm still unsure on how to apply it here. submitted by /u/SanderSohngen [link] [comm…

submitted by /u/deeplearningperson [link] [comments]

Just as with “linear” SVMs, the empirical parameter estimates of Kernelized SVMs do not have a closed-form analytic solution.

Alternatively, just like Ordinary Least Squares (OLS) Linear Regression, the empirical parameter estimates of Kernelized Linear Regression do have a closed-form solution; no iterative fitting procedure required!

Given this property, I fully advocate the teaching of Kernelized Linear Regression as the introduction to Mercer Kernels.

There’s nothing inherently special about SVMs in this respect over Linear Regression or Generalized Linear Models (GLMs).

Computational SimulationA computational simulation is provided in python for both Kernelized Linear Regression and Ke…

Abstract scripts into functions and classesSay you are working on a Jupyter notebook figuring out how to best visualize some data.

As soon as that code works and you don’t think it will need much more debugging, it’s time to abstract it!

Let’s look at an example,import matplotlib.pyplot as pltimport seaborn as snssns.set()import numpy as npimport pandas as pdsynthetic_data = np.random.normal(0,1,1000)plt.plot(synthetic_data, color="green")plt.title("Plotting Synthetic Data")plt.xlabel("x axis")plt.ylabel("y axis")plt.show()Here, we plotted some synthetic data.

If for any reason, we want to load the data and perform a couple of simple transformations to it, we might want to have a class that…

The relationships in the diagram below are captured using temporal word embeddings.

These embeddings are referred to as temporal word embeddings (also referred to as diachronic word embeddings or dynamic word embeddings).

Training Temporal Word EmbeddingsNeural word embeddings (such as Word2Vec) trained independently on different temporal corpora cannot be compared directly.

The authors suggest using this simpler method of comparing temporal word embeddings, as it is more interpretable and stable than using the common orthogonal Procrustes method for temporal alignment.

Temporal word embeddings are an exciting area of research within NLP and are currently being used in many different subfie…

The Hot Hand in ChessPhoto by GR Stocks on UnsplashBackgroundIn basketball, there is the idea of the “Hot Hand” where if a player has made several shots in the recent past, he’s believed to be “in the zone” and more likely to make future shots.

used to measure basketball’s hot hand, but instead focus on internet blitz chess games.

Using these players would be more analogous to the original Hot Hand study, which used professional basketball players.

The sample sizes are small, and it only mimics part of the original Hot Hand study.

All imperfections aside, this analysis strongly suggests that there is such thing as a “Hot Hand” in chess where a player gets in the zone and is more likely to w…

The Only Auto-Completion Extension You’ll Ever Need For Your Jupyter NotebooksPhoto by Nathan Dumlao on UnsplashOne of the most loved programming interfaces in Python is the Jupyter Notebook environment, and wanting code auto-completion enabled in it feels quite natural.

This extension is one of the most useful nbextensions that I’ve used, and it does exactly as proposed.

Now go ahead and open up your jupyter notebook!

For all the pipenv users out there, remember that you need to do:pipenv run jupyter notebookand not just:jupyter notebookThe second command should not even work if you’ve not installed Jupyter globally in your system.

You need to go here and enable it on the nbextensions tab,…

Understanding the building blocks and design choices of graph neural networks.

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

After five years, Distill will be taking a break.

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

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

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

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

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

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

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

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

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

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

They coined a new term, “Foundation Models” to characterize the new paradigm, joined forces in a “Center for Research on Foundation Models”, and published the massive 212-page report “On the Opportunities and Risks of Foundation Models.”Although the term is new, the general approach is not.

At the Workshop on Foundation Models, Jitendra Malik, a renowned expert in computer vision at Berkeley, said, “I am going to take a ... strongly critical role, when we talk about them as the foundation of AI ...

As Georgia Tech professor Mark Riedl wrote on Twitter “Branding very large pre-trained neural language models as “foundation” models is a brilliant … PR stunt.

The report says, unironically, “we …

An Introduction to AI Story GenerationIt’s All Training Data: Using Lessons from Machine Learning to Retrain Your Mind

Some events are goal events and some events are sub-goal events that are necessary steps in achieving a final goal event.

These sub-goal events precede the goal event and are also related to the goal event as part of a hierarchy.

Training a language model on a corpus of stories means the language model will attempt to emulate what it has learned from a corpus.

They train an explanation generation language model to answer the question and then make the resulting explanation the previous segment of the story.

CitationFor attribution in academic contexts or books, please cite this work asMark Riedl, "An Introduction to AI Story Generation", The Gradient, 2021.

Machine learning’s increasing importance to real-world applications brought awareness of a new field focused on ML in practice - machine learning systems (or, as some call it, MLOps).

This field acts as a bridging point between the domains of computer systems and machine learning, considering the new challenges of machine learning with a lens shaped by traditional systems research.

By identifying a shared problem in both systems and ML - combining data sources - we can apply traditional systems techniques to a machine learning setting.

Of course, there are some differences, reflecting how machine learning systems differ from the traditional paradigm.

Systems research is filling this need, b…

Language UnderstandingWhile NLP (Natural Language Processing) and NLU (Natural Language Understanding) are often used interchangeably, there is a substantial difference between the two and it is crucial to highlight this difference.

Thus, machine learning and language understanding are incompatible – in fact, they are contradictory.

Natural language is rampant with intensional phenomena, since objects of thoughts — that language conveys — have an intensional aspect that cannot be ignored.

CitationFor attribution in academic contexts or books, please cite this work asWalid Saba, "Machine Learning Won't Solve Natural Language Understanding", The Gradient, 2021.

BibTeX citation:@article{saba20…

Origins of Machine TranslationThe first machine translation efforts in the United States were spurred by the Cold War.

However, the United States government remained a faithful consumer of machine translation technology; in Tom Pedtke’s 1997 keynote address at the sixth Machine Translation Summit, he reflects on several key developments in the 1990s fostered by government demand.

This is not to say that machine translation is epistemologically bereft; the parallel text basis of contemporary machine translation paradigms aligns with Quine’s pragmatic, behaviorist approach to translation.

Translation remains a political act, and data-driven machine translation developments, largely centered i…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

BibTeX citation:@article{moranopensour…

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Each relevant technology trend in the last five decades has been accompanied by incremental progress in database technologies.

However, ML has the unique capability to not only improve the existing generation of database technologies but also reimagine the space with new databases we haven’t seen before.

The influence that ML can have in database technologies is unique because it is bidirectional.

Just this week, Facebook published a research paper unveiling what they call neural databases, a concept that combines the use of NLP for unstructured databases.

Like previous technology trends, ML is likely to bring fresh ideas that power innovation in the world of databases.

The technology transforms how analysts use their data to forecast, predict, and uncover new data insights.

It changes how data scientists connect, prepare, engineer, and model data and operationalize AI.

And it enables data science leaders to empower teams to deliver results that meet or surpass their goals faster.

Squark automates the entire data science process; a few highlights include:Intelligent data connectors that round-trip data between sources.

Automated data cleaning, pre-processing, feature engineering and selection make your data better, including natural language processing and much more.

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Делимся нашим исследованием вакансий и зарплат в сфере data science и data engineering.

Посмотрим еще на распределение грейдов для каждой специальностиСпрос на джунов в дата инжиниринге ниже, чем в аналитике и data science.

Зачастую зарплата указывается в тексте вакансии в неструктурированном виде, иногда в одной вакансии идет несколько позиций и несколько вилок.

После этого сделаем 4 регулярки: для текста, который часто идет до и после зарплаты в вакансии, для самих цифр зарплаты и для текста, который встречается между нижней и верхней границей зарплатной вилки.

В целом, знания deep learning становятся более востребованными: указаны в более 30% вакансий в 2021 году.

Также неправильно было бы говорить о том, что в отличии от классических компьютеров, где есть лишь и в квантовых есть все состояния сразу.

Но это для классического компьютера.

И сегодня мы вынуждены использовать лишь очень приближенные решения и концепции, точности которых часто не хватает.

На сегодня почти все известные технологии создания квантовых компьютеров требуют чего-то из:сверхнизкие температурысверхвысокий вакуумсверхточная юстировка лазеров на оптическом столеИли даже всего сразу.

Да и в этом нет особого смысла, ведь мало кому дома нужно взламывать биткоин, решать логистическую проблему или разрабатывать высокотемпературный сверхпроводник.

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

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

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

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

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

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

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

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

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

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

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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.)

That means I’ve felt greater feelings of responsibility and urgency for puzzlehunt writing compared to blogging.

markdown 6597 2021 - 01 - 29 - mh - 2021. markdown 4249 2021 - 02 - 18 - flash - games .

View CountsThese are the view counts from August 18, 2020 to today, for the posts I’ve written this year.

markdown 912 2021 - 01 - 29 - mh - 2021. markdown 167 2021 - 02 - 18 - flash - games .

Post about Dominion Online:Odds of writing this year: 25%Odds of writing eventually: 50%Post about puzzlehunts:Odds of writing this year: 70%Odds of writing eventually: 90%

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

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

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

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

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

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

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

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

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

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

Online Dominion is getting yet another online implementation!

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

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

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

There have been a few attempts at Dominion AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google implemented the similar approach in their neural machi…

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

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

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

However, while using Markov factoriza…

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

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

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

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

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

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

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

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

The second term is what Barret a…

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

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

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

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

We of course want to make contributions using the skills and in the scope of influence available to us, and so I thought we could hold a discussion on Harnessing Machine Learning to Achieve Net Zero.

Part I: Net Zero PossibilitiesWe are here at this ICML 2021, having witnessed Earth’s hottest decade in history.

The solutions for net zero are many, and they make many opportunities for high-impact machine learning research.

The Royal Society report on Digital Technology for the Planet develops the idea that ‘Computing for net zero’ could play an important role in global, regional and national net zero strategies.

There were three I wanted to bring together for your assessment and contestation…

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

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

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

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

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

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

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

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

Inventing Ourselves: Responsibility and Diversity in Research.

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

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

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

Despite the imp…

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

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

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

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

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

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

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

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

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

This gives us two potential ca…

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

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

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

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

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

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

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

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

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

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

When are Neural Networks more powerful than Neural Tangent Kernels?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Can implicit regularization in deep learning be explained by norms?

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

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

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

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

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

I introduce the cheat sheet in this brief video:

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

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

How the layers result in a final hidden state.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

I think the really A…

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

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

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

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

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

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

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

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

Large Transformer models have been central to recent advances in natural language processing.

Here we aim to reduce the costs of Transformers by searching for a more efficient variant.

We identify an architecture, named Primer, that has a smaller training cost than the original Transformer and other variants for auto-regressive language modeling.

For example, at a 500M parameter size, Primer improves the original T5 architecture on C4 auto-regressive language modeling, reducing the training cost by 4X.

Furthermore, the reduced training cost means Primer needs much less compute to reach a target one-shot performance.

Objectives: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficiency.

Methods: Multi-vendor cardiac MRI studies were retrospectively collected from 4 centres and 3 vendors... A two-head convolutional neural network ('CardiSort') was trained to classify 35 sequences by imaging sequence (n=17) and plane (n=10).

Single vendor training (SVT) on single centre images (n=234 patients) and multi-vendor training (MVT) with multicentre images (n = 479 patients, 3 centres) was performed.

MVTexternal yielded sequence accuracy of 92.7% and plane accuracy of 93.0%.

With refinement, it has pot…

This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot.

A dataset with ten main categories encountered under various field conditions was used for testing...

The effect on these conditions on the angular accuracy of crop row detection was compared.

A deep convolutional encoder decoder network is implemented to predict crop row masks using RGB input images.

The predicted mask is then sent to a post processing algorithm to extract the crop rows.

Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving.

However, LiDAR range image is different from a binary image which has a deterministic condition to tell if two pixels belong to the same component.

The heuristic condition used on the LiDAR range image only works empirically, which suggests the LiDAR clustering algorithm should be robust to potential failures of the empirical heuristic condition.

To overcome this challenge, this paper proposes a divide-and-merge LiDAR clustering algorithm.

We evaluate the divide-and-merge clustering algorithm on the SemanticKITTI panoptic segmentation benchmark by cascading it wi…

Square matrices appear in many machine learning problems and models.

Optimization over a large square matrix is expensive in memory and in time...

Conventional approximation approaches factorize the square matrix into a number matrices of much lower ranks.

In this paper, we propose to approximate a large square matrix with a product of sparse full-rank matrices.

The sparse factorization method is tested for a variety of synthetic and real-world square matrices.

Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier.

We introduce CompilerGym, a set of environments for real world compiler optimization tasks, and a toolkit for exposing new optimization tasks to compiler researchers.

CompilerGym enables anyone to experiment on production compiler optimization problems through an easy-to-use package, regardless of their experience with compilers.

We describe the CompilerGym architecture and implementation, characterize the optimization spaces and computational efficiencies of three included compiler environments, and provide extensive empirical evaluat…

While the rapid progress of deep learning fuels end-to-end reinforcement learning (RL), direct application, especially in high-dimensional space like robotic scenarios still suffers from high sample efficiency.

Therefore State Representation Learning (SRL) is proposed to specifically learn to encode task-relevant features from complex sensory data into low-dimensional states...

To handle such problem, we present a new algorithm called Policy Optimization via Abstract Representation which integrates SRL into the original RL scale.

Thirdly, we introduce a new prior called domain resemblance to leverage expert demonstration to train the SRL model.

Finally, we provide a real-time access by stat…

Privacy-preserving process mining enables the analysis of business processes using event logs, while giving guarantees on the protection of sensitive information on process stakeholders.

It lowers the utility of the published data and makes noise easily identifiable, as some traces will violate well-known semantic constraints.In this paper, we therefore argue for privacy preservation that incorporates a process semantics.

For common trace-variant queries, we show how, based on the exponential mechanism, semantic constraints are incorporated to ensure differential privacy of the query result.

Experiments demonstrate that our semantics-aware anonymization yields event logs of significantly hi…

Generating portrait images by controlling the motions of existing faces is an important task of great consequence to social media industries.

For easy use and intuitive control, semantically meaningful and fully disentangled parameters should be used as modifications...

In this paper, a Portrait Image Neural Renderer (PIRenderer) is proposed to control the face motions with the parameters of three-dimensional morphable face models (3DMMs).

The proposed model can generate photo-realistic portrait images with accurate movements according to intuitive modifications.

Meanwhile, we further extend this model to tackle the audio-driven facial reenactment task by extracting sequential motions from …

In some cases, a World Model offers an agent the opportunity to learn entirely inside of its own dream environment.

In this work we explore improving the generalization capabilities from dream environments to real environments (Dream2Real).

By training the World Model using dropout, the dream environment is capable of creating a nearly infinite number of different dream environments.

Dropout's Dream Land leverages each unique mask to create a diverse set of dream environments.

Our experimental results show that Dropout's Dream Land is an effective technique to bridge the reality gap between dream environments and reality.

Semantic understanding of 3D point clouds is important for various robotics applications.

Given that point-wise semantic annotation is expensive, in this paper, we address the challenge of learning models with extremely sparse labels...

To this end, we propose a self-supervised 3D representation learning framework named viewpoint bottleneck.

A principled analysis shows that viewpoint bottleneck leads to an elegant surrogate loss function that is suitable for large-scale point cloud data.

Compared with former arts based upon contrastive learning, viewpoint bottleneck operates on the feature dimension instead of the sample dimension.

Neural Architecture Search (NAS) provides state-of-the-art results when trained on well-curated datasets with annotated labels.

Our components build on top of recent developments in self-supervised learning~\citep{zbontar2021barlow}, self-supervised NAS~\citep{kaplan2020self} and extend them for the case of imbalanced datasets.

We conduct experiments on an (artificially) imbalanced version of CIFAR-10 and we demonstrate our proposed method outperforms standard neural networks, while using $27\times$ less parameters.

To validate our assumption on a naturally imbalanced dataset, we also conduct experiments on ChestMNIST and COVID-19 X-ray.

The results demonstrate how the proposed method can b…

In the FARFETCH Fashion Recommendation challenge, the participants needed to predict the order in which various products would be shown to a user in a recommendation impression.

