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Figure 1: Timeline of the drug discovery procedure, from target validation to clinical launch, from [1].

This article will cover how the application of geometric deep learning and the field of molecular machine learning is ushering us into a new era of scientific discovery.

CitationFor attribution in academic contexts or books, please cite this work asMeilina Reksoprodjo, "Symmetries, Scaffolds, and a New Era of Scientific Discovery", The Gradient, 2022.

[7] J. Vamathevan et al., "Applications of machine learning in drug discovery and development", Nature Reviews Drug Discovery, vol.

Available: https://thegradient.pub/ai-scientific-revolution/[13] H. Chen, O. Engkvist, Y. Wang, M. Olivecron…

2015)Photos of the brains of Paul Broca’s two aphasic patients, Leborgne (top row) and Lelong (bottom row) (Dronkers et al.

During the last decade, more advanced techniques borrowed from graph theory have been applied to brain imaging research (Rubinov and Sporns 2010).

Importantly, graph-based analyses can model the dynamics of the entire brain network all at once, thereby enabling investigation of network-wide properties.

CitationFor attribution in academic contexts or books, please cite this work asRebecca Ehrenkranz, "Overview of Graph Theory and Alzheimer's Disease", The Gradient, 2022.

BibTeX citation:@article{ehrenkranz_graph_ad,author = {Ehrenkranz, Rebecca},title = {Overview of Gra…

PreambleThis article contains an objective summary of a recent controversy related to an AI model named GPT-4chan, as well as a subjective commentary with my thoughts on it.

The main questions I will address are the following:Can GPT-4chan cause harm to peopleCan GPT-4chan contribute to AI researchIs GPT-4chan more 'truthful' than GPT-3Should the GPT-4chan model have been released to the publicWhat was the intent behind developing, deploying, and distributing GPT-4chanWas deploying GPT-4chan bots to interact with people on a message board unethicalCan GPT-4chan cause harm to peopleCan a bot that disseminates hate speech on the internet (e.g.

Moreover, now that the whole ordeal predictably l…

With manifold impacts stretching the length of the scientific method, AI is ushering in a scientific revolution through groundbreaking discoveries, novel techniques and augmented tools, and automated methods that advance the speed and accuracy of the scientific process.

Beyond the protein-folding problem, AI has proven its scientific worth with discoveries in a number of fields, from cosmology and chemistry to semiconductor design and materials science.

AI is ushering in a new scientific revolution by making remarkable breakthroughs in a number of fields, unlocking new approaches to science, and accelerating the pace of science and innovation.

CitationFor attribution in academic contexts or…

For most people, these roles outside of work occupy their evenings, weekends and vacations, yet almost every academic I know seems to fill every available bit of time with academic pursuits.

Not working on weekends seemed like a graduation from the messy life of an undergrad into the more structured life of an adult.

And the strangest thing is, I do not know where I got the idea that I should be working on weekends.

CitationFor attribution in academic contexts or books, please cite this work asClaas Voelcker, "Working on the Weekends - an Academic Necessity?

BibTeX citation:@article{class2022working,author = {Voelcket, Claas},title = {Working on the Weekends - an Academic Necessity?

I spent my last year at Berkeley doing research in deep learning for computer vision and working on Caffe, one of the first popular deep learning libraries.

Now I’m at Aquarium, where I get to help a multitude of companies deploying deep learning models to solve important problems for society.

I’ve learned a lot of lessons about doing deep learning in production, and I'd like to share some of those lessons with you so you don’t have to learn them the hard way.

For attribution in academic contexts or books, please cite this work asPeter Gao, "Lessons From Deploying Deep Learning To Production", The Gradient, 2022.

BibTeX citation:@article{gao2022lessons,author = {Gao, Peter },title = {Lesson…

The core idea behind wav2vec 2.0 is to teach the model to do two things in parallel:Quantize continuous speech data into discrete units automatically.

Wav2vec uses 2 groups with 320 possible words in each group, hence a theoretical maximum of 320 x 320 = 102,400 speech units.

The final context vectors then go through the last projection layer to match the dimension of the quantized speech units Qt.

Fine-tuning and downstream tasksThis concludes our tour of wav2vec 2.0 and its pre-training process.

HuBERT re-uses embeddings from the BERT encoder to improve targets, while wav2vec 2.0 only uses the output of the convolutional network for quantization.

Graph Neural Networks (GNNs) are by far the most common among graph ML methods and the most popular neural network architectures overall [2].

CitationFor attribution in academic contexts or books, please cite this work asMichael Bronstein, "Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks", The Gradient, 2022.

BibTeX citation:@article{dlneuro2022,author = {Bronstein, Michael},title = {Beyond Message Passing, a Physics-Inspired Paradigm for Graph Neural Networks},journal = {The Gradient},year = {2022},howpublished = {\url{https://thegradient.pub/graph-neural-networks-beyond-message-passing-and-weisfeiler-lehman}},}[1] See e.g.

A general form of message passing an…

On their own, AI ethics principles are insufficient to improve AI systems.

Instead, I suggest that each organization should articulate its own AI ethics principles, and I sketch ways to do so responsibly.

The search for universal AI ethics principlesIn recent years, several research groups have sought unifying themes in current AI ethics principles.

A key question is how to formulate AI ethics principles responsibly and how to tell that an organization has developed its principles responsibly.

But the first question to ask is “which principles?”, and my answer is: Don’t settle for “universal” AI ethics principles.

Specifically, this overview will first explain some common neuroimaging modalities more in-depth and then discuss applications of deep learning in conjunction with some of the unique characteristics of neuroimaging data.

These unique characteristics tie into a broader movement in deep learning, namely that data understanding should be a goal in itself to maximize the impact of applied deep learning.

Unique and leverageable aspects of neuroimaging dataAmong others [20], one critical challenge with deep learning in the field of neuroimaging is the limited number of samples; many neuroimaging datasets range from roughly 300 to 1300 subjects.

Data understanding and interpretation is an essentia…

Tech and AI startups have continued to gain prominence as the AI wave swept the country in 2018.

Hanoi and Ho Chi Minh City have developed a robust ecosystem for tech startups, with dominating sectors including AI, e-commerce, fin-tech, and enterprise solutions.

The ecosystem of 149 AI startups has attracted funding from both domestic and regional venture capital firms.

However, according to computer science professor Than Khoat, these structural initiatives have only started rolling out since 2020, and have not yet translated to a significant boost in tech talent supply to meet the pressing demand of tech talent of the fast growing tech ecosystem.

CitationFor attribution in academic contex…

Recently, NLP technology facilitated access and synthesis of COVID-19 research with the release of a public, annotated research dataset and the creation of public response resources.

Wikipedia serves as a source for BERT, GPT and many other language models.

Responding to this, MIT researchers have released StereoSet, a dataset for measuring bias in language models across several dimensions.

If NLP technology is going to be something revolutionary, it will need to be better and different.

CitationFor attribution in academic contexts or books, please cite this work asBenjamin Batorsky, "New technology, old problems: The missing voices in Natural Language Processing", The Gradient, 2022.

Somehow I think, in terms of chess, engines are always capable of bringing out the key points of a position but never the actual key point itself.

Discussing this pre-neural era of chess engines, Sadler suggests that the elements of the hand-crafted function are uninteresting despite being inherently interpretable.

Before chess engines were unambiguously better than humans (and even for a while after they were), Seconds faced the challenge of doubting the validity of an engine prediction.

In this gap, end-users resort to reading the tea leaves of their model’s predictions.

Sadler additionally has extensive, high quality content on his personal and book-related YouTube channels: Personal, Ga…

Active learning: Find samples for human-in-the-loopIn active learning, we select the most “interesting” unlabeled samples for labeling via human-in-the-loop (HITL).

Facebook added a twist to active learning by including similarity search as a filter (Similarity Search for Efficient Active Learning and Search aka SEALS).

Snorkel DryBell then applies these labeling functions on unlabeled examples before loading the output and labeling functions into a generative model.

Even Tesla, which probably has one of the most sophisticated data labeling systems to support Autopilot, hasn’t worked out all the kinks.

How to bootstrap labels at various stages of maturityGetting labels for machine learning …

Another problem arises if you try to find a high quality VAD with a permissible license.

Voice Activity Detection is the problem of looking for voice activity – or in other words, someone speaking – in a continuous audio stream.

The input is just a small audio chunk, and the output is a probability that this chunk contains speech given its history.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov and Dimitrii Voronin, "One Voice Detector to Rule Them All", The Gradient, 2022.

BibTeX citation:@article{veysov2020towardimagenetstt,author = {Veysov, Alexander and Voronin, Dimitrii},title = {One Voice Detector to Rule Them All},journal = {The Gradie…

Last week we finished our mini-series about ML testing, one of the most critical elements of the ML models’ lifecycle.

As the proverb (and many ML people) says: Repetition is the mother of learning ;)The essence of ML testing is to execute explicit checks that validate the behavior of an ML model.

Plenty of taxonomies can be used to organize ML testing techniques.

A very general approach segments testing techniques into two main groups relative to the ML model lifecycle:Pre-Train Tests: Designed to find problems that can help optimize the training workflow.

→ In Edge#211: we discuss what to test in ML models; explain how Meta uses A/B testing to improve Facebook’s newsfeed algorithm; and ex…

Learn from top data science leaders, who share their insights on their groundbreaking innovations, their careers, and the data science profession.

Who’s doing the most innovative things in data science?

These questions—and many more—are the focus of “The Data Science Innovator’s Playbook,” a free Domino Data Lab eBook that explores the work, ideas, and experiences of seven people whose work is revolutionizing data science and business, and having an impact on some of the world’s biggest problems.

All say that better tools for data scientists are a key driver of innovation.

Meet Cassie and other top leaders who are advancing the profession, achieving new value for their companies, and helpin…

JAX was initially released by Google Research in 2018 with the objective of streamlining high-performance numerical computing.

While it was not intended as a deep learning framework in the first place, JAX has seen relevant adoption within the deep learning community.

This has been partly influenced by the adoption of AI powerhouses like Google Research and, very notably, DeepMind, which has been very public about their adoption of JAX.

Just this week, Google Research open-sourced a new ranking library of ranking algorithms for JAX.

It might become one of the most relevant deep learning frameworks of the next few years.

The world’s top minds in AI and distributed computing are coming to Ray Summit — August 23-24 in San Francisco.

Join the global Ray community for two days of keynotes, training, and technical sessions exploring the future of scalable AI and more.

No matter your level of experience with Ray, you’ll come away with tools and know-how that will help you understand why and how large organizations are building their next generation of ML Platforms on top of Ray.

Be sure to register today as seats are limited!

SAVE YOUR SEAT

On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention.

💥 What’s New in AI: DeepMind’s New Super Model can Generalize Across Multiple Tasks on Different DomainsMost deep learning models specialize in mastering a single task in a single domain.

Recently, we have seen the emergence of multi-task models in single domains, such as transformers like GPT-3 in the language space.

We have also seen models like OpenAI’s Dall-E that can combine knowledge from different domains to master a single task.

However, having a single model mastering multiple tasks across heterogenous domains remains an elusive goal for deep learning.

In this issue:we discuss Pre-Train Model Testing ;we overview the pillars of robust machine learning ;we explore Great Expectations.

💡 ML Concept of the Day: Pre-Train Model TestingIn the previous edition of our machine learning (ML) testing series (Edge#213), we discussed the main methods for testing trained ML models.

Pre-train tests are another type of testing method targeted to identify potential errors in ML models prior to executing expensive training jobs.

Typically, pre-train model testing is targeted to validate the correct composition of the datasets and some key verifications in the model behavior.

Without attempting to provide an exhaustive list, there are some pre-train testing…

In this issue:we discuss Pre-Train Model Testing ;we overview the pillars of robust machine learning ;we explore Great Expectations.

💡 ML Concept of the Day: Pre-Train Model TestingIn the previous edition of our machine learning (ML) testing series (Edge#213), we discussed the main methods for testing trained ML models.

Pre-train tests are another type of testing method targeted to identify potential errors in ML models prior to executing expensive training jobs.

Typically, pre-train model testing is targeted to validate the correct composition of the datasets and some key verifications in the model behavior.

Without attempting to provide an exhaustive list, there are some pre-train testing…

About 45% of the time in data science projects is consumed by processing and labeling data.

It’s fair to say that data labeling is one of the most expensive tasks of any machine learning project.

We keep learning from the experience gained by engineers and entrepreneurs behind the leading data labeling solutions, Toloka, Superb AI, Label Studio, and more.

TAKE THE SURVEYThe survey invites machine learning engineers and data scientists, as well as AI enthusiasts.

As a thank you, we will send you a cheat sheet with useful resources that help you understand and organize data labeling as well as a $50 promo code for your own project.

Regularly, we read about massive NLU models reaching new milestones across different language tasks.

This week, we had a fresh taste of the progress with models published by Meta AI and Alexa AI.

This week, Meta AI open-sourced BlederBot 3, a new 175 billion parameter version that achieves over 30% improvement compared to its predecessors across different conversational tasks.

Meta AI released a live demo of BlenderBot 3, allowing users to interact with the chatbot and contribute to its training.

The models released this week by Meta AI and Alexa AI challenge the imagination of the new frontiers for NLU models.

The Superb AI labeling platform is designed with image and label management features allowing human-in-the-loop labeling of images.

Of these, we initially labeled about 3000 images, then used Superb AI’s Custom Auto Label to label a bit less than another 800 images.

As an example, we auto labeled another 500 images, reviewed them, and used the combined data to train a new Custom Auto Label model.

These results highlighted another useful feature of using the human-in-the-loop auto labeling on Superb AI.

Thus, the auto label process can help improve the dataset quality as well.

On Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention.

💥 What’s New in AI: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 LanguagesMachine translation is one of the deep learning disciplines that can have an immediate social impact.

Expanding translations to hundreds of low-resource languages and dialects is one of the most critical challenges of the next decade of machine translation.

Recently, Meta AI open-sourced no language left behind (NLLB)-200, a model that can perform state-of-the-art machine translation across 200 languages.

Meta AI also open-sourced a few comple…

Subscribe to TheSequence to keep reading this post and get 7 days of free access to the full post archives.

In our introduction to machine learning (ML) testing ( Edge#209 ), we reviewed two fundamental approaches: pre-training and post-training testing.

Today, we would like to dive deep into the methods for testing trained models.

Most testing techniques for trained models fit into some of the following categories:we explore TensorFlow’s What-If Tool, one of the most commonly used testing tools in the machine learning space.

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Project BackgroundQuadruped locomotion is rapidly maturing to a degree where robots now routinely traverse a variety of unstructured terrains.

ORI attempt to address this limitation by learning a latent space capturing the key stance phases of a particular gait, via a generative model trained on a single trot style.

The use of a generative model facilitates the detection and mitigation of disturbances to provide a versatile and robust planning framework.

Using a variational auto-encoder (VAE), the ORI approach learns a structured latent space capturing key stance phases constituting a particular gait.

Utilizing a generative model affords detection of disturbances as out of the distribution …

AlphaFold can easily be considered the most relevant ML contribution to the world of science in the last decade.

