I am trying to understand the masking in BERT model. I have confusion in following line taken from paper The training data generator chooses 15% of the token positions at random for prediction. If the i-th token is chosen, we replace the i-th token with (1) the [MASK] token 80% of the time (2) a random token 10% of the time (3) the unchanged i-th token 10% of the time at point 3 it say unchanged token (i think it mean unmasked token) 10% time. If we have to use original token 10% of 15% tokens, then why we need to mask it. This can be more clear in Attempt 4: Masked LM with Random Words and Unmasked Words section of this guide. The guide say So if we have a sequence of length 500, we will m…

Does anybody know how to load this large model (https://github.com/google-research/text-to-text-transfer-transformer) on multiple GPUs. So load some layers on some GPUs and other layers on other GPUs on the same machine. This model needs about 100GB of memory and though I'm trying to use model.parallelize(), I still run into Cuda out of memory error. Any assistance would be greatly appreciated! submitted by /u/rirhun [link] [comments]

Hi I'm very new to Machinelearning, and I want to use an SVM to classify a pose. I have a list of features (keypoint of the pose) in x and y coordinates. Since they are consistent, I want to use them as a single feature. I was wondering what the best method is to change the (x,y) coordinates to 1 feature. Thanks in advance submitted by /u/LilGucci2 [link] [comments]

Hi im new here glad to speak with u! is there any chat/forum where i can ask doubts? I've ended a math degree(in EU) this year, im still 21 yo and have a lot of time and enthusiasm to learn about this topic (and obviously make it a job) in the grade I ve learnt a lot about statistics and general maths(complex analysis , algebra, numeric methods, topology...) and i would like to apply it (at least the statistic part) I also dont undertand well the difference between the following terms "data science" "data analysis " "big data" "machine learning" "IA"... I want to follow my studies with a master and they focus in one of this terms and dont know what should i choose, is there any begginer gui…

"We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API." submitted by /u/deschaussures147 [link] [comments]

My undergrad capstone project involves an ML application in the healthcare field, and it has opened up a bit of a can of worms for me when considering the bias that may be involved. The use of machine learning in healthcare obviously has a lot of potential, but learning more about ML has created a few reservations for me as a patient. I wanted to hear thoughts on how others who are familiar with machine learning feel about the introduction of ML applications into healthcare *as a patient*, particularly considering the possibility for bias in these algorithms. submitted by /u/tiny_rugger [link] [comments]

Hello, I am reading Bishop's PRML book and trying to solve problem 2.16 (given in the picture) https://preview.redd.it/esfj9z89l9e81.png?width=850&format=png&auto=webp&s=8f89ec8b9cd4322a80d518dfcd396072e9ee4c3e I am having a hard time understanding the conditional probability p(x|x2 ) = N (x|μ1 + x2 , τ1^(-1) ), which is given in the solution. How did we get μ1 + x2 for the mean and τ1 for the precision? submitted by /u/Solid-Initiative-153 [link] [comments]

We have just launched sievable.com and are looking for beta testers. We are building a machine learning powered search engine to filter web content. You can contribute by training our search engine and get Sievable tokens in return. These tokens can then be used to index anything you want on the platform. Note: most features require to connect a solana wallet to the platform. Looking for feedbacks from other ML and Web3 enthusiats ! submitted by /u/Lopsided-Two-61 [link] [comments]

Hi all. I’m looking for around 5000 files that I can download for some training. I am hoping these models will be document files made up of content in a multitude of languages. Any ideas of a source I can use? Thanks submitted by /u/w3aryb0arpig [link] [comments]

Source: https://twitter.com/sama/status/1486497281884897281?s=20 After our pre-friends-and-family round in 2016, our F&F round in 2017, our angel round in 2018, our pre-seed round in 2019, our seed round in 2020, and our seed extension in 2021, we're delighted to share we’ve raised a Series A of $250 million. Humbled by such a strong start. submitted by /u/rantana [link] [comments]

I did not find such a thought, but wouldn't it be great to use it for solving zero-shot tasks? One can just get rid of the image input with data2vec, right? submitted by /u/MrLeylo [link] [comments]

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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.

My original copy of Gödel, Escher, Bach, as well as a punch card for programmingThough I considered pursuing a career in computer science or cognitive science, I wound up taking a different path.

This type of creativity, I believe, is one of the greatest things AI has to offer.

Human thought has expanded to include machine thought.

When I interviewed Gary Marcus, author of Rebooting AI: Building Artificial Intelligence We Can Trust, he said, “I think that at some point AI will fundamentally transform science and technology and medicine.

As a freelance science journalist, she regularly contributes to Muse magazine, Front Vision, and Science News for Students.

Quotations colored in blue are directly extracted from the MIT Work of the Future task force reports.

These ideas come from the task force research brief on “Artificial Intelligence and the Future of Work”.

[7] This description of the purpose of the MIT Future of Work Task Force is stated on their website homepage at https://workofthefuture.mit.edu/.

[9] All of the MIT Future of Work Task Force field study reports can be found on either their Research Brief webpage https://workofthefuture.mit.edu/research-type/briefs/ or their Working Paper webpage https://workofthefuture.mit.edu/research-type/working-papers/ .

[18] Thomas W. Malone, Daniela Rus, Robert Laubacher, “Artificial Intelligence a…

Over the last year, we at MLCommons.org set out to create public datasets to ease two pressing bottlenecks for open source speech recognition resources.

With over 30,000 hours of speech, this dataset is the largest and most diverse freely available English speech recognition corpus today.

While this may not be true for discriminative speech recognition models, it behooves the field of speech recognition to investigate this further.

Daniel Galvez is a AI developer technology engineer at NVIDIA, where he focuses on accelerating speech recognition and machine learning workloads.

CitationFor attribution in academic contexts or books, please cite this work asJuan Felipe Cerón and Mark Mazumder, …

Today, the two main approaches for training such agents are reinforcement learning (RL) and imitation learning (IL).

In reinforcement learning, humans provide rewards for completing discrete tasks, with the rewards typically being delayed and sparse.

How is training dogs different from training AIs using reinforcement learning or imitation learning?

His research interests include reinforcement learning, robotics, and computational neuroscience, with the goal of developing human-inspired and human-centered AIs.

CitationFor attribution in academic contexts or books, please cite this work asRuohan Zhang and Dhruva Bansal, "How to Train your Decision-Making AIs", The Gradient, 2021.

In episode 18 of The Gradient Podcast, we talked to Upol Ehsan, an Explainable AI (XAI) researcher who combines his background in Philosophy and Human-Computer Interaction to address problems in XAI beyond just opening the "black-box" of AI.

He is a Doctoral Candidate in the School of Interactive Computing at Georgia Tech and an affiliate at the Data & Society Research Institute.

Putting the human first and focusing on how our values shape the use and abuse of technology, his work has coined the term Human-centered Explainable AI (a sub-field of XAI) and charted its visions.

You can find his Gradient article charting this vision here, and listen to our interview with a full transcript and l…

Fig 2: A collection of outputs from different post-hoc interpretability techniques.

It becomes possible to evaluate different post-hoc interpretability techniques on the same underlying model, giving us more nuanced insights.

The same will be attested to, by interpretability methods as well as the target output do get influenced by perturbing these attributes.

CitationFor attribution in academic contexts or books, please cite this work asNirmal Sobha Kartha, "Explain Yourself - A Primer on ML Interpretability & Explainability", The Gradient, 2021.

BibTeX citation:@article{kartha2021explain,author = {Kartha, Nirmal Sobha},title = {Explain Yourself - A Primer on ML Interpretability & Explaina…

Why models must be learned and dynamically composedThe models that AI will use to reason, act, and communicate must be learned.

We can divide current models in AI into two classes: neural-network models and representation-based models.

Researchers often divide models into subsymbolic and symbolic models, but this work focuses on model composability, so dividing models into neural-network models and representation-based models is helpful, as we will see.

CitationFor attribution in academic contexts or books, please cite this work asJonathan Mugan, "Strong AI Requires Autonomous Building of Composable Models", The Gradient, 2021.

BibTeX citation:@article{mugan2021strong,author = {Mugan, Jonat…

Given this potential, we see foundation models as the subject of a growing paradigm shift, where many AI systems across domains will directly build upon or heavily integrate foundation models.

Foundation models are not just large language models; foundation models can also be trained using images, video, and other sensory and knowledge base data—and some already are.

People create the data that underpins foundation models, develop foundation models, adapt foundation models for specific applications, and interact with the resulting applications.

Foundation models are a strict superset of LLMs, though the most salient foundation models currently are LLMs (e.g., GPT-3).

Akin to how deep learni…

Sustainable AI can be thought of as another dimension along which we can guide the development of AI systems in addition to typical functional and business requirements.

Share the idea of sustainable AI widelyWe can begin first by sharing this idea of sustainable AI with our own communities of research and practice.

This will create a virtuous cycle of practice and reward enabling a transition into “Green AI”, AI systems that are built and used with the awareness of their carbon impact, as opposed to “Red AI” , AI systems that are built and used with only performance goals in mind ,which is currently favored and supported in the research and practice ecosystem.

Instrument your AI systems to…

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…

In this post, Toloka’s team offers you an insightful overview of the data labeling use cases in e-commerce.

Data labeling: use casesOf course, no AI is possible without relevant, accurately labeled data.

Because of this, data labeling has become the bedrock of AI, and thus also e-commerce.

Crowdsourcing, considered the quickest and most cost-effective method of data labeling that is also scalable, is how a lot of the data is labeled today.

Knowing that, let’s see in more detail what exactly data labeling is doing to improve e-commerce through these recent use cases:AliExpress and localizationAliExpress is one of the world’s e-commerce leaders.

This week we're finishing the MLOps series, one of our most popular series so far.

One of the challenges to understanding MLOps is that the term itself is used very loosely in the ML community.

In general, we should think about MLOps as an extension of DevOps methodologies but optimized for the lifecycle of ML applications.

In Edge#141, we discuss Model Monitoring; +Google’s research paper about the building blocks of interpretability; +a few ML monitoring platforms: Arize AI, Fiddler, WhyLabs, Neptune AI.

In Edge#147, we explain what model serving is; +the TensorFlow serving paper; +TorchServe, a super simple serving framework for PyTorch.

This week we're finishing the MLOps series, one of our most popular series so far.

Here is a full recap for you to catch up with the topics we covered.

As the proverb (and many ML people) says: Repetition is the mother of learning ;)Give a gift subscriptionLet’s have some useful intro about the whole category first:💡 ML Concept of the Day: What is MLOps?

One of the challenge…

The size and complexity of deep learning models are reaching unimaginable levels, particularly in models that try to master multiple tasks.

While MoE is not necessarily a novel ML technique, it has certainly experienced a renaissance with the rapid emergence of massively large deep learning models.

The greatest benefit of MoE models is that their computation costs scale sub-linearly with respect to their size.

Just this week, Microsoft and Google Research published papers outlining techniques to improve the scalability of MoE models.

As big ML models continue to dominate the deep learning space, MoE techniques are likely to become more mainstream in real-world ML solutions.

Our partner cnvrg.io is hosting another incredible virtual conference mlcon 2.0!

This is a FREE virtual ML community conference for AI and ML developers meant to bring together thousands of ML professionals to discuss proven strategies for building real-world AI applications.

Learn from 40+ AI experts from DeepMind, Spotify, Twitter, Disney, HuggingFace, Instacart, Colgate, Linkedin, Pinterest, Mobileye, HSBC, AstraZeneca, Verizon, BBC and more in sessions about building real-world AI applications.

REGISTER FOR FREEGet an inside look into:How DeepMind successfully deployed GNNs in production at Google MapsUnderstanding the impact of AI at DisneyBeyond Monitoring: Data & ML Observability in …

💥 What’s New in AI: Microsoft KEAR is a Deep Learning Model for Common Sense ReasoningCommon sense is one of the cognitive qualities of the human brain that are hard to quantify or even explain.

Not surprisingly, recreating common sense reasoning in ML models has become one of the most relevant challenges of the entire space.

The challenges recreating common sense are at the root of one of the fundamental philosophical dilemmas in ML: the friction between logic and knowledge.

Nowadays, architectures that combine logical reasoning and neural networks have become far from trivial.

From the ML perspective, the discipline that has become the center of common sense reasoning capabilities has bee…

Getting to know the experience gained by researchers, engineers, and entrepreneurs doing real ML work is an excellent source of insight and inspiration.

🛠 ML WorkBirchAI is focused on applying cutting edge natural language processing (NLP) and speech analysis techniques to the healthcare space.

The healthcare industry faces several related business challenges that drive our ML challenges.

How did you achieve high accuracy at scale and what are other ML challenges you are trying to address?

What’s the main value proposition of these techniques compared to previous NLP techniques relating to the healthcare industry?

In this issue:we explore CI/CD in ML Solutions;we discuss Amazon’s continual learning architecture that manages the ML models lifecycle;we overview CML, an open-source library for enabling CI/CD in ML pipelines.

💡 ML Concept of the Day: CI/CD in ML SolutionsToday we would like to bring together some of the ideas we have explored into what many considered the ultimate expression of MLOps: continuous integration and deployment (CI/CD) in ML pipelines.

CI/CD is a well-established concept in traditional software development.

Like in traditional software, CI/CD in ML focuses on streamlining the ML solutions’ delivery and management, but the specifics look quite different from established CI/CD c…

📝 EditorialWhen we think about active contributors to open-source machine learning (ML), we immediately gravitate towards big tech platforms providers like Google, Facebook, and Microsoft.

We do not immediately associate companies like Uber with open-source ML contributions.

However, the transportation giant has quietly become one of the most active sources of innovation for open-source ML projects.

Uber’s contribution to the open-source ML space should not come as a surprise.

The importance and speed of Uber’s open-source ML contributions are undoubtedly impressive, but they aren’t an exception by any stretch.

DOWNLOAD THE REPORTWe are happy to congratulate our partners Run:AI, a leader in compute orchestration for AI workloads, who have been recognized in The Forrester Wave™: AI infrastructure, Q4 2021 report published by Forrester Research.

You want to check this report for sure: subtitled “The 13 Providers That Matter Most And How They Stack Up”, this report is Forrester’s first ever AI Infrastructure Wave™.

The creation of this new Forrester Wave™ guide reflects the increasing momentum of AI adoption, and the demands that come with diverse AI workloads and scaling IT infrastructure.

Forrester applies a consistent methodology to scoring AI infrastructure vendors for their guides, offering IT p…

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

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

💥 What’s New in AI: The ML Powering LinkedIn’s Recruiting Recommendation …

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

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

Speakers include practitioners from Twitter, Etsy, Better.com, Walmart, Snowflake, Databricks, Redis, Tecton, and more.

We’d love for you to join us!

REGISTER FOR FREE🔦 A Few Highlights of the Agenda 🔦And other amazing speakers.

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For decades, most supervised ML models have been highly optimized for a single representation of the information.

In the last two years, we have seen the emergence of multimodal ML models applied to real-world scenarios.

This week, Meta AI Research released a new model that combines audio and visual inputs to improve speech recognition.

While there are still plenty of milestones to reach in individual deep learning modalities, multimodal learning is an essential step towards the goal of building general AI.

Little by little, such steps are making it more and more real.

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Также поговорим о проблемах метода SHAP и его дальнейшем развитии в виде метода Shapley Flow, объединяющего интерпретацию модели и многообразия данных.

Утечка данных является частным случаем сдвига данных, поскольку зависимость ID диагноз была в и , но ее не будет в .

Проблемы и ограничения SHAP valuesНа практике рассчет SHAP values позволяет интерпретировать модель, выявлять скрытые проблемы в модели и данных и даже выполнять кластеризацию, что мы увидим далее в разделе "SHAP на практике".

SHAP values можно посчитать по формуле , но количество слагаемых экспоненциально зависит от количества признаков, и алгоритм рассчета SHAP values в общем случае является NP-полным.

Можно также заметить, …

Список фичей в платной версиии вполне достойный, хоть и не самый впечатляющий (ничего такого, что я не нашел бы в labelstudio).

Документация на троечку и не отвечает на многие вопросы.

И это не так легко, особенно когда речь идет об аудиозаписях.

Это самая большая проблема подобных платформ - не имея собственных разметчиков вы не можете контролировать качество разметки и обучать людей тому, как размечать правильно (да, это не так просто, как кажется!).

Через UI загрузка и выгрузка работает отлично - ты кидаешь .txt или .csv/.tsv файл - и на каждую строчку создается отдельная задача внутри проекта.

В этом посте я расскажу историю появления Open Source библиотеки Albumentations как я ее запомнил.

Я, как и остальные участники команды, включая Александра Буслаева, начали пилить свои аугментационные велосипеды, которые планировалось сделать быстрее и удобнее.

Миша залез в соревнование Severstal: Steel Defect Detection и в кернелах увидел, что участники используют Albumentations.

Но потом, в разговоре всплыло, что для его компании самое большое откровение от соревнования было - что все в топе используют Albumentations.

Она активно использовала Albumentations по работе и, скорее всего, выбралась со мной только потому что ей было интересно пообщаться с её разработчиком :)Что мы по метрикам?

Кластеризация и маскирование таблицы MSAСложность вычислений и объем требуемой памяти в AlphaFold квадратично зависит от количества последовательностей в MSA, и лишь линейно от длины последовательности, поэтому количество последовательностей в MSA желательно уменьшить.

Этот блок принимает на вход массивы pair representation и MSA representation и возвращает два массива таких же размеров.

Таким образом, в MSA representation собирается вся доступная информация о позициях, а в pair representation собирается вся доступная информация о парах позиций.

Дополнительно выполняются следующие операции:Pair bias и outer product mean – операции, позволяющие передавать информацию от pair representation в …

TL;DR: Этой осенью сообщество Open Data Science и компания Huawei делают новый запуск курса.

Мы делаем новый запуск курса Natural Language Processing.

Полный syllabus курса можно посмотреть здесь.

После каждой лекции будет квиз.

Пару слов обо мне, как авторе курса, - в области NLP я работаю последние 9 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

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…

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In that moment, the brony fandom they never predicted clarified into something real.

Elements of this appear in many fandoms, but brony fandom was unusually prolific.

I think the explosion of MLP fan content is most easily explained by the MLP universe’s untapped potential.

Sturgeon’s Law still applies, but the MLP fandom was prolific enough to produce lots of content in the 10% that wasn’t crap.

Pony fandom was a sizable part of my life for 10 years.

How to Invent Everything is a book by Ryan North, of Dinosaur Comics fame.

How to Invent Everything doesn’t literally cover everything, but you can see it as a book about the highlights of civilization.

Hot air balloons took a really long time to invent, given that baskets, controlled fire, and fabric all existed for centuries.