The data was provided in two phases - a validation phase and a test phase...

The validation phase had a labelled training set that contained a binary column indicating whether a product has been clicked or not.

The dataset comprises over 5,000,000 recommendation events, 450,000 products and 230,000 unique users.

We have designed a unique context-aware system that takes the similarity of a product to the user context into account to rank products more effectively.

Automated Theorem Proving (ATP) deals with the development of computer programs being able to show that some conjectures (queries) are a logical consequence of a set of axioms (facts and rules).

There exists several successful ATPs where conjectures and axioms are formally provided (e.g.

Recent approaches, such as (Clark et al., 2020), have proposed transformer-based architectures for deriving conjectures given axioms expressed in natural language (English).

The conjecture is verified through a binary text classifier, where the transformers model is trained to predict the truth value of a conjecture given the axioms.

The approach is demonstrated in experiments using a diverse set of benchma…

Chatbot is increasingly thriving in different domains, however, because of unexpected discourse complexity and training data sparseness, its potential distrust hatches vital apprehension.

Recently, Machine-Human Chatting Handoff (MHCH), predicting chatbot failure and enabling human-algorithm collaboration to enhance chatbot quality, has attracted increasing attention from industry and academia...

In this study, we propose a novel model, Role-Selected Sharing Network (RSSN), which integrates both dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.

Unlike prior efforts in dialog mining, by utilizing local user satisfaction as a bridge, global satisfac…

Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples.

However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval... We first construct EntityQuestions, a set of simple, entity-rich questions based on facts from Wikidata (e.g., "Where was Arve Furset born?

"), and observe that dense retrievers drastically underperform sparse methods.

We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training.

Second, we argu…

Capitalization is an important feature in many NLP tasks such as Named Entity Recognition (NER) or Part of Speech Tagging (POS).

We are trying to reproduce results of paper which shows how to mitigate a significant performance drop when casing is mismatched between training and testing data...

In particular we show that lowercasing 50% of the dataset provides the best performance, matching the claims of the original paper.

We also show that we got slightly lower performance in almost all experiments we have tried to reproduce, suggesting that there might be some hidden factors impacting our performance.

Lastly, we make all of our work available in a public github repository.

We propose a novel architecture and training paradigm for training realistic PointGoal Navigation -- navigating to a target coordinate in an unseen environment under actuation and sensor noise without access to ground-truth localization.

We grant the agent restricted access to ground-truth localization readings during training via an information bottleneck.

Under this setting, the agent incurs a penalty for using this privileged information, encouraging the agent to only leverage this information when it is crucial to learning.

This enables the agent to first learn navigation and then learn localization instead of conflating these two objectives in training.

Specifically, our method outperf…

Models designed for intelligent process automation are required to be capable of grounding user interface elements.

This task of interface element grounding is centred on linking instructions in natural language to their target referents...

Even though BERT and similar pre-trained language models have excelled in several NLP tasks, their use has not been widely explored for the UI grounding domain.

This work concentrates on testing and probing the grounding abilities of three different transformer-based models: BERT, RoBERTa and LayoutLM.

Our primary focus is on these models' spatial reasoning skills, given their importance in this domain.

Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.

We aim to scrub such undesirable attributes and learn fair representations while maintaining performance on the target task...

In this paper, we present an adversarial learning framework "Adversarial Scrubber" (ADS), to debias contextual representations.

We perform theoretical analysis to show that our framework converges without leaking demographic information under certain conditions.

Experimental evaluations on 8 datasets show that ADS generates representations with minimal information about demographi…

Sarcasm detection is important for several NLP tasks such as sentiment identification in product reviews, user feedback, and online forums.

It is a challenging task requiring a deep understanding of language, context, and world knowledge...

In this paper, we investigate whether incorporating commonsense knowledge helps in sarcasm detection.

For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input.

Our experiments with three sarcasm detection datasets indicate that the approach does not outperform the baseline model.

Feature-based algorithm selection has recently received attention in the research field of black-box numerical optimization...

In addition, a benchmarking methodology for algorithm selection systems has not been well investigated in the literature.

In this context, this paper analyzes algorithm selection systems on the 24 noiseless black-box optimization benchmarking functions.

First, we demonstrate that the successful performance 1 measure is more reliable than the expected runtime measure for benchmarking algorithm selection systems.

These findings provide fundamental insights for algorithm selection for black-box optimization.

We propose CodeQA, a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated.

CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs... To obtain natural and faithful questions and answers, we implement syntactic rules and semantic analysis to transform code comments into question-answer pairs.

Experiment results achieved by several neural baselines on our dataset are shown and discussed.

While research on question-answering and machine reading comprehension develops rapidly, few prior work has drawn attention to code …

Dependency parse trees are helpful for discovering the opinion words in aspect-based sentiment analysis (ABSA).

This is because the syntactic trees are not designed for capturing the interactions between opinion words and aspect words.

In this work, we aim to shorten the distance between aspects and corresponding opinion words by learning an aspect-centric tree structure.

The aspect and opinion words are expected to be closer along such tree structure compared to the standard dependency parse tree.

The learning process allows the tree structure to adaptively correlate the aspect and opinion words, enabling us to better identify the polarity in the ABSA task.

Geo-tagged photo based tourist attraction recommendation can discover users' travel preferences from their taken photos, so as to recommend suitable tourist attractions to them.

However, existing visual content based methods cannot fully exploit the user and tourist attraction information of photos to extract visual features, and do not differentiate the significances of different photos...

In this paper, we propose multi-level visual similarity based personalized tourist attraction recommendation using geo-tagged photos (MEAL).

Specifically, by crossing the user and tourist attraction information of photos, we define four visual similarity levels and introduce a corresponding quintuplet lo…

We propose 3DETR, an end-to-end Transformer based object detection model for 3D point clouds.

Compared to existing detection methods that employ a number of 3D-specific inductive biases, 3DETR requires minimal modifications to the vanilla Transformer block...

Nevertheless, 3DETR is conceptually simple and easy to implement, enabling further improvements by incorporating 3D domain knowledge.

Through extensive experiments, we show 3DETR outperforms the well-established and highly optimized VoteNet baselines on the challenging ScanNetV2 dataset by 9.5%.

Furthermore, we show 3DETR is applicable to 3D tasks beyond detection, and can serve as a building block for future research.

Perceiver and Perceiver IO work as multi-purpose tools for AIMost architectures used by AI systems today are specialists.

A 2D residual network may be a good choice for processing images, but at best it’s a loose fit for other kinds of data — such as the Lidar signals used in self-driving cars or the torques used in robotics.

What’s more, standard architectures are often designed with only one task in mind, often leading engineers to bend over backwards to reshape, distort, or otherwise modify their inputs and outputs in hopes that a standard architecture can learn to handle their problem correctly.

Dealing with more than one kind of data, like the sounds and images that make up videos, is …

Through reinforcement learning (RL), this single system learnt by playing round after round of games through a repetitive process of trial and error.

But AlphaZero still trained separately on each game — unable to simply learn another game or task without repeating the RL process from scratch.

Today, we published "Open-Ended Learning Leads to Generally Capable Agents," a preprint detailing our first steps to train an agent capable of playing many different games without needing human interaction data.

The agent’s capabilities improve iteratively as a response to the challenges that arise in training, with the learning process continually refining the training tasks so the agent never stops …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In our previous post, we presented a high-level picture of Recommendations AI, showing how the product is typically used. In this post, we’ll take a deep dive into the first step of getting started, which is data ingestion. This post will answer all your questions on getting your data into Recommendations AI so you can train models and get recommendations.Recommendations AI uses your product catalog and user events to create machine learning models and deliver personalized product recommendations to your customers. Essentially, Recommendations AI uses a list of items available to be recommended (product catalog) and user's interactions with those products (events), allowing you to create va…

Waze AdsWaze is the world's largest community-based traffic and navigation app. As part of its offering, it lets advertisers put their businesses on the Waze map. By doing so, ads on Waze will reach consumers at key moments of their journey. Goals for advertising on Waze include getting customers to business locations, building brand awareness, and connecting with nearby customers at the right moments.Waze uses several ad formats, the most prominent of which is called a “Pin”. Like a store sign, Pins inform and remind customers that a business is on or near their route.Ad Serving @WazeWaze Ads is a reservation platform, which means we commit to a fixed number of ad impressions in advance an…

What if, instead, one could design neural networks that were smaller and faster, yet still more accurate?

For the same network, small image size leads to lower network capacity and thus requires weak regularization; vice versa, a large image size requires stronger regularization to combat overfitting.

In “CoAtNet: Marrying Convolution and Attention for All Data Sizes”, we systematically study how to combine convolution and self-attention to develop fast and accurate neural networks for large-scale image recognition.

CoAtNet models consistently outperform ViT models and its variants across a number of datasets, such as ImageNet1K, ImageNet21K, and JFT.

We hope these new neural networks can b…

This article is the next step in the series of PyTorch on Google Cloud using Vertex AI. In the preceding article, we fine-tuned a Hugging Face Transformers model for a sentiment classification task using PyTorch on Vertex Training service. In this post, we show how to deploy a PyTorch model on the Vertex Prediction service for serving predictions from trained model artifacts. Now let’s walk through the deployment of a Pytorch model using TorchServe as a custom container by deploying the model artifacts to a Vertex Endpoint. You can find the accompanying code for this blog post on the GitHub repository and the Jupyter Notebook.Deploying a PyTorch Model on Vertex Prediction ServiceVertex Pred…

In recent years, deep learning has made significant strides in solving the instance segmentation problem with architectures like Mask R-CNN.

However, these methods rely on collecting a large labeled instance segmentation dataset.

Moreover, this difference is only apparent when evaluating on unseen classes — if we evaluate on seen classes, all four architectures exhibit similar performance.

There is a significant difference in performance on unseen classes, even though the performance on seen classes barely changes.

Comparison of Deep-MAC and Deep-MARC to other partially supervised instance segmentation approaches like MaskX R-CNN, ShapeMask and CPMask.

Together with the model, we released a large-scale, multi-modal 3D dance motion dataset, AIST++, which contains 5.2 hours of 3D dance motion in 1408 sequences, covering 10 dance genres, each including multi-view videos with known camera poses.

We present a novel full-attention cross-modal transformer (FACT) network that can generate realistic 3D dance motion (right) conditioned on music and a new 3D dance dataset, AIST++ (left).

We generate the proposed 3D motion dataset from the existing AIST Dance Database — a collection of videos of dance with musical accompaniment, but without any 3D information.

The resulting database, AIST++, is a large-scale, 3D human dance motion dataset that contai…

Editor’s note: When German cooking platform Chefkoch was looking to bring treasured hand-me-down recipes into the 21st century it found a scaleable, well-supported solution with Google’s data cloud. Here’s how it was cooked up. Whether it’s salad dressing or chicken soup, most households have a favorite dish passed down across the generations. These recipes are often scribbled on scraps of paper and this personal culinary heritage is heavily guarded. Recognizing the significance of handwritten or printed recipes, German cooking platform Chefkoch wanted to make it possible to quickly and easily parse, extract and digitalize these time-honored tasty morsels using Google Cloud augmented analyt…

IntroductionML Ops is an ML engineering culture and practice that aims at unifying ML system development and ML system operation. An important ML Ops design pattern is the ability to formalize ML workflows. This allows them to be reproduced, tracked and analyzed, shared, and more.Pipelines frameworks support this pattern, and are the backbone of an ML Ops story. These frameworks help you to automate, monitor, and govern your ML systems by orchestrating your ML workflows. In this post, we’ll show examples of PyTorch-based ML workflows on two pipelines frameworks: OSS Kubeflow Pipelines, part of the Kubeflow project; and Vertex Pipelines. We are also excited to share some new PyTorch componen…

In 2019, we introduced Project Euphonia and discussed how we could use personalized ASR models of disordered speech to achieve accuracies on par with non-personalized ASR on typical speech.

Today we share the results of two studies, presented at Interspeech 2021, that aim to expand the availability of personalized ASR models to more users.

In “Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia”, we present a greatly expanded collection of disordered speech data, composed of over 1 million utterances.

Then, in “Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases”, we discuss our ef…

IntroVertex AI offers a fully managed training service, Vertex AI Training, which provides a set of prebuilt algorithms and enables you to create ML models using custom training.Machine learning (ML) engineers are responsible for training ML models. In most situations, training completes successfully, but it does sometimes fail. When it does, engineers often reach out to customer support for help in troubleshooting the problem. It turns out, however, that there are a fair number of ML training support cases that the ML engineers could handle themselves. This post covers seven common causes of ML model training failures, along with time-saving tips on how to avoid them and how to fix them. T…

Since the publishing of the inaugural post of PyTorch on Google Cloud blog series, we announced Vertex AI: Google Cloud’s end-to-end ML platform at Google I/O 2021. Vertex AI unifies Google Cloud’s existing ML offerings into a single platform for efficiently building and managing the lifecycle of ML projects. It provides tools for every step of the machine learning workflow across various model types, for varying levels of machine learning expertise.We will continue the blog series with Vertex AI to share how to build, train and deploy PyTorch models at scale and how to create reproducible machine learning pipelines on Google Cloud. Figure 1. What’s included in Vertex AI?In this post, we wi…

With people expecting to access products and services through easy, always-on experiences delivered across channels, transforming approaches to digital services is a must for every business. As a result, business leaders have to strive to provide employees with these same frictionless experiences. Sopra Steria is a European leader in consulting, digital services and software development, with 46,000 employees in 25 countries that generated revenue of €4.3 billion in 2020. It provides end-to-end solutions that help customers drive their digital transformation to obtain tangible and sustainable benefits, by combining in-depth knowledge of a wide range of business sectors and innovative techno…

Looking to translate your website? Google Cloud can help! Google Cloud Translation API is a service that dynamically translates between languages with Google’s state-of-the-art Machine Learning models. It is a highly scalable API that supports over one hundred languages, with built-in language detection. In this blog post, we will share some best practices for optimizing cost, increasing performance, and hardening the security posture while using the Translation API with your websites. Optimize Architecture for Performance, Cost and SecurityA common way to translate websites is to have site visitors select their language of choice, and then display the website in that language. However, on …

On the other hand, substantial progress has been made in learning visual representations from unlabeled data via self-supervised learning, including rotation prediction and contrastive learning.

The idea is that instead of learning representations from the training data only, the model learns from the union of the training data plus augmented training examples, where the augmented examples are considered to be different from the original training data.

With DA, the training data is no longer uniformly distributed in the representation space because some areas are occupied by the augmented data.

Texture Anomaly Detection for Industrial Defect DetectionIn many real-world applications of anoma…

A Deep Learning System for Detecting Abnormal Chest X-raysThe deep learning system we used is based on the EfficientNet-B7 architecture, pre-trained on ImageNet.

Each CXR was assigned a label of either “normal” or “abnormal” using a regular expression–based natural language processing approach on the associated radiology reports.

Sample chest X-rays of true and false positives, and true and false negatives for (A) general abnormalities, (B) tuberculosis, and (C) COVID-19.

Impact of a simulated deep learning model–based prioritization in comparison with random review order for (A) general abnormalities, (B) tuberculosis, and (C) COVID-19.

1Labels include atelectasis, cardiomegaly, effusion, …

Today, we’re excited to announce the appointment of Helen Toner to our Board of Directors.

As the Director of Strategy at Georgetown’s Center for Security and Emerging Technology (CSET), Helen has deep expertise in AI policy and global AI strategy research.

I greatly value Helen’s deep thinking around the long-term risks and effects of AI,” added Greg Brockman, OpenAI’s chairman and Chief Technology Officer.

“We are delighted to add her leadership to our board.”OpenAI is a unique organization in the AI research space, and has produced some of the advances, publications, and products I’m most excited about,” said Helen Toner.

She previously advised policymakers and grantmakers on AI strategy…

We are now inviting businesses and developers to build on top of OpenAI Codex through our API.