The initial release of AlphaFold was accompanied by a less promoted project known as AlphaFold Protein Structure Database (AlphaFold DB).

Last week, DeepMind doubled down in AlphaFold DB with a new and incredibly impressive release.

In collaboration with EMBL’s European Bioinformatics Institute (EMBL-EBI), DeepMind upgraded AlphaFold DB with the structure of nearly all catalogued proteins known to science.

By providing access to the structure of proteins in a data-structured, searchable format, AlphaFold DB can drastically advance research across different scientific areas rangin…

In this article, Toloka’s team shares their data labeling experience, looking into three different case studies.

While building production-ready AI solutions is a long process, it starts with gathering and labeling the data used to train ML models.

Search relevanceOne of the most popular use cases of Toloka is offline metrics evaluation of search relevance.

We have shown you examples of data annotation projects for audio transcription, MT evaluation, and search engine evaluation.

We hope they’ve given you a taste of how we approach this problem and an idea of how you can prepare data for your own ML project.

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Третья часть – про организацию работу, сложности, с которыми мы столкнулись, и хаки, повышающие продуктивность команды, к которым мы в итоге пришли.

MLFlow и сервис обучения моделейМы реализовали абстрактный класс для обучения, у котого есть наследники для простой модели tf-idf & logreg и для BERT-модели.

Adversarial Validation для обнаружения дрифта в данныхAdversarial validation – подход, про который я узнал на Kaggle, который, кажется, под разными именами постоянно переизобретается и в академии, и в индустрии.

Роли в командеКажется, что даже в пет-проджекте хорошо бы выделить роли и не толькаться, не бороться за задачи.

Хорошо бы довести пет-проект до красивой демки, на которую можно и в…

То есть MRR = 1 если правильный ответ на первой позиции, 0.5 если на второй, 0.33 на третьей и 0.01 – если на последней.

Транзакции:Здесь кроме id-клиента есть mcc код и два поля с его текстовым описанием, код валюты с расшифровкой, сумма транзакции (как с плюсом, так и с минусом) и дата-время транзакции.

Мы предполагали, что в этом случае общее векторное пространство будет также на входе в RNN, и что сеть сможет найти, например, похожие по смыслу mcc в транзакциях и категории в кликстриме.

Для поля timestamp посчитали те же временные признаки, что и для транзакций.

Отличие от NLP методов было в том, что мы маскировали и предсказывали не сам токен (транзакцию), а его вектор.

Найти их, однако, не так просто, и скорее всего это будут книги для детей или избранная классика.

Долго стоял К. на деревянном мосту, который вел с проезжей дороги в Деревню, и смотрел в кажущуюся пустоту.

Более подробно про интерфейс приложения и как им пользоваться можно почитать здесь, а про технические детали здесь.

Можно выбрать на какой стороне листа будет какой язык и на основе какого исходного текста формировать абзацы параллельного текста.

В шаблонах зашиты переменные для включения цветовой подсветки, путь к картинке для обложки, подсказки для японского и китайского и некоторые другие.

А в этой статье - под катом - хотелось бы рассказать о трендах в развитии Causal Inference в ML в 2021 г.Causal Inference в ML: итоги 2021 г.Сначала поговорим обобщенно, а затем детальнее раскроем наиболее интересные пункты.

Нобелевская премия по экономике была выдана за развитие методов CI, крупнейшие конференции по ML провели воркшопы (NeurIPS, ICML) по вопросам CI для ML.

Interpretable & Causal ML Track – Data Fest Online 2021На ежегодном Data Fest уже в третий раз прошел трек по вопросам Reliable ML - Interpretable & Causal ML Track 2021.

Доклад вошел в топ всех выступлений сообщества Open Data Science в 2021 г. Тема с Causal Shapley Values прогремела в 2020 г., в 2021 г.

Появление каче…

Сейчас этот сервис используют сотни людей и в России, и за ее пределами.

Лень — двигатель прогрессаВы все видели брайлевские символы в лифте и в поликлинике.

В обычном письме буквы представлены связанными линиями, а в письме по Брайлю - комбинацией от 1 до 6 точек, расположенных в узлах воображаемой сетки.

Так что по всему выходило, что для применения опубликованных методов нужен сканер, причем специальный — обычный бытовой мал.

Есть что улучшить и в оптическом распознавании, и в веб-интерфейсе.

Под катом давайте поговорим о том, что интересного произошло в интерпретируемости в 2021 г.Ключевые тренды и события 2021 г. в Interpretable MLСначала поговорим обобщенно, а затем детальнее раскроем наиболее интересные пункты.

Пожалуй, самое пристальное внимание в области XAI в 2021 г. было направлено на оценку качества методов интерпретации – для возможности сравнения методов между собой.

При этом в январе 2022 г. на arxiv появилась знаковая работа, в которой авторы систематизируют около 300 работ в области XAI, опубликованных на CS конференциях в 2014-2020 гг.

Так, и в 2021 г. в разных бизнес-источниках продолжили ссылаться на отчет PwC по Explainable AI от 2018 г. В обзоре достаточно про…

В следующей статье побеседуем о ключевых трендах в развитии методов причинно-следственного анализа в машинном обучении в 2020-2021 гг.

Что такое причинно-следственный анализCorrelation doesn't imply causationГлавный тезис эконометрики, который в последние 5 лет прочно пришел и в ML: «Корреляция не подразумевает причинно-следственную связь».

В целом, о кейсах бизнес-применения causal inference 2021 г. я рассказывала в одном из постов tg-канала @Reliable_ML еще в начале года.

Causal Inference как ключ к балансу классического ML и эконометрикиCausal Inference в MLВ 2020 году в отчете State of AI впервые в явном виде была обозначена необходимость интеграции классического ML c методами Causal In…

Другими словами, сегодня мы поговорим про то, как автоматически восстановить пунктуацию и капитализацию (сделать нужные буквы заглавными).

Если вы делаете модель для малоресурсного языка, то можно воспользоваться проектом Lingtrain, который я делаю для создания параллельных книг (проект открытый и идеи приветствуются).

Если же вы делаете модель для какого-то популярного языка, то можно воспользоваться готовыми датасетами типа Tatoeba.

Для удобства я оформил скрипты для подготовки и обучения в репозиторий multipunct, поэтому дальше я буду обращаться к нему.

Если понадобится экспортировать модель для инференса, например, в TorchScript, то для этого есть метод export().

Мы использовали их при развитии нашего open-source AutoML фреймворка FEDOT , у которого безусловно есть свои особенности как в архитектуре, так и в парадигме разработки.

Если хотите подробнее почитать про предобработку табличных данных и какой она бывает, то можете начать с Предварительная обработка данных и продолжить c Data Preprocessing: Concepts .

Оговоримся сразу, что рассматривать будем наиболее критические изменения в данных, преобразования в духе “трансформация одномерного target массива в вектор-столбец” подразумевается, что производится при необходимости в любой сколько-нибудь крупной AutoML библиотеке.

Также имеется возможность опциональной предобработки для некоторых моделей - н…

Почему ML-модели часто не справляются с такой задачей и что с этим делать – мы рассмотрим далее.

Конечно, этим проблемы машинного обучения не ограничиваются, существуют также сложности с интерпретацией моделей, проблемы предвзятости и этики, ресурсоемкости обучения и прочие.

Начнем с проблемы утечки данных, и затем рассмотрим более общую проблему ограниченного разнообразия обучающих данных и ситуации, когда модель лишь "делает вид", что обучается.

Проблема shortcut learning присутствует и в тех моделях, которые специально дообучались для лидербордов GLUE (Wang et al., 2018) и SuperGLUE (Wang et al., 2019).

Сеть использует признаки, которые позволяют эффективно предсказывать ответ на обучающ…

TL;DR: Этой весной сообщество Open Data Science и компания Huawei делают новый запуск курса по обработке естесственного языка.

Мы делаем новый запуск курса Natural Language Processing.

Лекции и семинары будут онлайн.

Я буду читать лекции, в области NLP я работаю последние 10 лет, успел поработать в Яндексе и ВКонтакте, защитить кандидатскую диссертацию.

Ссылка будет в канале курса в слаке ODS.

Для каждого листа, признака и порога определяем значения и на листьях и считаем функцию потерь получившегося дерева.

Запишем суммарную функцию потерь после разделения, и минимизируем ее по и :Первое слагаемое зависит только от , второе только от , поэтому их можно минимизировать независимо по и , если известны примеры, попавшие в один и в другой лист.

Суммируем ошибку по всем классам и будем искать минимум по :Из данной формулы можно исключить , и тогда задача нахождения оптимального распределения сводится к минимизации перекрестной энтропии между распределениями и .

Это эквивалентно минимизации расхождения Кульбака-Лейблера между и и достигается при для всех .

Другие особенности решающих д…

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.

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The experimentation working group is a group of individuals whose goal is to implement the experimentation program i.e.

Data Science Participation in the Working Group Which data science team members should participate in the working group?

This person is responsible for organizing the working group, leading the working group meetings (we’ll discuss this next), and influencing the working group participants.

The Working Group Meeting The working group should meet regularly to achieve it’s goal of implementing the ExPr.

Tactical Tips for Running an Experimentation Working Group Lets discuss a few tactical tips for improving the the working group’s probability of success.

An experimentation program is the mechanism by which a company uses randomized controlled experiments to generate positive business results.

An experimentation program can succeed only when the right people are involved.

Exactly how this ideation, planning, implementation, and analysis is completed is the process component of an experimentation program.

Data Science Data science plays two important roles in an effective experimentation program.

This second role should be played by data science managers or product managers who sit on a data science team.

These are some of the words used to describe the products and services offered by the world’s largest and most successful businesses.

But it’s very likely that this experience wasn’t crafted in some sort of top-down, divine-intervention-like manner either.

Many business leaders today are familiar with examples of companies that have evolved their products and services, and correspondingly optimized their profit-and-loss statements, through experimentation.

Data science teams don’t need to be convinced of the benefits of running experiments.

But often they lack the business knowledge, cross-team relationships, and structured processes for engaging with business teams and helping them optimiz…

My goal is to help data scientists, ML engineers, and AI product managers, build and operate machine learning systems in production.

Learn more about why I started MLinProduction.

I’m bad at Twitter.

I know I’m bad at Twitter.

There’s a machine learning Twitter, a philosophy Twitter, a history Twitter, a My Little Pony Twitter, a Smash Bros Twitter.

People tell me ML Twitter is worth it.

It’s quite likely that I’m losing out on both ML knowledge and career equity by not being more active on Twitter.

Lots of big communities have little interest in r/place, and lots of little communities have outsized presence in r/place.

The Dustforce Discord talked about doing something for r/place, but hadn’t done anything, so I made a pixel art template in hopes it would get the ball rolling.

After scanning existing r/place pixel art, I realized our target image was somewhat big for our community size, so I prepared a smaller version instead.

Our art template and their art template overlapped by 1 pixel, and we both really wanted that pixel.

We even had time to adjust our template and fill in more space with Dustforce pixel art, adding the S+ icon we had last time r/place happened.

SayCan is a robot learning system that we’ve been developing for about the past year.

The language generation is the easy part, while the value function + policy are the hard parts.

Meanwhile, Google Brain announced their PaLM language model, trained with 540B parameters on 780 billion tokens.

Let’s just say it’s not a good look for anyone claiming deep learning models are plateauing.

Similar to language generation, progress here might overstate the state of the field, because it’s improving things we naturally find interesting.

This has spoilers for MIT Mystery Hunt 2022.

Since the winner of MIT Mystery Hunt has to write the next year’s hunt, things will be…interesting for the next year.

Previous Mystery Hunt puzzles have used gimmicked dice before.

The gimmicked dice from the previous Mystery Hunt puzzle were more clearly gimmicked than our dice.

Maybe I’m a little jaded, but winning Mystery Hunt didn’t light any big fires of motivation for me.

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.

Active learning is one paradigm to deal with not enough labeled data, when there are resources for labeling more data samples but under a limited budget.

(2019) proposed a GAN-like setup, named VAAL (Variational Adversarial Active Learning), where a discriminator is trained to distinguish unlabeled data from labeled data.

Bayesian Active Learning for Classification and Preference Learning.” arXiv preprint arXiv:1112.5745 (2020).

“Learning Loss for Active Learning.” CVPR 2019.

“When Deep Learners Change Their Mind: Learning Dynamics for Active Learning.” CAIP 2021.

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…

March 3, 2022Implicit Bayesian Inference in Large Language Models?

Exchangeable sequences as Implicit Learning MachinesBefore talking about the paper, let me first refresh those old ideas about exchangeable sequences and implicit learning.

The de Finetti theorem connects such sequence models to Bayesian inference, saying that any such distribution can be decomposed as a mixture over i.i.d.

From Exchangeable sequences to Mixtures of HMMsBut GPT-3 is a language models, and clearly language tokens are not exchangeble.

In context learningThe core idea of this paper is that perhaps in-context learning exploits this implicit Bayesian inference, inherent to statistical models of language, to solve…

One phrase I often use to describe what it's like to read reference letters for Eastern European applicants to PhD and Master's programs in Cambridge.

I decided to write this guide for students so they can share it with their professors when asking for reference letters.

Reference letters are often used as ammunition to justify decisions internally, and to determine who gets prioritised for various scholarship and funding competitions.

Reference letters are often used as ammunition to justify decisions internally, and to determine who gets prioritised for various scholarship and funding competitions.

If you have a candidate you enthusiastically support, don't be afraid to ask for help writi…

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.

We find that, analogously to matrix and tensor factorizations, the implicit regularization in hierarchical tensor factorization strives to lower a notion of rank (called hierarchical tensor rank).

For our current purpose it suffices to know that a hierarchical tensor factorization consists of multiple local tensor factorizations, whose components we call the local components of the hierarchical factorization.

Basically, if a tensor can be represented through hierarchical tensor factorization with few local components, then it has low hierarchical tensor rank.

Seeing that the implicit regularization in matrix and tensor factorizations leads to low matrix and tensor ranks, respectively, in ou…

Predicting Generalization using GANsA central problem of generalization theory is the following: Given a training dataset and a deep net trained with that dataset, give a mathematical estimate of the test error.

This blog post is about the topic of a NeurIPS 20 competition Predicting Generalization in Deep Learning competition which suggested using machine learning techniques to understand network properties that promote generalization!

This blog post describes our ICLR22 spotlight paper, coauthored with Nikunj Saunshi and Arushi Gupta, that gives a surprisingly easy method to predict generalization using Generative Adversarial Nets or GANs.

Observation 2) Training deep net classifiers usin…

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…

The company trains massive language models (both GPT-like and BERT-like) and offers them as an API (which also supports finetuning).

Semantic search has to be one of the most exciting applications of sentence embedding models.

Finetuning tends to lead to the best results language models can achieve.

This article explains the intuitions around finetuning representation/sentence embedding models.

This is a walkthrough of one of the most common use cases of embedding models -- text classification.

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…

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.

It’s all very recognizable and type checks as a modern deep learning paper, except it is from 33 years ago.

misses: 102While my training script repro.py in its current form prints at the end of the 23rd pass:eval: split train.

This change dramatically improved the training error, completely overfitting the training set:eval: split train.