One question How To Invent Everything toys with is what point in history would let you most influence the trajectory of humanity.

Both these technologies are so incredibly useful to society that it’s hard to see how progress was made before their invention, and they look a long time to invent.

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.

Semi-supervised learning is one candidate, utilizing a large amount of labeled data conjunction with a small amount of labeled data.

They further set a minimum number of labeled samples in every mini batch by oversampling the labeled samples.

Given a batch of labeled data \(\mathcal{X}\) and unlabeled data \(\mathcal{U}\), we create augmented versions of them via \(\text{MixMatch}(.

A semi-supervised learning framework leverages unlabeled data corpus by (Left) task-agnostic unsupervised pretraining and (Right) task-specific self-training and distillation.

2020:Bigger models are more label-efficient;Bigger/deeper project heads in SimCLR improve representation learning;Distillation using unla…

Additionally training a large model often pairs with a large training corpus and thus a single process may just take forever.

2019)Pipeline ParallelismPipeline parallelism (PP) combines model parallelism with data parallelism to reduce inefficient time “bubbles’’.

The switch transformer paper summarized different data and model parallelism strategies for training large models with a nice illustration:Fig.

2019) optimizes the memory used for training large models based on the observation about two major memory consumption of large model training:The majority is occupied by model states, including optimizer states (e.g.

Cited as:@article{weng2021large, title = "How to Train Really Large Model…

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 \(\text{sim}(.,.)\).

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…

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…

Since Content Quality cannot be defined by formula, raters were given carefully chosen guidelines designed to evoke their individual judgment.

They knew this effort would be worthwhile because Content Quality was likely to be a factor in many important decisions.

Their chain of reasoning can be written out as follows:We wish to measure Content Quality.

Since Content Quality cannot be defined by formula, raters were given carefully chosen guidelines designed to evoke their individual judgment.

They knew this effort would be worthwhile because Content Quality was likely to be a factor in many important decisions.

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…

Does Gradient Flow Over Neural Networks Really Represent Gradient Descent?

Researchers often conduct such studies by considering gradient flow (GF), equivalent to GD with infinitesimally small step size.

This would imply $\boldsymbol\theta ( t + \eta ) = \boldsymbol\theta ( t ) - \etaabla f ( \boldsymbol\theta ( t ) )$, which we can write as $\frac{1}{\eta} ( \boldsymbol\theta ( t + \eta ) - \boldsymbol\theta ( t ) ) = -abla f ( \boldsymbol\theta ( t ) )$.

Figure 2: Experiment with NN comparing every iteration of GD with step size $\eta_0 := 0.001$, to every $r$'th iteration of GD with step size $\eta_0 / r$, where $r = 2 , 5 , 10 , 20$.

Left plot shows training loss values; right one shows…

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.

The last few years saw the rise of Large Language Models (LLMs) – machine learning models that rapidly improve how machines process and generate language.

By including a retrieval method in the language model, the model can be much smaller.

Aiding language models with retrieval methods allows us to reduce the amount of information a language model needs to encode in its parameters to perform well at text generation.

Aiding language models with retrieval methods allows us to reduce the amount of information a language model needs to encode in its parameters to perform well at text generation.

Input prompt reaches RETRO Decoder block to start information retrieval Input prompt reaches RETRO D…

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:

And that is exactly what is going on with Tesla FSD.

The excitement and media coverage of this event gathered attention of Silicon Valley and gave rise to a self driving car project at Google in January of 2009.

The appearance of Google "self driving" prototypes in the streets of San Francisco in the early part of 2010's ignited a hysteria in the Bay Area, where the "self driving car" became the next big thing.

It is not clear if a control system for a complex environment can exist in a form that does not continuously learn online.

What we have instead of a safe self driving car, is a farcical comedy of silly mistakes, even in the best of conditions.

We learned for example that everything we can write an equation for can be in principle calculated on a computer.

We take for granted that we can write equations for molecules, yet this isn't really the case.

We can write equations for "approximations" of molecules, ignoring some of the details.

This entire mental exercise that tries to fit the entire Universe into a giant Turing machine is fundamentally flawed!!!

And the straightforward consequence of this flawed philosophy is that brain is just yet another computer, and since we build faster computers every day, it's just a matter of time when we build one as sophisticated as the brain.

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…

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 …

Reinforcement learning agents typically perform poorly if provided with inputs that were not clearly defined in training.

For instance, most reinforcement learning (RL) agents require their inputs to be in a pre-specified format, or else they will fail.

Each sensory neuron integrates over time information from only their particular sensory input channel.

This action is also fed back into each sensory neuron in the next time-step, closing the communication loop.

Each sensory neuron is an identical neural network that is not confined to only process information from one particular sensory input.

“A drawing of a cat”CLIP + ES + TrianglesRedirecting to es-clip.github.io, where the article resides.

In the regime switching extension of Black-Scholes-Merton model of asset price dynamics, one assumes that the volatility coefficient evolves as a hidden pure jump process.

Under the assumption of Markov regime switching, we have considered the locally risk minimizing price of European vanilla options.

By pretending these prices or their noisy versions as traded prices, we have first computed the implied volatility (IV) of the underlying asset.

Then by performing several numerical experiments we have investigated the dependence of IV on the time to maturity (TTM) and strike price of the vanilla options.

Such regime recovery has also been proved in a theoretical setting.

Strategic behavior is a fundamental problem in a variety of real-world applications that require some form of peer assessment, such as peer grading of assignments, grant proposal review, conference peer review, and peer assessment of employees.

Although this method ensures strategyproofness, each submission may require a different type of expertise for effective evaluation.

In this paper, we focus on finding an assignment of evaluators to submissions that maximizes assigned expertise subject to the constraint of strategyproofness.

We analyze the price of strategyproofness: that is, the amount of compromise on the assignment quality required in order to get strategyproofness.

Finally, we eva…

However, CT resolution and radiation dose are tightly entangled, highlighting the importance of low-dose CT combined with sophisticated denoising algorithms.

Most data-driven denoising techniques are based on deep neural networks and, therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures.

Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity.

This work presents an open-source CT denoising framework based on the idea of bilateral filtering.

Although only using three spatial parameters and one range parameter per…

The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region.

Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear if they have the ability to solve the insideness problem as it requires evaluating long-range spatial dependencies.

In this paper, the insideness problem is analysed in isolation, without texture or semantic cues, such that other aspects of segmentation do not interfere in the analysis.

We demonstrate that DNNs for segmentation with few units have sufficient complexity to solve insideness for any curve.

Only recurrent networks trained with small images learn solutions that generali…

Targeted training-set attacks inject malicious instances into the training set to cause a trained model to mislabel one or more specific test instances.

This work proposes the task of target identification, which determines whether a specific test instance is the target of a training-set attack.

This can then be combined with adversarial-instance identification to find (and remove) the attack instances, mitigating the attack with minimal impact on other predictions.

We show that existing influence estimators' poor practical performance often derives from their over-reliance on instances and iterations with large losses.

Target identification then simplifies to detecting test instances with …

Visualizing these parcellations is critical to guide biological understanding of clinical and experimental datasets in humans and model organisms.

However, software used to visualize parcellations is different from the one used to analyze these datasets, greatly limiting the visualization of experimental data within parcellations.

We present coldcuts, an open source R package that allows to automatically generate, store and visualize any volume-based parcellation easily and with minimal manual curation.

coldcuts allows to integrate external datasets and offers rich 2D and 3D visualizations.

coldcuts is freely available at http://github.com/langleylab/coldcuts and several curated coldcuts ob…

The fundamental methodology including replication ("protocol") for hypothesis testing/validation to a state allowing inference, varies within medical and plant sciences with little justification.

Here, five protocols are distinguished which deal differently with systematic/random errors and vary considerably in result veracity.

49%:71%) plant studies had triple-result or triplicated global protocols, compared with, in both years, 4 (5%, C.I.

Plant sciences had a higher prevalence of protocols more likely to counter generalised systematic errors (the most likely cause of false positives) and random error than non-replicated protocols, without suffering from serious flaws found with random-In…

While neural networks have been remarkably successful in a wide array of applications, implementing them in resource-constrained hardware remains an area of intense research.

By replacing the weights of a neural network with quantized (e.g., 4-bit, or binary) counterparts, massive savings in computation cost, memory, and power consumption are attained.

We modify a post-training neural-network quantization method, GPFQ, that is based on a greedy path-following mechanism, and rigorously analyze its error.

We prove that for quantizing a single-layer network, the relative square error essentially decays linearly in the number of weights -- i.e., level of over-parametrization.

We also demonstrat…

Data shift robustness is an active research topic, however, it has been primarily investigated from a fully supervised perspective, and robustness of zero-shot learning (ZSL) models have been largely neglected.

In this paper, we present a novel analysis on the robustness of discriminative ZSL to image corruptions.

Our results show that discriminative ZSL suffer from corruptions and this trend is further exacerbated by the severe class imbalance and model weakness inherent in ZSL methods.

We also obtain new strong baselines for the label embedding model with certain corruption robustness enhancement methods.

Finally, our experiments show that although existing methods to improve robustness s…

Logistic regression training on an encrypted dataset has been an attractive idea to security concerns for years.

In this paper, we propose a faster gradient variant called Quadratic Gradient for logistic regression and implement it via a special homomorphic encryption scheme.

We evaluate various gradient $ascent$ methods with this gradient variant on the gene dataset provided by the 2017 iDASH competition and the image dataset from the MNIST database.

We also implement the gradient variant in full batch NAG and mini-batch NAG for training a logistic regression model on a large dataset in the encrypted domain.

Equipped with this gradient variant, full batch NAG and mini-batch NAG are both fa…

Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premise and hypothesis).

Many recent works have used contrastive learning by incorporating the relationship of the sentence pair from NLI datasets to learn sentence representation.

In this paper, we propose a Pair-level Supervised Contrastive Learning approach (PairSCL).

A contrastive learning objective is designed to distinguish the varied classes of sentence pairs by pulling those in one class together and pushing apart the pairs in other classes.

We evaluate PairSCL on two public datasets of NLI where the accuracy o…

Search-Oriented Conversational AI (SCAI) is an established venue that regularly puts a spotlight upon the recent work advancing the field of conversational search.

SCAI'21 was organised as an independent on-line event and featured a shared task on conversational question answering.

Since all of the participant teams experimented with answer generation models for this task, we identified evaluation of answer correctness in this settings as the major challenge and a current research gap.

Alongside the automatic evaluation, we conducted two crowdsourcing experiments to collect annotations for answer plausibility and faithfulness.

As a result of this shared task, the original conversational QA …

Attention mechanism has been widely believed as the key to success of vision transformers (ViTs), since it provides a flexible and powerful way to model spatial relationships.

However, is the attention mechanism truly an indispensable part of ViT?

To demystify the role of attention mechanism, we simplify it into an extremely simple case: ZERO FLOP and ZERO parameter.

Based on this simple operation, we construct a new backbone network, namely ShiftViT, where the attention layers in ViT are substituted by shift operations.

These results suggest that the attention mechanism might not be the vital factor that makes ViT successful.

State-of-the-art semantic segmentation models are characterized by high parameter counts and slow inference times, making them unsuitable for deployment in resource-constrained environments.

To address this challenge, we propose \textsc{Auto-Compressing Subset Pruning}, \acosp, as a new online compression method.

The core of \acosp consists of learning a channel selection mechanism for individual channels of each convolution in the segmentation model based on an effective temperature annealing schedule.

The results are competitive with existing baselines for compression of segmentation models at low compression ratios and outperform them significantly at high compression ratios, yielding ac…

Semi-supervised object detection (SSOD) has achieved substantial progress in recent years.

However, it is observed that the performances of self-labeling SSOD methods remain limited.

Based on our experimental analysis, we reveal that the reason behind such phenomenon lies in the mutual error amplification between the pseudo labels and the trained detector.

In this study, we propose a Cross Teaching (CT) method, aiming to mitigate the mutual error amplification by introducing a rectification mechanism of pseudo labels.

In this way, CT can enhance the pseudo label quality compared with self-labeling and existing mutual teaching methods, and reasonably mitigate the mutual error amplification.

Capsule networks are a class of neural networks that achieved promising results on many computer vision tasks.

However, baseline capsule networks have failed to reach state-of-the-art results on more complex datasets due to the high computation and memory requirements.

We tackle this problem by proposing a new network architecture, called Momentum Capsule Network (MoCapsNet).

In this paper, we provide a framework on how invertible residual building blocks can be applied to capsule networks.

We will show that MoCapsNet beats the accuracy of baseline capsule networks on MNIST, SVHN and CIFAR-10 while using considerably less memory.

Given the ubiquity of deep neural networks, it is important that these models do not reveal information about sensitive data that they have been trained on.

In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network.

A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset.

In this work, we provide a probabilistic interpretation of model inversion attacks, and formulate a variational objective that accounts for both diversity and accuracy.

In order to optimize this variational objective, we choose a variational family defined in the…

3D object detection is a fundamental and challenging task for 3D scene understanding, and the monocular-based methods can serve as an economical alternative to the stereo-based or LiDAR-based methods.

However, accurately detecting objects in the 3D space from a single image is extremely difficult due to the lack of spatial cues.

In particular, we first project the LiDAR signals into the image plane and align them with the RGB images.

After that, we use the resulting data to train a 3D detector (LiDAR Net) with the same architecture as the baseline model.

Finally, this LiDAR Net can serve as the teacher to transfer the learned knowledge to the baseline model.

HC4 is a new suite of test collections for ad hoc Cross-Language Information Retrieval (CLIR), with Common Crawl News documents in Chinese, Persian, and Russian, topics in English and in the document languages, and graded relevance judgments.

New test collections are needed because existing CLIR test collections built using pooling of traditional CLIR runs have systematic gaps in their relevance judgments when used to evaluate neural CLIR methods.

The HC4 collections contain 60 topics and about half a million documents for each of Chinese and Persian, and 54 topics and five million documents for Russian.

Active learning was used to determine which documents to annotate after being seeded us…

In source code search, a common information-seeking strategy involves providing a short initial query with a broad meaning, and then iteratively refining the query using terms gleaned from the results of subsequent searches.

This strategy requires programmers to spend time reading search results that are irrelevant to their development needs.

In contrast, when programmers seek information from other humans, they typically refine queries by asking and answering clarifying questions.

Clarifying questions have been shown to benefit general-purpose search engines, but have not been examined in the context of code search.

We present a method for generating natural-sounding clarifying questions u…

Given a union of non-linear manifolds, non-linear subspace clustering or manifold clustering aims to cluster data points based on manifold structures and also learn to parameterize each manifold as a linear subspace in a feature space.

Deep neural networks have the potential to achieve this goal under highly non-linear settings given their large capacity and flexibility.

We argue that achieving manifold clustering with neural networks requires two essential ingredients: a domain-specific constraint that ensures the identification of the manifolds, and a learning algorithm for embedding each manifold to a linear subspace in the feature space.

For subspace feature learning, Maximum Coding Rat…

Emotion recognition in smart eyewear devices is highly valuable but challenging.

However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those sentimental ones.

In this paper, we study the emotionship analysis problem in eyewear systems, an ambitious task that requires not only classifying the user's emotions but also semantically understanding the potential cause of such emotions.

To this end, we devise EMOShip, a deep-learning-based eyewear system that can automatically detect the wearer's emotional status and simultaneously analyze its associations with semantic-level visual perceptions.

Pilot studies with 20 participants further m…

The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models.

On the dual task, we design models to make branching decisions to promote the dual bound increase faster.

We propose a knowledge inheritance method to generalize knowledge of different models from the dataset aggregation process, named KIDA.

Further, we won the $1$\textsuperscript{st} Place on the dual task.

We hope this report can provide useful experience for developers and researchers.

Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do not provide optimality guarantees and error bounds.

In this paper, we propose a hybrid quantum-classical algorithm for robust fitting.

Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs and terminates with a global solution or an error bound.

While our usage of quantum computing does not surmount the fundamental intractability of robust fitting, by providing error bounds our algorithm is a practical improvement over randomised heuristics.

Moreover, our work represents a concrete application of quantum computing in computer vision.

Evaluating video captioning systems is a challenging task as there are multiple factors to consider; for instance: the fluency of the caption, multiple actions happening in a single scene, and the human bias of what is considered important.

Most metrics try to measure how similar the system generated captions are to a single or a set of human-annotated captions.

The aim is for the model to learn to perform an evaluation similar to that of a human.

To do so, we use a dataset that contains human evaluations of system generated captions.

The dataset consists of the human judgments of the captions produce by the system participating in various years of the TRECVid video to text task.

We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g.

explanations written in hieroglyphic -- by autonomously learning to interpret them.

In EL, the burden of interpreting symbols is not left to humans or rigid human-coded compilers, as done in Program Synthesis.

Rather, EL calls for a learned interpreter, built upon a limited collection of symbolic sequences paired with observations of several phenomena.

To these models, we oppose Critical Rationalist Networks (CRNs), which instead embrace a rationalist view on the acquisition of knowledge.

This paper reports on the first international competition on AI for the traveling salesman problem (TSP) at the International Joint Conference on Artificial Intelligence 2021 (IJCAI-21).

This first competition asked the participants to develop algorithms to solve a time-dependent orienteering problem with stochastic weights and time windows (TD-OPSWTW).

In this paper, we describe the problem, the setup of the competition, the winning methods, and give an overview of the results.

The winning methods described in this work have advanced the state-of-the-art in using AI for stochastic routing problems.

Overall, by organizing this competition we have introduced routing problems as an interestin…

Invariance principle-based methods, for example, Invariant Risk Minimization (IRM), have recently emerged as promising approaches for Domain Generalization (DG).

Despite the promising theory, invariance principle-based approaches fail in common classification tasks due to the mixture of the true invariant features and the spurious invariant features.

In this paper, we propose a framework based on the conditional entropy minimization principle to filter out the spurious invariant features leading to a new algorithm with a better generalization capability.

We theoretically prove that under some particular assumptions, the representation function can precisely recover the true invariant featur…

We address the problem of capturing temporal information for video classification in 2D networks, without increasing computational cost.

Instead, we propose a novel sampling strategy, where we re-order the channels of the input video, to capture short-term frame-to-frame changes.

We observe that without bells and whistles, the proposed sampling strategy improves performance on multiple architectures (e.g.

TSN, TRN, and TSM) and datasets (CATER, Something-Something-V1 and V2), up to 24% over the baseline of using the standard video input.

In addition, our sampling strategies do not require training from scratch and do not increase the computational cost of training and testing.

Research on machine learning for channel estimation, especially neural network solutions for wireless communications, is attracting significant current interest.

This is because conventional methods cannot meet the present demands of the high speed communication.