Watch Video Creating a Space Game with OpenAI Codex Tweet Watch Video “Hello World” with OpenAI Codex Tweet Watch Video Data Science with OpenAI Codex Tweet Watch Video Talking to Your Computer with OpenAI Codex Tweet Watch Video Converting Python to Ruby with OpenAI Codex Tweet Watch Video Giving OpenAI Codex a First Grade Math Test TweetOpenAI Codex is a descendant of GPT-3; its training data contains both natural language and billions of lines of source code from publicly available sources, including code in public GitHub repositories.

OpenAI Codex empowers computers to better understand people…

These issues can be mitigated by writing specialized GPU kernels, but doing so can be surprisingly difficult due to the many intricacies of GPU programming.

This has led us to extend and improve Triton, a recent language and compiler whose original creator now works at OpenAI.

# Z,X,Y are dense tensors Z[idx] = X[idx] + Y[idx] ... grid = (ceil_div(N, BLOCK),) block = (BLOCK,) add[grid, block](x, y, z, x.shape[0]) BLOCK = 512 # This is a GPU kernel in Triton.

Without a system like Triton, non-trivial modifications of matrix multiplication kernels would be out-of-reach for developers without exceptional GPU programming expertise.

If you’re interested in joining our team and working on Triton …

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

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

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

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

Please reach …

Investing in startups with big ideas about AI.

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

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

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

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

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

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

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

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

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

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

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

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

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

discovered that the human brain possesses multimodal neurons.

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

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

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

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

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

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

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

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

Unsolved problemsThere are many prob…

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

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

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

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

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

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

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

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

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

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

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

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

Live demos and discussions at our virtual booth.

Micro-climate predictions are beneficial in agriculture, forestry, architecture, urban design, ecology conservation, maritime and other domains.

Forecast error computation DeepMC uses weather station forecasts of the predicted variable to learn better models for micro-climate predictions.

Micro-climate wind speed prediction RMSE comparisons over 24-hour predictionsComparison: micro-wind speed predictionsFigure 3 shows the wind speed predictions at the 24th hour over a period of 10 days with one-hour resolution.

RMSE, MAPE and MAE comparison for micro-climate wind speed predictionsFigure 6.

The combination of accuracy, robustness, flexibility and scalability is important to help the renewabl…

Creating a programmable software infrastructure for telecommunication operations promises to reduce both the capital expenditure (CAPEX) and the operational expenses (OPEX) of the 5G telecommunications operators. In this blog, we focus on video, the dominant traffic type on the internet since the introduction of 4G networks.

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Intel and Microsoft Azure are working together to help enterprises deploy intelligent IoT technologies and services, including AI’s deep learning abilities, computer vision, and audio or speech capabilities. Adding these capabilities enables solutions to solve more business challenges, uniting two or more—adding both computer vision and AI, for example greatly expands the potential uses for IoT solutions.

Today, we are proud to announce DeepSpeed MoE, a high-performance system that supports massive scale mixture of experts (MoE) models as part of the DeepSpeed optimization library.

Besides supporting the most ambitious scale MoE models, DeepSpeed MoE boosts the development productivity and resource efficiency of training modestly sized MoE models in production scenarios, which may be of broader interest to the deep learning (DL) community.

Powered by DeepSpeed MoE, we can now train MoE models that are much larger compared with dense models.

By leveraging DeepSpeed MoE, the Z-code MoE model achieved improved convergence and higher quality with multitask training setting compared with the non-…

Today, AI and machine learning are enabling data-driven organizations to accelerate their journey to insights and decisions. With all the latest advancements, AI is no longer limited to only those with deep expertise or a cache of data scientists, and many organizations can now adopt AI and machine learning for better competitive advantage. Customers with analytics practices looking to adopt machine learning can read this report to get started.

So, you know, Microsoft, for example, Satya Nadella, after COVID hit, I couldn’t have been prouder to work for him.

It was like, you know, “Yeah, okay, productivity hasn’t fallen off a cliff.

This is really, really, really complicated accounting.

SURI: Yeah, yeah.

You know, right now, women caregivers are really, really struggling.

But with so many people independently altering code, it’s unsurprising that updates don’t always synchronize, resulting in bad merges.

In other cases, bad program merges can be more subtle and costly, introducing semantic merge conflicts that may either fail the compiler, break a test, or—worse—introduce a regression.

And third, there are project-specific patterns in how developers resolve bad merges that can be capitalized on by program synthesis.

Safe program merges—a long-standing research problemThe problem of ensuring safe program merges has been long studied and remains an open challenge in programming languages and software engineering research.

While the approach showed the potentia…

When compared to Transformer-based deep learning models, the MEB model also demonstrates interesting capabilities to learn beyond semantic relationships.

The input layer contains 9 billion features, generated from 49 feature groups, with each binary feature encoded into a 15-dimension embedding vector.

We define MaxQueryLength buckets for this feature so that query “Microsoft Windows” has the binary feature QueryLength_2 equal to 1.

One-hot encoding of categorical featuresCategorical features can be transformed into binary features through one-hot encoding in a straightforward way.

This is especially true if you have a large historical stream of user interactions and can easily construct si…

HUDSON: Yeah, I think initially it was things that were more grab-and-go, maybe a mouse, a keyboard, a monitor.

So many people in the office, as we well know, right, rely on multi-mon setups in their office, uh, environments to really maintain productivity.

When it’s back in person, I’m kind of scared.

I think a lot of that is to be determined as we really think about how this plays out in the hybrid environment.

And I’m really excited to see what happens.

The many benefits of chatChat has become essential in virtual meetings – many online meetings would be much less efficient, and some would be impossible, without chat.

Chat enables people to organize their collaboration and action around documents and follow-up meetings.

Used well, it can be a powerful and effective tool in online meetings.

RESOURCE Parallel Meeting Chat Guide for Moderators and ParticipantsChat to the futureImproving chat can lead to more effective conversations as well as enhanced meeting tools.

The age of online meetings has just begun.

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The action you just performed triggered the security solution.

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[MUSIC ENDS]In this episode of the series, we’re exploring the “IT and Security” chapter of The New Future of Work report published by Microsoft.

TEEVAN: You know one of the things that surprised me before I came to Microsoft was how important IT and security are to Microsoft.

So, making sure that Microsoft administrative experiences work is crucial in making sure that those hundreds of millions of people are efficient at work.

[MUSIC BREAK]TEEVAN: I know, uh, security threats have increased a lot this past year, as well.

TEEVAN: Has, um, what you’ve learned through your research and the research that you’ve read changed your own work practices?

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

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

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

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

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

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

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

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

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

“I want peo…

In this Q&A, Katabi, the Thuan (1990) and Nicole Pham Professor at MIT, discusses some of her recent work.

My lab is working on the next generation of wireless sensors and machine-learning models that can make more personalized predictions.

A: We’re developing “touchless” sensors that can track people’s movements, activities, and vital signs by analyzing radio signals that bounce off their bodies.

Here, the invisibles understand actions and movements.

Current deep-learning models are also limited whether wireless signals are collected from wearable or background sensors.

For this project, the researchers had multiple neural networks predict the potential energy surface from the same data.

The spread in the predictions of a “committee of neural networks” is the “uncertainty” at that point.

Instead, the new approach only samples data points from regions of low prediction confidence, corresponding to specific geometries of a molecule.

They used more than 15,000 examples to train a neural network to predict the potential energy surfaces for these systems.

However, when the adversarial approach is used to retrain the neural networks, the authors saw a performance jump to 97 percent using only 500 extra points.

MIT spinoff OpenSpace invented automated 360-degree video jobsite capture and mapping.

Enter OpenSpace, a company that’s propelling the construction of any built environment into the digital age.

It's essentially passive; the OpenSpace Vision System does all the work, mapping site photos to site plans automatically.

Prior to OpenSpace, CTO DeCamp was a computer vision and data visualization research scientist at the Institute.

No more trips to the site required when you can see a past-to-present 360-degree view from anywhere.

Scientist, writer, policy advocate, YouTuber – before Jordan Harrod established her many successful career identities, her first role was as a student athlete.

Mapping the brain to understand consciousnessToday, Harrod collaborates with professors Emery Brown, an anesthesiologist, and Ed Boyden, a neuroscientist, to study how different parts of the brain relate to consciousness and arousal.

Since beginning her neuroscience research, Harrod has been amazed to learn how much about the brain still needs to be uncovered.

She is the chair of the External Affairs Board of the Graduate Student Council, an Early Career Policy Ambassador for the Society for Neuroscience, and the co-founder of the MI…

Addressing what might not be totally apparent to the naked eye, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with “SensiCut,” a smart material-sensing platform for laser cutters.

In contrast to conventional, camera-based approaches that can easily misidentify materials, SensiCut uses a more nuanced fusion.

With the addition of computers, laser cutters have rapidly become a relatively simple and powerful tool, with software controlling shiny machinery that can chop metals, woods, papers, and plastics.

SensiCut is a smart material sensing platform for laser cutters.

Beyond laser cutters, the team envisions a future where SensiCut’s sensing tech…

For entrepreneurial MIT students looking to put their skills to work for a greater good, the Media Arts and Sciences class MAS.664 (AI for Impact) has been a destination point.

“It’s clear that the people who are graduating from here want to do something significant with their lives ... they want to have an impact on their world,” Pentland says.

A path toward confronting a pandemicRaskar began co-teaching the class in 2019, and brought a “Big AI” focus to the Development Ventures class, inspired by an AI for Impact team he had set up at his former employer, Facebook.

“This is something we should think about more seriously: how to use AI and data for positive social impact, while protecting …

“The key innovation is using machine learning to connect the sequence of a peptide, particularly a peptide that includes non-natural amino acids, to experimentally-measured biological activity.”Dream dataCPPs are relatively short chains, made up of between five and 20 amino acids.

Before a model could make any worthwhile predictions, researchers on the experimental side needed to create a robust dataset.

While only 20 amino acids naturally occur in the human body, hundreds more exist elsewhere — like an amino acid expansion pack for drug development.

To represent them in a machine-learning model, researchers typically use one-hot encoding, a method that assigns each component to a series of…

Now, aerospace engineers at MIT have devised an algorithm that helps drones find the fastest route around obstacles without crashing.

But the faster drones fly, the more unstable they become, and at high speeds their aerodynamics can be too complicated to predict.

Fast effectsTraining drones to fly around obstacles is relatively straightforward if they are meant to fly slowly.

Overall, the drone trained on the new algorithm “won” every race, completing the course in a shorter time than the conventionally trained drone.

The researchers plan to fly more experiments, at faster speeds, and through more complex environments, to further improve their algorithm.

MIT researchers have developed a new artificial intelligence programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives.

Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system.

SPPL is different from most probabilistic programming languages, as SPPL only allows users to write probabilistic programs for which it can automatically deliver exact probabilistic inference results.

Error from approximate probabilistic inference is tolerable in many AI applications.

This approach extends prior work on sum-product networks to models and queries expressed via a probabilistic programming language.

David Kaiser and Julie Shah are on a mission to prepare students and facilitate research to address the broad challenges and opportunities associated with computing.

As associate deans of Social and Ethical Responsibilities of Computing (SERC) in the MIT Stephen A. Schwarzman College of Computing, Kaiser and Shah are advancing a number of initiatives they hope will get students and faculty to reflect on the potential social, ethical, and policy implications of new technologies.

Q: Weaving social and ethical aspects of computing into the curricula is a key mandate of SERC.

It’s also part of SERC’s broader mission to incorporate humanist, social science, social considerations, and policy/civi…

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

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

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

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

As researchers model the…

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

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

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

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

M…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Thi…

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

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

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

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

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

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

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

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

ConclusionIn summary, our paper sho…

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

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

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

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

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

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

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

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

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

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

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

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

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

Does GPT-2 Know Your Phone Number?

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

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

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

Does Training Language Models Infringe on Copyright?

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

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

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

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

As phase one of Arçelik’s AI2 program, Arçelik hosted their first global AWS DeepRacer League to help upskill their employees on AI and ML.

Arçelik’s AWS DeepRacer journeyTo get started on their event, Arçelik established a steering committee and worked with the AWS customer team and AWS DeepRacer Specialist Solutions Architects (the AWS DeepRacer Pit Crew) to work through the League format, racetrack, and event calendar.

89% of the participants stated they would race in an AWS DeepRacer League again in the future.

Create an AWS DeepRacer LIVE event now and learn more in AWS DeepRacer documentation.

Crafted carefully in collaboration with AWS, the AI2-Immerse step consists of an AWS DeepRac…

Businesses can use Amazon Fraud Detector to detect online payment fraud.

Fortunately, the emergence of cloud computing makes the training and deployment of ML models easier and more cost-efficient.

The solution uses two foundational AWS services: Amazon S3 and Amazon SageMaker.

Amazon SageMakerAfter labeled card transcation data is uploaded to Amazon S3, SageMaker can run ML algorithms to process the data and train a model for detecting fraudulent transactions.

For a step-by-step tutorial on SageMaker Script Mode, check out Bring your own model with Amazon SageMaker script mode and Bring Your Own Custom ML Models with Amazon SageMaker.

Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML).

Query data using the PyHive library from the Python3 (Data Science) kernelIn this example, we use the Python 3 (Data Science) kernel.

Preprocess data and feature engineeringWe perform data preprocessing and feature engineering on the data using SageMaker Processing.

Delete Amazon SageMaker Studio AppsNavigate to Amazon SageMaker Studio Console.

He leads SageMaker Studio team to build it into the IDE of choice for interactive data science and data engineering workflows.

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, improving data science team productivity by up to 10 times.

In this post, we discuss how to launch Studio from external applications using presigned URLs.

Create and deploy a Lambda function to create a presigned URLTo create and deploy your Lambda function, complete the following steps:Create a Python Lambda function named sm-presigned .

For added security, we recommend controlling and managing access to your API using the API Gateway authentication and authorization method.

The following is the sample code to invoke the URL:index.html Choose Go to Amazon SageMaker Stud…

To train a custom entity recognition model that you can use on your PDF, Word, and plain text documents, you need to first annotate PDF documents using a custom Amazon SageMaker Ground Truth annotation template provided by Amazon Comprehend.

Historical options for processing PDF documents required converting documents to raw text format before processing through Amazon Comprehend custom entity recognition models.

You use this manifest file to create an Amazon Comprehend custom entity recognition training job and train your custom model.

For additional suggestions, see Improving Custom Entity Recognizer PerformanceConclusionWith this new contextual information included within labeled annotat…

Historically, you could only use Amazon Comprehend on plain text documents, which required you to flatten the documents into machine-readable text.

To extract text and spatial locations of text from scanned PDF documents, Amazon Comprehend calls Amazon Textract on your behalf before processing for custom entity recognition.

Train an Amazon Comprehend custom entity recognition model via the consoleIn the previous post, we showed you how to annotate finance documents via Amazon SageMaker Ground Truth using the custom annotation template provided by Amazon Comprehend.

To do so via the Amazon Comprehend console, complete the following steps:On the Amazon Comprehend console, choose Custom entity…

As a result, creating and managing a document processing pipeline remains a challenge for many companies.

AWS launched AI services like Amazon Textract, Amazon Comprehend, and others to help with the automation of extracting insights from documents.

Customer success storiesWith AWS, customers like Black Knight, Liberty Mutual, and Broadridge Financial Solutions have been able to process millions of documents that come through their pipeline using AWS AI.

With AWS AI, they can process insurance claims at a much higher speed and with greater accuracy.

AWS technologies have enabled underwriters to process documents to review results, adjust analyses and request additional documents and informa…

Today, we’re excited to announce a new feature of Amazon Transcribe that can help achieve this: PII identification and redaction in streaming transcriptions.

", "Items":[ { "Confidence":1, "Content":"My", "EndTime":0.67, "StartTime":0.6, "Type":"pronunciation", "VocabularyFilterMatch":false }, { "Confidence":1, "Content":"name", "EndTime":0.95, "StartTime":0.68, "Type":"pronunciation", "VocabularyFilterMatch":false }, { "Confidence":1, "Content":"is", "EndTime":1.14, "StartTime":0.96, "Type":"pronunciation", "VocabularyFilterMatch":false }, { "Confidence":0.96, "Content":"[NAME]", "EndTime":1.71, "StartTime":1.15, "Type":"pronunciation", "VocabularyFilterMatch":false }, { "Content":".