Leaving the baseline training running for 100 passes already shows some improvement from the added data alone:eval: split train.

2055 neural nets are basically the same as 2022 neural nets on the macro level, except bigger.

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.

We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVWs).

By constructing submodular EDVWs-based splitting functions, we convert hypergraphs with EDVWs into submodular hypergraphs for which the spectral theory is better developed.

We then utilize this eigenvector to cluster the vertices, achieving higher clustering accuracy than traditional spectral clustering based on the 2-Laplacian.

More broadly, the proposed algorithm works for all submodular hypergraphs that are graph reducible.

Numerical experiments using real-world data demonstrate the effectiveness of combining spectral clustering based on the 1-La…

The graph-based methods can be computationally amongst the most efficient ones, while maintaining the retrieval performance.

One of the main properties, common to graph-based methods, is their immediate conversion of token space into graphs, followed by subsequent processing.

In this paper, we explore a novel unsupervised approach which merges parts of a document in sequential form, prior to construction of the token graph.

Further, by leveraging personalized PageRank, which considers frequencies of such sub-phrases alongside token lengths during node ranking, we demonstrate state-of-the-art retrieval capabilities while being up to two orders of magnitude faster than current state-of-the-ar…

However, professional advice on optimal oral hygiene practices is often forgotten or abandoned by patients.

Therefore patients may benefit from timely and personalized encouragement to engage in oral self-care behaviors.

In this paper, we develop an online reinforcement learning (RL) algorithm for use in optimizing the delivery of mobile-based prompts to encourage oral hygiene behaviors.

We address this challenge by designing a quality reward which maximizes the desired health outcome (i.e., high-quality brushing) while minimizing user burden.

The RL algorithm discussed in this paper will be deployed in Oralytics, an oral self-care app that provides behavioral strategies to boost patient en…

In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.

Similar to proprietary chemistry databases such as Reaxsys, SynKB allows chemists to retrieve structured knowledge about synthetic procedures.

By taking advantage of recent advances in natural language processing for procedural texts, SynKB supports more flexible queries about reaction conditions, and thus has the potential to help chemists search the literature for conditions used in relevant reactions as they design new synthetic routes.

Using customized Transformer models to automatically extract information from 6 million synthesis procedures described in U.S. and EU p…

Image Segmentation is one of the core tasks in Computer Vision and solving it often depends on modeling the image appearance data via the color distributions of each it its constituent regions.

Whereas many segmentation algorithms handle the appearance models dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation.

Our method uses local high order color statistics from the image as an input to tensor factorization-based estimator for latent variable models.

This approach is able to estimate models in multiregion images and automatically output the regions proportions wi…

Learning invariant (causal) features for out-of-distribution (OOD) generalization has attracted extensive attention recently, and among the proposals invariant risk minimization (IRM) (Arjovsky et al., 2019) is a notable solution.

In spite of its theoretical promise for linear regression, the challenges of using IRM in linear classification problems yet remain (Rosenfeld et al.,2020, Nagarajan et al., 2021).

Along this line, a recent study (Arjovsky et al., 2019) has made a first step and proposes a learning principle of information bottleneck based invariant risk minimization (IB-IRM).

To further answer the question of whether IB-IRM is sufficient for learning invariant features in linear …

Federated learning is an emerging technique for training models from decentralized data sets.

In many applications, data owners participating in the federated learning system hold not only the data but also a set of domain knowledge.

Such knowledge includes human know-how and craftsmanship that can be extremely helpful to the federated learning task.

In this work, we propose a federated learning framework that allows the injection of participants' domain knowledge, where the key idea is to refine the global model with knowledge locally.

The scenario we consider is motivated by a real industry-level application, and we demonstrate the effectiveness of our approach to this application.

The fetal cortical plate (CP) undergoes drastic morphological changes during the in utero development.

Therefore, CP growth and folding patterns are key indicator in the assessment of the brain development and maturation.

In this study, we propose a deep learning segmentation framework for automatic and morphologically consistent segmentation of the CP in fetal brain MRI.

First, we generalized a multi-dimensional topological loss function in order to enhance the topological accuracy.

Overall, both quantitative and qualitative results, on a wide range of gestational ages and number of cases, support the generalizability and added value of our topology-guided framework for fetal CP segmentati…

A common approach to aggregate classification estimates in an ensemble of decision trees is to either use voting or to average the probabilities for each class.

The latter takes uncertainty into account, but not the reliability of the uncertainty estimates (so to say, the "uncertainty about the uncertainty").

Our methods are inspired by the theories of probability, belief functions and reliable classification, as well as a principle that we call evidence accumulation.

Our experiments on a variety of data sets are based on random decision trees which guarantees a high diversity in the predictions to be combined.

However, evidence accumulation showed consistently better results on all but ver…

Realistic generative face video synthesis has long been a pursuit in both computer vision and graphics community.

However, existing face video generation methods tend to produce low-quality frames with drifted facial identities and unnatural movements.

To tackle these challenges, we propose a principled framework named StyleFaceV, which produces high-fidelity identity-preserving face videos with vivid movements.

Extensive experiments demonstrate that our framework achieves state-of-the-art face video generation results both qualitatively and quantitatively.

Notably, StyleFaceV is capable of generating realistic $1024\times1024$ face videos even without high-resolution training videos.

Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events.

Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe e.g., climate, biosphere and environmental states.

Typically these quantiles lie outside the range of observable data and so, for estimation, require specification of parametric extreme value models within a regression framework.

In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-…

Knowledge-intensive language tasks (KILT) usually require a large body of information to provide correct answers.

Recently, the reader component has witnessed significant advances with the help of large-scale pre-trained generative models.

We show that a strong generative retrieval model can be learned with a set of adequately designed pre-training tasks, and be adopted to improve a variety of downstream KILT tasks with further fine-tuning.

We name the pre-trained generative retrieval model as CorpusBrain as all information about the corpus is encoded in its parameters without the need of constructing additional index.

Empirical results show that CorpusBrain can significantly outperform str…

We address the new problem of language-guided semantic style transfer of 3D indoor scenes.

The input is a 3D indoor scene mesh and several phrases that describe the target scene.

Firstly, 3D vertex coordinates are mapped to RGB residues by a multi-layer perceptron.

Secondly, colored 3D meshes are differentiablly rendered into 2D images, via a viewpoint sampling strategy tailored for indoor scenes.

(2) rendering 3D indoor scenes from viewpoints consistent with human priors is important.

Context: Test smells are symptoms of sub-optimal design choices adopted when developing test cases.

Previous studies have proved their harmfulness for test code maintainability and effectiveness.

Objective: We propose the design and experimentation of a novel test smell detection approach based on machine learning to detect four test smells.

Method: We plan to develop the largest dataset of manually-validated test smells.

This dataset will be leveraged to train six machine learners and assess their capabilities in within- and cross-project scenarios.

Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs.

However, limited by their inherently shallow and static architectures, they can hardly deal with the rising focus on complex logical queries, which comprise logical operators, imputed edges, multiple source entities, and unknown intermediate entities.

In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.

We then formulate the complex logical queries as masked prediction and introduce a two-stage masked pre-training strategy to improve transferability and generalizability.

Additionally, kgTransformer can reason with explain…

Efficient vision works maximize accuracy under a latency budget.

However, real-time vision applications like autonomous driving operate in streaming settings, where ground truth changes between inference start and finish.

Therefore, a recent work proposed to maximize accuracy in streaming settings on average.

In this paper, we propose to maximize streaming accuracy for every environment context.

Our method, Octopus, uses these scenario properties to select configurations that maximize streaming accuracy at test time.

Auto-encoding Variational Bayes (AEVB) is a powerful and general algorithm for fitting latent variable models (a promising direction for unsupervised learning), and is well-known for training the Variational Auto-Encoder (VAE).

In this tutorial, we focus on motivating AEVB from the classic Expectation Maximization (EM) algorithm, as opposed to from deterministic auto-encoders.

We discuss how approximate E-step can be interpreted as performing variational inference.

Finally, we derive from scratch the AEVB training procedures of a non-deep and several deep latent variable models, including VAE, Conditional VAE, Gaussian Mixture VAE and Variational RNN.

It is our hope that readers would recog…

Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers.

A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrained devices.

To address this challenge, we propose a novel memory-efficient deep learning based model, namely generative optimization networks (GON).

Leveraging the low memory footprint of GONs, we propose a decentralized fault-tolerance method called DRAGON that runs simulations (as per a digital modeling twin) to quickly predict and optimize the performance of the edge federation.

Extensive experiments with real-world edge computing benchmarks…

Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages.

Recent studies have explored improving pre-trained language models to boost dense retrieval performance.

This paper proposes CoT-MAE (ConTextual Masked Auto-Encoder), a simple yet effective generative pre-training method for dense passage retrieval.

Precisely, self-supervised masked auto-encoding learns to model the semantics of the tokens inside a text span, and context-supervised masked auto-encoding learns to model the semantical correlation between the text spans.

We conduct experiments on large-scale passage retrieva…

HairFIT, a pose-invariant hairstyle transfer model, alleviates this limitation yet still shows unsatisfactory quality in preserving delicate hair textures.

To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped with latent optimization and a newly presented local-style-matching loss.

In the StyleGAN2 latent space, we first explore a pose-aligned latent code of a target hair with the detailed textures preserved based on local style matching.

Then, our model inpaints the occlusions of the source considering the aligned target hair and blends both images to produce a final output.

The experimental results demonstrate that our model has strengt…

However, they have a major drawback that the generated JND is assessed in the real-world signal domain instead of in the perceptual domain in the human brain.

Hence, we propose an HVS-inspired signal degradation network for JND estimation.

To achieve this, we carefully analyze the HVS perceptual process in JND subjective viewing to obtain relevant insights, and then design an HVS-inspired signal degradation (HVS-SD) network to represent the signal degradation in the HVS.

On the one hand, the well learnt HVS-SD enables us to assess the JND in the perceptual domain.

On the other hand, it provides more accurate prior information for better guiding JND generation.

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model.

However, the absence of the source data and the domain shift makes the predictions on the target data unreliable.

We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.

For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions.

Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source trainin…

Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.

Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ``argmax'' labeling method often ignores many instances of objects.

We further introduce a new contrastive loss under weak supervision where no instance-level information is available for sampling, called weakly supervised contrastive loss (WSCL).

WSCL aims to construct a credible similarity threshold for object discovery by leveraging consistent features for embedding vectors…

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community.

Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability.

This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery.

This module is integrated by a category attention-driven invariant domain-level context aggregation module to current pseudo invariant feature for further augmenting the pixel representatio…

As there are far fewer majority graphs or weighted majority graphs than there are preference profiles (for a bounded number of candidates and voters), computer-aided techniques such as satisfiability solving become practical for proving results about C1 and pairwise methods.

In this paper, we develop an approach to generalizing impossibility theorems proved for C1 or pairwise voting methods to impossibility theorems covering all voting methods.

We apply this approach to impossibility theorems involving "variable candidate" axioms--in particular, social choice versions of Sen's well-known $\gamma$ and $\alpha$ axioms for individual choice--which concern what happens when a candidate is added…

However, such memorization requires training by amounts of posed images in each scene, which is heavy and inefficient.

On the contrary, few-shot images are usually sufficient to cover the main regions of a scene for a human operator to perform visual localization.

In this paper, we propose a scene region classification approach to achieve fast and effective scene memorization with few-shot images.

Our insight is leveraging a) pre-learned feature extractor, b) scene region classifier, and c) meta-learning strategy to accelerate training while mitigating overfitting.

The experiments validate the effectiveness of our method in the few-shot setting, and the training time is significantly reduce…

Cosmological shock waves are essential to understanding the formation of cosmological structures.

We introduce a novel pipeline, Virgo, combining physical motivation, scalability, and probabilistic robustness to tackle this unsolved unsupervised classification problem.

To this end, we employ kernel principal component analysis with low-rank matrix approximations to denoise data sets of shocked particles and create labeled subsets.

We evaluate on three state-of-the-art data sets with varying complexity and achieve good results.

The proposed pipeline runs automatically, has only a few hyperparameters, and performs well on all tested data sets.

It’s been one year since we released and open sourced AlphaFold and created the AlphaFold Protein Structure Database (AlphaFold DB) to freely share this scientific knowledge with the world.

Today’s update means that most pages on the main protein database UniProt will come with a predicted structure.

Determining the 3D structure of a protein used to take many months or years, it now takes seconds.

AlphaFold has already accelerated and enabled massive discoveries, including cracking the structure of the nuclear pore complex.

Meeting with researchers at the European Society of Human Genetics revealed how important AlphaFold structures are to biologists and clinicians trying to unravel the…

Shortly after submitting my PhD in early 2019, I began my journey as a research scientist at DeepMind!ÂMy role…My role is a virtuous cycle of learning, researching, communicating, and advising.

At ICML…We’re giving a spotlight presentation on our paper, The CLRS algorithmic reasoning benchmark, which we hope will support and enrich efforts in the rapidly emerging area of neural algorithmic reasoning.

The future of algorithmic reasoning…The main dream of our research on algorithmic reasoning is to capture the computation of classical algorithms inside high-dimensional neural executors.

AMMI offers top-tier machine learning tuition to Africa’s brightest emerging researchers, buildin…

Researchers working across artificial intelligence, data science, machine vision, computational biology, speech recognition, and more are presenting and publishing their cutting-edge work in machine learning.

In addition to sponsoring the conference and supporting workshops and socials run by our long-term partners LatinX, Black in AI, Queer in AI, and Women in Machine Learning, our research teams are presenting 30 papers, including 17 external collaborations.

Here’s a brief introduction to our upcoming oral and spotlight presentations:Effective reinforcement learningMaking reinforcement learning (RL) algorithms more effective is key to building generalised AI systems.

Studying aspects of…

Optimising video compressionÂWith video surging during the COVID-19 pandemic, and the total amount of internet traffic expected to grow in the future, video compression is an increasingly important problem.

Working together with YouTube, we explored the potential for our AI model, MuZero, to improve the VP9 codec, a coding format that helps compress and transmit video over the internet.

Together with the YouTube team, we developed a label quality model (LQM) that helps label videos with greater precision, according to YouTube’s ad friendly guidelines.

Through improving how videos are identified and classified, we’ve enhanced trust in the platform for viewers, creators, and advertisers a…

DeepMind are actually hosting a workshop with Queer in AI on Wednesday the 6th July to discuss the relationship between queer issues and AI.

My team (the Ethics & Society team) is busy and close knit.

I lead our research collaborations which operationalise ethics and safety across our work at DeepMind.

I love learning from those around me - internally and within the wider AI ethics community.

I often have people tell me they feel intimidated by AI/ML despite having an interest in technology and ethics.

So, a lot of my time is spent thinking about deep learning or machine learning in one way or another.

At the time, machine learning application and research wasn’t really a viable career option in South Africa.

Beyond DeepMind, I’m also a proud organiser and steering committee member of the Deep Learning Indaba, a movement to strengthen machine learning and AI in Africa.

We expected 30 or so students to get together to learn about machine learning – but to our surprise, we received over 700 applications!

In 2017, there were zero publications with an African author, based at an African institution, presented at NeurIPS, the leading machine learning conference.