In the paper, we deploy a general residual convolutional neural network to achieve channel estimation for the orthogonal frequency-division multiplexing (OFDM) signals in a downlink scenario.

Our method also deploys a simple interpolation layer to replace the transposed convolutional layer used in other networks to reduce the computation cost.

Compared with other deep learning methods for channel estimation, our results for 3GPP ch…

Despite decades of effort and several significant advances, accurately modelling the quantum mechanical behaviour of electrons remains an open challenge.

Instead, knowing the probability for any electron to be at each position (i.e., the electron density) is sufficient to exactly compute all interactions.

Kohn received a Nobel Prize in Chemistry after proving this, thus founding Density Functional Theory (DFT).

Over the years, researchers have proposed many approximations to the exact functional with varying levels of accuracy.

Despite their popularity, all of these approximations suffer from systematic errors because they fail to capture certain crucial mathematical properties of the exact…

It’s why our teams at DeepMind study aspects of language processing and communication, both in artificial agents and in humans.

Developing beneficial language models requires research into their potential impacts, including the risks they pose.

Today we are releasing three papers on language models that reflect this interdisciplinary approach.

They include a detailed study of a 280 billion parameter transformer language model called Gopher, a study of ethical and social risks associated with large language models, and a paper investigating a new architecture with better training efficiency.

Gopher - A 280 billion parameter language modelIn the quest to explore language models and develop ne…

More than a century ago, Srinivasa Ramanujan shocked the mathematical world with his extraordinary ability to see remarkable patterns in numbers that no one else could see.

The self-taught mathematician from India described his insights as deeply intuitive and spiritual, and patterns often came to him in vivid dreams.

These observations captured the tremendous beauty and sheer possibility of the abstract world of pure mathematics.

Our research paper, published today in the journal Nature, details our collaboration with top mathematicians to apply AI toward discovering new insights in two areas of pure mathematics: topology and representation theory.

We’re also releasing full companion paper…

AI is already enabling huge leaps in tackling fundamental challenges: from solving protein folding to predicting accurate weather patterns, scientists are increasingly using AI to deduce the rules and principles that underpin highly complex real-world domains - ones they might never have discovered unaided.

Two real-world domains that scientists at DeepMind are contributing to tackle climate change while developing what’s required to build AGI are weather prediction and plasma control for fusion.

Weather patterns are almost impossible to precisely model - it’s an example of nature’s variations at its fullest.

As we develop AGI, addressing global challenges such as climate change will not on…

This subtle complexity makes simulating physical contact — a vital component of robotics research — a tricky task.

Already widely used within the robotics community, including as the physics simulator of choice for DeepMind’s robotics team, MuJoCo features a rich contact model, powerful scene description language, and a well-designed API.

As we work to prepare the codebase, we are making MuJoCo freely available as a precompiled library.

MuJoCo, which stands for Multi-Joint Dynamics with Contact, hits a sweet spot with its contact model, which accurately and efficiently captures the salient features of contacting objects.

Unlike other simulators, MuJoCo resolves contact forces using the conv…

In “Skill Mastery,” our goal is to train a single agent that’s skilled in stacking a predefined set of five triplets.

To test for generalisation, these training objects exclude the family of objects from which the test triplets were chosen.

Next, we train a new policy in simulation that uses only realistic observations: images and the robot’s proprioceptive state.

The state policy serves as a teacher, providing the learning agent with corrections to its behaviours, and those corrections are distilled into the new policy.

This allows us to use the data that’s passively collected during the project instead of running a time-consuming online training algorithm on the real robots.

Based on Transformers, our new Enformer architecture advances genetic research by improving the ability to predict how DNA sequence influences gene expression.

Today Nature Methods published “Effective gene expression prediction from sequence by integrating long-range interactions” (first shared as a preprint on bioRxiv), in which we — in collaboration with our Alphabet colleagues at Calico — introduce a neural network architecture called Enformer that led to greatly increased accuracy in predicting gene expression from DNA sequence.

Previous work on gene expression has typically used convolutional neural networks as fundamental building blocks, but their limitations in modelling the influe…

In this evolving book of weather prediction, we now add a story on the role of machine learning for forecasting.

Today’s weather predictions are driven by powerful numerical weather prediction (NWP) systems.

Nowcasting fills the performance gap in this crucial time interval.

Nowcasting is essential for sectors like water management, agriculture, aviation, emergency planning, and outdoor events.

This combination of a crucial area where existing methods struggle and the availability of high-quality data provides the opportunity for machine learning to make its contributions to nowcasting.

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.

Quantum processors are made of superconducting quantum bits (qubits) that — being quantum objects — are highly susceptible to even tiny amounts of environmental noise.

In superconducting qubits, information is encoded in different patterns of oscillating supercurrent going back and forth through the Josephson junction.

When these arrive at the superconducting qubit layer, they break up the superconducting state and produce quasiparticles, which cause the qubit errors we observe.

By employing these techniques, future processors will be much more robust to these high-energy impact events.

We’re looking forward to future processor designs that can handle high energy impacts and enable the firs…

Businesses around the globe are continuing to benefit from innovations in Artificial Intelligence (AI) and Machine Learning (ML). At F5, we are using AI/MI in meaningful ways to improve data security, fraud detection, bot attack prevention and more. While the benefits of AI/ML for these business processes are well articulated, at F5, we also use AI/ML to optimize our software applications. Using AI/ML for better software engineering is still in its early days. We are seeing use cases around AI assisted code completion, auto-code generation for no-code/low-code platforms but we are not seeing broad usage of AI/ML in optimizing the software application architecture itself. In this blog, we wi…

Portrait Mode effect on a selfie shot using a low-resolution and coarse alpha matte compared to using the new high-quality alpha matte.

Portrait MattingIn designing Portrait Matting, we trained a fully convolutional neural network consisting of a sequence of encoder-decoder blocks to progressively estimate a high-quality alpha matte.

The network predicts a high-quality alpha matte from a color image and an initial coarse alpha matte.

Together with Light Stage data, we compute accurate alpha mattes using time-multiplexed lights and a previously recorded “clean plate”.

Portrait Mode effect on a selfie shot using a coarse alpha matte compared to using the new high quality alpha matte.

However, ML-based audio classification of bird species can be challenging for several reasons.

Moreover, in our new paper, “Improving Bird Classification with Unsupervised Sound Separation,” we use MixIT training to separate birdsong and improve species classification.

We found that including the separated audio in the classification improves precision and classification quality on three independent soundscape datasets.

We trained a new MixIT separation model using birdsong recordings from Xeno-Canto and the Macaulay Library.

We found that for separating birdsong, this new model outperformed a MixIT separation model trained on a large amount of general audio from the AudioSet dataset.

Organizations increasingly turn to AI to transform work processes, but this rapid adoption of models has amplified the need for explainable AI. Explaining AI helps us understand how and why models make predictions. For example, a financial institution might wish to use an AI model to automatically flag credit card transactions for fraudulent activity. While an accurate fraud model would be a first step, accuracy alone isn’t sufficient. Banks and regulators are often required to explain why an AI model is making a specific prediction. Was a fraud decision based on the transaction amount? The cardholder’s gender? Their spend history? Explainable AI helps answer these types of questions, promo…

The risks are mainly in three categories, namely “external cybersecurity”, “data exfiltration or insider threats”, and “cloud provider access to data”.

This blog post describes a set of controls to leverage when creating data products in compliance with security and regulatory requirements using Google Cloud services.

The default encryption uses AES-256 encryption standard and provides strict key access controls and auditing.

Google cloud services such as Dataflow, Dataproc and Data Fusion to build pipelines offer a level of controls using encryption and confidential VM offering.

Additionally, you can manage, monitor and govern data across data lakes and data warehouses using Dataplex (in p…

Today we’re excited to share recent advances in our “LaMDA: Language Models for Dialog Applications” project.

In this post, we’ll give an overview on how we’re making progress towards safe, grounded, and high-quality dialog applications.

LaMDA is built by fine-tuning a family of Transformer-based neural language models specialized for dialog, with up to 137B model parameters, and teaching the models to leverage external knowledge sources.

Objectives & MetricsDefining objectives and metrics is critical to guide training dialog models.

Comparing the pre-trained model (PT), fine-tuned model (LaMDA) and human-rater-generated dialogs (Human) across Sensibleness, Specificity, Interestingness, Saf…

At Google Cloud, we believe the products we bring to market should be strongly informed by our research efforts across Alphabet. For example, Vertex AI was ideated, incubated and developed based on the pioneering research from Google’s research entities. Features like Vertex AI Forecast, Explainable AI, Vertex AI Neural Architecture Search (NAS) and Vertex AI Matching Engine were born out of discoveries by Google’s researchers, internally tested and deployed, and shared with data scientists across the globe as an enterprise-ready solution, each within a matter of a few short years. Today, we’re proud to announce another deep integration between Google Cloud and Alphabet’s AI research organi…

For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.Today’s retail landscape has surfaced both exi…

In “Explaining in Style: Training a GAN to explain a classifier in StyleSpace”, presented at ICCV 2021, we propose a new approach for a visual explanation of classifiers.

Explaining a Cat vs. Dog Classifier: StylEx provides the top-K discovered disentangled attributes which explain the classification.

For perceived gender and age classifiers, below are the top four detected attributes per classifier.

In particular, these detected attributes may reveal biases in the classifier training or dataset, which is another key benefit of our method.

This is exemplified below for a retinal image classifier (DME disease) and a sick/healthy leaf classifier.

Explainable AI(XAI) helps you understand and interpret how your machine learning models make decisions. We're excited to announce that BigQuery Explainable AI is now generally available (GA). BigQuery is the data warehouse that supports explainable AI in a most comprehensive way w.r.t both XAI methodology and model types. It does this at BigQuery scale, enabling millions of explanations within seconds with a single SQL query.Why is Explainable AI so important? To demystify the inner workings of machine learning models, Explainable AI is quickly becoming an essential and growing need for businesses as they continue to invest in AI and ML. With 76% of enterprises now prioritizing artificial i…

Everyone wishes they had a crystal ball—especially retailers and consumer goods companies looking for the next big trend, or logistics companies worried about the next big storm. With a veritable universe of data now at their fingertips (or at least at their keyboards), these companies can now get closer to real-time forecasting across a range of areas when they leverage the right AI and machine learning tools.For retailers, supply chain, and consumer goods organizations, accurate demand forecasting has always been a key driver of efficient business planning, inventory management, streamlined logistics and customer satisfaction. Accurate forecasting is critical to ensure that the right prod…

We demonstrate the effectiveness of this method for multilingual neural machine translation (NMT) compared to other mixture of experts models and to models compressed using knowledge distillation.

Inference: Bypassing Distillation by Extracting SubnetworksA consequence of this difference in training between TaskMoE and models like TokenMoE is in how we approach inference.

We illustrate the process of training a TaskMoE network and then extracting per-task subnetworks for inference below.

Next StepsThe quality improvements often seen with scaling machine learning models has incentivized the research community to work toward advancing scaling technology to enable efficient training of large m…

In “Scaling Vision with Sparse Mixture of Experts”, we present V-MoE, a new vision architecture based on a sparse mixture of experts, which we then use to train the largest vision model to date.

We have also open-sourced the code to train sparse models and provided several pre-trained models.

Vision Mixture of Experts (V-MoEs)Vision Transformers (ViT) have emerged as one of the best architectures for vision tasks.

Still, compared to the largest language models, ViT models are several orders of magnitude smaller in terms of number of parameters and compute.

We thank Alex Kolesnikov, Lucas Beyer, and Xiaohua Zhai for providing continuous help and details about scaling ViT models.

text-search-{ada, babbage, curie, davinci}-{query, doc}-001 Search, context relevance, information retrieval Code search : Find relevant code with a query in natural language.

code-search-{ada, babbage}-{code, text}-001 Code search and relevanceText Similarity ModelsText similarity models provide embeddings that capture the semantic similarity of pieces of text.

To compare the similarity of two pieces of text, you simply use the dot product on the text embeddings.

2021 50.2% text-search-davinci-{doc, query}-001 52.8% Show more text-search-curie-{doc, query}-001 50.9% text-search-babbage-{doc, query}-001 50.4% text-search-ada-{doc, query}-001 49.0%Code Search ModelsCode search models provide…

It is trained to cite its sources, which makes it easier to give feedback to improve factual accuracy.

Evaluating factual accuracyIn order to provide feedback to improve factual accuracy, humans must be able to evaluate the factual accuracy of claims produced by models.

This allows humans to evaluate factual accuracy by checking whether a claim is supported by a reliable source.

What trade-off should be made between evaluations of factual accuracy and other criteria such as coherence?

Eventually, having models cite their sources will not be enough to evaluate factual accuracy.

Customizing GPT-3 can yield even better results because you can provide many more examples than what’s possible with prompt design.

Customizing GPT-3 improves the reliability of output, offering more consistent results that you can count on for production use-cases.

One customer found that customizing GPT-3 reduced the frequency of unreliable outputs from 17% to 5%.

Whether text generation, summarization, classification, or any other natural language task GPT-3 is capable of performing, customizing GPT-3 will improve performance.

By customizing GPT-3 with their data, Sana’s question and content generation went from grammatically correct but general responses to highly accurate outputs.

As part of our effort to support and develop AI talent, we're excited to announce the OpenAI Residency.

This new program offers a pathway to a full-time role at OpenAI for researchers and engineers who don't currently focus on artificial intelligence.

The Residency shifts the focus away from curriculum-based learning, instead giving Residents an opportunity to work collaboratively alongside OpenAI teams on active projects.

“The Residency aims to address that, by teaching participants the most important practical AI skills in a hands-on way as quickly as possible.

Join us for a discussion with a panel of former OpenAI Scholars and Fellows on December 8 to learn more about the program.

Since the launch of our API, we’ve made deploying applications faster and more streamlined while adding new safety features.

Starting today, developers in supported countries can sign up and start experimenting with our API right away.

Tens of thousands of developers are already taking advantage of powerful AI models through our platform.

As another step in this direction, we are also updating our Content Guidelines to clarify what kind of content our API can be used to generate.

As our safeguards continue to improve, we will expand how the API can be used while further improving the experience for our users.

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model.

However, they struggle to perform tasks that require accurate multistep reasoning, like solving grade school math word problems.

GSM8K DatasetGSM8K consists of 8.5K high quality grade school math word problems.

Training Verifiers: Models that Learn from their MistakesOne significant challenge in mathematical reasoning is the high sensitivity to individual mistakes.

Grade school math is an ideal testbed for these capabilities.

Our model works by first summarizing small sections of a book, then summarizing those summaries into a higher-level summary, and so on.

A zero-shot question-answering model can use our model’s summaries to obtain state-of-the-art on the NarrativeQA dataset for book-length question answering.

Our Approach: Combining Reinforcement Learning from Human Feedback and Recursive Task DecompositionConsider the task of summarizing a piece of text.

In the past we found that training a model with reinforcement learning from human feedback helped align model summaries with human preferences on short posts and articles.

In this case we break up summarizing a long piece of text into summarizing several sh…

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…

DeepSpeed uses a combination of data-parallel and expert-parallel training to effectively scale MoE model training and is capable of training MoE models with trillions of parameters on hundreds of GPUs.

This compute cost reduction can directly be translated into throughput gain, training time and training cost reduction by leveraging the efficient DeepSpeed MoE training system.

To reduce model size and improve parameter efficiency, we’ve made innovations in the MoE model architecture that reduce the overall model size by up to 3 times without affecting model quality.

DeepSpeed-MoE inference: Serving MoE models at unprecedented scale and speedOptimizing for MoE inference latency and cost is …

Additionally, there’s the risk that the AI vendor will use the test dataset to train the AI model, thereby “over-fitting” the model to the test dataset to show credible results.

EzPC: Easy Secure Multi-Party ComputationWe’re very interested in accelerating the AI model validation process while also ensuring dataset and model privacy.

With this test, EzPC enabled the first-ever secure validation of a production-grade AI model, proving that it’s not necessary to share data to accurately perform AI model validation.

Moreover, this technology has the potential to impact the negotiation of complex legal agreements required for the AI model validation process.

In addition to AI model validation, …

Microsoft is helping to reshape the automotive industry in the way it serves its drivers with in-vehicle infotainment systems. Together with the car manufacturers, Microsoft is creating new driving experiences with speech based on the text-to-speech and speech-to-text capabilities within Azure Cognitive Services for speech.

In June 2021, Microsoft and AT&T reached a major milestone when we announced an industry-first collaboration to adopt Microsoft cloud technology for AT&T’s 5G core network workloads. Since then, we have had requests from many operators, partners, and customers to share more details. This blog is intended to do just that.

Last month, our Azure Cognitive Services team, comprising researchers and engineers with expertise in AI, achieved a groundbreaking milestone by advancing commonsense language understanding.

With KEAR, we specifically equip language models with commonsense knowledge from a knowledge graph, dictionary, and publicly available machine learning data.

The final submission is generated by an ensemble of 39 language models, such as DeBERTa and ELECTRA, with majority voting.

In this way, the KEAR model can attend to related external knowledge for effective commonsense understanding.

These benefits can greatly facilitate the application of external attention technology to various natural language pr…

Neural Text-to-Speech (Neural TTS), a powerful speech synthesis capability of Azure Cognitive Services, enables developers to convert text to lifelike speech.

Neural TTS has now reached a significant milestone in Azure, with a new generation of Neural TTS model called Uni-TTSv4, whose quality shows no significant difference from sentence-level natural speech recordings.

Human recording Uni-TTSv4Zh-CN (Xiaoxiao) 另外,也要规避当前的地缘局势风险,等待合适的时机介入。 Human recording Uni-TTSv4It-IT (Elsa) La riunione del Consiglio di Federazione era prevista per ieri.

Human recording Uni-TTSv4Ko-KR (Sun-hi) 그는 마지막으로 이번 앨범 활동 각오를 밝히며 인터뷰를 마쳤다 Human recording Uni-TTSv4Es-ES (Alvaro) Al parecer, se trata de una operación v…

This year, Microsoft Research hosted the first-ever Microsoft Research Summit, a virtual event that embodied our aspiration to catalyze collaboration and innovation across traditional boundaries.

In January, Ashley Llorens joined Microsoft Research as VP, Distinguished Scientist, and Managing Director of Microsoft Research Outreach.

He’s helping Microsoft Research achieve its mission of amplifying the impact of research at Microsoft and advance the cause of science and technology research worldwide.

Building on work being pursued at Microsoft Research Cambridge and Microsoft Research Asia in a larger research effort at Microsoft, the Amsterdam lab will focus on molecular simulation using ML…

By Project Florence teamWith the new computer vision foundation model Florence v1.0, the Project Florence team set the new state of the art on the popular leaderboards TextCaps Challenge 2021, nocaps, Kinetics-400/Kinetics-600 action classification, and OK-VQA Leaderboard.