", "I…

Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy to add speech-to-text capabilities to your applications.

Automatic content redaction is a feature of Amazon Transcribe that can automatically remove information such as sensitive personally identifiable information (PII) from your transcription results.

Solution overviewThe following figure shows an example architecture for performing PII audio redaction, using Amazon Simple Storage Service (Amazon S3) and AWS Lambda.

For this post, we provide an AWS CloudFormation audio redaction template, which provides the full details of the implementation to enable repeatable deployments.

And I hope that Amazon transc…

Amazon Connect provides a unique opportunity for gathering data from these engagements and customer touchpoints that helps improve your business.

Call note storage and transcription, sentiment analysis, and text search are available out of the box from Connect Lens for Amazon Connect.

Your Amazon Kendra data source is scheduled to update itself every day at 10 AM.

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

You can also integrate these use cases into your CRM (such as Salesforce or Zendesk) by using Amazon Connect integration features.

Moreover, it allows you to stack your Amazon Fraud Detector resources with other AWS service resources that work with Amazon Fraud Detector, such as AWS Lambda functions that request event fraud predictions.

AWS CloudFormation conceptsBefore we get started, let’s review some core AWS CloudFormation concepts.

Solution overviewWe walk through the following high-level steps to create and update an Amazon Fraud Detector stack in AWS CloudFormation:Download a sample Amazon Fraud Detector template.

Verify that the Amazon Fraud Detector resources were createdAfter you create the stack, you’re redirected to the AWS CloudFormation console, where you can now see your Amazon Fraud Detector stack.

For …

The new Bundesliga Match Fact Passing Profile uncovers exactly that by providing real-time insights into the passing capabilities of all players and teams in the Bundesliga.

For example, they can be aggregated for each player to form a passing profile, showing which passing decisions players make.

Passing profile and efficiencyWe can use this passing or xPass (expected passes) model to estimate the passing profile of a player and his passing efficiency.

Given the experimental nature of model training, the actual training pipeline resides on our development environment.

SummaryIn this post, we demonstrated how the Bundesliga Match Fact Passing Profile makes it possible to objectively compare…

When transcribing content that is more specialized or domain-specific such as biology, Amazon Transcribe offers custom language models (CLM).

Solution overviewWith the CLM feature in Amazon Transcribe, you can build your own custom model for your class course content and improve the transcription accuracy of your class lectures.

For Training data , enter the S3 folder path for your training data.

Snippet 1 – Standard Amazon Transcribe Snippet 1 – Amazon Transcribe with CLM Another unique feature in some cells is flat Gela .

Standard Amazon Transcribe WER Amazon Transcribe CLM WER Standard Amazon Transcribe Accuracy Amazon Transcribe CLM Accuracy # Words Words Improved by CLM Lecture 1 9.5% …

Starting today, the model action space is fully customizable yet simplified with new dynamic graphics so developers have greater control and can easily unlock better model performance.

Advanced AWS DeepRacer developers need the ability to customize the action space to increase model performance.

Until now, you were limited to 20 options for modifying steering angle and speed in your action space on the AWS DeepRacer console.

A continuous action space allows the agent to select an action from a range of values for each state.

You can make discrete action space changes directly on the radial polar graph and by updating the action list.

Amazon S3 plugin for PyTorch is an open-source library which is built to be used with the deep learning framework PyTorch for streaming data from Amazon Simple Storage Service (Amazon S3).

The Amazon S3 plugin for PyTorch is designed to be a high-performance PyTorch dataset library to efficiently access data stored in S3 buckets.

Building blocksThe Amazon S3 plugin for PyTorch provides a native experience of using data from Amazon S3 to PyTorch without adding complexity in your code.

The Amazon S3 plugin for PyTorch is available to use through pre-configured PyTorch Docker images, or directly from the GitHub repository.

The Amazon S3 plugin for PyTorch was designed for ease of use and flexi…

They need something general and flexible, and that’s why we build what we build.”Large Language Models Are Changing the WorldOne of the most exciting areas of AI is language modeling, which is enabling groundbreaking applications in natural language understanding and conversational AI.

The complexity of large language models is growing at an incredible rate, with parameter counts doubling every two months.

A well-known example of a large and powerful language model is GPT-3, developed by OpenAI.

During his talk, Catanzaro showed an example of the surprising capabilities of large language models to solve new tasks without being explicitly trained to do so.

“These language models are first st…

Want to play awesome PC games for free without having to buy an expensive gaming rig?

This GFN Thursday takes a look at the 90+ free-to-play PC games — including this week’s Fortnite Season 8 release and the Epic Games Store free game of the week, Speed Brawl, free to claim Sept. 16-23 — all streaming on GeForce NOW.

Members can connect to popular PC game stores, like Steam and Epic Games Store, using their accounts to stream titles like Fortnite and Destiny 2.

GeForce NOW members can build their library with many of the Epic Games Store free games of the week.

Once claimed, Epic Games Store free games stay in your library, whether you’re playing on GeForce NOW or on a local PC.

A new AI-mapping tool is helping scientists assess how cities across the globe are using rooftops to combat climate change.

Named Roofpedia, the research creates an open-source and scalable map of sustainable rooftops—a promising strategy for climate mitigation.

Identifying areas with solar or green installations could help guide urban development, while also boosting community health, prosperity, and the environment.

Studies have shown the benefits of sustainable rooftops—most commonly roofs with solar or green space— are plentiful and wide-ranging.

There are over a million buildings in the current data set and the researchers note that more cities are being added as aerial or satellite im…

Highlighting NVIDIA’s fast-growing impact, Time magazine Wednesday named NVIDIA CEO Jensen Huang to its list of most influential people of 2021.

“Artificial intelligence is transforming our world,” writes Ng, who is founder of DeepLearning.AI, founder and CEO of Landing AI, and chairman and co-founder of Coursera.

Others profiled include U.S. President Joe Biden, Tesla CEO Elon Musk, Buccaneers Quarterback Tom Brady and singer Billie Eilish.

“In 2003, amid great skepticism, Huang directed his company Nvidia to adapt chips designed to paint graphics on computer screens, known as graphics processing units or GPUs, to perform other, more general-purpose computing tasks,” Ng explains.

“The resu…

Sperm whales, the largest of the toothed whales, boast enormous brains, explains David Gruber, a professor of biology and environmental science at the City University of New York.

Project CETI’s plan: decipher what sperm whales say to each other.

Marine biologists have long known sperm whales are unique.

To that end, Gruber’s effort is working to gather vast quantities of data about what sperm whales say to one another.

That said, putting these kinds of tools to work to understand whales will be a huge challenge.

Other large-scale federated learning projects are already underway in the healthcare industry, including a five-member study for mammogram assessment and pharmaceutical giant Bayer’s work training an AI model for spleen segmentation.

Federated Learning: AI Takes a VillageCompanies and research institutions developing AI models are typically limited by the data available to them.

It’s among the largest, most diverse clinical federated learning studies to date.

Businesses and research institutions getting started with federated learning can use the NVIDIA AI Enterprise software suite of AI tools and frameworks, optimized to run on NVIDIA-Certified Systems.

Learn more about the science behind …

For the first time, the NVIDIA Deep Learning Institute (DLI) is offering free, one-click notebooks for exploratory hands-on experience in deep learning, accelerated computing, and accelerated data science.

This free notebook demonstrates significant speed-up by moving common DataFrame operations to the GPU with minimal changes to existing code.

You will explore common data preparation processes and compare data manipulation performance using GPUs compared to CPUs.

An Even Easier Introduction to CUDALearn the basics of writing parallel CUDA kernels to run on NVIDIA GPUs.

You will have access to GPUs in the cloud and develop skills in AI, accelerated computing, accelerated data science, graph…

“We’re doing AI in medicine, AI in drugs, AI in agriculture, AI in business … we think that this is where higher education is going to inevitably go,” Glover added in a podcast with John Koetsier of TechFirst.

Plugged Into an AI SupercomputerPowering UF’s vision is HiPerGator AI, the eighth most powerful supercomputer in higher education and 22nd most powerful supercomputer in the world.

“All of these things involve huge amounts of data, and this is where the HiPerGator AI really excels,” Glover said.

An AI Hub for FloridaUF will make the supercomputer available through the state university system, creating an AI hub for others to enhance education and research.

“We’re glad to see the unive…

The Invisible Mobile Keyboard, or IMK, created by researchers at the Korea Advanced Institute of Science and Technology, lets users blast through 51.6 words per minute while reporting lower strain levels.

It then converts it into a character sequence by using the touch locations on the invisible keyboard.

Then, the semantic decoder corrects decoding errors in the character sequence estimated by the geometric decoder by considering semantic meanings.

The researchers trained the semantic decoder on the One Billion Word Benchmark created by Cambridge University and the University of Edinburgh and Google in 2014.

The result: users could type 157.5% faster using the Invisible Mobile Keyboard tha…

Highlights is supported in popular GeForce NOW titles such as Apex Legends, Destiny 2 and Rocket League.

Selecting or deselecting these moments, and toggling Highlights on or off, can also be done from the GeForce NOW in-game overlay.

Nearly all GeForce NOW games support Freestyle and can be enabled in game through the overlay.

Titles that support both NVIDIA Highlights and Freestyle can capture gameplay video with filters turned on, for even more colorful and artistic videos.

The Perfect Highlight ReelUsing NVIDIA Highlights and existing video clips, members can create custom highlight videos to show off their favorite gaming moments with NVIDIA Montage.

Grassroot communities are essential to driving AI innovation, according to Kate Kallot, head of emerging areas at NVIDIA.

On its opening day, Kallot gave a keynote speech at the largest AI Expo Africa to date, addressing a virtual crowd of 10,000 people.

“I hope to inspire you on ways to fuel your own applications and help advance the African AI revolution,” Kallot said.

To do so, she illustrated how the AI revolution is underway in Africa.

According to Kallot, NVIDIA GPUs are used by many research groups across Africa — including the Kenya Education Network Trust, Makerere University, Stellenbosch University and the University of Cape Town — which enable and nurture the cutting-edge techno…

The PyCaret team added NVIDIA GPU support in version 2.2, including all the latest and greatest from RAPIDS.

With GPU acceleration, PyCaret modeling times can be between 2 and 200 times faster depending on the workload.

This post will go over how to use PyCaret on GPUs to save both development and computation costs by an order of magnitude.

For simplicity, GPU code was written to run on a single GPU.

The compare_models command trains all the models in PyCaret’s model library using default hyperparameters and evaluates performance metrics using cross-validation.

The NVIDIA DRIVE platform, Kani said, can handle all of the critical layers necessary for AVs to safely reach our streets.

Cars, Concepts and CollaborationSeveral NVIDIA DRIVE partners demonstrated their latest green tech, concepts and innovations.

It’s a single widescreen panel that runs on the high-performance, energy-efficient NVIDIA DRIVE platform for instantaneous AI processing and dynamic graphics capabilities.

Powered by NVIDIA DRIVE, ZF ProAI is available in scalable performance levels up to 1,000 TOPS.

Learn more about the NVIDIA DRIVE platform, and don’t forget to register for GTC, which is coming up Nov. 8-11.

Global retailers and suppliers are faced with navigating rapidly changing consumer demand, behavior, and expectations.

For online shoppers, AI is helping create personalized shopping journeys and product recommendations.

From customer engagement, to operational agility, to seamless omnichannel management, AI at the edge is transforming retail.

Retailers Adopt AI for Improved In-Store Experiences, Less ShrinkageRetailers are leveraging AI at the edge to analyze data from in-store cameras and sensors, and create intelligent stores.

Future of Edge Computing in Retail and BeyondBillions of connected sensors are coming online in retail stores, city streets, hospitals, warehouses, and more.

It shards an AI model’s parameters across data parallel workers and can optionally offload part of the training computation to the CPUs.

For example, typical data parallel training requires maintaining redundant copies of the model on each GPU, and model parallel training introduces additional communication costs to move activations between workers (GPUs).

Using FSDP in computer vision modelsFor computer vision models, FSDP is supported in VISSL and tested on RegNets architectures.

Users may need to carefully tune the activation checkpointing strategy to fit a large model within limited GPU memory space.

We look forward to developing algorithms for auto-tuning both GPU memory usage and trai…

We will be hosting a talk about our work on, “A Platform Agnostic Observability System for AI Accelerators” during our virtual Systems @Scale event at 10:20 a.m. PT on Wednesday, June 30, followed by a live Q&A session.

To meet these challenges, we’ve introduced three new tools:ASIC Monitoring (Asicmon) , a scalable observability framework.

However, with an accelerator system, we can imagine the CPU now has a complicated and brawnier sibling!

Since implementing Asicmon we’ve been able to increase our AI accelerator metrics support from ~30 percent to ~75 percentAtrace: Accelerator tracing at scaleWhy tracing?

This would allow us to debug the end-to-end latency of microservices that use AI a…

People upload hundreds of millions of videos to Facebook every day.

From a pure computing perspective, applying the most advanced codecs to every video uploaded to Facebook would be prohibitively inefficient.

A relatively small percentage (roughly one-third) of all videos on Facebook generate the majority of overall watch time.

The impact of the new video encoding modelIn addition to improving viewer experience with newly uploaded videos, the new model can identify older videos on Facebook that should have been encoded with more advanced encodings and route more computing resources to them.

The improved compression has also allowed people on Facebook with limited data plans, such as those i…

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

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

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

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

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

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

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

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

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

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

In this blog, we discuss how moving to distributed XGBoost on Ray helps address these concerns and how finding the right abstractions allows us to seamlessly incorporate Ray and XGBoost Ray into Uber’s ML ecosystem.

To run XGBoost Ray, we simply pass in XGBoost Ray’s train function to the Ray Estimator.

In this example, the XGBoost Ray Estimator will create 50 GPU workers that form the Ray Cluster, each participating in data-parallel XGBoost training and synchronization.

To go from running a single distributed XGBoost Ray job to running distributed hyperparameter search using the Ray Estimator, we can simply pass in Ray Tune’s tune.run() with XGBoost Ray’s train() as serializable functions …

Different cluster models are employed, and for each of these cluster models, different algorithms can be given.

Centroid-based clustering algorithms / Partitioning clustering algorithmsIn centroid/partitioning clustering, clusters are represented by a central vector, which may not necessarily be a member of the dataset.

Note: An example of K-Means clustering is explained with customer segmentation examples in the use cases section below.

Mini-Batch K-Means clustering algorithmK-Means is one of the popular clustering algorithms, mainly because of its good time performance.

SummaryThis blog covered the most critical aspects of clustering, image compression, digit classification, customer segm…

Let’s explore what model stores are, how they help, and how to pick the right model store for your project.

In a model store, you have logging, discovery, examples, metadata, all you need, and the model store contains a model registry.

In terms of artifact management, model stores manage the model lifecycle, including packaging the model for staging or release.

Model metadataThe model metadata that you can find within the model store include:Model name set by the user.

ClearML Open Architecture Stack | SourceWhile a model store is not part of the ClearML application stack, you can build a custom model store on the open-source MLOps engine that pretty much provides the core functionalities o…

Read also How to Compare Machine Learning Models and AlgorithmsTeaching conceptsPerhaps teaching is where visualization is most useful, for educating novice users about fundamental concepts of machine learning.

Such visualization tools are really useful for those who only wish to use pretrained models to get predictions for their own tasks.

As described by its creators, Netron is a viewer tool for deep learning and machine learning models which can generate pretty descriptive visualization for the model’s architecture.

We’ve explored tools and frameworks for visualizing our model architecture, now let’s move on to the next part – training visualization.

We’ve covered a considerable number o…

Alternative title suggestions:Guide: Version Control for ML ModelsWhy Do You Need Version Control In Machine Learning, and How To Implement It?

Why do we need version control in ML?

Machine learning version control typesThere are two types of ML version control:Centralized Version Control System.

A Distributed Version Control System (DVCS) is a version control system where the full codebase is available locally on the developer’s computer, including the history.

Distributed Version control [Source]A Centralized Version Control System (CVCS) is a version control where the developer has to check out the repository from a single centralized server containing all the files and file history.