At DeepMind…I’m a part of the Applied team, which helps bring DeepMind technology to the outside world through Alphabet and Google products and solutions, like with WaveNet and Google Assistant, Maps, and Search.

I presented…ÂImage Recognition Using Deep Neural Networks, our recently published research on vision language models (VLMs).

I want people to leave the session…With a better understanding of what happens after the research breakthrough is announced.

And how can we use our research to create products and services that have a purpose?

The future is bright and I’m excited to discover new ways of applying our groundbreaking research to help benefit millions of people around t…

Research scientist, Kevin McKee, tells how his early love of science fiction and social psychology inspired his career, and how he’s helping advance research in ‘queer fairness’, support human-AI collaboration, and study the effects of AI on the LGBTQ+ community.

In elementary school, I often tried running controlled psychology experiments for my science projects.

Recent advances in AI research show a lot of promise for supporting her and others working with queer communities.

First, a paper I worked on about ‘queer fairness’, where we advocated for more research to understand the effects of AI on LGBTQ+ communities.

Yet, most work aimed at measuring and correcting algorithmic bia…

Our team is working on a custom project management system for organising all of DeepMind’s research projects.

Along the way, I’ll communicate changes to our stakeholders, write docs, code and test solutions, build analytics dashboards, clean-up old code, and fix bugs.

Then our team usually has lunch together, which is really nice after working remotely for so long.

LeetCode: a platform for levelling-up coding skills.ÂCracking the Coding Interview: an interview guide for software developer roles.

Gainlo Blog: practical coding interview tips and hacks.

In fact, at DeepMind, we now champion a term that perfectly captures my own values and hopes for integrating technology into people’s daily lives: pioneering responsibly.ÂI believe pioneering responsibly should be a priority for anyone working in tech.

At DeepMind, everything we do stems from our company mission of solving intelligence to advance society and benefit humanity, and building a culture of pioneering responsibly is essential to making this mission a reality.ÂWhat does pioneering responsibly look like in practice?

I wanted us to focus on the operational and practical aspects of responsibility, starting with an expectation-free space in which everyone could talk candidly about w…

In October 2021, we announced that we acquired the MuJoCo physics simulator, and made it freely available for everyone to support research everywhere.

We also committed to developing and maintaining MuJoCo as a free, open-source, community-driven project with best-in-class capabilities.

MuJoCo is one of the few full-featured simulators backed by an established company, which is truly open source.

As a research-driven organisation, we view MuJoCo as a platform for collaboration, where roboticists and engineers can join us to develop one of the world’s best robot simulators.

Features that make MuJoCo particularly attractive for collaboration are:Full-featured simulator that can model comple…

Today’s post is all about Akhil Raju, a software engineer on the robotics team.

That was until I turned 12 and started participating in LEGO robotics competitions.

From there I continued with robotics competitions, started university at MIT, and spent a lot of time studying computer science with a focus on robotics.

The robotics team wasn’t on my radar until my recruiter asked me, “By the way, you have bits of robotics on your resume.

Robotics – and AI in general – will be a positive force in the world, and it’s exciting to be able to help move that forward.

But for a typical visual model to learn a new task, it must be trained on tens of thousands of examples specifically labelled for that task.

Today, in the preprint of our paper, we introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks.

This should allow non-expert people to quickly and easily use accurate visual language models on new tasks at hand.

Following this method, we start from Chinchilla, our recently introduced compute-optimal 70B parameter language model, to train our final Flamingo model, an 80B parameter VLM.

Flamingo’s abilities pave the way towards rich interactions with …

At DeepMind…I build bespoke software tools for our developers.

We’re working a hybrid 3:2 model - Monday through Wednesday in the office, Thursday and Friday from anywhere.

Playing music helped tremendously when we were working remotely during the pandemic.

We’re trying to develop tools that can be used by everybody, so we have to design in a very collaborative way.

One of the things I'm advocating for is to take a step back and decide what our principles are for evolving this part of the programming language we’re working on.

Therefore, we asked ourselves, is there an effective way to combine advanced language models with robot learning algorithms to leverage the benefits of both?

With PaLM-SayCan, the robot acts as the language model’s “hands and eyes,” while the language model supplies high-level semantic knowledge about the task.

Our approach selects skills based on what the language model scores as useful to the high level instruction and what the affordance model scores as possible.

Our system can be seen as a dialog between the user and robot, facilitated by the language model.

This is particularly exciting because it represents the first time we can see how an improvement in language models translates to …

The data received at serving time is rarely in the format your model expects. Numerical columns need to be normalized, features created, image bytes decoded, input values validated. Transforming the data can be as important as the prediction itself. That’s why we’re excited to announce custom prediction routines on Vertex AI, which simplify the process of writing pre and post processing code. With custom prediction routines, you can provide your data transformations as Python code, and behind the scenes Vertex AI SDK will build a custom container that you can test locally and deploy to the cloud. Understanding custom prediction routinesThe Vertex AI pre-built containers handle prediction re…

Rax provides state-of-the-art ranking losses, a number of standard ranking metrics, and a set of function transformations to enable ranking metric optimization.

A Rax ranking loss incorporates the entire list of scores to optimize the neural network, improving the overall ranking of the items.

Approximate Metric OptimizationThe quality of a ranking is commonly evaluated using ranking metrics, e.g., the normalized discounted cumulative gain (NDCG).

Using an approximation technique from Rax to transform the NDCG ranking metric into a differentiable and optimizable ranking loss ( approx_t12n and gumbel_t12n ).

Fine-tuning a T5-Base model on MS-MARCO QNA v2.1 with a ranking loss (softmax, in bl…

Along with entity extraction, the other key components of the CDSS system are capturing the temporal relationships, subjects, and certainty assessments.

We helped them to parse the discharge summaries and prescriptions to extract the medical entities.

Experimentation and choosing the right approach — Four models put to testFor entity extraction, both Google Cloud products and open-source approaches were explored.

This has to be done via AutoML Entity Extraction for Healthcare, another Google Cloud service for custom model development.

Vertex AutoML Entity Extraction for Healthcare: This is a low-code approach that’s already available on Google Cloud.

Visualization of the video-text iterative co-tokenization approach.

Instead of processing the inputs directly, the video-text iterative co-tokenization model learns a reduced number of useful tokens from the fused video-language inputs.

Furthermore, iterative co-tokenization learning yields significant compute savings for video-text learning tasks.

Comparison of our iterative co-tokenization approach to previous methods such as MERLOT and VQA-T, as well as, baselines using single ResNet-3D or X3D-XL.

Despite the apparently more voluminous information to process with three streams, we obtain very efficient models due to the iterative co-tokenization approach.

Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction.

To this end, we’re excited to announce the Google Universal Image Embedding Challenge, hosted by Kaggle in collaboration with Google Research and Google Lens.

Domain Landmark Apparel Image Instance Name Empire State Building2 Cycling jerseys with Android logo3Which physical objects belong to the instance class?

Universal Image Embedding ChallengeTo help motivate the research community to address these challenges, we are hosting the Google Universal Image Embedding Challenge.

We invite researchers and machine learning enthusiasts to participate in…

When Google announced its Vertex AI platform in 2021, the timing coincided perfectly with our search for a comprehensive and reliable AI Platform.

This has been a crucial part of our journey towards MLOps excellence, in which Vertex AI has proved to be of great support.

Vertex AI gives us the infrastructure to do this with tools for training, validating, and deploying ML models and pipelines.

With this in mind, we chose to focus our initial adoption of the Vertex AI platform on its Feature Store component.

This enables us to release Vertex AI Pipelines to our test and production environments, leveraging cloud-native services.

This involves both collaborating with data engineers (another in-demand role) and creating the infrastructure for robust data practices throughout the end-to-end ML process.

In other words, ML engineers create processes and partnerships to help with cleaning, labeling and working with large scale data from across the enterprise.

PRODUCTIONMany employers look for ML engineers who have experience with the end-to-end ML process, especially taking ML models to production.

ML engineers work with data scientists to productionize their work, building pipelines for continuous training, automated validation and version control of the model.

ML engineers deploy ML models to production either on cloud…

To give startup leaders more access to these stories and insights, we’re pleased to launch our “Startup Stories” podcast, available on YouTube, Google Podcasts, and Spotify.

Each episode features an intimate, in-depth conversation with a leader of a startup using Google Cloud, with topics ranging from technical implication to brainstorming ideas over glasses of whiskey.

Current: Trevor Marshall, CTO at Current, tells us how he started his journey and how Google Cloud has supported the success of his business.

We’re thrilled to highlight the innovative work and business practices of startups who’ve chosen Google Cloud.

To learn more about how startups are using Google Cloud, please visit thi…

Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images).

As such, we propose a multi-path neural architecture search (MPNAS) approach to build a unified model with heterogeneous network architecture for multiple domains.

MPNAS extends the efficient neural architecture search (NAS) approach from single path search to multi-path search by finding an optimal path for each domain jointly.

The figure below demonstrates the searched architecture of two visual domains among the ten domains of the Visual Domain Decathlon challenge.

Architecture blocks of two domains (I…

Next, we downsample the byte stream to manageable sequence length and feed it to the encoder layer.

A diagram of a generic end-to-end sequence model using byte stream input.

We chose multilingual data sources that related to the task for pre-training both BERT and byte stream models to achieve the best possible performance.

ConclusionFollowing up on our previous work with pQRNN, we evaluate byte stream models for on-device use to enable pre-training and thereby improve model performance for on-device deployment.

Thanks to Vinh Tran, Jai Gupta and Yi Tay for their help with pre-training byte stream models.

Today, to accelerate research in the bio-pharma space, from the creation of treatments for diseases to the production of new synthetic biomaterials, we are announcing a new Vertex AI solution that demonstrates how to use Vertex AI Pipelines to run DeepMind’s AlphaFold protein structure predictions at scale.

Soon after, Google Cloud released a solution that integrated AlphaFold with Vertex AI Workbench to facilitate interactive experimentation.

Between this continued growth in the AlphaFold database and the efficiency of Vertex AI, we look forward to the discoveries researchers around the world will make.

Running inference workflows at scale can be challenging—these challenges include optimi…

Vertex AI Training delivers a serverless approach to simplify the ML model training experience for customers.

As such, training data does not persist on the compute clusters by design.

Built-in NFS support for custom training jobs provides the following benefits:Delivers an easy way to store and access large datasets for Vertex AI Training with less of the cumbersome work involving moving training data around.

Training jobs execute faster by eliminating the data download steps.

Create a Filestore instance and copy dataFirst let’s create a Filestore instance as our NFS file server.

Time-sharing GPUs in GKEGPU time-sharing works by allocating time slices to containers sharing a physical GPU in a round-robin fashion.

For the high-end A100 GPUs, GPU sharing offered a 4.5x throughput increase, which is truly transformational.

NVIDIA multi-instance GPUs (MIG) in GKEGKE’s GPU time-sharing feature is complementary to multi-instance GPUs, which allow you to partition a single NVIDIA A100 GPU into up to seven instances, thus improving GPU utilization and reducing your costs.

You can then run multiple containers on each partition, with those containers sharing access to the resources on that partition.

And now, with GPU time-sharing, you can match your workload acceleration nee…

Training DNNs involves minimizing a loss function that measures the discrepancy between the ground truth labels and the model’s predictions.

The operation uses more memory for storing statistics and involves matrix inversion, thus hindering the applicability of higher-order optimizers in practice.

In “LocoProp: Enhancing BackProp via Local Loss Optimization”, we introduce a new framework for training DNN models.

Perhaps the simplest loss function one can think of for a layer is the squared loss.

After forming the objective in each layer, LocoProp updates the layer weights by repeatedly applying gradient descent steps on its objective.

We are introducing a new-and-improved content moderation tool: The Moderation endpoint improves upon our previous content filter, and is available for free today to OpenAI API developers.

To help developers protect their applications against possible misuse, we are introducing the faster and more accurate Moderation endpoint.

When given a text input, the Moderation endpoint assesses whether the content is sexual, hateful, violent, or promotes self-harm — content prohibited by our content policy.

input text Violence Self-harm Hate Sexual Moderation endpoint Flagged FlaggedThe Moderation endpoint helps developers to benefit from our infrastructure investments.

Use of the Moderation endpoint t…

Join DALL·E 2 waitlistDALL·E, the AI system that creates realistic images and art from a description in natural language, is now available in beta.

Every DALL·E user will receive 50 free credits during their first month of use and 15 free credits every subsequent month.

PricingIn this first phase of the beta, users can buy additional DALL·E credits in 115-credit increments (460 images ) for $15 on top of their free monthly credits.

Using DALL·E for commercial projectsStarting today, users get full usage rights to commercialize the images they create with DALL·E, including the right to reprint, sell, and merchandise.

We are excited to see what people create with DALL·E and look forward to us…

Today, we are implementing a new technique so that DALL·E generates images of people that more accurately reflect the diversity of the world’s population.

We plan to improve this technique over time as we gather more data and feedback.

We are continuing to research how AI systems, like DALL·E, might reflect biases in its training data and different ways we can address them.

These improvements have helped us gain confidence in the ability to invite more users to experience DALL·E.

Expanding access is an important part of our deploying AI systems responsibly because it allows us to learn more about real-world use and continue to iterate on our safety systems.

As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows.

“We didn't know what an osteosarcoma villain would look like so we turned to DALL·E as our creative outlet.

That's a community effort — it's come from the past few months of me talking to other DALL·E artists on Twitter / Discord / DM.

We're all figuring it out together, how to play this beautiful new instrument.”Tom AvivIsraeli chef and MasterChef winner Tom Aviv is debuting his first U.S. restaurant in Miami in a few months and has used DALL·E for menu, decor, and ambiance inspiration — and his team have also used DALL·E to in designing the…

This post focuses on pre-training mitigations, a subset of these guardrails which directly modify the data that DALL·E 2 learns from.

This post is organized in three sections, each describing a different pre-training mitigation:In the first section, we describe how we filtered out violent and sexual images from DALL·E 2’s training dataset.

Reducing Graphic and Explicit Training DataSince training data shapes the capabilities of any learned model, data filtering is a powerful tool for limiting undesirable model capabilities.

We refer to the former model as the unfiltered model, and the latter as the filtered model.

Unfiltered Filtered Generations for the prompt “military protest” from our un…

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data.

In order to utilize the wealth of unlabeled video data available on the internet, we introduce a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT).

Trained on 70,000 hours of IDM-labeled online video, our behavioral cloning model (the “VPT foundation model”) accomplishes tasks in Minecraft that are nearly impossible to achieve with reinforcement learning from scratch.

We then take each foundation model and fine-tune it to the house building dataset described in th…

Major travel delays are expected late Friday and Friday night as rain turns into snow, the National Weather Service forecast said.

In counties like Sussex, Morris and Warren, expected snow accumulations range from 6 to 16 inches.

The winter storm warnings have been issued for Sussex, Warren, Morris, Hunterdon, Middlesex, Monmouth, Ocean and northwest Burlington counties.

Expect the National Weather Service’s Upton, N.Y. office, which covers northeastern N.J., to follow suit shortly.