Last year, the Project Florence team achieved its first milestone, reaching state-of-the-art performance on the nocaps benchmark.

Today, we’re thrilled to announce another important milestone: Florence v1.0, a computer vision foundation model that successfully scales a large variety of vision and vision-language tasks.

Supported by Florence v1.0, we’ve also achieved the new state of the art on multiple popular vision and …

To this end, we’re publishing a paper at the thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) 2021, FS-Mol: A Few-Shot Learning Dataset of Molecules.

In this work, we curated a few-shot learning dataset of molecules that reflects this challenge by providing small datasets for protein-ligand binding prediction alongside a principled method for using these datasets in few-shot learning.

Applying few-shot learning to drug-discoveryFew-shot learning is a widely used concept in the computer vision and reinforcement learning communities.

Making an assessmentHow well do few-shot learning models perform in comparison to those ML models that are not given access to pretrain…

Finding and fixing bugs in code is a time-consuming, and often frustrating, part of everyday work for software developers.

Our deep learning model was able to correctly identify this bug and alert the developer.

The bug selector tries to learn to “hide” interesting bugs within each code snippet and the detector aims to beat the selector by finding and fixing them.

Through this process, the detector becomes increasingly capable of detecting and fixing bugs, while the bug selector learns to generate increasingly challenging training examples.

How can deep learning models “understand” what a snippet of code is doing?

As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. Join our digital event, Put Responsible AI into Practice, to learn more about these updates, including new guidelines for product leaders and a Responsible AI dashboard for data scientists and developers.

CLUES: Evaluating few-shot learning in NLUDespite increasing interest in data-efficient, few-shot learning with language models, no standardized evaluation benchmark exists for few-shot natural language understanding (NLU).

To help accelerate work on few-shot learning for NLU, Microsoft researchers have created CLUES, a benchmark for evaluating the few-shot learning capabilities of NLU models.

Finally, we discuss several principles and choices in designing the experimental settings for evaluating the true few-shot learning performance and suggest a unified standardized approach to few-shot learning evaluation.

Systematic human annotations: Systematic evaluation and annotation over the gener…

In addition, T-NLRv5 is more efficient than recent pretraining models, achieving comparable effectiveness with 50% fewer parameters and pretraining computing costs.

Model architecture and pretraining taskT-NLRv5 is largely based on our recent work, COCO-LM, a natural evolution of pretraining paradigm converging the benefits of ELECTRA-style models and corrective language model pretraining.

Efficiently scaling up language model pretrainingTraining billion-parameter neural models can be prohibitively expensive in both time and computing costs.

The latter is one of the most important needs when pretraining language representation models with billions of parameters.

Customers interested in usin…

AI is transforming business and the world. However, AI models learn from the data. Biases that exist in society will exist in the models. Human judgment must be the overriding factor, ensuring that AI models benefit and are inclusive of everyone. Equally important, AI must inspire trust in customers that their data is being used appropriately. These are key reasons that responsible approaches to AI are so critical, and you can learn how to put responsible AI into practice.

Our paper published in Science Advances, “Scaling DNA Data Storage with Nanoscale Electrode Wells,” introduces a proof-of-concept molecular controller in the form of a tiny DNA storage writing mechanism on a chip.

We’ve already conducted research into a number of areas of DNA data storage, including an end-to-end data storage system capable of random access and viable methods for preservation of DNA.

We are always interested in learning about new chemistries and other technology that could make DNA data storage more sustainable.

If you’d like to learn more about the DNA storage work happening at Microsoft Research, check out the DNA Storage project page.

To learn more about the emerging ind…

Their goal is to develop machine learning algorithms that can intake this tremendous amount of data, and make it meaningful — identifying when an individual may be struggling and what might be helpful to them.

While early research focused on determining if machine learning could use data to identify a participant’s current emotion, Picard and Pedrelli’s current work at MIT’s Jameel Clinic goes several steps further.

They want to know if machine learning can estimate disorder trajectory, identify changes in an individual’s behavior, and provide data that informs personalized medical care.

Machine-learning algorithms may be able to make sense of these data, mapping them onto the individual’s …

In a letter to the MIT community today, Vice President for Open Learning Sanjay Sarma announced the appointment of Professor Cynthia Breazeal as dean for digital learning, effective Feb. 1.

As dean, she will supervise numerous business units and research initiatives centered on developing and deploying digital technologies for learning.

These include MIT xPRO, Bootcamps, Horizon, the Center for Advanced Virtuality, MIT Integrated Learning Initiative, RAISE, and other strategic initiatives.

Breazeal has served as senior associate dean for open learning since the fall.

She will also lead research efforts at MIT Open Learning into teaching, learning, and how new technologies can enhance both, …

Annaswamy serves as the director of MIT’s Active Adaptive Control Laboratory.

A world-leading expert in adaptive control theory, she was named president of the Institute of Electrical and Electronics Engineers Control Systems Society for 2020.

In a recent interview, Annaswamy spoke about how these smart systems could help support a safer and more sustainable future.

Q: How is your team using adaptive control to make air travel safer?

Q: What other ways are you using intelligent systems to promote sustainability?

There are few environments as unforgiving as the ocean.

“At the same time, the ocean holds countless opportunities — from aquaculture to energy harvesting and exploring the many ocean creatures we haven’t discovered yet.”Ocean engineers and mechanical engineers, like van Rees, are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities.

This allows them to propel themselves in many different ways, well beyond what any man-made vehicle can do in terms of maneuverability, agility, or adaptivity,” explains van Rees.

While researchers like Benjamin and van Rees use machine learning and multi-objective optimization to address the complexity of v…

Out of this came Tempo, a technology for creating risk-based screening guidelines.

At MGH, it recommended roughly a mammogram a year, and obtained a simulated early detection benefit of roughly four-and-a-half months better.

This model then estimates patient risk at unobserved time points, and it enables simulation of the risk-based screening policies.

The model is dynamically changing the patient’s screening frequency, based on how the risk profile is changing.

Tempo uses a simple metric for early detection, which assumes that cancer can be caught up to 18 months in advance.

It has been hypothesized that X (3872) and other exotic particles might be better illuminated in quark-gluon plasma.

“So we had to beat down this background so that we could eventually see the X particles in our data.”To do this, the team used a machine-learning algorithm which they trained to pick out decay patterns characteristic of X particles.

Immediately after particles form in quark-gluon plasma, they quickly break down into “daughter” particles that scatter away.

For X particles, this decay pattern, or angular distribution, is distinct from all other particles.

Now that the team has shown X particles can be detected in quark-gluon plasma, they plan to probe this particle with quark-g…

Using the AI system can help her make faster diagnoses, but how does she know when to trust the AI’s predictions?

By showing people how the AI complements their abilities, the training technique could help humans make better decisions or come to conclusions faster when working with AI agents.

The user then has to answer the question and can click a button to “let the AI answer.” The user can't see the AI answer in advance, however, requiring them to rely on their mental model of the AI.

The human can answer on her own or let the AI system answer.

And those who didn’t receive teaching but could see the AI answers were right on 57 percent of the questions.

Then they use this modified dataset to evaluate whether feature-attribution methods can correctly identify those important features.

But these feature-attribution methods could be wrong in the first place.

Then we can see if all these feature-attribution methods rush to highlight that location rather than everything else,” Zhou says.

“Especially alarming” resultsThey applied this technique to a number of different feature-attribution methods.

The team’s work shows that it is critical to test feature-attribution methods before applying them to a real-world model, especially in high-stakes situations.

Investigating interactionsThis work grew out of an earlier study where the researchers explored how people use voice-user interfaces at home.

At the start of the study, users familiarized themselves with three devices before taking one home for a month.

The researchers noticed that people spent more time interacting with a Jibo social robot than they did the smart speakers, Amazon Alexa and Google Home.

They changed the “wake word” (the word the user says aloud to engage the device) of the Amazon Echo to “Hey, Amazon!” instead of “Hey, Alexa!,” but kept the “wake word” the same for the Google Home (“Hey, Google!”) and the Jibo robot (“Hey, Jibo!”).

“It also drastically changed how much peop…

I am also really inspired by the work of Louis Stewart, an amazing jazz guitarist who was born and raised in Dublin.

Learning about this unique intersection combining music and technology, I began to think about bigger questions, like, “What kind of creative future can technology create?

Technology has this incredible potential to make anyone a creator — I’d like to build the tools to make it happen.

Most importantly, though, I think music and jazz have taught me patience and discipline, and that mastery of a skill takes a lifetime.

Q: You’ve focused in on music and arts education, and the potential of technology to bolster both.

Scientists, students, and community members came together last month to discuss the promise and pitfalls of artificial intelligence at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for the fourth TEDxMIT event held at MIT.

But these intriguing possibilities should only be pursued if we can simultaneously resolve the challenges that these technologies bring,” said Rus.

The next step, Kellis said, is to harness insights about evolution in order to combat an individual’s genetic susceptibility to disease.

But in order for that to happen, its models must be trained on accurate, diverse, and unbiased medical data.

And if a human makes a mistake, and we train an AI from th…

Machine-learning algorithms are often referred to as a “black box.” Once data are put into an algorithm, it’s not always known exactly how the algorithm arrives at its prediction.

A new mechanical engineering (MechE) course at MIT teaches students how to tackle the “black box” problem, through a combination of data science and physics-based engineering.

“I wanted to take 2.C161 because machine-learning models are usually a “black box,” but this class taught us how to construct a system model that is informed by physics so we can peek inside,” explains Crystal Owens, a mechanical engineering graduate student who took the course in spring 2021.

The two classes are taught concurrently during t…

This year’s Accenture Fellows work across disciplines including robotics, manufacturing, artificial intelligence, and biomedicine.

Xinming (Lily) Liu is a PhD student in operations research at MIT Sloan School of Management.

Soumya Sudhakar SM '20 is a PhD student in aeronautics and astronautics.

Her contributions bring together the emerging robotics industry, integrated circuits industry, aerospace industry, and consumer electronics industry.

Yang earned her BS in electrical and computer engineering from Seoul National University in South Korea and her MS in electrical engineering from Caltech.

While large-scale RL agents can achieve stunning results, even the best RL agents today are narrow.

Unsupervised RL as a path forwardTo date, the most promising path toward generalist AI systems in language and vision has been through unsupervised pre-training.

The unsupervised RL frameworkUnsupervised RL is very similar to supervised RL.

The Unsupervised Reinforcement Learning Benchmark (URLB)While a variety of unsupervised RL algorithms have been proposed over the last few years, it has been impossible to compare them fairly due to differences in evaluation, environments, and optimization.

Paper: URLB: Unsupervised Reinforcement Learning Benchmark Michael Laskin*, Denis Yarats*, Hao Liu, …

Sequence Modeling Solutionsfor Reinforcement Learning ProblemsSequence Modeling Solutions for Reinforcement Learning ProblemsLong-horizon predictions of (top) the Trajectory Transformer compared to those of (bottom) a single-step dynamics model.

While it has been possible to apply reinforcement learning algorithms to large-scale problems, generally there has been much more friction in doing so.

In this post, we explore whether we can alleviate these difficulties by tackling the reinforcement learning problem with the toolbox of sequence modeling.

Though there is value in studying the most streamlined approaches that can tackle reinforcement learning problems, it is possible that the most ef…

Which Mutual Information Representation Learning Objectives are Sufficient for Control?

To simplify the analysis, we analyze representation learning in isolation from the other aspects of RL by assuming the existence of an offline dataset on which to perform representation learning.

An objective may have more than one maximizing representation, so we call a representation learning objective sufficient if all the representations that maximize that objective are sufficient.

To separate representation learning from RL, we first optimize each representation learning objective on a dataset of offline data, (similar to the protocol in Stooke et al.

This post is based on the paper Which Mutual Inf…

Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain DatasetsFig.

For comparison, we include the performance of the policy when trained only on the target domain data, without bridge data (Target Domain Only), a baseline that uses only the bridge data without any target domain data (Direct Transfer), as well as a baseline that trains a single-task policy on data in the target domain only (Single Task).

However, we do include the “direct transfer” baseline, which utilizes a policy trained only on the bridge data.

Figure 10: Example rollouts of policies jointly trained on target domain data and bridge data in each of the three transfer scenarios.

This means that bridge…

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Which Mutual Information Representation Learning Objectives are Sufficient for Control?

To simplify the analysis, we analyze representation learning in isolation from the other aspects of RL by assuming the existence of an offline dataset on which to perform representation learning.

An objective may have more than one maximizing representation, so we call a representation learning objective sufficient if all the representations that maximize that objective are sufficient.

To separate representation learning from RL, we first optimize each representation learning objective on a dataset of offline data, (similar to the protocol in Stooke et al.

This post is based on the paper Which Mutual Inf…

How should we compare neural network representations?

To understand neural networks, researchers often use similarity metrics to measure how similar or different two neural networks are to each other.

Thus, for a given pair of neural network representations, we measure both their (dis)similarity and the difference between their accuracies on some task.

“Insights on representational similarity in neural networks with canonical correlation.” Proceedings of the 32nd International Conference on Neural Information Processing Systems.

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020.

Epistemic POMDPs and Implicit Partial ObservabilityActively steering towards regions of low uncertainty or taking information-gathering actions are two of a multitude of avenues an RL agent has to handle its epistemic uncertainty.

What makes the epistemic POMDP particularly exciting is the following equivalence:An RL agent is Bayes-optimal for generalization if and only if it maximizes expected return in the corresponding epistemic POMDP.

LEEP, an algorithm based on the epistemic POMDP objective, generalizes better than PPO in four Procgen tasks.

This is surprisingly not true; limited training data in RL introduces implicit partial observability into an otherwise fully-observable problem.

T…

RECON: Learning to Explore the Real World with a Ground RobotAn example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset).

To build a robot capable of exploring and navigating like this, we need to learn from diverse prior datasets in the real world.

Our method learns both components using datasets or real-world robot interactions gathered in prior work.

In a new environment, RECON encourages the robot to explore at the frontier of the map – while the robot is not at the frontier, RECON directs it to navigate to a previously seen subgoal at the frontier of the map.

This dataset presents an exciting benchmark f…

RECON: Learning to Explore the Real World with a Ground RobotAn example of our method deployed on a Clearpath Jackal ground robot (left) exploring a suburban environment to find a visual target (inset).

To build a robot capable of exploring and navigating like this, we need to learn from diverse prior datasets in the real world.

Our method learns both components using datasets or real-world robot interactions gathered in prior work.

In a new environment, RECON encourages the robot to explore at the frontier of the map – while the robot is not at the frontier, RECON directs it to navigate to a previously seen subgoal at the frontier of the map.

This dataset presents an exciting benchmark f…

Epistemic POMDPs and Implicit Partial ObservabilityActively steering towards regions of low uncertainty or taking information-gathering actions are two of a multitude of avenues an RL agent has to handle its epistemic uncertainty.

What makes the epistemic POMDP particularly exciting is the following equivalence:An RL agent is Bayes-optimal for generalization if and only if it maximizes expected return in the corresponding epistemic POMDP.

LEEP, an algorithm based on the epistemic POMDP objective, generalizes better than PPO in four Procgen tasks.

This is surprisingly not true; limited training data in RL introduces implicit partial observability into an otherwise fully-observable problem.

T…

Designs from Data: Offline Black-Box Optimization via Conservative TrainingFigure 1: Offline Model-Based Optimization (MBO): The goal of offline MBO is to optimize an unknown objective function $f(x)$ with respect to $x$, provided access to only as static, previously-collected dataset of designs.

Conventionally, such problems have been tackled with black-box optimization procedures that repeatedly query an objective function.

We call this offline model-based optimization (offline MBO), and in this post, we discuss offline MBO methods and some recent advances.

Offline Model-Based Optimization (Offline MBO)Formally, the goal in offline model-based optimization is to maximize a black-box objec…

Designs from Data: Offline Black-Box Optimization via Conservative TrainingFigure 1: Offline Model-Based Optimization (MBO): The goal of offline MBO is to optimize an unknown objective function $f(x)$ with respect to $x$, provided access to only as static, previously-collected dataset of designs.

Conventionally, such problems have been tackled with black-box optimization procedures that repeatedly query an objective function.

We call this offline model-based optimization (offline MBO), and in this post, we discuss offline MBO methods and some recent advances.

Offline Model-Based Optimization (Offline MBO)Formally, the goal in offline model-based optimization is to maximize a black-box objec…

Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size.

It turns out this is possible: in our recent paper, we derive a first-principles theory that allows one to make accurate predictions of neural network generalization (at least in certain settings).

Finite network $\approx$ infinite-width network $=$ kernel regressionA major vein of deep learning theory in the last few years has studied neural networks of infinite width.

Trusting that this first approximation will bear weight, our challenge now is to understand kernel regression.

It also applies to linear regression, a special case of kernel regressi…

Measures of generalization performance for neural networks trained on four different boolean functions (colors) with varying training set size.

It turns out this is possible: in our recent paper, we derive a first-principles theory that allows one to make accurate predictions of neural network generalization (at least in certain settings).

Finite network $\approx$ infinite-width network $=$ kernel regressionA major vein of deep learning theory in the last few years has studied neural networks of infinite width.

Trusting that this first approximation will bear weight, our challenge now is to understand kernel regression.

It also applies to linear regression, a special case of kernel regressi…

SageMaker Processing job – We can run this data processing job on any ML machine, and it runs our notebook with parameters.

With the SageMaker notebook instance lifecycle, we can control the maximum notebook instance runtime, using the autostop.py template script.

To implement this solution at scale, we tested most of the SageMaker endpoint solutions in our anomaly-detection research.

We measured the response time, CPU, memory, and other metrics (for more information, see Monitor Amazon SageMaker with Amazon CloudWatch).

set -e # OVERVIEW # This script installs the sagemaker_run_notebook extension package in SageMaker Notebook Instance # # There are two parameters you need to set: # 1.

Solution OverviewThe solution consists of two components:An Amazon Rekognition Custom Labels model — To enable Amazon Rekognition to detect mitotic figures, we complete the following steps: Sample the WSI dataset to produce adequately sized images using Amazon SageMaker Studio and a Python code running on a Jupyter notebook.

Train a Amazon Rekognition Custom Labels model to recognize mitotic figures in hematoxylin-eosin samples using the data prepared in the previous step.

After onboarding to Studio, follow these instructions to grant Studio the necessary permissions to call Amazon Rekognition on your behalf.

Amazon Rekognition Custom Labels modelBefore you shut down your Studio notebook, m…

Based on what we want to scale (model or data) there are two approaches to distributed training: data parallel and model parallel.