We need to narrow down on techniques by comparing machine learning models thoroughly with parallel experiments.

The goal of comparing machine learning algorithmsComparing machine learning algorithms is important in itself, but there are some not-so-obvious benefits of comparing various experiments effectively.

Parameters of machine learning algorithms and how to compare themLet’s dive right into analyzing and understanding how to compare the different characteristics of algorithms that can be used to sort and choose the best machine learning models.

Bias is the assumption used by machine learning models to make the learning process easier.

Final noteThere’s no scarcity of comparable techniq…

In this article, we’re going to discuss Jupyter Notebooks and the use of Notebooks in production environments.

It gets easier to use Jupyter Notebooks as templates to generate reports by automating the process of notebook execution.

Jupyter notebooks can be scheduled as jobs over the cloud.

What to consider when choosing your production workflowReliability: Jupyter notebooks are more stable than they were years ago.

Many people believe and take it as undeniable truth saying Jupyter notebooks are just for experimenting and prototyping, but I don’t completely agree with them.

So, in this article, we’re going to explore:Data Version Control (DVC),Best Alternatives to Data Version Control (DVC),Comparison of Experiment Tracking ToolsTo learn more about Experiment Tracking, check – ML Experiment Tracking: What It Is, Why It Matters, and How to Implement ItData Version Control (DVC)DVC is an open-source platform for machine learning projects.

Researchers and engineers use Neptune for experiment tracking and model registry to control their experimentation and model development.

Choosing the right ML experiment tracking tool for your workflowChoosing the right ML experiment tracking tool for your team can be hard.

First, let’s take a look at the things you need to con…

Classification is a time-consuming operation, which is why the two-stage object detection approach performs slower compared to one-stage detection.

SSD and YOLO are one stage object detectors whereas Faster-RCNNand R-FCN are two-stage object detectors.

To know what object types a pre-trained YOLO model is able to detect, check out the coco_classes.txt file available in …/yolo-v4-tf.kers/class_names/.

How to Train Your Custom YOLO Object Detection ModelTask StatementTo design an object detection model, you need to know what object types you want to detect.

Model Object InitializationTo get ready for a training job, initialize the YOLOv4 model object.

Adversarial networks (Deep Convolutional Generative Adversarial Networks) have been a very active playground lately for Deep Learning practitioners.

These architectures were first introduced in the paper Unsupervised Representational Learning With Deep Convolutional Generative Adversarial Networks.

import torchvision.datasets as datasets def data_preprocessing (root_dir, batch_size= 128 , image-size= 64 , num_workers= 2 ) : data = datasets.ImageFolder(root=root_dir, transform=transforms.Compose([ transforms.Resize(image_size), transforms.CenterCrop(image_size), transforms.ToTensor(), transforms.Normalize(( 0.5 , 0.5 , 0.5 ), ( 0.5 , 0.5 , 0.5 )) ])) dataloader = torch.utils.data.DataLoader(…

In this article, we’re going to explore what data lineage means in machine learning, and see several paid and open-source data lineage tools.

Learn more Data Lineage in Machine Learning: Methods and Best PracticesCriteria for choosing Data Lineage toolsData lineage tools help you visualize and manage the whole journey of your data.

Open-source: open source data lineage tools have the advantage of being free to use and are constantly being improved on.

Big Data Handling: many machine learning models require big data, so data lineage tools should be able to handle and process big data efficiently.

SPLINESpline (SPark LINEage) is a free, open-source tool for automated data lineage tracking and…

Data lineage vs. data provenanceData lineage is often confused with data provenance, as the difference is quite subtle and easy to miss.

So, Data Lineage takes care of data about the data (metadata), while Data Provenance takes care of the information about the processes that influence the data.

Data Lineage can help in combining new data with older relevant data such that the data consumers like developers, business teams, and stakeholders can derive the maximum value from the data assets.

Since several departments in an organization rely on Data Lineage, let’s get a closer view of the dependents to understand the need for Data Lineage further.

Data lineage across the pipelineTo capture en…

These tools provide useful statistics and model performance details that provide deep insights and help you improve model performance.

If we know the weak points of our model, we can plan a course of action that won’t hurt model performance.

Set realistic goalsOne of the key steps when designing a machine learning model is choosing appropriate metrics for evaluating model performance.

In particular, does a decrease in model performance necessarily mean that the model is performing worse?

These tools have excellent visualization capabilities and track different model performance statistics (check this blog post on model monitoring tools for more details).

Balanced Accuracy = (Sensitivity + Specificity) / 2 = 40 + 98.92 / 2 = 69.46 %Balanced Accuracy does a great job because we want to identify the positives present in our classifier.

Balanced Accuracy Multiclass ClassificationAs it goes for binary, Balanced Accuracy is also useful for multiclass classification.

F1-Score and Balanced Accuracy will be:Precision = 5 / 15 = 0.33 Sensitivity = 5 / 10 = 0.5 Specificity = 990 / 1000 = 0.99 F1-score = 2 * ( 0.5 * 0.33 ) / ( 0.5 + 0.33 ) = 0.4 Balanced Accuracy = ( 0.5 + 0.99 ) / 2 = 0.745You can see that balanced accuracy still cares more about the negative in the data than F1.

Balanced Accuracy vs ROC_AUCHow is Balanced Accuracy different from roc_…

Face detection and recognition – as previously discussed, one of the major applications of object detection is face detection and recognition.

Object detection algorithmsSince the popularization of deep learning in the early 2010s, there’s been a continuous progression and improvement in the quality of algorithms used to solve object detection.

→ Working process of YOLOYOLO – Object Detection Algorithm | SourceThe YOLO architecture utilizes three primary terminologies to achieve its goal of object detection.

RetinaNet→ IntroductionThe RetinaNet model introduced in 2017 became one of the best models with single-shot object detection capabilities that could surpass other popular object detect…

#schmidhuber #tiktok #roomba Your regularly irregluar update on what's happening in the world of Machine Learning. OUTLINE:

0:00 - Intro

0:15 - Sponsor: Weights & Biases

1:55 - ML YouTuber reaches 100k subscribers

2:40 - Facebook AI pushes Textless NLP

5:30 - Schmidhuber blog post: I invented everything

7:55 - TikTok algorithm rabbitholes users

10:45 - Roomba learns to avoid poop

11:50 - AI can spot art forgeries

14:55 - Deepmind's plans to separate from Google

16:15 - Cohere raises 40M

16:55 - US Judge rejects AI inventor on patent

17:55 - Altman: GPT-4 not much bigger than GPT-3

18:45 - Salesforce CodeT5

19:45 - DeepMind Reinforcement Learning Lecture Series

20:15 - WikiGraphs Dataset

20:…

#yannickilcher #machinelearning #100k OUTLINE:

0:00 - 100k!

1:00 - Announcements & Thanks

3:55 - Channel Statistics Links:

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

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

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BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely o…

#mlnews #schmidhuber #muzero Your regular updates on what's happening in the ML world! OUTLINE:

0:00 - Intro

0:15 - Sponsor: Weights & Biases

1:45 - Google shuts down health streams

4:25 - AI predicts race from blurry X-Rays

7:35 - Facebook labels black men as primates

11:05 - Distill papers on Graph Neural Networks

11:50 - Jürgen Schmidhuber to lead KAUST AI Initiative

12:35 - GitHub brief on DMCA notices for source code

14:55 - Helpful Reddit Threads

19:40 - Simple Tricks to improve Transformers

20:40 - Apple's Unconstrained Scene Generation

21:40 - Common Objects in 3D dataset

22:20 - WarpDrive Multi-Agent RL framework

23:10 - My new paper: Boosting Search Agents & MuZero

25:15 - Can AI …

#inftyformer #infinityformer #transformer Vanilla Transformers are excellent sequence models, but suffer from very harsch constraints on the length of the sequences they can process. Several attempts have been made to extend the Transformer's sequence length, but few have successfully gone beyond a constant factor improvement. This paper presents a method, based on continuous attention mechanisms, to attend to an unbounded past sequence by representing the past as a continuous signal, rather than a sequence. This enables the Infty-Former to effectively enrich the current context with global information, which increases performance on long-range dependencies in sequence tasks. Further, the p…

#mlnews #chess #neurips OUTLINE:

0:00 - Intro

0:30 - Reconnaissance Blind Chess NeurIPS 2021 Competition

3:40 - Colab Pro no longer top priority for GPUs

4:45 - DeepMind uses Graph NNs to do traffic prediction

6:00 - Helpful Libraries: Isaac Gym, Differentiable Human, LVIS, BEHAVIOR

10:25 - Cerebras Wafer Scale Engine Cluster

12:15 - AI Voice Synthesis for Val Kilmer

14:20 - Can AI give thoughtful gifts? References:

Reconnaissance Blind Chess NeurIPS 2021 Competition

https://rbc.jhuapl.edu/

https://rbc.jhuapl.edu/gameRules Colab Pro no longer top priority

https://www.reddit.com/r/MachineLearning/comments/pdwxxz/d_colab_pro_no_longer_gives_you_a_v100_not_even_a/ Google Maps ETA prediction us…

#alibi #transformers #attention Transformers are essentially set models that need additional inputs to make sense of sequence data. The most widespread additional inputs are position encodings or position embeddings, which add sequence index information in various forms. However, this has put a limit on the resulting model, which cannot run inference on sequences longer than it has been trained on, as it would encounter unfamiliar position encodings. ALiBi solves this by proposing simple linear fixed biases as position information, adding negligible overhead in time and memory, but surprisingly, the resulting model is able to handle inference on sequences many times as long as its training …

#plagiarism #foundationmodels #tesla The best place to keep up to date with the latest and greatest from the ML world! OUTLINE:

0:00 - Intro & Sponsor

3:15 - A high-profile case of plagiarism shocks the ML world

11:55 - Stanford AI releases paper on "Foundation Models"

19:45 - Updates on Apple's NeuralHash

20:45 - RL control for two-player splorts

21:45 - Tesla's AI Day

23:55 - COMMA THREE announced

24:40 - Intel winding down RealSense cameras

25:20 - IBM unveils Telum Processor

25:50 - Lux AI Challenge & Neural MMO Challenge

26:50 - Dribnet's CLIP PixelArt

27:40 - Multi-Agent RL papers are mostly fake

28:50 - I can't even come up with a segment title

29:25 - AI News Questions

31:20 - Frame…

#attention #transformer #fastformer Transformers have become the dominant model class in the last few years for large data, but their quadratic complexity in terms of sequence length has plagued them until now. Fastformer claims to be the fastest and most performant linear attention variant, able to consume long contexts at once. This is achieved by a combination of additive attention and elementwise products. While initial results look promising, I have my reservations... OUTLINE:

0:00 - Intro & Outline

2:15 - Fastformer description

5:20 - Baseline: Classic Attention

10:00 - Fastformer architecture

12:50 - Additive Attention

18:05 - Query-Key element-wise multiplication

21:35 - Redundant m…

#pondernet #deepmind #machinelearning Humans don't spend the same amount of mental effort on all problems equally. Instead, we respond quickly to easy tasks, and we take our time to deliberate hard tasks. DeepMind's PonderNet attempts to achieve the same by dynamically deciding how many computation steps to allocate to any single input sample. This is done via a recurrent architecture and a trainable function that computes a halting probability. The resulting model performs well in dynamic computation tasks and is surprisingly robust to different hyperparameter settings. OUTLINE:

0:00 - Intro & Overview

2:30 - Problem Statement

8:00 - Probabilistic formulation of dynamic halting

14:40 - Tra…

#apple #icloud #neuralhash Send your Apple fanboy friends to prison with this one simple trick ;) We break Apple's NeuralHash algorithm used to detect CSAM for iCloud photos. I show how it's possible to craft arbitrary hash collisions from any source / target image pair using an adversarial example attack. This can be used for many purposes, such as evading detection, or forging false positives, triggering manual reviews. OUTLINE:

0:00 - Intro

1:30 - Forced Hash Collisions via Adversarial Attacks

2:30 - My Successful Attack

5:40 - Results

7:15 - Discussion DISCLAIMER: This is for demonstration and educational purposes only. This is not an endorsement of illegal activity or circumvention of …

#mlnews #nvidia #openai An in-depth look over what's going on in the world of Machine Learning and Artificial intelligence. Subscribe now and make Monday the best day of the week! OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

3:00 - Nvidia's CEO was rendered during Keynote

5:00 - AI21 Labs releases Jurassic-1 language model

7:00 - Tortured Phrases reveal plagiarism

10:05 - Cortical neurons are computationally complex

11:55 - OpenAI Codex Update & Challenge

13:30 - Automated drug abuse prevention gone wrong

17:55 - Rapid News Questions

18:40 - SoundStream learned neural audio codec

19:40 - RoboMimic framework for robotics research

20:05 - Droidlet framework for agent training

20:40 …

#apple #icloud #privacy Apple recently announced scanning all images uploaded to iCloud for CSAM (child abuse material), and that this scan would happen locally on users' phones. We take a look at the technical report and explore how the system works in detail, how it is designed to preserve user privacy, and what weak points it still has. OUTLINE:

0:00 - Introduction

3:05 - System Requirements

9:15 - System Overview

14:00 - NeuralHash

20:45 - Private Set Intersection

31:15 - Threshold Secret Sharing

35:25 - Synthetic Match Vouchers

38:20 - Problem 1: Who controls the database?

42:40 - Problem 2: Adversarial Attacks

49:40 - Comments & Conclusion Paper: https://www.apple.com/child-safety/pdf…

#mlnews #apple #nolamarck Your update on the latest news in the AI and Machine Learning world. OUTLINE:

0:00 - Intro

0:15 - Sponsor: Weights & Biases

3:30 - Apple to scan iDevices for illegal content

14:10 - EU approves chatcontrol

15:20 - Machine Learning FAQ book

17:40 - TimeDial & Disfl-QA Conversation Datasets

20:30 - VoxPopuli Speech Dataset

21:00 - Google Tensor chip coming to Pixel 6

21:30 - Pentagon uses AI to predict events

23:10 - Sketch your own GAN

24:45 - Can a Fruit Fly learn Word Embeddings?