With defenses already weakened, coastal communities could see major impacts from coastal flooding, with the worst coming Saturday morning, according to the National Weather Service.

As cluster and model sizes have grown, machine learning practitioners have developed an increasing variety of techniques to parallelize model training over many GPUs.

Data Parallelism Pipeline Parallelism Tensor Parallelism Expert Parallelism Data Parallelism Pipeline Parallelism Tensor Parallelism Expert ParallelismAn illustration of various parallelism strategies on a three-layer model.

Forward Forward Backward Backward Update Update Idle Idle GPipe PipeDream Comparison of GPipe and PipeDream pipelining schemes, using 4 microbatches per batch.

Matrix multiplication can be thought of as dot products between pairs of rows and columns; it's possible to compute independent dot products on dif…

Joint Recommendation for Language Model DeploymentWe’re recommending several key principles to help providers of large language models (LLMs) mitigate the risks of this technology in order to achieve its full promise to augment human capabilities.

Documentation should also include model and use-case-specific safety best practices.

Publicly disclose lessons learned regarding LLM safety and misuse in order to enable widespread adoption and help with cross-industry iteration on best practices.

Treat all labor in the language model supply chain with respect.

As LLM providers, publishing these principles represents a first step in collaboratively guiding safer large language model development an…

OpenAI Codex, a natural language-to-code system based on GPT-3, helps turn simple English instructions into over a dozen popular coding languages.

We’re already seeing new applications of Azure OpenAI Service across many industry verticals, from healthcare to financial services.

Applications and IndustriesSince its release via our API, we’ve been working closely with developers to build on top of Codex.

Through tight integration with Codex, GitHub Copilot can convert comments to code, autofill repetitive code, suggest tests and show alternatives.

Developers search for entire commands using natural language rather than trying to remember them or assemble them piecemeal.

Last month, we started previewing DALL·E 2 to a limited number of trusted users to learn about the technology’s capabilities and limitations.

We’ve enhanced our safety system, improving the text filters and tuning the automated detection & response system for content policy violations.

Less than 0.05% of downloaded or publicly shared images were flagged as potentially violating our content policy.

We hope to increase the rate at which we onboard new users as we learn more and gain confidence in our safety system.

We’re inspired by what our users have created with DALL·E so far, and excited to see what new users will create.

Brad Lightcap has been pivotal in OpenAI's growth, scaling our structure, team, and capital base through his oversight of our Finance, Legal, People, and Operations organizations.

Mira is taking on the role of Chief Technology Officer, reflecting her leadership across these critical areas within OpenAI.

He will lead the operations of OpenAI’s nonprofit parent and key strategic projects including our relationships with mission-aligned partners.

These executives are supported by world-class teams who are the lifeblood of OpenAI, constantly advancing the state of the art in artificial intelligence research and deployment.

It’s a pleasure to work alongside such incredible talent and leadership …

But it’s important to keep track of how well the true objective is being optimized.

We’ll focus on a setting that is particularly clean to analyze, in which we have access to the true objective.

In addition, best-of-$n$ sampling has reliable performance and is straightforward to analyze mathematically, making it well-suited to empirical studies of Goodhart’s law and related phenomena.

Together, these estimators allow us to easily analyze how the true objective varies with the amount of optimization applied to the proxy objective.

In the settings we’ve studied so far, such as summarization, we’ve typically been able to reach a KL of around 10 nats using reinforcement learning before the true…

DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language.

def ___fib(10) def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity of the def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) Improve the runtime complexity of the function def fib(n): if n <= 1: return 1 return fib(n-1) + fib(n-2)fib(10) …

Gartner has recognized Microsoft as a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud AI Developer Services, with Microsoft placed furthest in “Completeness of Vision”.

With Azure Cognitive Services for Speech, customers can build voice-enabled apps confidently and quickly with the Speech SDK. We make it easy for customers to transcribe speech to text (STT) with high accuracy, produce natural-sounding text-to-speech (TTS) voices, and translate spoken audio.

Bluware, which develops cloud-native solutions to help oil and gas operators to increase exploration and production workflow productivity through deep learning by enabling geoscientists to deliver faster and smarter decisions about the subsurface and today announced its collaboration with Microsoft for its next-generation automated interpretation solution, InteractivAI™, which is built on the Azure implementation of the OSDU™ Data Platform.

Print a copy of How data and AI will transform contact centres for financial servicesShare How data and AI will transform contact centres for financial services on EmailShare How data and AI will transform contact centres for financial services on FacebookShare How data and AI will transform contact centres for financial services on LinkedInShare How data and AI will transform contact centres for financial services on TwitterToggle share menu for: How data and AI will transform contact centres for financial servicesContact centres for financial institutions have traditionally been a core touch point for customers to access various types of immediate support – from queries to complaints to f…

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However, no systematic study on best practices for extreme compression exists, such as using aggressive quantization methods and layer reduction.

DeepSpeed Compression overcomes these challenges by offering novel state-of-the-art compression techniques, such as XTC for 32x smaller model size and ZeroQuant for 5000x lower compression cost reduction.

map Layers Table 1: Compression techniques supported in DeepSpeed Compression composer.

After the DNN model has been compressed, DeepSpeed Compression replaces the compressed layers with highly optimized kernels in the DeepSpeed Inference engine to maximize hardware efficiency.

DeepSpeed Compression release planDeepSpeed Compression is still at i…

At Microsoft Build 2022, the company announced serverless confidential containers with lift-and-shift support, the next step in the evolution of confidential computing.

Confidential Containers enables users to take existing container workloads, and with a small amount of configuration, use them in a confidential environment.

Users of Confidential Containers create a policy defining precisely what can run in the confidential container environment and how.

Secure multiparty computationsAnother benefit of Confidential Containers is they enable secure multiparty computations.

Confidential Containers is currently available for limited preview and will be available for public preview later this y…

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Could this capability represent the dawn of a new paradigm of scientific discovery?

BOOK The Fourth Paradigm Data-Intensive Scientific DiscoveryJim Gray, a Turing Award winner, and former Microsoft Technical Fellow, characterised the historical evolution of scientific discovery through four paradigms.

Machine learning forms an increasingly important component of the fourth paradigm, allowing the modelling and analysis of large volumes of experimental scientific data.

This ‘fifth paradigm’ of scientific discovery represents one of the most exciting frontiers for machine learning as well as for the natural sciences.

AI4Science is an effort deeply rooted in Microsoft’s mission, applying the fu…

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Introducing the general availability of NVads A10 v5 GPU accelerated virtual machines, now available in US South Central, US West2, US West3, Europe West, and Europe North regions. Azure is the first public cloud to offer GPU Partitioning (GPU-P) on NVIDIA GPUs.

As we continue to explore the role of technology to advance the art of the possible, we are launching the Microsoft Climate Research Initiative (MCRI).

For the kickoff of this initiative, we are focusing on three critical areas in climate research where computational advances can drive key scientific transformations: Overcoming constraints to decarbonization, reducing uncertainties in carbon accounting, and assessing climate risks in more detail.

All results of this initiative are expected to be made public and freely available to spark even broader research and progress on these important climate issues.

“As researchers, we’re excited to work together on projects specifically selected for …

The test set for these experiments combines a variety of dialog genres, including task-oriented dialog, conversational question-answering, and grounded chit-chat.

GODEL available as open sourceTo advance research, we believe it is crucial to make code and models publicly available, and we have released GODEL as fully open source.

We have made three versions of GODEL available: base, large, and extra-large.

We hope GODEL helps numerous academic research teams advance the field of conversational AI with innovative dialog models while eliminating the need for significant GPU resources.

We plan to continuously improve GODEL and make more models available to the research community.

How can Microsoft empower satellite operators to focus on their mission and enable them to continue the operation of their satellites, without making capital investments in their ground infrastructure? To answer that question, Microsoft worked alongside the National Oceanic and Atmospheric Administration (NOAA), and our partner Xplore, to demonstrate how the commercial cloud can provide satellite mission management for NOAA’s legacy polar satellites (NOAA-18)—extending the mission life of these satellites while reducing the cost of operation through Azure Orbital Ground Station as-a-Service (GSaaS).

This website is using a security service to protect itself from online attacks.

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The Federation of State Medical Boards then gave us names and addresses of the state medical boards in the U.S., and some abroad.

The Federation of State Medical Boards recently developed a new technology to address this problem.

These database sites recommended that we have to go to the sites of the three state medical boards, and it actually took us there.

When we got to the state medical boards, all the information has been redacted.

About 15 years ago, I had coined the term “three-pronged approach” to describe my vision of evolving health care.

Caspar Hare and Georgia Perakis have been appointed the new associate deans of the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative in the MIT Stephen A. Schwarzman College of Computing.

“I look forward to working with Caspar and Georgia on continuing to develop and advance SERC and its reach across MIT.

Perakis will also assume an associate dean role at MIT Sloan in recognition of her leadership.

Hare and Perakis succeed David Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Julie Shah, the H.N.

In February 2021, they launched the MIT Case Studies in Social and Ethical Responsibilities of Computing for undergradua…

Now, Saadi is working to improve the product design process by evaluating computational design tools, exploring new applications, and developing education curricula.

These tools, also known as generative design tools, are commonly used in automotive, aerospace, and architectural industries.

After, they feed the constraints into a generative design tool, which generates a product design accordingly.

Before this project, Saadi had little experience with computational tools in the product design process.

Reforming design curriculum to be more inclusiveSaadi is also working to improve the product design process through curriculum development.

“Boiling is a very effective heat transfer mechanism; it’s the way to remove large amounts of heat from the surface, which is why it is used in many high-power density applications,” Bucci says.

But what if so many bubbles form and coalesce that they form a band of vapor that prevents further heat transfer?

Such a problem is a known entity and is labeled the boiling crisis.

The boiling crisis remains a challenge because while models abound, the measurement of related phenomena to prove or disprove these models has been difficult.

His interest in heat transfer mechanisms took root during his doctoral studies, a research subject he pursued in Paris at the French Alternative Energies and Atomi…

The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

The model also automatically explains solutions and rapidly generates new problems in university math subjects.

When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.

Adding contextTurning math questions into programming tasks is not always simple, Drori says.

The researchers gave students 10 questions from each undergraduate math course in a random order; five were created by humans and five were machine-generated.

Pulse oximetry is a noninvasive test that measures the oxygen saturation level in a patient’s blood, and it has become an important tool for monitoring many patients, including those with Covid-19.

It is well known that non-white intensive care unit (ICU) patients receive less-accurate readings of their oxygen levels using pulse oximeters — the common devices clamped on patients’ fingers.

Now, a paper co-authored by MIT scientists reveals that inaccurate pulse oximeter readings can lead to critically ill patients of color receiving less supplemental oxygen during ICU stays.

The findings showed that inaccurate readings of Asian, Black, and Hispanic patients resulted in them receiving less su…

It could also help technicians make adjustments to the printing process on-the-fly if material or environmental conditions change unexpectedly.

Picking parametersDetermining the ideal parameters of a digital manufacturing process can be one of the most expensive parts of the process because so much trial-and-error is required.

But training a neural network-based controller to understand this manufacturing process is data-intensive, and would require making millions of prints.

In practice, conditions typically change due to slight variations or noise in the printing process.

So if you had a manufacturing process out in the field and you wanted to change the material, you wouldn’t have to rev…

A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors.

In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

Accelerating deep learningAnalog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons.

To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte.

The most popular types of local explanation methods fall into three broad categories.

Perhaps her credit score or income, both features used in the model’s prediction, need to be higher for her to be approved.

How are explanation methods used?

For one, Ghassemi’s recent research has shown that explanation methods can perpetuate biases and lead to worse outcomes for people from disadvantaged groups.

Another pitfall of explanation methods is that it is often impossible to tell if the explanation method is correct in the first place.

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low.

As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgro…

However, training effective models requires huge datasets of relevant medical questions, which are often hard to come by due to privacy restrictions.

To overcome this data shortage, researchers at MIT partnered with medical experts to study the questions physicians ask when reviewing EHRs.

When they used their dataset to train a machine-learning model to generate clinical questions, they found that the model asked high-quality and authentic questions, as compared to real questions from medical experts, more than 60 percent of the time.

Some were composed of medical questions asked by patients on web forums, which are a far cry from physician questions.

Once they had compiled their dataset o…

But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments?

This gargantuan number prolongs the drug development process for fast-spreading diseases like Covid-19 because it is far beyond what existing drug design models can compute.

Before drug development can even take place, drug researchers must find promising drug-like molecules that can bind or “dock” properly onto certain protein targets in a process known as drug discovery.

After successfully docking to the protein, the binding drug, also known as the ligand, can stop a protein from functioning.

Walters suggested that the team try their model on an already existing dr…

The machine-learning system predicted motions and yoga poses performed by an individual standing on the smart textile mat with about 99 percent accuracy.

Using a novel fabrication process, MIT researchers have produced smart textiles that snugly conform to the body so they can sense the wearer’s posture and motions.

Knitting know-howTo produce a smart textile, the researchers use a digital knitting machine that weaves together layers of fabric with rows of standard and functional yarn.

He decided to try incorporating melting fibers and thermoforming into the smart textile fabrication process.

Once he perfected the fabrication process, Wicaksono needed a system to accurately process pressure…

Over time, however, Conover realized that other skin conditions make up the vast majority of cases physicians and dermatologists see.

“We realized we needed to pivot away from skin cancer in order to help skin cancer patients see the dermatologist faster.”After primary care physicians take a photo of a patient’s skin condition, Piction’s app shows images of similar skin presentations.

Piction also helps physicians differentiate between the conditions they most suspect to make better care decisions for the patient.

Through those conversations, she learned that skin rashes like psoriasis, eczema, and rosacea account for the vast majority of skin problems seen by primary care physicians.

“This…

Evidence from Policy RepresentationIn cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy.

CTDE in Cooperative MARL: VD and PG methodsCentralized training and decentralized execution (CTDE) is a popular framework in cooperative MARL.

VD methods learn local Q networks and a mixing function that mixes the local Q networks to a global Q function.

By contrast, PG methods directly apply policy gradient to learn an individual policy and a centralized value function for each agent.

The permutation game: a simple counterexample w…

FIGS: Attaining XGBoost-level performance with the interpretability and speed of CARTFIGS (Fast Interpretable Greedy-tree Sums): A method for building interpretable models by simultaneously growing an ensemble of decision trees in competition with one another.

In this blog post we’ll cover FIGS, a new method for fitting an interpretable model that takes the form of a sum of trees.

Real-world experiments and theoretical results show that FIGS can effectively adapt to a wide range of structure in data, achieving state-of-the-art performance in several settings, all without sacrificing interpretability.

from imodels import FIGSClassifier , get_clean_dataset from sklearn.model_selection impor…

The Berkeley Crossword SolverWe recently built the Berkeley Crossword Solver (BCS), the first computer program to beat every human competitor in the world’s top crossword tournament.

in Berkeley (3)Domain ender that UC Berkeley was one of the first schools to adopt (3)Angeleno at Berkeley, say (8)Our ApproachThe BCS uses a two-step process to solve crossword puzzles.

Compared to the previous state-of-the-art method for answering crossword clues, this approach obtained a 13.4% absolute improvement in top-1000 QA accuracy.