PrerequisitesTo perform distributed training of Hugging Face Transformers models in SageMaker, you need to complete the following prerequisites:Implement distributed trainingThe Hugging Face Transformers library provides a Trainer API that is optimized to train or fine-tune the models the library provides.

Train a model using SageMaker Hugging Face EstimatorsAn Estimator is a high-level interface for SageMaker training and handles end-to-end SageMaker training and deployment tasks.

You can use git_config to run the Hugging Face Transformers examples scripts and …

As a proactive measure against model degradation, you can use Amazon SageMaker Model Monitor to continuously monitor the quality of your ML models in real time.

Model bias –Model Monitor is integrated with Amazon SageMaker Clarify to improve visibility into potential bias.

We also present an approach to detecting data drift in text data using Model Monitor.

The following diagram shows how we use SageMaker Model Monitoring to establish baseline and detect data drift using cosine distance similarity.

In this post, we highlighted the challenges with monitoring data drift on unstructured data like text, and provided an intuitive approach to detect data drift using a custom monitoring script.

This post demonstrates how to set up a computer vision-based anomaly detection solution for failing product carriers (or similar manufacturing line assembly) using AWS Panorama and Lookout for Vision.

The object detection model crops the image and passes the result to the Lookout for Vision anomaly detection model that classifies the pin images.

Training a Lookout for Vision model follows a four-step process:Create a Lookout for Vision project.

Train and tune the Lookout for Vision modelTraining an anomaly detection model in Lookout for Vision is as simple as a click of a button.

ConclusionIn this post, we demonstrated how to set up a vision-based anomaly detection system in a production en…

The Amazon Machine Learning Solutions Lab (MLSL) recently created a tool for annotating text with named-entity recognition (NER) and relationship labels using Amazon SageMaker Ground Truth.

Labeling RequirementsAlthough Ground Truth provides a built-in NER text annotation capability, it doesn’t provide the ability to link entities together.

: { id: string; start: number; end: number; text: string; label: string; labelCategory?

Ground Truth can be configured to build a data labeling job using the new NER tool as a custom template.

Input ManifestThe Ground Truth input data manifest is a JSON-lines file where each line contains a single worker task.

For offline applications, you can use SageMaker batch transform jobs.

Because TorchServe is natively integrated with SageMaker via the SageMaker PyTorch inference toolkit, you can easily deploy a PyTorch model onto TorchServe using SageMaker Hosting.

With TorchServe integrations with SageMaker, you can now deploy PyTorch models natively on SageMaker, where you can define a SageMaker PyTorch model.

SageMaker batch transform jobFor offline use cases where requests are batched from a data source such as a dataset, SageMaker provides batch transform jobs.

A full example of batch inference using batch transform jobs can be found in the following notebook, where we use a machine translation model…

When framed as a link prediction problem, the task is to assign a score to any possible link between the two nodes.

By learning link structures already present in the graph, a link prediction model can generalize new link predictions that ‘complete’ the graph.

Neptune ML supports common graph prediction tasks, such as node classification and regression, edge classification and regression, and link prediction.

Neptune ML uses DGL to automatically choose and train the best ML model for your workload.

Therefore, the new graph data must be processed using the same feature encodings, and it must adhere to the same graph schema as the original graph data.

This post shows how to implement this use case while keeping AWS SSO capabilities to access Studio.

The next section shows how to address these challenges and implement IAM-based access control with AWS SSO access to Studio.

Architecture overviewStudio is published as a SAML application, which is assigned to a specific SageMaker Studio user profile.

Choose the SageMaker Secure Demo AWS SSO application from the AWS SSO portal.

Choose the SageMaker Secure Demo AWS SSO application from the AWS SSO portal.

AWS Panorama removes these requirements and enables Tyson to process video streams at the edge on the AWS Panorama Appliance.

Upon application deployment, AWS Panorama first creates an AWS SageMaker Neo Compilation job to compile the model for the AWS Panorama device.

The inference results are captured in AWS SiteWise Monitor through MQTT messages from the AWS Panorama device via AWS IoT core.

ConclusionBy combining AWS Cloud service like Amazon SageMaker, Amazon S3 and edge service like AWS Panorama, Tyson Foods Inc., is infusing artificial intelligence to automate human-intensive industrial processes like inventory counting in its manufacturing plants.

Model: Model training and evaluation…

We will go into detail about how we addressed our problems using ML models and used Amazon SageMaker along the way.

Automation of the Review Image Inspection ProcessThe first step toward automating the Image Review Inspection process was to manually label images, thereby matching them to the appropriate categories and inspection criteria.

It supports Amazon SageMaker, so it easily migrates the code developed with SageMaker Studio to Apache Airflow.

However, we went through some trial and error, as it was our first time integrating Apache Airflow with Amazon SageMaker.

We believe that Amazon SageMaker is ideal for businesses requiring rapid service developments, as in the case of the MUSINSA…

In 2021, ReliaQuest turned to AWS to help it enhance its artificial intelligence (AI) capabilities and build new features faster.

Using Amazon SageMaker, Amazon Elastic Container Registry (ECR), and AWS Step Functions, ReliaQuest reduced the time needed to deploy and test critical new AI capabilities for its GreyMatter platform from eighteen months to two weeks.

To solve this, ReliaQuest’s Data Scientist, Mattie Langford, and ML Ops Engineer, Riley Rohloff, turned to Amazon SageMaker.

ReliaQuest chose Amazon ECR because of its native integration with Amazon SageMaker.

Learn more about how you can accelerate your ability to innovate with AI by visiting Getting Started with Amazon SageMaker o…

Inside the workflow, we use a Lambda function and a Wait State until the Amazon Rekognition Video asynchronous analysis started earlier finishes execution.

Afterward, another Lambda function retrieves the result of the completed process from Amazon Rekognition and passes it to another Lambda function that uses OpenCV to blur the detected faces.

Amazon Rekognition Video supports MPEG-4 and MOV file formats, encoded using the H.264 codec.

The Lambda function uses the video’s attributes (name and location on Amazon S3) to start the face detection job on Amazon Rekognition through an API call.

CMD ["app.lambda_function"]If you want to build your own application using Amazon Rekognition face det…

Thousands of Wix customer care experts support tens of thousands of calls a day in various languages from countries around the world.

For example, you can provide Amazon Transcribe with industry-specific terms or acronyms that it might not otherwise recognize.

Sentiment analysis is just one example of the many use cases that we can achieve with Amazon Transcribe.

In the future, we plan to use Amazon Transcribe to understand not just how users feel, but what topics they’re talking about.

Mykhailo Ulianchenko is an Engineering Manager at Customer Care, Wix.

The NVIDIA NGC catalog is a hub for GPU-optimized deep learning, machine learning, and HPC applications.

Container security scan reportsAll the container images in the NGC catalog are scanned for CVEs, malware, crypto keys, open ports, and more.

The scan reports are available on the latest as well as the previous versions of the images and with the entire NGC catalog scanned every 30 days.

HPC applicationsLatest versions of the popular HPC applications are also available in the NGC catalog.

Visit the NGC catalog to see how the GPU-optimized software can help simplify workflows and speedup solution times.

Fisheries collect millions upon millions of fish eggs, protecting them from predators to increase fish yield and support the propagation of endangered species — but an issue with gathering so many eggs at once is that those infected with parasites can put healthy ones at risk.

Jensorter, an Oregon-based startup, has created AI-powered fish egg sorters that can rapidly identify healthy versus unhealthy eggs.

The machines, built on the NVIDIA Jetson Nano module, can also detect egg characteristics such as size and fertility status.

Using AI, Jensorter machines look at characteristics like color to discern an egg’s health status and determine whether it’s fertilized — at a speed of about 30 mi…

The Regeneron team used NVIDIA Clara Parabricks, a software suite for secondary genomic analysis of next-generation sequencing data, during the exome sequencing process.

It was developed by bioinformatics platform DNAnexus, which lets scientists use Clara Parabricks running on NVIDIA GPUs in the AWS cloud.

This capability will allow scientists to harmonize their own exome data with sequenced exome data from UK Biobank by running the same bioinformatics pipeline used to generate the initial reference dataset.

Get started with NVIDIA Clara Parabricks on the DNAnexus-developed UK Biobank Research Analysis Platform.

Main image shows the freezer facility at UK Biobank where participant samples a…

After your team has identified a business problem to solve using ML, you can select from NVIDIA collection of pretrained AI models in computer vision and conversational AI.

To speed up the data collection and augmentation process, you can now use the Appen Data Annotation Platform to create the right training data for your use case.

The Appen Data Annotation Platform (ADAP) works with a diverse set of formats:Audio (.wav, .mp3)Image (.jpeg, .png)Text (.txt)Video (URL)When you’re done with the data collection phase, unless you plan to work with Appen for your data collection needs, you can use Appen’s platform to quickly label the data you’ve collected.

You need an Appen platform license and…

Editor’s note: This post is a part of our Meet the Omnivore series, which features individual creators and developers who use NVIDIA Omniverse to boost their artistic or engineering processes.

Based in north-central Nigeria, Dazhi is building a team for his indie animation studio, JUST ART, which creates animation films focused on action, sci-fi, horror and humor.

Music, Movies and Masterful RenderingFrom animated music videos to clips for action films, Dazhi has a multitude of projects — and accompanying deadlines.

Using Omniverse, Dazhi accomplishes lighting and materials setup, rendering, simulation and post-production processes.

Creators can download NVIDIA Omniverse for free and get st…

If you use Vulkan, NVIDIA GPUs are a no-brainer.

NVIDIA designs hardware to provide the fastest Vulkan performance for your games and applications.

NVIDIA provides the broadest range of Vulkan functionality to ensure you can run the games and apps that you want and need.

NVIDIA works hard to be the platform of choice for Vulkan development with tools that are often the first to support the latest Vulkan functionality, encouraging apps and games to be optimized first for NVIDIA.

Look for more details about our commitment and leadership in Vulkan on NVIDIA’s Vulkan web page.

The AI Research SuperCluster (RSC), announced today, is already training new models to advance AI.

Under the HoodThe new AI supercomputer currently uses 760 NVIDIA DGX A100 systems as its compute nodes.

In 2017, Meta built the first generation of this infrastructure for AI research with 22,000 NVIDIA V100 Tensor Core GPUs that handles 35,000 AI training jobs a day.

NVIDIA DGX, which includes a full stack of NVIDIA AI software, scales easily from a single system to a DGX SuperPOD running on-premises or at a colocation provider.

Customers can also rent DGX systems through NVIDIA DGX Foundry.

Let’s face it, the global supply chain may not be the most scintillating subject matter.

The $9 trillion logistics industry is responding by investing in automation and using AI and big data to gain more insights throughout the supply chain.

At Manifest 2022, a logistics and supply chain conference taking place in Las Vegas, the industry is discussing how to refine supply chains and create cost efficiencies using AI and machine learning.

Leaning InWhile manufacturers, supply chain operators and retailers each will have their own approaches to solving challenges, they’re leaning in on AI as a key differentiator.

For more on how NVIDIA AI is powering the most innovative AI solutions for the s…

A specific implementation is called a hash, hashing function, or hash function.

Our hash function mapped “this” to the 0-indexed column, “is” to the 1-indexed column and “the” to the 3-indexed columns.

Advantages of vocabulary based hashingUsing the vocabulary as a hash function allows us to invert the hash.

Mathematical hashingFortunately, there is an alternative way of hashing tokens: hash each instance with a non-cryptographic mathematical hash function.

This parallelization, which is enabled by the use of a mathematical hash function, can dramatically speed up the training pipeline by removing bottlenecks.

Today, there are three types of edge architectures that are commonly being used by organizations: streaming data, edge preprocessing, and autonomous systems.

Edge Architecture 1: Streaming dataImage 1: The streaming data architecture collects data at the edge and processes it in the cloud.

Edge Architecture 2: Edge Pre-ProcessingImage 2: Edge-preprocessing models are considered to be a hybrid edge and cloud model.

Instead of sensor data feeding directly into a pipeline running in the data center, data is fed into an intelligent data reduction application.

Industry Insights for Building the Intelligent EdgeBuilding an intelligent edge solution is not just about pushing a container to tens or…

Every living cell contains its own bustling microcosm, with thousands of components responsible for energy production, protein building, gene transcription and more.

Published in the journal Cell, the project simulates a living minimal cell, which contains a pared-down set of genes essential for the cell’s survival, function and replication.

The model uses NVIDIA GPUs to simulate 7,000 genetic information processes over a 20-minute span of the cell cycle – making it what the scientists believe is the longest, most complex cell simulation to date.

Minimal Cell With Maximum RealismTo build the living cell model, the Illinois researchers simulated one of the simplest living cells, a parasitic …

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…

We recently built a new system, Capacity Recommendation Engine (CRE), with a new algorithm that relies on throughput and utilization based scaling with machine learning modeling.

Take throughput estimation for weekly scaling as an example: the target throughput RPS Target should be the estimation of the next week’s peak traffic.

Define Target UtilizationTarget utilization (Utilization Target ) is the one of the signals required to deduce the capacity number in CRE.

Linear Regression: Normalized Throughput and UtilizationFor utilization-bound service, utilization, throughput, capacity, service, and hardware performance are common factors, which influence each other.

ConclusionIn this article…

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 …

In this article, we’ll look into some of the best frameworks and tools for distributed training.

Distributed training is of two types:1 Data-parallel trainingData-parallel training 2 Model-parallel trainingDistributed training model parallelism vs data parallelism | SourceIn data-parallel training, the data is divided into subsets based upon the number of nodes available for training.

Frameworks for distributed trainingNow, let’s discuss some of the libraries that offer distributed training.

Related informationCloud platforms for distributed trainingSo far, we have discussed the frameworks and the libraries that can be used to enable distributed training.

Distributed training: Google Cloud …

There are a number of tools available in the market to help professionals in tracking model experiments and their management.

Amazon SageMaker Experiments and Model Monitoring are two capabilities that are integrated with Amazon SageMaker Studio.

Amazon SageMaker Model RegistryAmazon SageMaker Model Registry helps to catalog different versions of a model.

AWS SageMaker: model groups and registry | SourceWhen is AWS Sagemaker not the best choice?

MLflow: registering model and model stage transitioning | SourceMLflow: registering model and model stage transitioning | SourceRead more 👉 Best Alternatives to MLflow Model Registry6.

Precisely, in distributed training, we divide our training workload across multiple processors while training a huge deep learning model.

Distributed training explanation: data parallelism | SourceLet’s see the two ways of carrying out distributed training loops and the difference between them.

Distributed training explained: centralized synchronous systems | Source: AuthorWhy do we need distributed training?

But is distributed training better in every case, even when we have simpler models with smaller training data?

RPC-Based Distributed Training : RPC allows general training topologies that aren’t suitable for data-parallel training, such as distributed pipeline parallelism, the paramete…

As you (probably) know, developing and deploying traditional software applications is quite different from building and deploying machine learning (ML) applications in a number of ways.

The questions then become:How would ML teams adopt existing CI/CD tools to suit their machine learning use cases?

Continuous Integration and Delivery (CI/CD) for Machine Learning (ML) with Azure DevOpsIn this section, we walk through the workflow of a team that orchestrates CI/CD processes for their machine learning workloads in Azure DevOps.

Core CI/CD toolsOverviewThe team’s CI/CD workflow and deployments are infrastructure as code (IaC) and versioned along with the code.

ConclusionOne interesting point th…

The most popular boosting algorithms: Catboost, XGBoost, LightGBM | Source: AuthorThe three algorithms in scope (CatBoost, XGBoost, and LightGBM) are all variants of gradient boosting algorithms.

Categorical features | Source: AuthorCategorical features can be more complex in high cardinality features like ‘id‘ features.

CatBoost vs XGBoost and LightGBM: hands-on comparison of performance and speedThe previous sections covered some of CatBoost’s features that will serve as potent criteria in choosing CatBoost over LightGBM and XGBoost.

CatBoost vs XGBoost vs LightGBM: tuned hyperparametersFollowing are the tuned hyperparameters that we will be using in this run.

LightBGM XGBoost CatBoost Pa…

Overview of the three methods: ARIMA, Prophet and LSTMARIMAARIMA is a class of time series prediction models, and the name is an abbreviation for AutoRegressive Integrated Moving Average.

The backbone of ARIMA is a mathematical model that represents the time series values using its past values.

ProphetProphet FB was developed by Facebook as an algorithm for the in-house prediction of time series values for different business applications.

The colored dots in Figure 11 show the mean square error values for different ARIMA parameters over a validation set.

This shows that the LSTM model is too advanced for a rather small dataset and is prone to overfitting.

As a result, the AI community believes that model-centric machine learning is more promising.

Let’s now talk about how a data-centric approach differs from a model-centric approach and the need for it in the first place.

Data-centric approach vs model-centric approachTo data scientists and machine learning engineers, the model-centric approach may seem more pleasant.

However, in today’s machine learning, data is crucial, yet it’s often overlooked and mishandled in AI initiatives.

ConclusionIn this article, we learned how a data-centric approach differs from a model-centric approach, and how to make your machine learning application more data-centric.

Deploying machine learning models is hard!

They deployed a standalone ad predictor endpoint on an external service that would score data from the data pipeline and perform serverless inference.

Auto-scaling both the data pipeline and the prediction service to compensate for the high traffic from the pipeline.

ConclusionIn this article, we learned that model deployment challenges faced by ML Engineers and data teams go beyond putting models into production.

Hopefully, one or more of these cases are useful for you as you also look to address challenges in deploying ML models in your organization.

Recommended for you Best 8 Machine Learning Model Deployment ToolsIntegration in DockerCloud providers and physical servers may be provisioned using Docker Cloud to construct Docker nodes.

Best practices to use Docker for Machine Learning (ML)Docker explained | Source1.

Using Docker, it is possible to replicate the working environment that is needed to train and operate the machine learning model on any system.

docker run -e NEPTUNE_API_TOKEN= "" After you run the docker container you will get a link on the terminal.

Here are some references that can help you better understand Docker best practices and how a Machine Learning practitioner can benefit from this.

Oversampling before cross-validationThis is a very common mistake that I have observed when doing cross-validation when working with classification problems.

Knowledge leakIt is a major problem in machine learning and this is common when doing cross-validation.

With such models, it is unlikely for you to get an accurate estimation of model performance using the normal cross-validation.

To avoid any surprises when testing your model with your hold-out test dataset, you should do cross-validation on multiple seeds and average the model performance.

This would enable you to get a far better understanding of your model performance.

Time series data examples: a dataset with dependent observations | Source: AuthorIn the next part of this article, you will discover the specifics of time series data in more detail.

Multivariate Time Series models are the Univariate Time Series models that are adapted to integrate external variables.