26:00 - Master Faces beat facial recognition system

27:25 - PyTorch profiler 1.9

27:55 - 0 A.D. gets reinforcement learning interface

28:40 - BeatBot cleans up cigarette butts on the beac…

We solve the OpenAI Codex Challenge

https://challenge.openai.com/ Links:

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#mlnews #dabus #alephalpha OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

3:45 - AI legally recognized as patent inventor

8:35 - Alpeh Alpha raises USD 27Mio to build European OpenAI

10:20 - AMP advances AI aided recycling

11:20 - DeepMind builds XLand RL environment

13:15 - Cognitive Behavioral Therapy as an app

16:15 - Wordcraft interactive AI text editor

17:05 - ML used to cheat in console games

18:10 - Google's OpenBuildings Dataset

20:00 - Most ML COVID tools are flawed

21:10 - DALL-E mini released

21:55 - Helpful Libraries

25:20 - FSF funds papers discussing CoPilot SPONSOR: Weights & Biases

https://wandb.ai References:

AI legally recognized as patent inventor

https://www.glob…

Researchers from the University of Washington, Columbia University, Open AI, the Allen Institute of Artificial Intelligence, and Toyota Research have teamed up to present a new method for fine-tuning these pre-trained models such as GPT-3, BERT, DALL-E, EfficientNet, or CLIP for application specific datasets. The key insight is that as you fine-tune these models, you gain in-distribution accuracy, but sacrifice the zero-shot flexibility, or out-of-distribution generalization, of these pre-trained “foundation” models. The authors present Weight-Space Ensembling, where you take a linear interpolation between the weights of the zero-shot and fine-tuned model to make new inference. This achieve…

Researchers from the University of Washington, Columbia University, Open AI, the Allen Institute of Artificial Intelligence, and Toyota Research have teamed up to present a new method for fine-tuning these pre-trained models such as GPT-3, BERT, DALL-E, EfficientNet, or CLIP for application specific datasets. The key insight is that as you fine-tune these models, you gain in-distribution accuracy, but sacrifice the zero-shot flexibility, or out-of-distribution generalization, of these pre-trained “foundation” models. The authors present Weight-Space Ensembling, where you take a linear interpolation between the weights of the zero-shot and fine-tuned model to make new inference. This achieve…

AI Weekly Update Notion Page: https://ebony-scissor-725.notion.site/AI-Weekly-Update-September-8th-2021-a2119851b5b74470b4971d064665e77e

New AI Weekly Update GitHub Repo: https://github.com/CShorten/AIWeeklyUpdates I am looking for collaborators for two survey papers on Text-to-Image Generation and Data Augmentation Controllers. If you are interested in helping out and being a co-author of either paper, please send me a quick overview of what you think about the topic to cshorten2015@fau.edu! Content Links in the Video:

Challenges in Generalization in Open Domain Question Answering: https://arxiv.org/pdf/2109.01156.pdf

Hurdles to Progress in Long-Form Question Answering: https://arxiv.org/p…

Notion Link: https://ebony-scissor-725.notion.site/AI-Weekly-Update-August-7th-2021-3b21331c5c6a45e1a5638955dda7923c Chapters:

0:00 Introduction

0:06 Open-Ended Learning

2:38 Persistent Reinforcement Learning

3:34 Domain-Matched Pre-training for Retrieval

4:22 Growing Knowledge Culturally

5:06 Language Grounding with 3D Objects

6:03 Pre-train, Prompt, and Predict

6:32 QA Dataset Explosion

7:32 ProtoTransformer

9:32 Don’t sweep your Learning Rate under the Rug

10:34 AAVAE

11:40 Domain-Agnostic Contrastive Learning

12:43 Dataset Distillation

14:04 Pointer Value Retrieval

16:01 Go Wider Instead of Deeper

16:52 Geometric Deep Learning on Molecules

18:37 Simulation Framework for Label Noise

19:5…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Follow Katherine Lee on Twitter @katherine1ee

Twitter Thread Link: https://twitter.com/katherine1ee/status/1415496898241339400 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Demo Video: https://www.youtube.com/watch?v=9p4mfA0Fyd8 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thank you for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thumbnail Goldfish Image Credit - Photo by zhengtao tang on Unsplash Thanks for watching! Please Subscribe! Chapters:

0:00 Introduction

1:54 Multi-Session Chat

4:12 Models

6:40 Drawbacks of Info Retrieval

9:40 Memory Augmented Models

10:09 Comparison with Language Models

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Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-22nd-2021-0c43042b93a3459c901f7f5973b949bf Thank you for watching! Please Subscribe! Chapters

0:00 Introduction

0:13 BlenderBot 2.0

2:28 Wordcraft

3:20 Hyper-Text Pre-Training and Prompting

4:18 Deduplicating Training Data

5:00 Turning Tables

6:26 Reasoning with LMs and KGs

7:06 FLEX: Few-Shot NLP

8:33 AlphaFold2

9:27 Semi-Supervised Learning in Action

10:18 AI-Generating Algorithms

10:52 Adaptable Agent Populations

11:36 Recurrent Parameter Generators

12:17 Conservative Objective Models

13:20 Representation Learning for OOD Robots

13:54 MultiBench

14:40 Align before Fuse

15:06 CLIP Benefit in V-L tasks

15:4…

This video will present the Determined AI Model Hub for using HuggingFace transformers, tokenizers, and datasets with the Determined training platform! I hope you find this video useful! Overview of Determined on Henry AI Labs:

https://www.youtube.com/watch?v=9wbn2ikhbpg

Challenges of Advanced Hyperparameter Search on Henry AI Labs:

https://www.youtube.com/watch?v=5F5LlmO10AM

Walkthrough of the Determined CIFAR-10 Example on Henry AI Labs:

https://www.youtube.com/watch?v=WEYu8DI4LOU Determined Transformers Examples: https://docs.determined.ai/latest/model-hub/transformers/examples.html

HuggingFace Models: https://huggingface.co/models

HuggingFace Datasets: https://huggingface.co/datasets/sw…

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Notion Link: https://ebony-scissor-725.notion.site/Henry-AI-Labs-Weekly-Update-July-15th-2021-a68f599395e3428c878dc74c5f0e1124 Thanks for watching! Please Subscribe!

Learn more and submit: https://3b1b.co/SoME1

Podcast/New channel: https://youtu.be/C-i4q-Xlnis

↓↓Things referenced through the video↓↓ Join the discord channel:

https://discord.gg/SRTErdZ9 James Schloss:

https://www.youtube.com/user/LeiosOS Free will theorem:

https://www.ams.org/notices/200902/rtx090200226p.pdf Kolmogorov complexity and primes:

https://people.cs.uchicago.edu/~fortnow/papers/kaikoura.pdf Tadashi Tokieda talk:

https://youtu.be/tQQ3oiB32GI Boarbarktree:

https://www.youtube.com/channel/UCFeIEAkqvS4fJMTwUtF4OFw Mathologer:

https://youtu.be/N-KXStupwsc Manim:

https://github.com/3b1b/manim Manim Community edition:

https://github.com/ManimCommunity/manim/ Reanimate:

https://github.…

How to write the eigenvalues of a 2x2 matrix just by looking at it.

Thanks to Tim for the jingle: https://www.youtube.com/acapellascience

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos.

Special thanks to these supporters: https://3b1b.co/quick-eigen-thanks Introduction to eigenvectors and eigenvalues:

https://youtu.be/PFDu9oVAE-g Lockdown math lecture talking about the mean product formula:

https://youtu.be/MHXO86wKeDY Timestamps:

0:00 - Background

4:53 - Examples

10:24 - Relation to the characteristic polynomial

12:00 - Last thoughts ------------------ These animations are largely made using a custom python …

General exponentials, Love, Schrödinger, and more.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/thanks ------------------

The Romeo-Juliet example is based on this essay by Steven Strogatz:

http://www.stevenstrogatz.com/essays/loves-me-loves-me-not-do-the-math The book shown at the start is Vladimir Arnold's (excellent) textbook on ordinary differential equations.

https://amzn.to/3dtXSwj Need a review of ordinary powers of e?

https://youtu.be/m2MIpDrF7Es Or of linear algebra?

https://youtu.be/kYB8IZa5AuE Timetable

0:00 - Definition

6:40 - Dynamics of love

13:17 - General equation

20:03 - On general rotations

22:11 - Visualizing with flow ------------------

C…

Bayes factors, aka Likelihood Ratios*, offer a very clear view of how medical test probabilities work.

Home page: https://www.3blue1brown.com

Brought to you by you: https://3b1b.co/bayes-factor-thanks The book by my friend Matt Cook about paradoxes mentioned at then end:

https://amzn.to/3aBrEzg On the topic, I can't help also mentioning another paradox book I'm rather fond of by Bunch:

https://amzn.to/3mBDSKE *As mentioned in the on-screen note at the end, while the terms "Bayes Factor" and "Likelihood Ratio" refer to the same term in this setting, where Bayes rule is used on the probability of an event with only two possible outcomes (you either have the disease or you don't), they do take…

❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "The Relightables: Volumetric Performance Capture of Humans with Realistic Relighting" is available here:

https://augmentedperception.github.io/therelightables/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Ca…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/ayush-thakur/interpretability/reports/Interpretability-in-Deep-Learning-With-W-B-CAM-and-GradCAM--Vmlldzo5MTIyNw 📝 The paper "Adversarial Reinforcement Learning for Procedural Content Generation" is available here:

https://www.ea.com/seed/news/cog2021-adversarial-rl-content-generation 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace …

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Evaluating Large Language Models Trained on Code" is available here:

https://openai.com/blog/openai-codex/ Codex tweet/application links:

https://twitter.com/CristiVlad25/status/1432017112885833734

https://twitter.com/slava__bobrov/status/1425904829013102602

https://www.youtube.com/watch?v=MvHbrVfEuyk GPT-3 tweet/application links:

Website layout: https://twitter.com/sharifshameem/status/1283322990625607681

Plots: https://twitter.com/aquariusacquah/status/1285415144017797126?s=12

Typesetting math: https://twitter.com/sh_reya/status/1284746918959239168

Population data: https://twitter…

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi.

If you wish to appear here…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "ManipNet: Neural Manipulation Synthesis with a Hand-Object Spatial Representation" is available here:

https://github.com/cghezhang/ManipNet ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'…

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper Editable Free-Viewpoint Video using a Layered Neural Representation"" is available here:

https://jiakai-zhang.github.io/st-nerf/

https://github.com/DarlingHang/st-nerf 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael A…

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports" is available here:

https://research.fb.com/publications/control-strategies-for-physically-simulated-characters-performing-two-player-competitive-sports/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Ange…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned report is available here: https://wandb.ai/_scott/omnimatte/reports/Omnimatte-Associating-Objects-and-Their-Effects--Vmlldzo5MDQxNTc 📝 The paper "Omnimatte: Associating Objects and Their Effects in Video" is available here:

https://omnimatte.github.io/ Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://discordapp.com/invite/hbcTJu2 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eri…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Open-Ended Learning Leads to Generally Capable Agents" is available here:

https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play

https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Ange…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/jxmorris12/huggingface-demo/reports/A-Step-by-Step-Guide-to-Tracking-Hugging-Face-Model-Performance--VmlldzoxMDE2MTU 📝 The paper "Direct Delta Mush Skinning Compression with Continuous Examples" is available here:

https://binh.graphics/papers/2021s-DDMC/

https://media.contentapi.ea.com/content/dam/ea/seed/presentations/ddm-compression-with-continuous-examples.pdf 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan …

❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Thin-Film Smoothed Particle Hydrodynamics Fluid" is available here:

https://arxiv.org/abs/2105.07656 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbashee…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Time Lens: Event-based Video Frame Interpolation" is available here:

http://rpg.ifi.uzh.ch/TimeLens.html ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, …

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "NeX: Real-time View Synthesis with Neural Basis Expansion " is available here:

https://nex-mpi.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras B…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Fast Linking Numbers for Topology Verification of Loopy Structures " is available here:

https://graphics.stanford.edu/papers/fastlinkingnumbers/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Ja…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: https://wandb.ai/stacey/yolo-drive/reports/Bounding-Boxes-for-Object-Detection--Vmlldzo4Nzg4MQ 📝 The paper "Scaling data-driven robotics with reward sketching and batch reinforcement learning" is available here:

https://sites.google.com/view/data-driven-robotics/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jo…

Data Fest Online 2021 https://fest.ai/2021/

ML Art track https://ods.ai/tracks/ml-art-df2021 Speaker introduces himself in Russian, and then presents the material in English.

Data Fest Online 2021 https://fest.ai/2021/

ML in Marketing track https://ods.ai/tracks/ml-in-marketing-df2021 Modern Digital Advertising Platforms Leverage Machine Learning and AI to help Advertisers to achieve their goals. Being managed by humans, Advertising technological potential is often remains under-utilised as Humans tend to follow stereotypes and rely on “gut feeling” when making decisions. Understanding of the underlying principles behind “Googles and Facebook’s of our world” therefore becomes a crucial skill a modern marketer needs to acquire to stay relevant. In this talk, we will shed the light into the complex Digital Advertising ecosystem and will show you techniques, such a…

Data Fest Online 2021 https://fest.ai/2021/

ML REPA track https://ods.ai/tracks/ml-repa-df2021 Presentation: https://yadi.sk/i/a25573AB8IZUyw In this video we will analyse the requirements for modern MLOps and the main trends: Human-Centered AI, Fairness, Explainability, Model Monitoring, Human Augmented AI

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

Data Fest Online 2021 https://fest.ai/2021/

RL + Catalyst track https://ods.ai/tracks/catalyst-and-rl-df2021

ML Training 2019 Artur Kuzin tells about his participation in the competition Driven Data Hakuna Ma-data: Identify Wildlife on the Serengeti with AI for Earth. He took second place. In this video, you will find out: - Overview of a training procedure on Imagenet1k from scratch

- Implementation Details of Hacks & Tricks

- The specialty of working with JPEG pictures and resize in different frameworks Presentation - https://gh.mltrainings.ru/presentations/Kuzin_DrivenDataHakuna.pdf

ML Training 2019 Yury Bolkonsky and Andrei Dukhounik tell about their participation in Kaggle Understanding Clouds from Satellite Images. The team got a silver medal. In this video you will find out:

- Thresholding is an evil, believe in your classification models

- Why you should always use modern best practices

- Why it is not recommended to use postprocessing without local validation Presentation - https://gh.mltrainings.ru/presentations/Bolkonsky_KaggleUnderstandingClouds.pdf

ML Training 2019 Yury Bolkonsky tells about his participation in Kaggle ASHRAE - Great Energy Predictor III. His team won a gold medal. In this video you will find out:

- How to create timestamp features

- Do you need to use a leak if it is noisy?

- Leak validation for the best solution

ML Training 2019 Denis Vorotyntsev won AutoSeries - AutoML competition on time-series regression. In his presentation, he talks about the competition organization, his final solution, and solutions of other top placed participants. In this video, you will find out:

- How AutoML competition differs from most common Kaggle-alike and why you should try them

- Features engineering approach for time-series tasks when you have no idea about domain

- Why validation split should emulate train-test split

- Why you should always check the code of top participants and how small bugs might drop your score Presentation - https://gh.mltrainings.ru/presentations/Vorotyntsev_CodalabAutoML.pdf

ML Training 2019 Adel Valiullin tells about his participation in the competition Kaggle Peking University/Baidu - Autonomous Driving. He won a silver medal. In this video, you will find out: - Overview of the Autonomous Vehicles problem

- Dataset description and exploration: images with 6D pose information, taken from the roof of a car, 3D models of cars and input data analysis - Problems with mAP metric and dataset in this challenge

- The implementation of CenterNet Neural Network for 6D car pose estimation

- Score boosters and other better and high scored approaches

ML Training 2019 Ilya Kibardin tells about his participation in 2 competitions: Topcoder SpaceNet 5 Challenge & Signate The 3rd Tellus Satellite Challenge. He took fourth and second places. In this video you will find out:

- Spacenet 5 challenge at Topcoder, dataset and metric description

- Overview of a UNet pipeline for road graph extraction from satellite images

- The same pipeline applied to ice segmentation at Signate

- Hacks & Tricks for better performance Presentation - https://gh.mltrainings.ru/presentations/Kibardin_Spacenet5Tellus_v2.pdf

Data Fest Online 2020

Uncertainty Estimation in ML track https://ods.ai/tracks/uncertainty-estimation-in-ml-df2020 Speaker: Bruno Mlodozeniec, University of Cambridge In this video we discuss how ensembles of models can be effectively emulated using a single “Prior Network” model via a technique called Ensemble Distribution Detection. This enables a single model to efficiently retain both the ensemble’s predictive performance and uncertainty measures at low computational and memory cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

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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

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

DataFest Online 2020

AI Hardware track https://ods.ai/tracks/ai-hardware-df2020 Speaker: Evgenii Zheltonozhskii, Technion, Israel Institute of Technology Register and get access to the tracks: https://ods.ai/events/datafest2020

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

Идея применения машинного обучения для выявления/исправления ошибок в коде волнует умы исследователей по всему миру. Важным шагом на пути к этой светлой цели является получение обширной, репрезентативной и незашумлённой выборки bug-fix коммитов. На данный момент подавляющее число подобных датасетов собирается либо с помощью баг-трекеров, либо с помощью фильтрации коммитов по ключевым словам. Однако, точность и полнота подобных подходов оставляет желать лучшего. Мы решили исследовать применимость методов машинного обучения для майнинга bug-fix коммитов. На семинаре мы рассмотрим существующие исследования в области классификации коммитов, уделив отдельное внимание проблемам использовавшихся д…

"Aggregation model for stack trace grouping" Разработка программного обеспечения – сложный итеративный процесс. Программы поддерживаются, реорганизуются, исправляются и обновляются на постоянной основе. При этом очень часто существуют ошибки, которые не обнаружили на этапе тестирования. В большинстве современных программ при возникновении ошибки автоматически формируется отчет об ошибке, содержащий в себе стек вызовов, информацию о системе, состояние окружения, версию аварийного приложения, установленные плагины, библиотеки и их версии.