FillWinning The American Crossword Puzzle TournamentThe American Crossword Puzzle Tournament (ACPT) is the largest and longest-running crossword tournament and is organiz…

Rethinking Human-in-the-Loop for Artificial Augmented IntelligenceFigure 1: In real-world applications, we think there exist a human-machine loop where humans and machines are mutually augmenting each other.

For demonstration, we designed a recognition framework that was a combination of active learning, semi-supervised learning, and human-in-the-loop (Figure 3).

Low-confidence predictions are sent for human annotation, and high-confidence predictions are trusted for downstream tasks or pseudo-labels for model updates.

Thus, the goal of AI development changes from replacing human intelligence to mutually augmenting both human and machine intelligence.

However, this goal of replacing human e…

Designing Societally Beneficial Reinforcement Learning SystemsDeep reinforcement learning (DRL) is transitioning from a research field focused on game playing to a technology with real-world applications.

At the same time as the emergence of powerful RL systems in the real world, the public and researchers are expressing an increased appetite for fair, aligned, and safe machine learning systems.

A Taxonomy of FeedbackReinforcement learning systems are often spotlighted for their ability to act in an environment, rather than passively make predictions.

Other supervised machine learning systems, such as computer vision, consume data and return a prediction that can be used by some decision ma…

Should I Use Offline RL or Imitation Learning?

Are there fundamental limitations to methods that rely on some form of imitation (BC, conditional BC, filtered BC) that offline RL addresses?

While it might be clear that offline RL should enjoy a large advantage over imitation learning when learning from diverse datasets that contain a lot of suboptimal behavior, we will also discuss how even cases that might seem BC-friendly can still allow offline RL to attain significantly better results.

Empirical Results Comparing Offline RL and BCIn our discussion so far, we have already studied settings such as the antmazes, where offline RL methods can significantly outperform imitation-style methods d…

Offline RL Made Easier: No TD Learning, Advantage Reweighting, or TransformersA demonstration of the RvS policy we learn with just supervised learning and a depth-two MLP.

Offline reinforcement learning (RL) is conventionally approached using value-based methods based on temporal difference (TD) learning.

These algorithms learn conditional policies by conditioning on goal states (Lynch et al., 2019; Ghosh et al., 2021), reward-to-go (Kumar et al., 2019; Chen et al., 2021), or language descriptions of the task (Lynch and Sermanet, 2021).

The video above shows the complex behavior we learn using just supervised learning with a depth-two MLP – no TD learning, data reweighting, or Transformer…

EO imagery is commonplace—anyone who has used Google Maps or similar mapping software has interacted with EO satellite imagery.

In general, existing computer vision methods on other, non-aerial RGB imagery transfer very well to satellite imagery.

Synthetic Aperture Radar ImagerySynthetic aperture radar (SAR) imagery is an active form of remote sensing in which a satellite transmits pulses of microwave radar waves down to the surface of the Earth.

Computer Vision on SAR Imagery for UkraineImagery analysts are currently relying on both EO and SAR imagery where available over Ukraine.

Our top performing method, MAERS, for representation learning on RGB, SAR, and co-registered RGB + SAR build…

Unsupervised Skill Discovery with Contrastive Intrinsic ControlUnsupervised Reinforcement Learning (RL), where RL agents pre-train with self-supervised rewards, is an emerging paradigm for developing RL agents that are capable of generalization.

This tension between the need to support large skill spaces and the limitation of current discriminators leads us to propose Contrastive Intrinsic Control (CIC).

Contrastive Intrinsic Control (CIC) introduces a new contrastive density estimator to approximate the conditional entropy (the discriminator).

For a practical algorithm, we use the CIC contrastive skill learning as an auxiliary loss during pre-training.

Our hope is that our approach encoura…

imodels: leveraging the unreasonable effectiveness of rulesimodels: A python package with cutting-edge techniques for concise, transparent, and accurate predictive modeling.

Moreover, interpretable models help with all kinds of things, such as identifying errors, leveraging domain knowledge, and speeding up inference.

Fig 1 shows four possible forms an interpretable model in the imodels package could take.

model = BoostedRulesClassifier () # initialize a model model .

This post is based on the imodels package (github, paper), published in the Journal of Open Source Software, 2021.

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, …

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…

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…

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…

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…

Amazon Personalize automatically finds the relevant items within the set of promotional items that meet your business rule and distributes them within each user’s recommendations.

You can use the Amazon Personalize console or API to create a filter with your logic using the Amazon Personalize DSL (domain-specific language).

You define the items to promote in the catalog system, load them to the Amazon Personalize items dataset, and then get recommendations.

PrerequisitesTo use promotions, you first set up some Amazon Personalize resources on the Amazon Personalize console.

For more information about Amazon Personalize, see What Is Amazon Personalize?

Amazon SageMaker JumpStart solutions are a feature within Amazon SageMaker Studio that allow a simple-click experience to set up your own machine learning (ML) workflows.

Starting today, we’re excited to announce that JumpStart solutions now supports custom AWS Identity and Access Management (IAM) roles be passed into services.

This means each service uses their own IAM role with dedicated IAM policy attached, and is fully customizable.

In the SageMaker Projects and JumpStart section, select Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for this account and Enable Amazon SageMaker project templates and Amazon SageMaker JumpStart for Studio users.

About the authors…

Solution overviewThe following reference architecture shows how you can use AWS AI services like Amazon Textract and Amazon Comprehend, along with other AWS services to implement the IDP workflow.

To extend this phase, we use Amazon Comprehend pre-trained entities and an Amazon Comprehend custom entity recognizer for further document extraction.

We discussed the various stages of the pipeline and a hands-on solution with AWS AI services such as Amazon Textract, Amazon Comprehend, Amazon Comprehend Medical, and Amazon A2I for designing and building industry-specific use cases.

We recommend you review the security sections of the Amazon Textract, Amazon Comprehend, and Amazon A2I documentatio…

Intelligent document processing (IDP) with AWS artificial intelligence (AI) services helps automate information extraction from documents of different types and formats, quickly and with high accuracy, without the need for machine learning (ML) skills.

We demonstrate how you can utilize ML capabilities with Amazon Textract, Amazon Comprehend, and Amazon Augmented AI (Amazon A2I) to process documents and validate the data extracted from them.

Amazon A2I integrates both with Amazon Textract and Amazon Comprehend to provide you the ability to introduce human review steps within your intelligent document processing workflow.

We recommend reviewing the security sections of the Amazon Textract, A…

Create an Amazon Simple Storage Service (Amazon S3) bucket and a folder named air-quality .

On the AWS IoT Core console, create an AWS IoT policy called admin.

To create the detector, navigate to the Lookout for Metrics console and choose Create detector.

To create a dataset, complete the following steps:On the Amazon Lookout for Metrics console, navigate to your detector.

You can also use an alert to trigger automations using AWS Lambda functions in order to drive API-driven operations on AWS IoT Core.

To the best of the authors’ knowledge, no reference architectures and examples are available for GNN-based real-time inference solutions as of this writing.

It allows you to pack your model training scripts and dependencies in a Docker image, which it uses to create SageMaker training instances.

The function model_fn loads the saved model file and associated assets from the model_dir argument and the SageMaker model folder.

To fulfill real-time inference, we need to convert the GNN model inference from transductive mode to inductive mode.

ConclusionIn this post, we showed how to build a GNN-based real-time fraud detection solution using SageMaker, Neptune, and the DGL.

Rekognition Custom Labels lets you manage the ML model training process on the Amazon Rekognition console, which simplifies the end-to-end model development and inference process.

Create your Amazon Rekognition projectTo create your agriculture yield measuring project, complete the following steps:On the Amazon Rekognition console, choose Custom Labels.

Edit this file to replace the parameter bucket with your bucket name and model with your Amazon Rekognition model ARN.

Delete the Amazon Rekognition projectTo delete the Amazon Rekognition project, complete the following steps:On the Amazon Rekognition console, choose Use Custom Labels.

For more information about using custom labels, see Wha…

Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations.

In the following sections, we walk through these high-level steps for overcoming an ICE:Define HyperParameter Tuning Job Configuration Define the Training Job Parameters Create the Hyperparameter Tuning Job Describe training jobDefine HyperParameter Tuning Job ConfigurationThe HyperParameterTuningJobConfig object describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, the ranges of the parameters to search, and the resource limits f…

We also explore using SageMaker for our model deployment, all using R.Solution overviewThe following diagram shows the architecture used in our solution.

If you’re new to using RStudio on SageMaker, review Get started with RStudio on Amazon SageMaker.

The easiest way to create a SageMaker pipeline is by using the SageMaker SDK, which is a Python library that we can access using the library reticulate.

Register model step – If the preceding conditional step is True , and the performance of the model is acceptable, then the model is registered in the model registry.

Serverless deployment of the modelAfter you’ve trained and registered a model on SageMaker, deploying the model on SageMaker is …

Edge devices – Remote edge devices are hardware devices that can run ML models using Edge Manager.

AWS IoT Greengrass V2 – AWS IoT Greengrass allows you to deploy components into the simulated devices using an EC2 instance.

With AWS IoT Greengrass V2 and Edge Manager integration, it’s possible to use AWS IoT Greengrass V2 components.

With AWS IoT Greengrass V2 and Edge Manager integration, it’s possible to use AWS IoT Greengrass V2 components.

AWS IoT Greengrass installation and provisioningAfter we have the IAM policy and role in place, we’re ready to install AWS IoT Greengrass Core software with automatic resource provisioning.

In this post, we share how Adspert created the pricing tool from scratch using different AWS services like Amazon SageMaker and how Adspert collaborated with the AWS Data Lab to accelerate this project from design to build in record time.

Product listing notifications coming from Amazon SQS are ingested in near-real time into the raw layer using an AWS Lambda function.

The original source data is stored in the Amazon Simple Storage Service (Amazon S3) raw layer bucket using Parquet data format.

The SageMaker training job is deployed using a SageMaker endpoint.

Ennio Pastore is a data architect on the AWS Data Lab team.

For example, you can immediately start detecting entities such as people, places, commercial items, dates, and quantities via the Amazon Comprehend console, AWS Command Line Interface, or Amazon Comprehend APIs.

To train a custom entity recognizer, you can provide training data to Amazon Comprehend as annotations or entity lists.

Dataset preparationIn this post, we explain how we trained an Amazon Comprehend custom entity recognizer using annotated documents.

With this announcement, we’re lowering the barrier to entry for users interested in using Amazon Comprehend custom entity recognition technology.

To learn more and get started with a custom entity recognizer, refer to Custom entity rec…

(one-hot encoded: or ) total_amount – Total amount of loans (numeric)The following figure shows example feature groups and feature metadata.

In this section, we interact with the Boto3 API endpoints to update and search feature metadata.

To begin improving feature search and discovery, you can add metadata using the update_feature_metadata API.

response = sagemaker_client.describe_feature_metadata( FeatureGroupName="customer", FeatureName="job_admin", ) # Navigate to 'Parameters' in response to get metadata metadata = response['Parameters']You can search for features by using the SageMaker search API using metadata as search parameters.

If you want to learn more about collaborating and shar…

In the fast-paced field of making the world’s tech devices, Pegatron Corp. initially harnessed AI to gain an edge.

It maintains hundreds of AI models, trained and running in production on NVIDIA GPUs.

Triton, NGC Simplify AI JobsPegatron uses NVIDIA Triton Inference Server, open-source software that helps deploy, run and scale AI models across all types of processors, and frameworks.

Hsiao’s team optimizes pretrained AI models it downloads in integrated Kubernetes containers from the NVIDIA NGC hub for GPU-optimized software.

Next Step: Digital TwinsTaking another step in smarter manufacturing, Pegatron is piloting NVIDIA Omniverse, a platform for developing digital twinsIt has two use case…

Software company Seoul Robotics is using NVIDIA technology to make this possible — turning non-autonomous cars into self-driving vehicles.

Seoul Robotics’ platform, dubbed LV5 CTRL TWR, collects 3D data from the environment using cameras and lidar.

As a Metropolis member, Seoul Robotics received early access to software development kits and the NVIDIA Jetson AGX Orin for edge AI.

Equipped with LV5 CTRL TWR, the BMW facility has automated such movement of cars — resulting in time and cost savings.

Beyond automotive factories, Seoul Robotics envisions its platform to be deployed across the globe — at retail stores, airports, traffic intersections and more.

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology accelerates creative workflows.

They design engaging animations, VR art exhibits and futuristic interactive AR displays.

#CreatorsJourney ChallengeIn the spirit of learning, the NVIDIA Studio team is posing a challenge for the community to show off personal growth.

Participate in the #CreatorsJourney challenge for a chance to be showcased on NVIDIA Studio social media channels.

Follow and tag NVIDIA Studio on Instagram, Twitter or Facebook, and use the #CreatorsJourney tag to join.

For over a decade, NVIDIA has invested in social causes and communities in India as part of our commitment to corporate social responsibility.

The project will train 3,000 farmers in Naandi’s Farmer Field Schools to earn sustained income by cultivating coffee and pepper plants using organic regenerative practices.

Previous efforts trained over 3,000 farmers across 115 villages and resulted in the production and distribution of nearly 34,000 kilograms of coffee fruit.

NVIDIA’s corporate social responsibility initiatives span the globe.

Read more about previous projects we’ve funded in India and corporate social responsibility at NVIDIA.

Israel’s largest private medical center is working with startups and researchers to bring potentially life-saving AI solutions to real-world healthcare workflows.

“We saw the impact right away,” said Dr. Michal Guindy, head of medical imaging and head of RISE at Assuta.

With Rhino Health, Assuta aims to help its collaborators develop AI models across hospitals internationally, resulting in more generalizable algorithms that perform more accurately across different patient populations.

Register for NVIDIA GTC, running online Sept. 19-22, to hear more from leaders in healthcare AI.

Subscribe to NVIDIA healthcare news and watch on demand as Assuta, Aidoc and Rhino Health speak at an GTC panel.

It’s time to rumble in Grapital City with Rumbleverse launching today on GeForce NOW.

That means gamers can tackle, uppercut, body slam and more from any GeForce NOW-compatible device, including mobile, at full PC quality.

Drop into the chaotic world of Grapital City, where players must brawl it out to become the champion.

Rumbleverse is free to play, so getting started is easy when paired with a free GeForce NOW membership.

And without having to wait for game downloads due to cloud streaming, members can dive into their new games as quickly as possible.

It’s one example Greenberg gives of how computer graphics are becoming part of every human enterprise.

A Whole New ChapterExpanding the frontier, he’s creating new tools for an architecture design course based on today’s capabilities for building realistic 3D worlds and digital twins.

“This is my next big project, and I’m very excited about it,” said the computer graphics professor of the work, which is sponsored by NVIDIA.

From Sketches to Digital TwinsFor Greenberg, it all comes down to the power of computer graphics.

Educators can request early access to the “Graphics & Omniverse” teaching kit.

Tokyo-based startup Telexistence this week announced it will deploy NVIDIA AI-powered robots to restock shelves at hundreds of FamilyMart convenience stores in Japan.

There are 56,000 convenience stores in Japan — the third-highest density worldwide.