Time series model selectionIn the previous part of this article, you have seen a large number of time series models, divided into classical time series models, supervised machine learning models, and recent developments including LSTMs, Prophet, and DeepAR.

Time series model experimentsIn conclusion, when doing time series model selection, the following questions are key to define before start…

Recall that the four pillars of MLOps include:1 Production model deploymentProduction model deployment 2 Production model monitoringProduction model monitoring 3 Model lifecycle managementModel lifecycle management 4 Production model governanceNow let’s learn how a model registry component in your MLOps workflow can enable the deployment, management, and governance pillars.

Model registry in MLOps level 0If you are at level 0 implementation of MLOps, your workflow with a model registry could look like this:MLOps level 0 workflow with a model registry | Source (modified)The output from the experimentation step is fed into the model registry.

MLflow model registry dashboard | SourceThe MLflow…

It does this by learning the original distribution from the real data and then evaluating between the two.

Loss function and trainingThe loss function is one of the most important components in any deep learning algorithm.

See also 🔎 Understanding GAN Loss FunctionsThe above point is an important one that we must keep in mind.

In the following section, we will understand some of the key components of the same like the architecture, loss function et cetera.

Pix2Pix is a conditional GAN | Source: AuthorApplication of Pix2Pix | SourceThe idea with Pix2Pix relies on the dataset provided for the training.

LightGBM parametersHere are the most important LightGBM parameters:max_depth – Similar to XGBoost, this parameter instructs the trees to not grow beyond the specified depth.

Comparing XGoost’s model performance against LightGBM’s model performance | DashboardFrom the above chart, we see a very surprising result.

Also if the hardware is available, since XGBoost scales better, as discussed before we could train using LightGBM, get an understanding of the parameters required, and train the final model as an XGBoost model.

XGBoost and LightGBM which are based on GBDTs have had great success both in enterprise applications and data science competitions.

Both the algorithms perform similarly in t…

#mlnews #convnext #mt3 Your update on what's new in the Machine Learning world! OUTLINE:

0:00 - Intro

0:15 - ConvNeXt: Return of the Convolutions

2:50 - Investigating Saliency Cropping Algorithms

9:40 - YourTTS: SOTA zero-shot Text-to-Speech

10:40 - MT3: Multi-Track Music Transcription

11:35 - China regulates addictive algorithms

13:00 - A collection of Deep Learning interview questions & solutions

13:35 - Helpful Things

16:05 - AlphaZero explained blog post

16:45 - Ru-DOLPH: HyperModal Text-to-Image-to-Text model

17:45 - Google AI 2021 Review References:

ConvNeXt: Return of the Convolutions

https://arxiv.org/abs/2201.03545

https://github.com/facebookresearch/ConvNeXt

https://twitter.com/gi…

#deeplearning #neuralinterpreter #ai This video includes an interview with the paper's authors!

What if we treated deep networks like modular programs? Neural Interpreters divide computation into small modules and route data to them via a dynamic type inference system. The resulting model combines recurrent elements, weight sharing, attention, and more to tackle both abstract reasoning, as well as computer vision tasks. OUTLINE:

0:00 - Intro & Overview

3:00 - Model Overview

7:00 - Interpreter weights and function code

9:40 - Routing data to functions via neural type inference

14:55 - ModLin layers

18:25 - Experiments

21:35 - Interview Start

24:50 - General Model Structure

30:10 - Function c…

#deeplearning #noether #symmetries This video includes an interview with first author Ferran Alet!

Encoding inductive biases has been a long established methods to provide deep networks with the ability to learn from less data. Especially useful are encodings of symmetry properties of the data, such as the convolution's translation invariance. But such symmetries are often hard to program explicitly, and can only be encoded exactly when done in a direct fashion. Noether Networks use Noether's theorem connecting symmetries to conserved quantities and are able to dynamically and approximately enforce symmetry properties upon deep neural networks. OUTLINE:

0:00 - Intro & Overview

18:10 - Inter…

#minerl #minecraft #deeplearning The MineRL BASALT challenge has no reward functions or technical descriptions of what's to be achieved. Instead, the goal of each task is given as a short natural language string, and the agent is evaluated by a team of human judges who rate both how well the goal has been fulfilled, as well as how human-like the agent behaved. In this video, I interview KAIROS, the winning team of the 2021 challenge, and discuss how they used a combination of machine learning, efficient data collection, hand engineering, and a bit of knowledge about Minecraft to beat all other teams. OUTLINE:

0:00 - Introduction

4:10 - Paper Overview

11:15 - Start of Interview

17:05 - First…

#tesla #fsd #elon Watch the original podcast: https://www.youtube.com/watch?v=DxREm3s1scA An analysis of Elon's appearance on Lex Fridman. Very interesting conversation and a good overview of past, current, and future versions of Tesla's Autopilot system. OUTLINE:

0:00 - Intro

0:40 - Tesla Autopilot: How hard is it?

9:05 - Building an accurate understanding of the world

16:25 - History of Tesla's neural network stack

26:00 - When is full self-driving ready?

29:55 - FSD 11: Less code, more neural networks

37:00 - Auto-labelling is essential

39:05 - Tesla Bot & Discussion Links:

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

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

…

#playerofgames #deepmind #alphazero Special Guest: First author Martin Schmid (https://twitter.com/Lifrordi)

Games have been used throughout research as testbeds for AI algorithms, such as reinforcement learning agents. However, different types of games usually require different solution approaches, such as AlphaZero for Go or Chess, and Counterfactual Regret Minimization (CFR) for Poker. Player of Games bridges this gap between perfect and imperfect information games and delivers a single algorithm that uses tree search over public information states, and is trained via self-play. The resulting algorithm can play Go, Chess, Poker, Scotland Yard, and many more games, as well as non-game env…

The greatest news, now fully live!

#glide #openai #diffusion Diffusion models learn to iteratively reverse a noising process that is applied repeatedly during training. The result can be used for conditional generation as well as various other tasks such as inpainting. OpenAI's GLIDE builds on recent advances in diffusion models and combines text-conditional diffusion with classifier-free guidance and upsampling to achieve unprecedented quality in text-to-image samples. Try it yourself: https://huggingface.co/spaces/valhalla/glide-text2im OUTLINE:

0:00 - Intro & Overview

6:10 - What is a Diffusion Model?

18:20 - Conditional Generation and Guided Diffusion

31:30 - Architecture Recap

34:05 - Training & Result metrics

36:55 - F…

Chatting & Coding

Just chilling, coding, talking

#webgpt #aiart #mlnews The latest and greatest from the Machine Learning world. OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

2:40 - WebGPT: When GPT-3 can search the Internet

15:45 - MetaAI brings children's drawings to life

17:15 - OpenAI lets anyone fine-tune GPT-3

18:15 - New Journal: Transactions on Machine Learning Research

21:20 - Hugging Face buys Gradio

22:45 - Helpful Things

28:35 - NetHack Challenge winners announced

29:20 - Characters for good, created by AI Sponsor: Weights & Biases

https://wandb.me/yannic References:

WebGPT: When GPT-3 can search the Internet

https://openai.com/blog/improving-factual-accuracy/

https://cdn.openai.com/WebGPT.pdf MetaAI brings children's…

#mlnews #gopher #glam Your updates on everything going on in the Machine Learning world. Sponsor: Weights & Biases

https://wandb.me/yannic OUTLINE:

0:00 - Intro & Overview

0:20 - Sponsor: Weights & Biases

3:05 - DeepMind releases 3 papers on large language models

11:45 - Hugging Face Blog: Training CodeParrot from scratch

14:25 - Paper: Pre-Training vision systems with noise

15:45 - DeepMind advances Quantum Mechanics

16:45 - GoogleAI trains GLaM: 1 Trillion Parameters Mixture of Experts Model

18:45 - Colin Raffel calls for building ML models like we build Open-Source software

22:05 - A rebuke of the hype around DeepMind's math paper

24:45 - Helpful Things

32:25 - Suicide Capsule plans AI t…

#lama #inpainting #deeplearning At the end of the video is an interview with the paper authors!

LaMa is a system that is amazing at removing foreground objects from images, especially when those objects cover a large part of the image itself. LaMa is specifically trained to reconstruct large masked areas and includes global information throughout its forward propagation by using Fourier Convolutions in its layers. This makes it incredibly effective at reconstructing periodic structures with long-range consistency, compared to regular convolutions. OUTLINE:

0:00 - Intro

0:45 - Sponsor: ClearML

3:30 - Inpainting Examples

5:05 - Live Demo

6:40 - Locality as a weakness of convolutions

10:30 - U…

#mlnews #deepmind #ai The most trusted model in News! Get started with Weights & Biases here: https://wandb.me/yannic

(it's free forever for personal use) OUTLINE:

0:00 - Intro

0:15 - Sponsor: Weights & Biases

3:10 - DeepMind tackles fundamental math

6:45 - Microsoft focuses on scaling effectively and efficiently

10:15 - NeurIPS Anthology Visualization

13:30 - Timnit Gebru launches research institute independent from big tech

16:50 - SageMaker Canvas for no-code ML

17:50 - Help, Help!

21:40 - Cornelius Emde wins the 3090

21:55 - A retrospective on the NeurIPS 2021 ethics review process References:

DeepMind tackles fundamental math

https://deepmind.com/blog/article/exploring-the-beauty-of-pu…

This video quickly presents the idea of having some kind of trusted 3rd party library to handle data privacy for Deep Learning applications. The current state of Deep Learning applications are very data heavy, although recent advances such as Data Augmentation and Transfer Learning may circumvent that bottleneck. Federated Learning is a promising solution to data privacy and Deep Learning. In this framework, only the model weights are sent to a local user and the updates are done within the local machine. Only the parameters are traded globally, rather than having a central data store with sensitive user data. Federated Learning seems to be the leading technique for this -- Differentiable P…

One of the biggest lessons I've learned in developing KerasBERT and trying to apply language modeling to Keras information is the value of data. More particularly, the value of automated pipelines to collect this data. In this clip, I asked Michael about the ease of integrating slack conversations into 3rd party applications built on Deep Learning. This reminded me of the conversation with Charles Pierse at Keenious (Weaviate Podcast #2) and how they integrated their search and recommendation system directly into Google Docs and Microsoft Word. I hope you find this video interesting, it is really exciting to see this modularity with established software platforms to allow Deep Learning deve…

Democratic AI is an important goal to enable entrepreneurship and the development of AI technology. Particularly what we mean by this is generally overcoming bottlenecks of needing very expensive computers, massive private datasets, and long training times to get started with Deep Learning. I think that the decomposition of Retrieve-then-Read can be very promising for overcoming this bottleneck, in addition to ideas around efficient training. For those interested, I highly recommend checking out the "methods" outlined on the MosaicML website. They are a leading research lab doing amazing work on efficiency in Deep Learning, which has additional implications such as limiting climate damage f…

The discussion around categories of Generalization has been very exciting. We are used to evaluating these models with independent and identically distributed (i.i.d.) train-test splits. However, this doesn't really capture the Distribution Shift that happens from train to test sets in real-world deployment or the kind of behavior we are trying to achieve. This video outlines two sides of Generalization that I think are really interesting. Robustness has obvious implications for these systems, probably most vividly communicated with self-driving cars and corruption tests like artificially adding rain or snow to an image. Compositional generalization is probably the more exciting one, especi…

Deep Learning has made remarkable successes processing data domains such as images, text, audio, video, graph-structured, tabular, and more. One of the most exciting emerging applications is the combination of data types for one task. For example, combining images and text for visual question answering and text-based image search. This chapter discusses the idea of using text-based queries to search through tabular descriptions of bicycle models in order to find a local repair shop, as well as generalizing this to image-image search and other ideas to help us better connect with our local communities through the use of more targeted search!

Robustness to the somewhat esoteric concept of "style" was well explored in Computer Vision with the construction of the "Stylized ImageNet" dataset. This dataset was used to show things like the bias towards texture rather than a more human, shape bias. The concept of "style" has similarly been used in images to render a photograph of a dog as if it was painted by Vincent van Gogh. This chapter is focused on the analog of "style" to Natural Language Processing. For example, people have a different style of asking questions to each other compared to a search engine like Google or Bing. I think there are a lot of interesting ideas to this and the notion of style transfer between casual and f…

I recently read a really interesting survey paper about Cellular Automata and Self-Organizing systems. I first became aware of this idea from the Distil publication and their awesome animations of re-generation with Cellular Automata. I was thinking about how language models similarly try to recover from the damage of self-supervised masking. This short video presents the idea of local damage recovery with global message passing. This idea isn't very well developed, but hopefully there is something of interest in there for you. I like this idea lot, but again, and am still not sure how to really bring it to life.

Nature-inspired Artificial Intelligence is one of the most interesting ways of thinking about the technology. More particularly, System 1 / System 2 thinking, outlined in the book "Thinking Fast and Slow" has been an exciting framework for this. System 1 thinking refers to subconscious intuition, or quick thinking. System 2 refers to conscious, deliberate, logical, reasoning -- slow thinking. This chapter tries to relate this idea to the study of Search systems such as quick retrieval and slow reasoning over retrieved context.

Debugging is a common practice in software engineering to understand why the thing isn't working. There are some special considerations when debugging Deep Learning-based software systems such as robustness and domain generalization in addition to i.i.d. train-test data splits. When debugging Question-Answering systems it may be useful to visualize the nearest neighbors of the vector embedding of the Question to get a sense of what the model is predicting. This idea is very similar to viewing the retrieved-context however, a graph-structured User Interface may be more intuitive for human developers and debuggers. I hope you find this interesting!

In this video, Michael explains the architecture they are using to bring Deep Learning for Search, NLP, and Weaviate to Slack Chats. I think it is really interesting to see how each Application / Use Case customizes the general framework of Search components and which pieces are the most useful. If you like this video you will probably also find "Deep Learning for Search - January 15th, 2022" to be useful, which is also in this playlist. Thanks for watching!

This is one of the most important topics in Deep Learning at the moment. GPT-3 has, somewhat unreasonably, been able to perform Few-Shot Learning by giving repeatedly applying a fixed task description with a few input-output examples in the input. Although this is amazing, many people studying Machine Learning may be skeptical that this can surpass Fine-Tuned models. Fine-Tuned models in NLP are particularly adapted to the vocabulary with custom tokenizers, as well as nuances to the domain. This is really interesting with Retrieve-then-Read pipelines where we might not need to fine-tune both the retriever AND the reader, maybe just the reader. I hope you find this interesting!

This video touches on the topic of Confidence in Deep Learning. In our conversation, we are primarily concerned with how Confidence and Certainty can aid in Human-Computer Interaction and trust in our search systems. Confidence is additionally used in all sorts of ways from regularizing self- and semi-supervised learning (sorry forgot to include that in the video), to early exiting architectures such as PonderNet, and Active Learning. Mind your Outliers! is an interesting paper that makes us question how well confidence really works in Active Learning. Please subscribe to SeMI Technologies on YouTube!

Michael Wechner is developing Katie, a duplicate question detection system for slack chats. In addition to academic datasets such as SQuAD for question answering and FEVER for fact verification, we have one of the best academic datasets out there for duplicate question detection in QQP (Quora Question Pairs). Quora has published over 400K duplicate question annotations, and even hosted a Kaggle competition to develop this! I think this is an extremely interesting case of understanding how well these academic benchmarks generalize to startup ideas and real-world applications! HuggingFace Datasets: https://huggingface.co/datasets

This is one of my favorite books I have read -- to be honest one of very few I have completely read from start to finish recently. Working in Public describes the ecosystem around software contributions, blog posts, youtube videos, and more. As described in the video, there are 2 parts to the book. The first of which is about the platforms and trying to figure out why people contribute to open-source to begin with. The second part of the book is about the challenge of maintaining these projects, especially the strain on the core developer team's time. Michael Wechner is building a tool to add question answering, duplicate question detection, and search to problem like this! I am really exci…

A tale of two problem solvers.

Numberphile video on Bertrand's paradox: https://youtu.be/mZBwsm6B280

Help fund future projects: https://www.patreon.com/3blue1brown​

Special thanks to these supporters: https://3b1b.co/lessons/newtons-fractal#thanks

An equally valuable form of support is to simply share the videos. The general result here was originally proved by Cauchy.

Mémoire sur la rectification des courbes et la quadrature des surfaces courbes par M. Augustin Cauchy

https://ia600208.us.archive.org/27/items/bub_gb_EomNI7m8__UC/bub_gb_EomNI7m8__UC.pdf ------------------- Timestamps

0:00 - The players

5:22 - How to start

9:12 - Alice's initial thoughts

13:37 - Piecing together the cube

22:1…

Take a look at the full playlist (really): https://www.youtube.com/watch?v=fJWnA4j0_ho&list=PLnQX-jgAF5pTkwtUuVpqS5tuWmJ-6ZM-Z

Blog post with more details: https://3b1b.co/some1-results

Thanks, as always, to the supporters of this channel for helping to make this whole project possible: http://3b1b.co/thanks ------------------ Typo at 2:00, it should read "Burkard Polster" Videos and posts mentioned here. That weird light at the bottom of a mug — ENVELOPES

https://youtu.be/fJWnA4j0_ho Hiding Images in Plain Sight: The Physics Of Magic Windows

mattferraro.dev/posts/caustics-engineering The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw What Is The Most Complicated Lock Pattern?

https:/…

How the right question about Newton's method results in a Mandelbrot set.

Video on Newton's fractal: https://youtu.be/T_S2j5GaLRQ

Special thanks: https://3b1b.co/lessons/newtons-fractal#thanks Extra special thanks to Sergey Shemyakov, of Aix-Marseille University, for helpful conversations and for introducing me to this phenomenon. ------------------ Introduction to Fatou sets and Julia sets, including a discussion of Montel's theorem and its consequences:

http://www.math.stonybrook.edu/~scott/Papers/India/Fatou-Julia.pdf Numberphile with Ben Sparks on the Mandelbrot set:

https://youtu.be/FFftmWSzgmk Excellent article on Acko.net, from the basics of building up complex numbers to Julia sets.…

Who knew root-finding could be so complicated?