Отчеты об ошибках приходят постоянно, и их становится очень много. Одна из проблем, с которой сталкиваются разработчики, — одинаковые ошибки могут дать немн…

Статья (https://sarahfakhoury.com/2021-FSE-Summarization-Metrics.pdf) посвящена сравнению эффективности метрик для автоматической оценки качества моделей, которые суммаризуют код в документацию. Помимо стандартных BLEU, METEOR и ROUGE авторы также рассматривают BERTScore (метрику, основанную на претренированных эмбеддингах BERT'а) и chrF (метрику, которая считает F-score на n-граммах символов, а не токенов). Авторы изучили, насколько значительной должна быть разница в оценках моделей на уровне корпуса, чтобы мнение асессоров о сравнительном качестве моделей совпадало с мнением метрик. Также авторы изучили, насколько можно доверять оценкам метрик для отдельных примеров. В рамках семинара мы …

Программные продукты часто имеют ошибки, которые не были обнаружены при тестировании и в результате попали к пользователям. Тогда при возникновении такой ошибки разработчикам отправляется автоматически сгенерированный отчет о ней, состоящий преимущественно из стека вызовов. Таких отчетов приходит очень много и чтобы справиться с их анализом, часто их группируют по исходным причинам ошибки. Для группировки надо понять, какие стеки вызовов похожи друг на друга и относятся к одной ошибке, а какие нет. На семинаре будет рассказано, какие существуют подходы к этой задаче. Также будут рассмотрены предложенные нами модели и возможные их улучшения. В конце будут описаны другие задачи, связанные с о…

На практике нам хотелось бы иметь предиктивные способности во многих ситуациях: классифицировать объекты, генерировать недостающую информацию, определять наилучшее поведение, и всё это в автоматизированном режиме. Для этого обычно формулируется некоторая параметрическая модель явления или зависимости, после чего параметры подгоняются под наблюдаемую реальность в надежде на то, что зависимость легко обобщаема. В этом сущность машинного обучения. Оптимизация возникает на этапе подгона параметров — оказывается, однако, что минимизировать ошибку модели можно существенно различными методами. А знание закулисной математики при этом помогает как объяснять поведение методов, так и модифицировать по…

Инструменты выделения сценариев редактирования AST используются в самых разных областях анализа изменений кода. В их работе присутствует важный шаг, которому уделяется не так много внимания — процесс сопоставления вершин абстрактных синтаксических деревьев кода “до” и “после” изменения. Авторы статьи “A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms” предлагают новый подход к автоматическому сравнению алгоритмов сопоставления вершин AST, который достигает 98-100% точности в соответствии с оценками сторонних экспертов. Более того, в ходе описываемого эксперимента с изменениями в коде на Java авторы выяснили, что около 25% всех считающихся “соответствующи…

При работе над крупными (и не очень) проектами активно используются issue-трекеры. Если проект большой и разработчиков достаточно много, то возникает задача выбора программиста, который будет работать над конкретным сообщением о проблеме. Этот процесс занимает время, и в нём также могут возникать ошибки: если issue начинают “передавать” между разработчиками, то время решения проблемы продолжает расти. В рамках данного семинара будут разобраны несколько подходов, которые предложили исследователи для автоматизации процесса рекомендации программистов. Также будет рассмотрен разработанный нами подход для построения эмбеддингов экспертизы программистов, его технические особенности, и то, как с е…

Видео включает два доклада: "Fast reinforcement learning with generalized policy updates" Одной из важных проблем в обучении с подкреплением является то, что зачастую, чтобы выучить удовлетворительную политику, нужно провести достаточно большое число взаимодействий агента с окружением. Авторами рассматриваемой статьи предлагается решать эту проблему с помощью подхода «разделяй и властвуй». Часто, сложную задачу можно представить в виде последовательно или параллельно выполняемых простых задач. Для такого перехода авторами вводятся обобщенные версии policy evaluation и improvement. Таким образом, можно использовать решение одной задачи для решения других. Решение авторов помогает добиться си…

The code generation task can be formulated as a translation from a natural language to a specific programming language. To evaluate the output of different code generation models (against each other and during validation), generated code is compared to reference snippets. Unfortunately, it is impossible to evaluate the output of every slightly different model by an experienced human programmer. Therefore, it is necessary to apply some kind of metric that would score generated code snippets, and the desired metric should be strongly correlated with human judgment. The metrics currently used to evaluate code generation models (BLEU, ROUGE, METEOR) have originated from the neural machine trans…

Одной из важных областей исследования в программной инженерии в последние пару десятилетий являются так называемые «запахи кода» (code smells), то есть определенные архитектурные и программные решения, которые, не являясь сами по себе ошибкой, тем не менее могут усложнить восприятие кода или привести к ошибке в дальнейшем. Учёные уже собрали большие списки таких запахов, разработали инструменты для их нахождения и проанализировали их частоту в реальном коде. Отдельным подвидом запахов кода являются «запахи в тестах» (test smells). По сути дела, это тоже запахи кода, но специфичные для тестирования и тестировочного кода. Их выделение в отдельный класс и термин связано с тем, что тестировочны…

При разработке лекарственных препаратов на основе терапевтических антител возникают различные задачи, связанные со структурами антитела и/или антигена. Например, к ним относятся фолдинг (определение структуры по последовательности), докинг (определение комплекса антитело-антиген), эпитопное картирование (определение эпитопа — аминокислот, участвующих в связывании). Эти задачи можно решать экспериментальным путем, но это долго и дорого. Поэтому на помощь приходят вычислительные методы. При этом в последнее время в научном сообществе возникает все больше и больше решений, основанных на методах глубокого обучения. Один из примеров хорошо известен — Alphafold для задачи фолдинга. На семинаре бу…

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В задаче сегментации медицинских изображений наилучших результатов достигают модификации архитектуры UNet. Однако, полагаясь исключительно на свертки, подобные сети принимают решение для каждого пикселя основываясь лишь на небольшой его окрестности. Данное ограничение авторы предлагают обойти с помощью механизма self-attention, как части encoder'a модели. Представленная модель(MedT) учитывает ограничение на небольшой размер датасета, типичный для возможных приложений. Для учета отношений между различными участками изображения вводится новая стратегия обучения(LoGo) — совместное использование двух похожих по архитектуре частей сети: локальной(для небольших областей) и глобальной(для всего из…

В стандартном процессе машинного обучения ставится задача поиска глобального минимума функции потерь. При этом ландшафт функции потерь для задач глубокого обучения обычно чрезвычайно сложен, до сих пор неизвестна форма локальных минимумов, их устройство и взаимное расположение. Это приводит к тому, что наиболее популярные на данный момент методы оптимизации (SGD, Adam) могут сойтись в локальный минимум, не являющийся глобальным. К счастью, в последние несколько лет появилось множество подходов, которые модифицируют стандартные SGD и Adam для более качественного обучения моделей и показывают значимое улучшение результатов для исследуемых моделей. Однако, исследователи обычно изучают эффектив…

В 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…

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

Телеграм-канал https://t.me/mlinmarketing Спикер: Данил Гиздатуллин, Data Scientist В рекомендательных системах есть понятия пользователя и айтема. Типичная задача рекомендаций - рекомендации айтемов пользователям, но также одной из важных задач является задача рекомендации айтемов к айтемам. В докладе рассмотрим несколько вариантов её решения: от использования коллаборативной фильтрации до модели ранжирования. Презентация доклада: https://drive.google.com/file/d/1F0mmU0W4mbOPKRpfYr0JaP6I82L7PS8z/view?usp=sharing Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Спикер: Андрей Ануфриев, Старший инженер-исследователь в области глубокого обучения, Intel Вступить в сообщество: https://ods.ai/events/datafest2021/join Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

ODS Course Fest #1 https://datafest.ru/course/ Зарегистрироваться и получить доступ ко всем курсам и мероприятиям сезона: https://ods.ai/events/course_fest_1 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & Course Fest: https://t.me/datafest

https://vk.com/datafest

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

Телеграм-канал https://t.me/mlinmarketing Спикер: Антон Киселев, Marketing Analytics Producer at Playrix

* 10 лет в data (ритейл, игры, телеком)

* 15 DS проектов в проде

* 40 DS специалистов принял на работу

* 3 DS отдела построил с нуля Антон расскажет, как оптимизировали и автоматизировали ценообразование моторного топлива в одной российской топливной компании. Как учились менять цены на лету, как Марков, RL и многорукие бандиты помогли вывести argmax в продакшн и что из всего этого вышло. 00:00 introduction

01:15 динамическое ценообразование в бизнесе

04:00 цели и задачи использования

05:25 ценообразование в топливном рит…

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

Телеграм-канал https://t.me/mlinmarketing Спикер: Артем Агафонов, Data Scientist at Mail Ru Group Выбор удачной геопозиции является одной из важных стратегических задач для бизнеса, будь это строительная компания или компания розничной торговли (магазины, рестораны, банки). Популярность локации, доступность с точки зрения пешеходного или транспортного потока, наличие конкурентов в геоточке определяют важнейшие характеристики успешности бизнеса. В докладе рассмотрены примеры того, как большие данные помогают автоматизировать процесс отбора географического положения для нового объекта. Презентация доклада: https://drive.google…

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 В пятницу 27.08.2021 в 17:00 в нашем ODS спейсе live.ods.ai (пароль odssummerofcodeison) состоится митап, на котором Даниил Чесаков, Data Scientist в SberAI, расскажет о современных методах переноса лица и детально рассмотрит методы переноса лица по одной source фотографии на target фотографию или видео. Даниил остановится на двух моделях FaceShifter, одна из которых делает непосредственно перенос, а вторая добавляет детали, которые были изначально на target. Также он расскажет про то, как удалось улучшить качество переноса и сделать свой сервис FaceSwap Зарегистрироваться на ODS Summer of Code и получить …

Data Fest Online 2021

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

Телеграм-канал https://t.me/mlinmarketing Спикер: Виталий Поздняков, Преподаватель, исследователь в ВШЭ, MADE Разберемся, что такое сообщество в социальной сети и как его определить. Рассмотрим современные научные подходы к обнаружению сообществ в социальных сетях: методы на основе случайных блужданий, спектральной кластеризации и графовых нейронных сетей. Обсудим библиотеки Python, которые можно использовать для поиска сообществ, посмотрим примеры визуализации найденных сообществ. Дополнительные материалы по графам: Ссылка на гитхаб курса http://github.com/netspractice/network-science

там же в описании ссы…

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Спикер: Евгений Изутов, Deep Learning R&D Engineer, Intel В последнее время мы видим все больше примеров успешного использования методов глубокого обучения для решения практических задач. Проделан большой путь от решения базовой задачи классификации, сделавшей имя глубокому обучению, до более полезных решений, таких как система помощи водителю или автоматический перевод иностранной речи. Последний пример включает расширенное определение языка – использование жестов и мимики вместо традиционных звуков. Жестовые языки долгое время оставались незамеченными, и машинный перевод на них не распространялся. Это бы…

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 https://ods.ai/tracks/summer-of-code-2021

LightAutoML Hackathon https://ods.ai/competitions/LAMA-hack-soc2021 Приветствуем участников Summer School Of Code! В этом видео вы узнаете, на какие темы мы, разработчики LightAutoML, предлагаем вам подумать, чтобы совместными усилиями сделать наше решение чуть лучше. Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Прикладные применения ruGPT-3, GPT-Neo, GPT-3 OpenAI: что можно и нужно сделать уже сейчас Спикер: Татьяна Шаврина, Главный эксперт по технологиям, SberDevices В субботу 14.08.21 в 13:00 в нашем ODS спейсе https://live.ods.ai/ (пароль odssummerofcodeison) вместе с Татьяной Шавриной посмотрим на возможности моделей, их актуальные применения и продукты после года выхода модели. Сравним возможности моделей разных размеров, успешность прототипов, продуктов для русского, английского и других языков. Ждём вас! Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/d…

ODS Summer of Code 2021 | Intel & SberCloud track https://ods.ai/tracks/cloudcity2021 Зарегистрироваться на ODS Summer of Code и получить доступ к проектам и трекам: https://ods.ai/events/datafest2021 Вступить в сообщество: https://ods.ai/ Соцсети Data Fest & ODS Summer of Code:

https://t.me/datafest

https://vk.com/datafest

Try NordVPN free for 30 days at https://nordvpn.com/Primer More than you ever wanted to know about Hamilton's rule:

https://users.ox.ac.uk/~grafen/cv/oseb.pdf For discussion and updates

- Twitter: @primerlearning

- Discord: https://discord.gg/NbruaNW

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning Made with Unity and Manim

https://github.com/Helpsypoo/PrimerUnity

https://www.manim.community 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 Made possible by …

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…

Douglas Lenat is the founder of Cyc, a 37 year project aiming to solve common-sense knowledge and reasoning in AI.

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

(00:00) – Introduction(07:39) – What is Cyc?

(2:01:21) – The open source community and OpenCyc(2:11:48) – The inference problem(2:13:31) – Cyc’s programming language(2:21:05) – Ontological engineering(2:28:30) – Do machines think?

(2:37:15) – Death and consciousness(2:47:16) – What would you say to AI?

Niels Jorgensen is a former New York firefighter for over 21 years, who was there at Ground Zero on September 11th, 2001.

Please support this podcast by checking out our sponsors:– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– MUD\WTR: https://mudwtr.com/lex and use code LEX to get 5% off– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:Niels’s 20 for 20 Podcast: https://ironlightlabs.org/20-for-20/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.…

Donald Knuth is a computer scientist, Turing Award winner, father of algorithm analysis, author of The Art of Computer Programming, and creator of TeX.

Please support this podcast by checking out our sponsors:– Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– NetSuite: http://netsuite.com/lex to get free product tour– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Donald’s Stanford Page: https://profiles.stanford.edu/donald-knuthDonald’s Books: https://amzn.to/3heyBsCPODCAST INFO:Podcast website: …

Jaron Lanier is a computer scientist, composer, artist, author, and founder of the field of virtual reality.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.org/lex to get early access– Novo: https://banknovo.com/lex– Onnit: https://lexfridman.com/onnit to get up to 10% off– Indeed: https://indeed.com/lex to get $75 credit– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Jaron’s Website: http://www.jaronlanier.com/Jaron’s Books: https://amzn.to/3tlhl9TPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/f…

Rodney Brooks is a roboticist, former head of CSAIL at MIT, and co-founder of iRobot, Rethink Robotics, and Robust.AI.

Please support this podcast by checking out our sponsors:– Paperspace: https://gradient.run/lex to get $15 credit– GiveDirectly: https://givedirectly.org/lex to get gift matched up to $300– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security cameraEPISODE LINKS:Rodney’s Twitter: https://twitter.com/rodneyabrooksRodney’s Blog: http://rodneybrooks.com/blog/PODCAST INFO:Podcast website: https://lexfrid…

Vincent Racaniello is a virologist, immunologist, and microbiologist at Columbia.

He is a co-author of the textbook Principles of Virology and co-host of This Week in Virology podcast.

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

(1:35:45) – Vaccines(1:41:43) – Lex on his reaction to the COVID-19 vaccine shot(1:47:39) – Modern vaccines(1:52:39) – How does mRNA vaccine work?

(3:06:37) – Masks(3:15:05) – Bret Weinstein vs Sam Harris(3:18:39) – This Week in Virology(3:28:19) – Advice for young people(3:30:42) – Meaning of life

Wojciech Zaremba is a co-founder of OpenAI.

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

(00:00) – Introduction(07:57) – The Fermi paradox(14:59) – Systems of government(17:36) – Life, intelligence, and consciousness(24:49) – GPT language model(27:02) – Engineering consciousness(31:19) – Is there an algorithm for intelligence?

(1:31:35) – AI safety(1:38:21) – OpenAI Codex(1:51:54) – Robotics(2:01:11) – Developing self driving cars and robots(2:12:02) – What is the benchmark for intelligence?

(2:17:18) – AI Friendships(2:26:48) – Sleep(2:29:22) – Generating good ideas(2:35:47) – Advice for young people(2:40:31) – Getting started with machine learnin…

Jed Buchwald is a historian and philosopher of science at Caltech.