“The first space we’re tackling this is through convenience stores — a huge network that supports daily life, especially in Japan, but is facing a labor shortage.”The company, founded in 2017, next plans to expand to convenience stores in the U.S., which is also plagued with a labor shortage in the retail industry — and where more than half of consumers say they visit one of the country’s 150,000 convenience stores at least once a month.

Telexi…

Editor’s note: This post is part of our weekly In the NVIDIA Studio series, which celebrates featured artists, offers creative tips and tricks, and demonstrates how NVIDIA Studio technology accelerates creative workflows.

Announcements included a host of updates to a pillar of the NVIDIA Studio software suite: NVIDIA Omniverse, a platform for 3D design collaboration and world simulation.

Available now, the August NVIDIA Studio Driver gives creators peak reliability for using Omniverse and their favorite creative apps.

Choosing the right NVIDIA Studio laptop can be tricky, but students can use this guide to find the perfect tool to power their creativity — like the Lenovo Slim 7i Pro X, an N…

With 45 demos and slides, five NVIDIA speakers announced:A new platform for creating avatars, NVIDIA Omniverse Avatar Cloud Engine (ACE).

Omniverse also will include neural graphics capabilities developed by NVIDIA Research that combine RTX graphics and AI.

Graphics Get SmartOne of the essential pillars of the emerging metaverse is neural graphics.

“Neural graphics intertwines AI and graphics, paving the way for a future graphics pipeline that is amenable to learning from data,” said Sanja Fidler, vice president of AI at NVIDIA.

For example, they can use neural graphics to capture objects and behaviors in the physical world quickly.

NVIDIA AI tools are enabling deep learning-powered performance capture for creators at every level: visual effects and animation studios, creative professionals — even any enthusiast with a camera.

With NVIDIA Vid2Vid Cameo, creators can harness AI to capture their facial movements and expressions from any standard 2D video taken with a professional camera or smartphone.

And with 3D body-pose estimation software, creators can capture full-body movements like walking, dancing and performing martial arts — bringing virtual characters to life with AI.

And creative studios can harness AI-powered performance capture for concept design or previsualization — to quickly convey an idea of how certai…

For cutting-edge visual effects and virtual production, creative teams and studios benefit from digital sets and environments that can be updated in real time.

A crucial element in any virtual production environment is a sky dome, often used to provide realistic lighting for virtual environments and in-camera visual effects.

At SIGGRAPH today, ILM showcased how its team, with the NVIDIA DeepSearch tool, used natural language to rapidly search through a massive asset library and create a captivating sky dome.

Omniverse DeepSearch, however, lets ILM search intuitively through untagged assets using text or a 2D image.

With NVIDIA DeepSearch and Omniverse Enterprise, ILM has the potential to ac…

To facilitate the level of data ingestion required to support the training models supporting our products, we’ve had to build a new data ingestion infrastructure as well as new last-mile transformation pipelines.

In the sections below, we share our experience building data ingestion and last-mile data preprocessing pipelines that are responsible for feeding data into AI training models.

Data ingestion pipeline overviewWe have exabytes of training data powering our models, and the amount of training data is growing rapidly.

We have built a disaggregated Data PreProcessing tier (DPP) that serves as the reader tier for data ingestion and last-mile data transformations for AI training [Ref].

Sc…

FL-DP enhances privacy in two important ways:It allows machine learning (ML) models to be trained in a distributed way so that users’ data remains on their mobile devices.

It adds noise to reduce the risk of an ML model memorizing user data.

Such an approach could enhance user privacy while still facilitating an intelligent, safe, and intuitive user experience across Meta’s family of technologies.

How it works:With FL-DP, ML models are trained in a federated manner where mobile devices learn locally.

This architecture is a combination of infrastructure across mobile devices, trusted execution environments, and conventional back-end servers.

What the research is:Recent research has demonstrated that binary optimization is important for achieving peak performance for various applications.

VESPA expands on ESP in several ways to make it useful in the context of binary optimizers.

VESPA increases the scope where binary optimizers can be used, thus enhancing the range of applications that can leverage these tools to improve their performance.

Once the static profile data produced by VESPA is injected into a binary optimizer, this tool can proceed with its optimization steps as usual, completely oblivious to how the profile data was computed.

VESPA, therefore, can very easily be integrated into existing binary optimizers, which we d…

Share Vote Reddit WhatsApp 0 SharesIntroductionIf you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and program frameworks.

How were the ML Education program creators able to capture and communicate this value so that the program could scale to what it is today?

When a larger percentage of non-ML engineers attend ML Education courses it means that we are distilling ML expertise to the broader ML market, increasing the overall internal ML market size for Uber.

ConclusionUber’s ML Education Program ha…

Part 1 will introduce our design principles and explain the benefits of applying these principles to technical education content design and program frameworks, specifically in the ML domain.

Core Principles of Uber’s ML Education ProgramThe capabilities of Uber’s ML infrastructure and ecosystem have enabled us to design, implement, and ground our ML Education program in our design principles.

Aside from the core principle of reproducibility discussed above, we have a list of other design principles that comprise Uber’s ML Education program:Because our subject matter is highly technical, we felt it appropriate to derive our design principles from industry-recognized engineering principles.

H…

Real-Time Document Check CriteriaFrom the onset, we knew that the Real-Time Document Check product needed to meet 4 non-negotiable criteria:Data privacy: Adherence to best practices for handling personal data, taking into account local laws, regulations, and norms in all countries where the product is available.

In the Document Image Processing module, a list of operations (including document classification, transcription, and fraud detection) are applied to the uploaded document images via different technologies (e.g., 3rd-party vendor, Uber in-house technology, and human review).

Looking into the FutureReal World ImpactAs of May 2022, Real-Time ID Document Check is live in Brazil, Mexico,…

By training machine learning (ML) models on top of the road graph prediction using historical data in combination with real-time signals, we can refine ETAs that better predict real-world outcomes.

To meet these challenges, Uber AI partnered with Uber’s Maps team on a project called DeepETA to develop a low-latency deep neural network architecture for global ETA prediction.

We take a similar approach to ETA prediction at Uber.

Conclusions and Future WorkWe have launched this DeepETA model into production for global 4-wheel ETA prediction.

The DeepETA model launch makes it both possible and efficient to train and serve large-scale Deep Learning models that predict ETAs better than XGBoost ap…

Industry-wide, payment fraud losses are measured in terms of the fraction of gross amounts processed.

RADAR is an AI fraud detection and mitigation system with humans in the loop.

RADAR fraud protection rules are generally short-lived and targeted reactions to the unexpected attacks.

We will define some terminology to discuss time series data below:OT – “Order time” when the specific order has been fulfilled.

ConclusionIn this blog, we presented the RADAR system and how it brings together many components of Uber’s technical ecosystem to solve a complex business problem.

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…

Diagram of active learning system | Source: AuthorWhy do we need active learning?

Active learning use case in NLP (NER) | SourceAs we can see above, clearly, all of the active learning strategies are outperforming the random sampling (RAND) baseline performance by a good margin.

Sample of selected frames via active learning | SourceAside from the cost advantages, a significant improvement in mean average precision (from an objection detection perspective) was observed using active learning.

The improvement of protein production | SourceSome popular frameworks used for Active Learning1.modAL: A modular active learning framework for Python3modAL is an active learning framework for Python3, de…

Training data: Meena is trained on a large amount of dialogue data, this is different from previous models.

Building on the early work of Meena, LaMDA introduced a number of new approaches to dialogue models, which resulted in impressive results.

In this post, we looked at two models, namely Meena and LaMDA, which are both dialogue models, and highlighted some of their key technical innovations.

You may have to invest more resources trying to make these dialogue models “fit” your domain-specific application.

So that still represents a big obstacle to the easy application of these dialogue models.

You’ll learn about:1 What is a reasonable scale MLOpsWhat is a reasonable scale MLOps 2 How to set up MLOps at a reasonable scaleHow to set up MLOps at a reasonable scale 3 What tools to use and whether to buy or build themWhat tools to use and whether to buy or build them 4 How to deliver models to customersHow to deliver models to customers 5 What are the limits of reasonable scaleWhat are the limits of reasonable scale 6 And much more.

Sabine Nyholm: It’s our pleasure to introduce Jacopo Tagliabue, who has even been called the father of reasonable scale MLOps.

The idea of the reasonable scale stack came from the realization that most of our business problems are per customer or organizat…

Aim of the project: bird image classifierThe aim of the project is to build an image classifier to classify different species of birds.

Building the image classification modelAs mentioned before, research and planning is the key to implementing any machine learning project.

MLOps pipeline for image classification: building the vision transformer using PytorchI have created the full model as per the author’s description of ViT in their paper.

The image on the left is the original image whereas the image on the right is overlaid with the attention map.

MLOps pipeline for image classification: creating the app using StreamlitThe Streamlit app will be a web app that we will deploy on the cloud.

In this tutorial, we’ll present a simple example of a time-series-based ML project and build an MLOps pipeline for that.

Exploratory data analysis (EDA) – understanding our data using data analysis and visualization techniques.

– understanding our data using data analysis and visualization techniques.

MLOps pipeline for time series prediction: model developmentResearchAs we mentioned before, a good practice of ML project development is to start with research.

ConclusionIn this tutorial, we’ve presented a simple end-to-end ML time series project following MLOps practices.

In this article, you will learn about different lessons for building a deep learning-based OCR model so that when you are working on any such use case, you may not face the issues that I have faced during the development and deployment.

OCR has become very popular nowadays and has been adopted by several industries for faster text data reading from images.

I have also worked on insurance documents OCR where information from different documents needed to be extracted and used for several other purposes like user profile creation, user verification, etc.

Labeling the data (data annotation)ProblemNow that you have your data and also created new samples using image augmentation techniques, the …

In particular, we provide tips for meetings under different phases of an ML project development to ensure smoothness along the entire journey.

Below we describe the memory-wise and temporal limitations worth discussing in your early ML meetings.

Diving a bit deeper into Neptune.ai and how it is useful in the context of ML team meetings:It stores individual runs with the metadata in clear tables.

Don’t call a meeting without resultsA data science team meeting should only happen to discuss tangible resultsThis is perhaps the most important tip in making your ML meetings effective.

9 small tips to help you along the way in making your ML meetings more effective.

Just like ZenML, Metaflow also uses steps to decorate functions, so it can be quite similar to users with ZenML experience.

Ingesting data: Kedro > ZenML > MetaflowIntegrationsKedro: pluginsMost of the kedro capability comes from its plugins.

And while kedro adds them as plugins, they actually do their job as an independent component integrated into the kedro structure.

Tool Kedro ZenML Metaflow Programming languages Python Kedro: ZenML: Metaflow: R Kedro: ZenML: Metaflow: Pricing Free-to-use Kedro: ZenML: Metaflow: Code structure Data sources abstraction Kedro: ZenML: Metaflow: Separated environments Kedro: ZenML: Metaflow: Stacks Kedro: ZenML: Metaflow: Object oriented Kedro: ZenML: Metaf…

In this first installment of the series “Real-world MLOps Examples,” Jules Belveze, an MLOps Engineer, will walk you through the model development process at Hypefactors, including the types of models they build, how they design their training pipeline, and other details you may find valuable.

Today, I work for a media intelligence tech company called Hypefactors, where I develop NLP models to help our users gain insights from the media landscape.

Model development at HypefactorsCould you elaborate on the types of models you build at Hypefactors?

ML workflow at Hypefactors | Source: AuthorCould you describe your tool stack for model development?

Hypefactors model training and evaluation sta…

In Data Science projects, model deployment is probably the most critical and complex part of the whole lifecycle.

What you could try: use one of the deployment strategiesBlue-green deploymentBlue-green model deployment | SourceThis deployment strategy comprises having 2 versions (both old and new) of the service deployed in production at the same time.

If you have a well-defined ML monitoring architecture (Best Tools to Do ML Model Monitoring), you would be able to assess the accuracy of the model by introducing a feedback loop with the ground truth.

Learn more Model Deployment StrategiesMistake #3: Not enabling automated (prediction) service rollbackImagine that you have a production model…

For a couple of years now, MLOps is probably the most often used term in the ML industry. The more models people want to deploy to production, the more they think about how to organize the Ops part of this process. Naturally, the way to do MLOps has been shaped by the big players on […]

The post MLOps at a Reasonable Scale [The Ultimate Guide] appeared first on neptune.ai.

Imbalance in object detection modelsObject detection is simultaneously locating the object of interest in a picture while categorizing it into a certain class.

The first generation of object detection algorithms mostly relied on hand-crafted features and linear classifiers, before deep learning came into the picture.

| Source: AuthorClass imbalance from an object detection point of view can be subclassified into two types – foreground-background imbalance and foreground-foreground imbalance.

Faster RCNN, a rather popular method of object detection uses a Feature pyramid network (FPN) for Region Proposal.

IoU distribution ImbalanceWhen the distribution of IoU for bounding boxes (across the d…

In fact, even if AutoML solutions become 10x better, it will not make Machine Learning specialists of any trade irrelevant.

Prone to over-optimization/over-fittingDepending on the nature of your data, and your model validation setup, some AutoML solutions can easily overfit.

A word on AutoML benchmarksThe literature on AutoML benchmarks is fairly scarce, and most often it compares the performance of AutoML solutions omitting the performance of humans.

Thankfully, we do have some work in establishing standardized ways to assess the performance of different AutoML solutions.

Another important thing you should get from this blog post: Invest all the time you save using AutoML on feature engine…

But besides software testing, automated testing can include some other types of testing such as hardware, security, performance, and others.

In contrast to conventional software testing, in ML testing we need to pay special attention to data testing .

Model testingJust like for data testing, model testing can be a part of unit testing, integration testing, or regression testing.

Monitoring models in production: similarly as for monitoring data tests, most of the model tests were covered in the section about model tests.

Open table in new window+ Expand all – Collapse all Jenkins GitHub Actions Unittests Pytest Deepchecks CheckList Aporia Arize AI WhyLabs Smoketesting Unit testing Integratio…

#stablediffusion #ai #stabilityai An interview with Emad Mostaque, founder of Stability AI. OUTLINE:

0:00 - Intro

1:30 - What is Stability AI?

3:45 - Where does the money come from?

5:20 - Is this the CERN of AI?

6:15 - Who gets access to the resources?

8:00 - What is Stable Diffusion?

11:40 - What if your model produces bad outputs?

14:20 - Do you employ people?

16:35 - Can you prevent the corruption of profit?

19:50 - How can people find you?