Early view of the next part: https://www.patreon.com/3blue1brown

An equally valuable form of support is to simply share the videos. ------------------ On fractal dimension:

https://youtu.be/gB9n2gHsHN4 Mathologer on the cubic formula:

https://youtu.be/N-KXStupwsc Some of the videos from this year's Summer of Math Exposition are fairly relevant to the topics covered here. Take a look at these ones, The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw The insolubility of the quintic:

https://youtu.be/BSHv9Elk1MU The math behind rasterizing fonts:

https://youtu.be/LaYPoMPRSlk --- These animations are largely made using a custom python library,…

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 …

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "SketchHairSalon: Deep Sketch-based Hair Image Synthesis" is available here:

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NVIDIA Canvas - https://www.nvidia.com/en-us/studio/canvas/ 📝 The previous paper "Semantic Image Synthesis with Spatially-Adaptive Normalization" is available here:

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Weatherscapes: Nowcasting Heat Transfer and Water Continuity" is available here:

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❤️ Check out Perceptilabs and sign up for a free demo here: https://www.perceptilabs.com/papers 📝 The paper "Plenoxels: Radiance Fields without Neural Networks" is available here:

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- Input video, output sound - https://www.youtube.com/watch?v=kwqme8mEgz4

- Input sound, output video - https://www.youtube.com/watch?v=aMo7pkkaZ9o ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

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❤️ Check out the Gradient Dissent podcast by Weights & Biases: http://wandb.me/gd 📝 The paper "VGPNN: Diverse Generation from a Single Video Made Possible" is available here:

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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

Разметка репозитория на темы — полезная практическая задача, которая может помочь в понимании сути проекта, ответственности разработчиков в команде и т.д. В данный момент у GitHub существует возможность ручной разметки на топики, и пользователи проставили уже очень много тем. Например, у репозитория pytorch есть тэги “python”, “deep-learning”, “autograd”, а у React — “javascript”, “frontend”, “ui”. Размечено довольно много разных проектов, поэтому можно попробовать применить обучение с учителем. На семинаре мы поговорим о том, как проект Code Tagger решает задачу предсказания топиков для репозиториев, какие при этом встретились сложности и как хочется их решать в будущем. Докладчики: Алекса…

Одной из стратегий лечения пациентов со сложными заболеваниями является полифармация – тип комбинаторной терапии, которая включает одновременный прием нескольких лекарств. Следствием такого подхода является высокий риск неблагоприятных побочных эффектов для пациента. В статье представляют Decagon – подход к моделированию побочных эффектов полифармации. При этом подходе строится мультимодальный граф взаимодействий белков с белками, лекарств с белками и лекарств с лекарствами. Побочные эффекты полифармации представлены как лекарственные взаимодействия, где каждому побочному эффекту соответствует свой тип ребра. В отличие от других подходов, Decagon может предсказать точный побочный эффект, ес…

В последние годы набирают популярность визуально-языковые модели. Это мультимодальные модели, которые позволяют, например, осуществлять поиск изображений по тексту. Как правило, в таких моделях используются качественные данные, фильтрация и разметка которых стоит очень дорого. На семинаре мы рассмотрим результаты авторов рассматриваемой статьи. Они показали, что в визуально-языковых моделях можно пренебречь качеством текстовых данных и получить высокое качество модели за счёт возникающего большого объёма данных, который компенсирует их зашумлённость. Такой подход позволил авторам получить SOTA модель при использовании архитектуры dual-encoder и contrastive loss на бенчмарках для image-text …

RL остается одной из немногих областей, в которых трансформер еще не нашел повсеместного признания и использования. Исследователи пробуют использовать его в разных местах традиционного RL пайплайна, однако чудо не случается. В новой статье авторы решили подойти к использованию трансформера максимально буквально - представили проблему RL как проблему предсказания последовательности, а именно следующего токена в траектории, будь это состояние, действие или награда. Получился Trajectory Transformer. Оказалось, что у такого подхода есть множество применений, в которых он показывает себя даже лучше, чем традиционные решения на основе динамического программирования. На семинаре мы подробно разбер…

Приглашаем вас посетить семинар “Building IntelliJ-based plugins”, посвященный разработке плагинов для IntelliJ-based IDEs. В лаборатории мы активно разрабатываем плагины для IDEs (в частности, для IntelliJ IDEA и PyCharm) и будем рады поделиться нашим опытом. Семинар будет полезен как тем, кто только начинает знакомство с разработкой плагинов, так и опытным разработчикам плагинов. Мы расскажем базовые вещи про возможности IntelliJ Platform, про способы расширения поведения IDE, про запуск плагинов в headless режиме и их тестирование. Мы набили некоторое количество шишек и с удовольствием поделимся советами по тому, как их можно избежать и поделимся полезными ссылками. Докладчики: Зарина Ку…

В задачах обучения с подкреплением агенту необходимо научиться действовать оптимальным образом в среде, максимизируя получаемую награду. Для решения поставленной задачи агент в процессе обучения должен не только научиться эффективно использовать уже выученную стратегию, но и исследовать окружение для поиска потенциально лучшего решения, так называемый exploration-exploitation trade-off. Существует множество подходов для решения задачи исследования среды, однако авторами статьи Self-imitation learning (SIL) был предложен относительно простой алгоритм, в котором дополнительное обучение агента воспроизводить собственные полезные решения может неявно привести к более глубокому изучение среды.

Э…

Статья посвящена методу генерации изображений (и других сложных распределений), который является комбинацией метода EBM и VAE. Первый основан на представлении плотности в виде распределения Гиббса. Второй основан на использовании концепции скрытых переменных. Мы обсудим, как авторы статьи предлагают совместить эти два подхода для получения резких изображений с небольшими вычислительными затратами. Докладчик: Станислав Лебедев.

Алгоритмы обучения с подкреплением обычно работают в парадигме активного (online) обучения. Во многих случаях онлайн-взаимодействие непрактично либо из-за дороговизны сбора данных, либо из-за опасности такого подхода. Более того, даже в тех областях, где возможно онлайн-взаимодействие, мы все же можем предпочесть использовать вместо этого ранее собранные данные - например, если домен сложный и эффективное обобщение требует больших наборов данных. На семинаре мы познакомимся с методами автономного (offline) обучения с подкреплением, обсудим область применимости и основные проблемы оффлайн обучения с подкреплением. Докладчики: Камиль Мазитов.

В последнее время все большую популярность набирает model-based подход в обучении с подкреплением, который заключается в использовании обучаемой модели мира для обучения агента или планирования. Как оказалось, алгоритмы, получаемые при таком подходе, требуют значительно меньшего количества взаимодействий с окружением, чем model-free методы, а также могут быть использованы для offline обучения с подкреплением. Однако несмотря на то, что model-based подход уже сейчас демонстрирует хорошие результаты в решении большого количества задач, направление все еще активно развивается. На семинаре мы разберем алгоритм Dreamer, который является одним из самых популярных model-based алгоритмов на данный …

На следующей встрече мы будем разбирать статью Challenges for Static Analysis of Java Reflection — Literature Review and Empirical Study, посвящённую изучению рефлексии в Java и ее влиянию на статический анализ кода. Так как рефлексия выполняется в runtime, предсказание поведения программы сильно усложняется, поэтому инструменты анализа кода могут опираться лишь на отдельные предположения о ее работе. Авторы исследуют существующие способы преодоления описанных трудностей и их применимость в реальных проектах.Для ответа на первый вопрос они провели систематический литературный обзор существующих исследований методов статистического анализа и их ограничений. Для ответа на второй — собирали да…

На сегодняшний день трансформеры широко применяются в задачах обработки естественного языка и компьютерного зрения. На семинаре мы рассмотрим работу, авторы которой предлагают применять трансформеры и для решения задач обучения с подкреплением (RL). Для этого предлагается рассматривать RL как задачу моделирования последовательности принимаемых решений. Такой подход, по мнению авторов, позволяет применить современные достижения в области моделирования языка к RL, использовать преимущества трансформеров -- простоту и масштабируемость, а также позволяет бороться с одной из основных проблем RL -- долгосрочным присвоением заслуги. На семинаре мы обсудим преимущества и недостатки предлагаемого по…

Novelty detection или определение того, лежит ли образец в заданном распределении или пришел из другого - одна из важных задач машинного обучения. В этой статье авторы представляют новую схему обучения при помощи contrastive loss и аугментаций для вышеописанной задачи в области компьютерного зрения.

Предложенное решение оценивается на ряде задач классификации с разметкой и без. Подход авторов достигает SOTA результатов на большинстве задач. На семинаре разберем то, как им удалось добиться таких результатов, и более детально обсудим все эксперименты. Докладчик: Алексей Корепанов.

Эффективное использование данных в обучении с подкреплением развивалось в несколько этапов: от использования одного текущего примера на каждой итерации обучения до использования на каждой итерации сразу всего накопленного опыта. И, несмотря на то, что данные стали использоваться значительно эффективнее, вопрос о том, как эти данные собирать, остается недостаточно изученным. На семинаре мы обсудим, почему этот аспект являются не менее важным для эффективного использования данных и рассмотрим статью "Collect & Infer - a fresh look at data-efficient Reinforcement Learning", предлагающую представить обучение с подкреплением в виде двух отдельных процессов, один из которых отвечает за сбор данны…

Сегодня на семинаре будет доклад на тему Documenting research projects: How to structure your ideas and make yourself understood. Мы расскажем, как писать тех. документацию, чтобы было понятно читателям: основные приемы, которые упрощают процесс изложения мыслей и делают его более результативным. Также немного остановимся на примерах из ридми и научных статей, а затем поделимся советами по улучшению навыков английского языка. После презентации будет сессия вопросов и ответов. Докладчик: Елена Тихомирова.

Графовые нейронные сети и молекулярные отпечатки пальцев являются наиболее частыми подходами предсказанию свойств малых молекул. Однако развитие и повышение доступности инструментов для обработки естественного языка, в том числе трансформеров, позволяет предпринять попытку использования этих методов для предсказания свойств малых молекул с помощью создания модели ChemBERTa. На семинаре мы рассмотрим статью "ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction", обсудим применявшиеся методы и сравним результаты авторов с результатами текущих state-of-the-art моделей. Докладчики: Михаил Лебедев.

Рождественский коллоквиум — ежегодное мероприятие, на котором выступают молодые исследователи ведущих лабораторий России и мира. Вместе с авторами статей топовых международных конференций мы обсудим тенденции из мира компьютерного зрения. Поговорим о достижениях и исследованиях российских и зарубежных учёных, подведём итоги года и обсудим будущее. Выступления спикеров будут на русском и английском языках. Программа: 12:10 - Making DensePose Fast and Light / Эмиль Богомолов

12:40 - Motion-Augmented Self-Training for Video Recognition at Smaller Scale / Кирилл Гаврилюк

13:10 - QPP: Real-Time Quantization Parameter Prediction for Deep Neural Networks / Владимир Крыжановский Перерыв 14:00- Out-…

В лекции дается обзор применений сверточных нейросетей в компьютерном зрении. Рассматриваются задачи семантической и instance сегментации, обнаружение объектов, распознавание лиц/людей. В каждом случае рассматриваются формулировка задачи, основные архитектурные паттерны для нейросетей, решающих данные задачи, популярные архитектуры. В конце обсуждаются последние работы по контрастивному предобучению нейросетей без учителя.

В лекции дается неформальное определение глубокого обучения, очень кратко освещаются история глубокого обучения, обсуждается алгоритм обратного распространения ошибки, обсуждается концепция слоев сети. В конце лекции очень кратко обсуждаются биологические нейросети.

Лекция посвящена сверточным нейросетям. Дается определение сверточных слоев, расматриваются их вариации (паддинг, варианты с пропуском, depthwise-separable и 1-by-1 convolutions). Обсуждается обратное распространение через сверточные слои (а также через max pooling слой). Во второй половине лекции рассматриваются популярные архитектуры сверточных нейросетей для классификации изображений (LeNet, AlexNet, VGGNet, Inception, Resnet, ResNext, MobileNet, EfficientNet).

Лекция посвящена представлениям данных, выучиваемым сверточными нейросетями. Рассматриваются разные способы визуализации и анализа подобных представлений. Обсуждаются атаки на нейросети, а также генерация искусственных изображений-иллюзий для нейросетей. Обсуждается способность больших сверточных нейросетей к переобучению под произвольные выборки. Заключительная часть лекции посвящена трансферу знаний с применением сверточных нейросетей.

Последняя лекция курса посвящена применению нейросетей для задач 3D-реконструкции и представления 3D-сцен. Первая половина лекции посвящена задаче восстановления геометрии. Рассматриваются стереосопоставление, монокулярная оценка глубины, нейросети, восстанавливающие облака точек, а также объекты в неявном представлении. Во второй половине лекции рассматриваются системы, позволяющие синтезировать новые виды сцены. Рассматриваются системы, основанные на волюметрическом ренедеринге и неявных представлениях (NeRF и подобные), системы с нейротекстурированными триангулированными представлениями и точечным представлением геометрии. Дается краткий обзор пакетов для нейрорендеринга.

Рассматриваются основные алгоритмы для оптимизации функций потерь в глубинном обучении, а именно стохастический пакетный градиентный спуск и его модификации. Особое внимание уделяется градиентному спуску с моментом. Обсуждается так же пакетная нормализация (batch normalization).

В лекции описываются латентные порождающие модели на основе нейросетей, обучающиеся без учителя. В начале рассматривается модель Generative Latent Optimization (GLO), далее рассматриваются автокодировщики и их применение, включая варационные автокодировщики. Во второй половине лекции рассматриваются нормализующие потоки, а также вводятся противоборствующие сети (generative adversarial networks).

Лекция посвящена современным вариациям противоборствующих нейросетей и обучению с дискриминаторными функциями потери. Рассматриваются как продвинутые варианты латентных моделей (StyleGAN1, StyleGAN2), так и применение дискриминаторных функций потерь в задачах трансляции изображений и им подобным. Рассматриваются основные паттерны, применяемые при построении генераторов. Рассматриваются основные применения этих технологий, такие как нейросинтезаторы реалистичных изображений и нейроаватары.

В лекции рассматриваются нейросети, обрабатывающие изображения (на примере повышения разрешения), и нейросети, синтезирующие изображения. Рассматриваются функции потери (включая перцептивные -- perceptual losses, текстурные функции потери). Рассматриваются задачи синтеза текстур и стилизации изображений. Обсуждается модуль адаптивной instance нормализации и его применение.

- расскажу про задачу ранжирования, решаемую в Lamoda для каталога товаров, и про ее решение, не опирающееся на синхронный запуск модели в ответ на событие пользователя

- приложу подход к нескольким примерам других задач и на их примере расскажу для каких случаев он может быть полезен

- на примере рабочей системы обработки данных покажу как она отличается от классического варианта (с деплоем модельки для синхронного ответа на событие)

Закрываем наш небольшой должок с прошедшей Ёлки 🤗 В программе ещё 100 минут итогов года:

1. Итоги 2021 в NLP - Валентин Малых

2. Итоги 2021 в Quantum Computing - Сергей Ширкин

3. Итоги 2021 в GraphML - Михаил Галкин, Антон Цицулин, Анвар Курмуков

4. Итоги 2021 в A/B тестировании - Аслан Байрамкулов

...

5. ODS Awards 2021 recap + Зимняя школа ODS 2022 Плейлист лучших видео 2021: https://www.youtube.com/playlist?list=PLTlO6nV_TaGA9qov5W093NlMJbN7aeGa1

Зимняя школа ODS: https://ods.ai/competitions/ods-winter-projects-22

Владимир Грановский, Data Scientist at KION

Нейросетевая матричная факторизация и dssm (Практика)

В лекции мы соберем простой пайплайн для обучения DSSM:

- в очередной раз разгребем данные;

- сформируем датасет для обучения;

- напишем функцию потерь;

- соберем и обучим модель;

- посмотрим как ее применять. В конце видео вас ждет пара бонусов, которые позволят вам использовать разработанную модель по максимуму🔥 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия соощества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Спикер: Глеб Волохов, ML_developer@KION Нейросетевая матричная факторизация и dssm (Теория) Мы поговорим о применении нейросетевых подходов для построения рекомендаций. В частности мы поговорим о:

-автоенкодерах

-item 2 vec подходе

-графовом представлении матричной факторизации и подробно обсудим нейронную матричную факторизацию, лоссы для ранжирования и сиамкие сети. Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия соощества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Ирина Елисова, DS Teamlead [RecSys] В этой лекции вас ждет рассказ про двухуровневую или двухэтапную модель. Мы разберем в теории и на примере с кодом зачем ее строить, какие модели стоит использовать на каждом этапе. Как правильно собрать трейн и тест и настроить схему валидации. Первая часть лекции - теория, вторая - практика на датасете от онлайн-кинотеатра Кион. Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия соощества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

ODS Course Fest 2021

https://ods.ai/events/course_fest_1 Спикер: Михаил Хасыков, ML Engineer в MTS@BigData В видео мы расскажем про визуальный анализ рекомендаций и про сложные метрики (diversity + novelty + serendipity) с примером кода Занятие состоит из двух частей - теории и практики. В первой половине лекции обсудим способы получения качественной оценки качества рекомендаций. В частности, познакомимся с «визуальным анализом» - методом оценки рекомендательной системы «глазами», узнаем кто такие «аватары» и как они применяются. Во второй половине теоретической части поговорим о таких свойствах рекомендаций как разнообразие и новизна: зачем они нужны, как их оценивать и оптимизировать. В п…

ODS Course Fest 2021

https://ods.ai/events/course_fest_1 Спикер: Ильдар Сафило, Head of Recommender Systems, MTS@BigData Мы расскажем о том, как можно оценивать эффект от влияния рекомендаций на продукт, зачем делать А/Б тест и что с помощью него оценивать. В конце мы поговорим о том, как можно связывать оффлайн и онлайн метрики( бизнесовые метрики) в рекомендательных системах, также обсудим проблему bias и feedback loop

Спикер: Александр Бутенко, MLE @ BigData MTS На лекции поговорим про некоторые способы ускорения инференса рекомендательных систем преимущественно в nearline/online сеттингах.

В частности, обсудим методы приближенного поиска соседей, почему их можно применять и какие могут быть трудности и ограничения. Подробнее разберем один.

Также немного затронем тему кэширования. Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия соощества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Спикер: Эмилий Фельдман, ML developer @ BigData MTS На лекции обсудим, что такое production (прод) для рекомендательных систем и зачем он нужен. Затем рассмотрим различные варианты реализации прода - offline, nearine, online - и поговорим о том, когда какой вариант лучше использовать. После чего детально рассмотрим как грамотно построить офлайн-продд, какие инструменты использовать и на что обратить внимание. В конце осветим несколько общих моментов про то, как сделать прод лучше.

Наше финальное мероприятие 2021 года уже завтра! Завтрашняя трансляция уже ждёт вас на нашем ютубе: https://youtu.be/xkl5oXtsjTc Проголосовать в премии ODS Awards нужно тут:

https://ods.ai/tracks/ods-awards-2021 А полное расписание Ёлки доступно на ODS.AI: https://ods.ai/events/data-elka-2021 Ждём вас и ваших голосов!