Please support this podcast by checking out our sponsors:– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1k– Theragun: https://therabody.com/lex to get 30 day trial– LMNT: https://drinkLMNT.com/lex to get free sample pack– Fundrise: https://fundrise.com/lex– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Jed’s Caltech page: https://bit.ly/38eLLRFJed’s Books: https://amzn.to/2WoxGPiPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Epi…

Barry Barish is a theoretical physicist at Caltech and the winner of the Nobel Prize in Physics.

Please support this podcast by checking out our sponsors:– MUD\WTR: https://mudwtr.com/lex and use code LEX to get 5% off– GiveDirectly: https://givedirectly.org/lex to get gift matched up to $300– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% 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 offEPISODE LINKS:Barry’s Nobel Prize entry: https://www.nobelprize.org/prizes/physics/2017/barish/facts/Barry’s Caltech profile: https://pma.caltech.edu/people/barry-c-barishLIGO’s Websi…

Joscha Bach is a cognitive scientist, AI researcher, and philosopher.

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

(00:00) – Introduction(07:15) – Life is hard(09:38) – Consciousness(16:24) – What is life?

(26:33) – Free will(40:38) – Simulation(42:49) – Base layer of reality(58:24) – Boston Dynamics(1:06:43) – Engineering consciousness(1:17:12) – Suffering(1:26:06) – Postmodernism(1:30:25) – Psychedelics(1:43:40) – GPT-3(1:52:22) – GPT-4(1:58:47) – OpenAI Codex(2:01:02) – Humans vs AI: Who is more dangerous?

(2:17:47) – Hitler(2:22:44) – Autonomous weapon systems(2:30:11) – Mark Zuckerberg(2:35:47) – Love(2:50:00) – Michael Malice and anarchism(3:…

Brian Muraresku is the author of The Immortality Key.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1k– NI: https://www.ni.com/perspectives– Indeed: https://indeed.com/lex to get $75 credit– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Brian’s Twitter: https://twitter.com/brianmurareskuBrian’s website: https://www.brianmuraresku.comImmortality Key (book):PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: htt…

Matt Walker is a sleep scientist at Berkeley, author of Why We Sleep, and the host of a new podcast called The Matt Walker Podcast.

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

(00:00) – Introduction(09:56) – Putin moment: Lex takes Matt’s sunglasses(10:17) – Fascination with sleep(14:26) – Why do we sleep?

(22:58) – Computer vision for driver assistance(32:19) – Consciousness is fundamental(40:25) – Lex on human to robot connection(42:52) – Scent of a Woman is better than “John Wick”(54:33) – Distinction between coffee and caffeine(1:20:17) – The science of ‘sleeping on it’(1:34:10) – Lex on his sleeping schedule(1:59:14) – Chronotypes(2:06:44) – …

Luis and Joao Batalha are co-founders of Fermat’s Library.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.org/lex to get early access– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Indeed: https://indeed.com/lex to get $75 credit– NetSuite: http://netsuite.com/lex to get free product tour– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% offEPISODE LINKS:Fermat’s Library Twitter: https://twitter.com/fermatslibraryLuis’s Twitter: https://twitter.com/luismbatJoao’s Twitter: https://twitter.com/joao_batalhaFermat’s Library Website: https://fermatslibrary.comPODCAST INFO:Podcast website: ht…

Jeff Hawkins is a neuroscientist and cofounder of Numenta, a neuroscience research company.

Please support this podcast by checking out our sponsors:– Codecademy: https://codecademy.com and use code LEX to get 15% off– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:A Thousand Brain (book): https://amzn.to/3AmxJt7Numenta’s Twitter: https://twitter.com/NumentaNumenta’s Website: https://numenta.com/PODCAST INFO:Podcast webs…

For Microsoft researchers, COVID-19 was a call to action.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Senior Principal Researcher Siddharth Suri explore the many ways people were impacted by work shifts during the COVID-19 pandemic.

The research that Siddharth Suri describes in this podcast was jointly done with Hana Wolf of Li…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

They also talk about what an “anatomy of hybrid work” might look like and som…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

They also explore why remote work came with a spike in phishing threats, what…

Unlocking the challenge of molecular simulation has the potential to yield significant breakthroughs in how we tackle such societal issues as climate change, drug discovery, and the treatment of disease, and Microsoft is ramping up its efforts in the space.

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

Connecting over their shared physics background and vision for molecular simulation, Bishop and Welling explore several fascinating topics, including a future in which machine learning and quantum computing will be used in tandem to model mo…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Principal Productivity Engineer Brian Houck discuss what the massive shift to remote work meant for developers—both employees of Microsoft and customers using Microsoft developer platforms to support their work.

They’ll talk about how taking a holistic approach to developer productivi…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

They also explore how people already working from home helped them better und…

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Abigail Sellen, Deputy Lab Director at Microsoft Research Cambridge in the United Kingdom, explore the dynamics of meetings and collaborations in the context of remote work.

They specifically address the difference between weak and strong ties in our professional networ…

For Microsoft researchers, COVID-19 was a call to action.

The reimagining of work practices had long been an area of study, but existing and new questions that needed immediate answers surfaced as companies and their employees quickly adjusted to significantly different working conditions.

Teams from across the Microsoft organizational chart pooled their unique expertise together under The New Future of Work initiative.

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

They’ll discuss the role of research during times of disruption, the widening…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Hunt Allcott talks with Postdoctoral Researcher Evan Rose about Allcott’s work exploring the everyday decisions people face, like buying fuel-efficient cars or taking out payday loans, and how a clearer understanding of these decisions can shape meaningful public policy.

Allcott shares how his and others’ research shows that policy can often have compl…

Interviewed by Senior Principal Researcher Hunt Allcott, Economist David Rothschild discusses how the news media has evolved alongside social media and the internet, from story development to distribution of news via aggregators and wire services.

Rothschild illuminates how and where people are consuming news and shares some of the strategies he’s seeing news outlets use to appeal to their audiences.

He also covers research insights into media bias, misinformation, and how this knowledge could inform the future of news for the better.

In addition, the researchers talk about Rothschild’s work with Project Ratio, which looks at how the news ecosystem impacts public opinion and political polar…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Senior Principal Researcher Dr. Hunt Allcott speaks with Microsoft Research New England office mate and Senior Principal Researcher Dr. Greg Lewis.

Together, they cover the connection between causal machine learning and economics research, the motivations of buyers and sellers on e-commerce platforms, and how ad targeting and data practices could evolve to foster a more symbiotic…

In the world of economics, researchers at Microsoft are examining a range of complex systems—from those that impact the technologies we use to those that inform the laws and policies we create—through the lens of a social science that goes beyond the numbers to better understand people and society.

In this episode, Dr. Hunt Allcott, Senior Principal Researcher at Microsoft Research New England, talks with Dr. Evan Rose, Postdoctoral Researcher, whom Allcott describes as “one of the most engaging and talented researchers in applied microeconomics today.” They’ll discuss how Rose’s experience teaching adult learners at San Quentin State Prison has resonated throughout his research, and they’l…

Today, people around the globe—from teachers to small-business owners to finance executives—use Microsoft Excel to make sense of the information that occupies their respective worlds, and whether they realize it or not, in doing so, they’re taking on the role of programmer.

In this episode, Senior Principal Research Manager Andy Gordon, who leads the Calc Intelligence team at Microsoft Research, and Senior Principal Researcher Simon Peyton Jones provide an inside account of the journey Excel has taken as a programming language, including the expansion of data types that has unlocked greater functionality and the release of the LAMBDA function, which makes the Excel formula language Turing-c…

What can NLP researchers learn from Human Computer Interaction (HCI) research?

We chatted with Nanna Inie and Leon Derczynski to find out.

We discussed HCI's research processes including methods of inquiry, the data anno…

In this episode, we talk with Lisa Beinborn, an assistant professor at Vrije Universiteit Amsterdam, about how to use human cognitive signals to improve and analyze NLP models.

We start by discussing different kinds of c…

In this episode, we talk to Shunyu Yao about recent insights into how transformers can represent hierarchical structure in language.

Bounded-depth hierarchical structure is thought to be a key feature of natural language…

We discussed adversarial dataset construction and dynamic benchmarking in this episode with Douwe Kiela, a research scientist at Facebook AI Research who has been working on a dynamic benchmarking platform called Dynaben…

We invited members of Masakhane, Tosin Adewumi and Perez Ogayo, to talk about their EMNLP Findings paper that discusses why typical research is limited for low-resourced NLP and how participatory research can help.

We invited Lisa Li to talk about her recent work, Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Prefix tuning is a lightweight alternative to finetuning, and the idea is to tune only a fixed-length task-sp…

How can we build Visual Question Answering systems for real users?

For this episode, we chatted with Danna Gurari, about her work in building datasets and models towards VQA for people who are blind.

We talked about the …

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Dr. Feng Li, (@f3ngli) is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He joins us today to discuss his work Distributed ARIMA Models for Ultra-long Time Series.

Angus Dempster, PhD Student at Monash University in Australia, comes on today to talk about MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification, a fast deterministic transform for time series classification. MINIROCKET reformulates ROCKET, gaining a 75x improvement on larger datasets with essentially the same performance. In this episode, we talk about the insights that realized this speedup as well as use cases.

Chongshou Li, Associate Professor at Southwest Jiaotong University in China, joins us today to talk about his work Why are the ARIMA and SARIMA not Sufficient.

Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine.

Nitin Pundir, PhD candidate at University Florida and works at the Florida Institute for Cybersecurity Research, comes on today to talk about his work “RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique.”

Florian Eckerli, a recent graduate of Zurich University of Applied Sciences, comes on the show today to discuss his work Generative Adversarial Networks in Finance: An Overview.

Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.

Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site. Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost! In this interview, we explore the use of machine learning to help identify certain conditions under which the weather …

Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction. We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.

Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting.

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work “Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.”

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior

Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?”

In this episode, I continue with last week’s theme and discuss the differences between supervised and unsupervised learning.

Additional materials: www.superdatascience.com/506

Hadelin de Ponteves joins us to discuss his latest educational work and how his skills as a data science educator helped him start his career in acting.

In this episode you will learn:• What has Hadelin been up to?

In this episode, I give a quick introduction to subcategories of supervised learning problems.

Additional materials: www.superdatascience.com/504

Pieter Abbeel joins us to discuss his work as an academic and entrepreneur in the field of AI robotics and what the future of the industry holds.

In this episode you will learn:• How does Pieter do it all?

[5:45]• Pie…

In this episode, I explore a common issue plaguing people across fields: imposter syndrome.

Additional materials: www.superdatascience.com/502

Jared Lander joins us to discuss his work as an R meetup organizer, the upcoming virtual R Conference, and his work as a consultant for a variety of companies from metal workers to professional football teams.

Barr Moses joins us to discuss the importance of data reliability for pipelines and how companies can achieve data mesh.

In this episode you will learn:• Data meshes [4:25]• Self-serve data reliability [15:36]• How …

In this very special episode, we delve into a live yoga Nidra practice with Jes Allen and go over how you can open up to consciousness through yoga practice.

In this episode you will learn:• [3:40] What Yoga means• [1…

In this episode, I dive into a reoccurring pattern I’ve noticed where beginners, myself included, think they’re more skilled and experienced than they really are.

Additional materials: www.superdatascience.com/498

Benjamin Todd joins us to discuss his work helping professionals maximize their career capital, the top skills to learn across professions, and more.

In this episode you will learn:• How Benjamin helped me become a dat…

In this episode, you’ll enjoy a fictional narrative I’ve titled “2040: A Brain-Computer Interface Story”. Additional materials: www.superdatascience.com/496

Greg Coquillo joins us to discuss his work on ROI for startups and the best ways to make the most of your company’s AI investment.

In this episode you will learn:• Our connection through Harpreet’s happy hours and DSGO…

In this episode, I talk about an interesting thought experiment that helps you appreciate your existence.

Additional materials: www.superdatascience.com/494

Anjali Shrivastava joins us to discuss her data science degree and her content creation efforts to bring data science to the people.

In this episode you will learn:• Anjali’s studies [2:00]• Anjali’s YouTube channel […

In this episode, I discuss the changing child mortality rate as evidence of how much better the world is and how much better it could be.

Additional materials: www.superdatascience.com/492

August 18, 2021 podcastIs reinforcement learning sufficient to build truly intelligent machines?

Our SponsorsQuantum MetricStay off the naughty list this holiday season by reducing customer friction, increasing conversions, and personalizing the shopping experience.

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Amethix TechnologiesAmethix 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…

August 18, 2021 podcastIn this episode I have a wonderful chat with Ronald Schmelzer and Kathleen Walch, authors of “AI Today” the top podcast for those wanting a no-hype, practical, real-world insight into what enterprises, public sector agencies, thought leaders, leading technology companies, pundits, and experts are doing with AI today.

Sponsored byQuantum Metric Did you know that 2021 holiday ecommerce sales are expected to exceed 2020 benchmarks?Are you prepared to capture every customer revenue opportunity?

Visit their website at quantummetric.com/podoffer and see if you qualify to receive their “12 Days of Insights” offer with code DATASCIENCE.

Sponsored by Amethix TechnologiesAmethi…

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Remember the Netflix challenge? It was a ton of money for the one who would have cracked the problem of recommending the best possible movie. Was it a fair challenge? […]

The post The Netflix challenge. Fair or what? (Ep. 162) appeared first on Podcast Data science at home.

In this episode Fetch AI CTO Jonathan Ward speaks about decentralization, AI, blockchain for smart cities and the enterprise.Below some great links about collective learning, smart contracts in Rust and […]

The post Artificial Intelligence for Blockchains with Jonathan Ward CTO of Fetch AI (Ep. 161) appeared first on Podcast Data science at home.

August 4, 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.

Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

July 6, 2021 podcastIt made already quite some noise in the news, GitHub copilot promises to be your pair programmer for life.

In this episode I explain how and what GitHub copilot does.

Should developers be happy, scared or just keep coding the traditional way?

SponsorsGet one of the best VPN at a massive discount with coupon code DATASCIENCE.

It provides you with an 83% discount which unlocks the best price in the market plus 3 extra months for free.

June 22, 2021 podcastData from the real world are never perfectly balanced.

In this episode I explain a simple yet effective trick to train models with very unbalanced data.

SponsorsGet one of the best VPN at a massive discount with coupon code DATASCIENCE.

It provides you with an 83% discount which unlocks the best price in the market plus 3 extra months for free.

Here is the link https://surfshark.deals/DATASCIENCEReferences

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June 10, 2021 podcastIn this episode I have a really interesting conversation with Karan Grewal, member of the research staff at Numenta where he investigates how biological principles of intelligence can be translated into silicon.

We speak about the thousand brains theory and why neural networks forget.

May 26, 2021 podcastDelivering unstoppable data to unstoppable apps is now possible with Streamr NetworkStreamr is a layer zero protocol for real-time data which powers the decentralized Streamr pub/sub network.

The technology works in tandem with companion blockchains – currently Ethereum and xDai chain – which are used for identity, security and payments.

On top is the application layer, including the Data Union framework, Marketplace and Core, and all third party applications.

In this episode I have a very interesting conversation with Streamr founder and CEO Henri PihkalaReferences

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits.

By clicking “Accept”, you consent to the use of ALL the cookies.

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By clicking “Accept”, you consent to the use of ALL the cookies.

May 11, 2021 podcastIf you think that knowing Tensorflow and Scikit-learn is enough, think again.

What is MLOps and why is it important?

It’s a podcast for techies by techies.

Amethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

April 28, 2021 podcastYour data is worth thousands a year.

Why aren’t you getting your fair share?

There is a company that has a mission: they want you to take back control and get paid for your data.

In this episode I speak about knowledge graphs, data confidentiality and privacy with Mike Audi, CEO of MyTiki.

You can reach them on their website https://mytiki.com/Discord official channelhttps://discord.com/invite/evjYQq48BeTelegramhttps://t.me/mytikiappSignalhttps://signal.group/#CjQKIA66Eq2VHecpcCd-cu-dziozMRSH3EuQdcZJNyMOYNi5EhC0coWtjWzKQ1dDKEjMqhkP

🎙 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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