По уже сложившейся традиции, Дата Ёлка это наш финальный аккорд года:

🎄 Вместе с главными экспертами своих областей, подводим итоги года по самым бурным направлениям современного DS/ML

🏆 А тем, кто знатно фигачил в 2021, самое время вручить подарки за их вклад в Open Data Science на ODS Awards

🔥 Широченный набор подарков и возможностей их заполучить: -Самим победителям премии ODS Awards 2021

-Топ участникам каждого из наших осенних курсов

...а также участникам голосований и активностей Дата Ёлки Полное расписание конференции доступно на ODS.AI: https://ods.ai/events/data-elka-2021 Проголосовать в премии ODS Awards нужно здесь:

https://ods.ai/tracks/ods-awards-2021 И вступить в сообщество, к…

https://ods.ai/tracks/qmlcourse

Ваагн Минасян, Lead Data Scientist at X5 Retail Group

Ускорение A/Б тестов линейными методами: сравнительный анализ. Задача ускорения А/Б тестов ( a.k.a. повышения чувствительности ) является одной из самых актуальных в индустрии. К наиболее удобным методами ускорения А/Б тестов, статистические свойства которых можно вывести аналитически, относятся линейная регрессия и методы на остатках, например, CUPED. Однако, некоторые реализации этих методов и, в частности, реализации CUPED'a, незаметно приводят к очень нежелательным последствиям - оценки получаются смещёнными, т.е. результаты A/Б теста искажаются и перестают отражать реальность на ограниченном количестве данных. В других же методах не…

ML in Marketing hub: https://ods.ai/hubs/ml-in-marketing

Телеграм-канал https://t.me/mlinmarketing Спикер: Иван Максимов, Data Science Team Lead at Delivery Club Многие аналитики для ускорения А/В тестов в первую очередь используют достаточно сложные статистические приемы (например, CUPED). Однако существует огромное множество более простых и эффективных способов ускорить А/В тесты. Мы обсудим 13 таких способов: от улучшения процесса дизайна теста до применения стат критерия и финального принятия решения о выкатке фичи. А также оценим потенциальный trade-off эффект-затраты от внедрения каждого из способов. Что было в докладе:

00:00 начало видео

01:18 сразу о результатах

02:07 содержание док…

ODS Course Fest #1 https://datafest.ru/course/ Полное расписание конференции доступно на ODS.AI: https://ods.ai/events/halloween2021 Курсы ODS: https://ods.ai/tracks/groups/courses 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

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

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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 …

Thomas Tull is founder of Legendary Entertainment, Tulco, part-owner of Pittsburgh Steelers, and guitarist for the band Ghost Hounds.

Please support this podcast by checking out our sponsors:– Paperspace: https://gradient.run/lex to get $15 credit– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Tulco: https://tulcoholdings.com/Ghost Hounds: https://www.ghosthounds.com/Pittsburgh Steelers: https://ww…

Yann LeCun is the Chief AI Scientist at Meta, professor at NYU, Turing Award winner, and one of the seminal researchers in the history of machine learning.

Please support this podcast by checking out our sponsors:– Public Goods: https://publicgoods.com/lex and use code LEX to get $15 off– Indeed: https://indeed.com/lex to get $75 credit– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– NetSuite: http://netsuite.com/lex to get free product tour– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 offEPISODE LINKS:Yann’s Twitter: https://twitter.com/ylecunYann’s Facebook: https://www.facebook.com/yann.lecunYann’s Website: http://yann.lecun.com/Books and…

Brian Keating is an experimental physicist at the UCSD, author of Losing the Nobel Prize, and host of the Into the Impossible podcast.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– MasterClass: https://masterclass.com/lex to get 15% off– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Brian’s Twitter: https://twitter.com/DrBrianKeatingBrian’s YouTube: https://www.youtube.com/c/DrBrianKeatingBooks and resources menti…

Yaron Brook is an objectivist.

Yoram Hazony is a national conservative.

This is a conversation and debate about national conservatism vs individualism.

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

(00:00) – Introduction(09:11) – Conservatism(15:07) – Importance of history(27:09) – Rationalism vs empiricism(32:39) – Communism(40:43) – Otto von Bismarck(45:04) – Edmund Burke and the French Revolution(49:52) – USA’s founding fathers(1:02:47) – Founding documents(1:24:36) – Cohesion and Individualism(1:45:29) – Love and relationships(1:53:41) – Individual freedom(2:08:08) – Having children(2:21:54) – Reason vs emotions(2:28:44) – Nationalism(2:40:56) –…

Mark Normand is a stand-up comedian.

Please support this podcast by checking out our sponsors:– Calm: https://calm.com/lex to get 40% off– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Grammarly: https://grammarly.com/lex to get 20% off premium– ROKA: https://roka.com/ and use code LEX to get 20% off your first orderEPISODE LINKS:Mark’s Twitter: https://twitter.com/marknormMark’s YouTube: https://youtube.com/c/marknormandOut to Lunch (special): https://www.youtube.com/watch?v=tDolNU89SXIThe Standups: Season 3 (Netflix): https://www.netflix.com/title/80175685PODCAST INFO:Podcast website: https://lexfr…

Jay Bhattacharya is a professor of medicine at Stanford University and co-author of the Great Barrington Declaration.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Jay’s Twitter: https://twitter.com/DrJBhattacharyaGreat Barrington Declaration: https://gbdeclaration.org/PODCAST INFO:Podcast website: https://lexfrid…

Michael Malice is a political thinker, podcaster, author, and anarchist.

Please support this podcast by checking out our sponsors:– FightCamp: https://joinfightcamp.com/lex to get free shipping– Linode: https://linode.com/lex to get $100 free credit– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– Sunbasket: https://sunbasket.com/lex and use code LEX to get $35 off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmaliceMichael’s Community: https://malice.locals.com/Michael’s YouTube: https://www.youtube.com/channel/UC5tj5QCpJKIl-KIa4Gib5XwMichael’s Website: http://michaelmal…

Elon Musk is CEO of SpaceX, Tesla, Neuralink, and Boring Company.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– ButcherBox: https://butcherbox.com/lex to get offers & discounts– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Elon’s Twitter: https://twitter.com/elonmuskPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spo…

Ray Dalio is an investor, author, and founder of Bridgewater Associates.

Please support this podcast by checking out our sponsors:– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1k– 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 premium– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Ray’s Twitter: https://twitter.com/RayDalioRay’s Instagram: https://www.instagram.com/raydalioBooks & resources mentioned:Ray’s books: https://amzn.to/3swN5uwRay’s free …

Peter Wang is the co-founder & CEO of Anaconda and one of the most impactful leaders and developers in the Python community.

Also, he is a physicist and philosopher.

Please support this podcast by checking out our sponsors:– Quip: https://getquip.com/lex to get first refill free– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 off– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1k– Four Sigmatic: https://foursigmatic.com/lex and use code LexPod to get up to 60% off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Peter’s Twitter: https://twitter.com/pwangAnaconda’s Website: https://www.anaconda.com/Books & resources …

Albert Bourla is the Chairman and CEO of Pfizer.

Please support this podcast by checking out our sponsors:– Notion: https://notion.com/startups to get up to $1000 off team plan– Calm: https://calm.com/lex to get 40% off– Hunter Douglas: https://www.hunterdouglas.com/lex to get a free design guide– LMNT: https://drinkLMNT.com/lex to get free sample pack– Grammarly: https://grammarly.com/lex to get 20% off premiumEPISODE LINKS:Albert’s Twitter: https://twitter.com/AlbertBourlaPfizer’s Website: https://www.pfizer.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTu…

Norman Naimark is a historian at Stanford, specializing in the history of genocide.

Please support this podcast by checking out our sponsors:– Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin– Quip: https://getquip.com/lex to get first refill free– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– NetSuite: http://netsuite.com/lex to get free product tour– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Norman’s Website: https://history.stanford.edu/people/norman-naimarkStalin’s Genocides (book): https://amzn.to/3oO0HzbStalin and the Fate of Europe (book): https://amzn.to/3pLbWrkBooks & resources …

Jamie Metzl is an author specializing in topics of genetic engineering, biotechnology, and geopolitics.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– NI: https://www.ni.com/perspectives– GiveDirectly: https://givedirectly.org/lex to get gift matched up to $300– Indeed: https://indeed.com/lex to get $75 credit– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premiumEPISODE LINKS:Jamie’s Twitter: https://twitter.com/JamieMetzlJamie’s Website: https://jamiemetzl.com/Jamie’s lab leak blog post: https://jamiemetzl.com/origins-of-sars-cov-2/Hacking Darwin (book): https://amzn.to/3lLqLsMPODCAST …

Peter Woit is a theoretical physicist, mathematician, critic of string theory, and author of the popular science blog Not Even Wrong.

Please support this podcast by checking out our sponsors:– The Prisoner Wine Company: https://theprisonerwine.com/lex to get 20% off & free shipping– Linode: https://linode.com/lex to get $100 free credit– Sunbasket: https://sunbasket.com/lex and use code LEX to get $35 off– BetterHelp: https://betterhelp.com/lex to get 10% off– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security cameraEPISODE LINKS:Peter’s website: http://www.math.columbia.edu/~woit/Peter’s blog: https://bit.ly/3xCwm9FNot Even Wrong (book): https://amzn.to/3peDzZsQ…

Tom Brands is an Olympic and World Champion in freestyle wrestling and the head wrestling coach at the University of Iowa.

Please support this podcast by checking out our sponsors:– FightCamp: https://joinfightcamp.com/lex to get free shipping– InsideTracker: https://insidetracker.com/lex and use code Lex25 to get 25% off– ROKA: https://roka.com/ and use code LEX to get 20% off your first order– Theragun: https://therabody.com/lex to get 30 day trial– GiveWell: https://www.givewell.org/ and use code LEX to get donation matched up to $1kEPISODE LINKS:Tom’s Twitter: https://twitter.com/tombrandshawkHawkeyes’ Website: https://hawkeyesports.com/PODCAST INFO:Podcast website: https://lexfridman.c…

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…

This episode is the second in our current series on PhD applications.

How do PhD programs in Europe differ from PhD programs in the US, and how should people decide between them?

In this episode, we invite Barbara Plank…

This episode is the first in our current series on PhD applications.

How should people prepare their applications to PhD programs in NLP?

In this episode, we invite Nathan Schneider (Professor of Linguistics and Compute…

In this episode, we discussed the Alexa Prize Socialbot Grand Challenge and this year's winning submission, Alquist 4.0, with Petr Marek, a member of the winning team.

Petr gave us an overview of their submission, the de…

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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Sean Law, Principle Data Scientist, R&D at a Fortune 500 Company, comes on to talk about his creation of the STUMPY Python Library. Sponsored by Hello Fresh and mParticle: Go to Hellofresh.com/dataskeptic16 for up to 16 free meals AND 3 free gifts! Visit mparticle.com to learn how teams at Postmates, NBCUniversal, Spotify, and Airbnb use mParticle’s customer data infrastructure to accelerate their customer data strategies.

Georgia Papacharalampous, Researcher at the National Technical University of Athens, joins us today to talk about her work “Probabilistic water demand forecasting using quantile regression algorithms.”

Data scientists and psychics have at least one major thing in common. Both professions attempt to predict the future. In the case of a data scientist, this is done using algorithms, data, and often comes with some measure of quality such as a confidence interval or estimated accuracy. In contrast, psychics rely on their intuition or an appeal to the supernatural as the source for their predictions. Still, in the interest of empirical evidence, the quality of predictions made by psychics can be put to the test. The Great Australian Psychic Prediction Project seeks to do exactly that. It's the longest known project tracking annual predictions made by psychics, and the accuracy of those predic…

John Watson, Principal Software Engineer at Splunk, joins us today to talk about Splunk and OpenTelemetry.

Yusan Lin, a Research Scientist at Visa Research, comes on today to talk about her work "Predicting Next-Season Designs on High Fashion Runway."

Time series topics on Data Skeptic predate our current season. This holiday special collects three popular mini-episodes from the archive that discuss time series topics with a few new comments from Kyle.

Dr. Darren Shannon, a Lecturer in Quantitative Finance in the Department of Accounting and Finance, University of Limerick, joins us today to talk about his work "Extending the Heston Model to Forecast Motor Vehicle Collision Rates."

Eric Manibardo, PhD Student at the University of the Basque Country in Spain, comes on today to share his work, "Deep Learning for Road Traffic Forecasting: Does it Make a Difference?"

Daniele Gammelli, PhD Student in Machine Learning at Technical University of Denmark and visiting PhD Student at Stanford University, joins us today to talk about his work "Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management."

Mahdi Abolghasemi, Lecturer at Monash University, joins us today to talk about his work "Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion."

The retail holiday “black Friday” occurs the day after Thanksgiving in the United States. It’s dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as much as 80% of their revenue in the last two months of the year. This episode features four interviews with guests bringing unique data-driven perspectives on the topic of analyzing this seeming outlier in a time series dataset.

Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."

Today we are joined again by Ben Fulcher, leader of the Dynamics and Neural Systems Group at the University of Sydney in Australia, to talk about hctsa, a software package for running highly comparative time-series analysis.

Gerrit van den Burg, Postdoctoral Researcher at The Alan Turing Institute, joins us today to discuss his work "An Evaluation of Change Point Detection Algorithms."

Nicole Büttner (Founder and CEO of Merantix Labs) joins the podcast to discuss driving A.I.

innovation, automation, and transformation and building the ideal A.I.

start-up founding team.

In this episode you will learn:…

Revisit the much-underrated continuous calendar and get started with this uncommon planning method thanks to Jon's 2022 template.

Additional materials: www.superdatascience.com/542

In this episode, Kevin Hu joins the podcast to talk about founding and growing the data observability startup, Metaplane.

Listen in to hear about his time in academia at MIT, his experience with Y Combinator, and his cur…

In this episode, Jon opens up about starting his day with a glass of water – his first morning habit that sets his day off on a healthy and successful note.

Additional materials: www.superdatascience.com/540

In this episode, Serg Masís joins the podcast to share his in-depth technical knowledge of Interpretable Machine Learning.

Together they discuss why this field matters, how it’s evolving, and so much more.

In this episo…

In this episode, Jon shares his "life-changing" habit tracking system that has allowed him to achieve more, create more structure within his day and cut out bad habits.

Additional materials: www.superdatascience.com/53…

Sadie St. Lawrence returns to discuss the biggest data science trends that are set to take over the industry in 2022.

In this episode you will learn:• A look back at data science trends for 2021 [4:03]• Micro and macr…

Jon goes over his five biggest learnings from 2021 and what he hopes to work on in 2022.

Additional materials: www.superdatascience.com/536

Prolific data science entrepreneur and Y Combinator alum Austin Ogilvie (Laika, Yhat) joins Jon Krohn for a revealing look into his journey of starting, growing, and selling a data science startup.

From liberal arts grad…

Jon sends a holiday greeting to all listeners. Additional materials: www.superdatascience.com/534

Dr. Brett Tully joins us on the podcast to discuss his work as Director of AI Output Systems at Nearmap and his previous research in biomedical topics and nuclear fusion.

In this episode you will learn:• What is Nearma…

Jon discusses one helpful framework when it comes to problem-solving and how data scientists are uniquely positioned to employ this technique.

Additional materials: www.superdatascience.com/532

Jeroen Janssen joins on the podcast to discuss his book on utilizing the command line for data science and the importance of polyglot data science work.

In this episode you will learn:• The genesis of Jeroen’s book [3:…

Jon details his top ten AI thought leaders hoping that his suggestions prove valuable to you in your data science journey.

Additional materials: www.superdatascience.com/530

Dave Niewinski joins us to discuss his prolific work in robotics both as a consultant and a popular YouTuber.

In this episode you will learn:• Dave’s Armoury [4:44]• Robotic cornhole tournament [12:33]• Dave’s many r…

January 20, 2022 podcastIn this episode I speak about neural network quantization, a technique that makes networks feasible for embedded systems and small devices.

There are many quantization techniques depending on several factors that are all important to consider during design and implementation.

Chat with meJoin us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by 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 hig…

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January 10, 2022 podcastThis episode is the first of a series about Embedded Machine Learning.

I explain the requirements of tiny devices and how it is possible to run machine learning models.

Join us on Discord community chat to discuss the show, suggest new episodes and chat with other listeners!

Sponsored by 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, and build the statistical models that will support your business.

Who invented the methods data scientists use every day?

We answer such questions and much more in this wonderful episode with Triveni Gandhi, Senior Data Scientist and Shaun McGirr, AI Evangelist at Dataiku.

We cover topics about the history of data science, ethical AI and…This episode is brought to you by DataikuWith Dataiku, you have everything you need to build and deploy AI projects in one place, including easy-to-use data preparation and pipelines, AutoML, and advanced automation.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, ener…

December 23, 2021 podcastIn this episode I speak with Manavalan Krishnan from Tsecond about capturing massive amounts of data at the edge with security and reliability in mind.

This episode is brought to you by TsecondThe growth of data being created at static and moving edges across industries such as air travel, ocean and space exploration, shipping and freight, oil and gas, media, and more proposes numerous challenges in capturing, processing, and analyzing large amounts of data.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.…

December 23, 2021 podcastIf you think deep learning is a method to get to AGI, think again.

Humans, as well as all mammals think in a… composable way.

Come chat with us on DiscordSponsorsThis episode is brought to you by 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, and build the statistical models that will support your business.

December 23, 2021 podcastSponsorsThis episode is brought to you by 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, and build the statistical models that will support your business.

Join us on DiscordFeel free to drop by and have a chat with the host and the followers of the show

December 23, 2021 podcastSponsorsThis episode is brought to you by Advanced RISC Machines (ARM).

ARM is a family of reduced instruction set computing architectures for computer processors https://www.arm.com/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.

November 16, 2021 podcastThis episode summarizes a study about trends of AI in 2021, the way AI is perceived by people of different background and some other weird questions.

For instance, would you have sexual intercourse with a robot?

Would you be in a relationship with an artificial intelligence?

The study has been conducted by Tidio and reported at https://www.tidio.com/blog/ai-trends/SponsorsThis episode is supported by Amethix Technologies.

Amethix uses machine learning and advanced analytics to empower people and organizations to ask and answer complex questions like never before.

November 9, 2021 podcastIf you think deep learning is a method to get to AGI, think again.

Humans, as well as all mammals think in a… composable way.

SponsorsThis episode is brought to you by Advanced RISC Machines (ARM).

ARM is a family of reduced instruction set computing architectures for computer processors https://www.arm.com/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.

November 2, 2021 podcastAI, ethics, and explainability.

We answer such questions in this amazing episode with Erika Agostinelli from the AI Elite team at IBM.

Sponsored by 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, and build the statistical models that will support your business.

ReferencesAIX360:https://aix360.mybluemix.net/Questioning the AI: Informing Design Practices for Explainable AI User Experienceshttps://arxiv.org/abs/2001.…

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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.

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.