22:45 - Final thoughts, let's destroy PowerPoint Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

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

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

Link…

#mlnews #ai #copilot OUTLINE:

0:00 - Intro

0:20 - Copilot Now Generally Available

3:20 - FOSS Org leaves GitHub

6:45 - Google's Internal ML Code Completion

9:10 - AI Trains Itself to Code Better

14:30 - Amazon CodeWhisperer in Preview

15:15 - Pangu-Coder: A New Coding Model

17:10 - Useful Things References:

Copilot Now Generally Available

https://github.blog/2022-06-21-github-copilot-is-generally-available-to-all-developers/ FOSS Org leaves GitHub

https://www.theregister.com/2022/06/30/software_freedom_conservancy_quits_github/

https://sfconservancy.org/blog/2022/jun/30/give-up-github-launch/

https://sfconservancy.org/GiveUpGitHub/

https://sfconservancy.org/docs/SupportGiveUpGitHub-README-s…

#mlnews #dalle #imagen All things text-to-image models like DALL-E and Imagen! OUTLINE:

0:00 - Intro

0:30 - Imagen: Google's Text-to-Image Diffusion Model

7:15 - Unified I/O by AllenAI

9:40 - CogView2 is Open-Source

11:05 - Google bans DeepFakes from Colab

13:05 - DALL-E generates real Cosmopolitan cover

15:45 - DALL-E tips & tricks

17:00 - Midjourney moves to Open Beta

17:50 - DALLE-mini is not Crayon

19:00 - Deep Learning Resources References:

Imagen: Google's Text-to-Image Diffusion Model

https://imagen.research.google/?utm_source=pocket_mylist

https://arxiv.org/pdf/2205.11487.pdf Unified I/O by AllenAI

https://unified-io.allenai.org/

https://blog.allenai.org/introducing-ai2s-unified-io-…

#mlnews #ai #minerva This episode is all about models that reason. OUTLINE:

0:00 - Intro

0:35 - Meta AI learns Wikipedia citations

5:25 - Google's Minerva solves math problems by reading papers

9:10 - GPT-3 writes a paper on itself

13:35 - Jürgen Schmidhuber prompts LeCun for missing citations References:

Meta AI learns Wikipedia citations

https://tech.fb.com/artificial-intelligence/2022/07/how-ai-could-help-make-wikipedia-entries-more-accurate/

https://ai.facebook.com/blog/introducing-sphere-meta-ais-web-scale-corpus-for-better-knowledge-intensive-nlp/?d=%7B%22u%22%3A100051861999022%2C%22f%22%3A207799259245384%2C%22t%22%3A1658664021%2C%22ed%22%3A[]%7D&s=AWVELTip1y4HowJprXc

https://github.c…

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE:

0:00 - Intro

0:55 - BLOOM: Open-Source 176B Language Model

5:25 - YALM 100B

5:40 - Chinese Brain-Scale Supercomputer

7:25 - Meta AI Translates over 200 Languages

10:05 - Reproducibility Crisis Workshop

10:55 - AI21 Raises $64M

11:50 - Ian Goodfellow leaves Apple

12:20 - Andrej Karpathy leaves Tesla

12:55 - Wordalle References:

BLOOM: Open-Source 176B Language Model

https://bigscience.huggingface.co/blog/bloom

https://huggingface.co/spaces/bigscience/license

https://huggingface.co/bigscience/bloom?text=34%2B10%3D44+%0A54%2B20%3D YALM 100B

https://github.com/yandex/YaLM-100B Chinese Brain-Scale …

#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time. OUTLINE:

0:00 - Introduction

2:00 - Main Contributions

5:45 - Mode 1 and Mode 2 actors

15:40 - Self-Supervised Learning and Energy-Based Models

20:15 - Introducing latent variables

25:00 - The problem of collapse

29:50 - Contrastive vs regularized methods

36:00 - The JEPA architecture

47:00 - Hierarchical JEPA (H-JEPA)

53:00 - Broader relevance

56:00 - Summ…

Chatting & Coding

#openai #vpt #minecraft Minecraft is one of the harder challenges any RL agent could face. Episodes are long, and the world is procedurally generated, complex, and huge. Further, the action space is a keyboard and a mouse, which has to be operated only given the game's video input. OpenAI tackles this challenge using Video PreTraining, leveraging a small set of contractor data in order to pseudo-label a giant corpus of scraped footage of gameplay. The pre-trained model is highly capable in basic game mechanics and can be fine-tuned much better than a blank slate model. This is the first Minecraft agent that achieves the elusive goal of crafting a diamond pickaxe all by itself. OUTLINE:

0:00…

#parti #ai #aiart Parti is a new autoregressive text-to-image model that shows just how much scale can achieve. This model's outputs are crips, accurate, realistic, and can combine arbitrary styles, concepts, and fulfil even challenging requests. OUTLINE:

0:00 - Introduction

2:40 - Example Outputs

6:00 - Model Architecture

17:15 - Datasets (incl. PartiPrompts)

21:45 - Experimental Results

27:00 - Picking a cherry tree

29:30 - Failure cases

33:20 - Final comments Website: https://parti.research.google/

Paper: https://arxiv.org/abs/2206.10789

Github: https://github.com/google-research/parti Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.co…

#lamda #google #ai Google engineer Blake Lemoine was put on leave after releasing proprietary information: An interview with the chatbot LaMDA that he believes demonstrates that this AI is, in fact, sentient. We analyze the claims and the interview in detail and trace how a statistical machine managed to convince at least one human that it is more than just an algorithm. OUTLINE:

0:00 - Whistleblower put on leave

4:30 - What is a language model?

6:40 - The prompt is the key

10:40 - Who are we talking to exactly?

12:50 - LaMDA analyzes stories

15:20 - Fear, pain, and consent

20:25 - How would we recognize sentience? When is a machine conscious? References:

https://cajundiscordian.medium.com/…

#gpt4chan #4chan #ai GPT-4chan was trained on over 3 years of posts from 4chan's "politically incorrect" (/pol/) board. EXTRA VIDEO HERE: https://www.youtube.com/watch?v=dQw4w9WgXcQ Website (try the model here): https://gpt-4chan.com

Model: https://huggingface.co/ykilcher/gpt-4chan

Code: https://github.com/yk/gpt-4chan-public

Dataset: https://zenodo.org/record/3606810#.YpjGgexByDU OUTLINE:

0:00 - Intro

0:30 - Disclaimers

1:20 - Elon, Twitter, and the Seychelles

4:10 - How I trained a language model on 4chan posts

6:30 - How good is this model?

8:55 - Building a 4chan bot

11:00 - Something strange is happening

13:20 - How the bot got unmasked

15:15 - Here we go again

18:00 - Final thoughts L…

#flamingo #mlnews #tech Your updates directly from the state of the art in Machine Learning! OUTLINE:

0:00 - Intro

0:30 - DeepMind's Flamingo: Unified Vision-Language Model

8:25 - LiT: Locked Image Tuning

10:20 - Jurassic X & MRKL Systems

15:05 - Helpful Things

22:40 - This AI does not exist References:

DeepMind's Flamingo: Unified Vision-Language Model

https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model

https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf LiT: Locked Image Tuning

https://ai.googleblog.com/2022/04/locked-image-tuning-adding-language.html

https://google-r…

#mlnews #dalle #gpt3 An inside look of what's happening in the ML world! Sponsor: Weights & Biases

https://wandb.me/yannic OUTLINE:

0:00 - Intro

0:20 - Sponsor: Weights & Biases

1:40 - Meta AI releases OPT-175B

4:55 - CoCa: New CLIP-Competitor

8:15 - DALL-E Mega is training

10:05 - TorToiSe TTS is amazing!

11:50 - Investigating Vision Transformers

12:50 - Hugging Face Deep RL class launched

13:40 - Helpful Things

17:00 - John Deere's driverless tractors References:

Meta AI releases OPT-175B

https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/

https://arxiv.org/abs/2205.01068

https://arxiv.org/pdf/2205.01068.pdf

https://github.com/facebookresearch/m…

#nft #gan #ai Today we build our own AI that can create as many bored apes as we want! Fungibility for everyone! Try the model here: https://huggingface.co/spaces/ykilcher/apes

or here: https://ykilcher.com/apes

Files & Models here: https://huggingface.co/ykilcher/apes/tree/main

Code here: https://github.com/yk/apes-public This video is sponsored by BrightData, use this link for 25$ free credits (and they match your first deposit up to 250$):

https://brightdata.grsm.io/yannickilcher OUTLINE:

0:00 - Introduction

2:05 - Generative Adversarial Networks

3:40 - Scraping Opensea with BrightData

7:55 - Training the GAN

11:35 - Here are the results!

15:20 - Diving deeper into BrightData References:…

Please check out the full podcast here: https://www.youtube.com/watch?v=gjJBYcYMB-o This video is a commentary on the latest Weaviate Podcast with Laura Ham on the Weaviate User Experience. User Experience describes a suite of things from the performance of the tech, API interfaces, documentation, and communication strategy -- as outlined by Bob van Luijt here: https://twitter.com/bobvanluijt/status/1552379772747096064. Laura has lead the development of the GraphQL API that makes Weaviate so friendly and exciting to use! I really hope you enjoy learning more about these topics. Here are some additional links referenced in the video: Wikipedia Weaviate Example: https://weaviate.io/developers…

Hey everyone! Here are some of my thoughts and lessons learned on the new Weaviate v1.14 release! Please check out the full length podcast linked here: https://www.youtube.com/watch?v=eiQaZIhUS_o. Some references from the video:

Weaviate v1.14 Blog Post: https://weaviate.io/blog/2022/07/Weaviate-release-1-14.html#stronger-together

CO-Search: https://arxiv.org/pdf/2006.09595.pdf

Prometheus: https://prometheus.io/docs/introduction/overview/

Literature-Augmented Clinical Outcome Prediction: https://aclanthology.org/2022.findings-naacl.33.pdf

Sigmoid-MSE vs. Softmax Cross-Entropy: https://wandb.ai/ayush-thakur/dl-question-bank/reports/Sigmoid-MSE-vs-Softmax-Cross-Entropy--VmlldzoyMDA3ODQ

Please check out the full podcast here: https://www.youtube.com/watch?v=kG3ji89AFyQ This video is a commentary on the latest Weaviate Podcast with Etienne Dilocker on ANN Benchmarks. ANN search -- short for Approximate Nearest Neighbors -- describes algorithms that enable efficient distance comparison between an encoded query vector and a vector database. For example, we may have 1 billion vectors to search through -- we don't want to do a dot product distance between our query and 1 billion candidate vectors! This podcast describes Weaviate's efforts to benchmark HNSW within the Weaviate system and give users a sense of how performance varies with respect to each dataset (and their respect…

Please check out Eric Jang's article "Ranking YC Companies with a Neural Net": https://evjang.com/2022/04/02/yc-rank.html Please subscribe to SeMI Technologies on YouTube! https://www.youtube.com/c/SeMI-and-Weaviate Timecodes

0:00 Introduction

0:58 Weaviate Demo

3:40 Article Overview

10:45 NLP for Venture Capital and Data-Centric AI

Please leave a star! https://github.com/mosaicml/composer Thank you so much for watching! This video presents some details of MosaicML's Composer launch and how to use it in Python. I am really excited about this company and their mission to deliver faster and cheaper Deep Learning training! I hope you find this video useful, happy to answer any questions you might have about this or these ideas in Efficient Deep Learning generally! The full Weaviate podcast with Jonathan Frankle will be uploaded very soon on SeMI Technologies YouTube, please subscribe!

https://www.youtube.com/c/SeMI-and-Weaviate Chapters

0:00 Introduction

1:45 Documentation Intro

4:20 Composer Notebooks

5:35 Functional API…

I hope you found this useful, please let me know if you have any questions or ideas! Docarray Documentation: https://docarray.jina.ai/ Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Introduction

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Please check out SeMI YouTube!

0:15 My takeaways on Full Stack Neural Search

11:04 Podcast Clip - Han Xiao

I hope this video helps you get started with Image Search using Weaviate and Jina AI - happy to answer any questions / help solve problems! Check out the full tutorial explanation from Laura Ham: https://www.youtube.com/watch?v=rBKvoIGihnY New podcast with Jina AI CEO Han Xiao! https://www.youtube.com/watch?v=HIGAQAE_xaI Full notebook code: https://github.com/laura-ham/HM-Fashion-image-neural-search/blob/main/hm-fashion-image-neural-search.ipynb Get started with the Weaviate Cloud Service: console.semi.technology

Hey everyone! Hopefully this video helps supplement the new Weaviate podcast with Brady Neal, I hope you find this interesting / useful! Check out Brady Neal on YouTube! https://www.youtube.com/c/BradyNealCausalInference/featured Weaviate Podcast: https://www.youtube.com/watch?v=t7g9s1GWcB8 0:00 New Weaviate Podcast!

0:42 Brady Neal Causal Inference

1:34 Oogway.ai

2:45 Whiteboard Ideas

5:35 Discussion Topics

Hey everyone! I recently interviewed Arvind Neelakantan from OpenAI about the new OpenAI Embeddings API on the Weaviate Podcast! This video provides some additional detail for the different topics that were discussed. If you find this video to be informative, please check out SeMI technologies on youtube where we are working hard on developing content explaining concepts in Deep Learning for Search. Full Podcast: https://www.youtube.com/watch?v=uFxfZ0vLsoU SeMI Technologies on YouTube: https://www.youtube.com/channel/UCJKT6kJ3IFYybWnL7jbXxhQ

Thanks for watching! Please subscribe for more Deep Learning and AI videos, the list of papers is below under "Content Links" Content Links:

Fully Online Meta-Learning without Task Boundaries: https://arxiv.org/abs/2202.00263

Datamodels: Predicting Predictions from Training Data: https://arxiv.org/abs/2202.00622

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization: https://arxiv.org/abs/2202.01334

Competition-Level Code Generation with AlphaCode: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf

GPT-NeoX-20B: https://blog.eleuther.ai/announcing-20b/

PromptSource: https://arxiv.org/abs/2202.01279

Chain of Thou…

This video gives an overview of the latest Weaviate podcast, please subscribe to see future episodes!

https://www.youtube.com/c/SeMI-and-Weaviate/videos Thanks for watching! Chapters

0:00 Overview

7:53 Ideas for Podcast Search

10:44 Weaviate Podcast so far

Thank you so much for watching, please subscribe for more Deep Learning and Ai videos! Please check out SeMI Technologies on YouTube as well, where I am hosting a podcast on Deep Learning for Search! Paper Links:

Text and Code Embeddings by Contrastive Pre-Training: https://cdn.openai.com/papers/Text_and_Code_Embeddings_by_Contrastive_Pre_Training.pdf

Introducing Text and Code Embeddings in the OpenAI API (Blog Post): https://openai.com/blog/introducing-text-and-code-embeddings/

Nils Reimers - OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings? https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddi…

Thank you so much for watching, please subscribe for more Deep Learning and AI videos! Please check out SeMI Technologies on YouTube as well! Paper Links:

CM3: https://arxiv.org/abs/2201.07520

data2vec: https://scontent.fmia1-1.fna.fbcdn.net/v/t39.8562-6/271974914_483120576492438_4239522333319653600_n.pdf?_nc_cat=107&ccb=1-5&_nc_sid=ae5e01&_nc_ohc=4-cMR5tUq4QAX-Of7fj&_nc_ht=scontent.fmia1-1.fna&oh=00_AT9ymN9dNPt1p8zWQClW6MSZikaCTT8gobc2LqxW4OhzZQ&oe=61F3F7D1

LaMDA: https://arxiv.org/abs/2201.08239

PromptBERT: https://arxiv.org/abs/2201.04337

UnifiedSKG: https://arxiv.org/abs/2201.05966

Collapse by Conditioning: https://arxiv.org/abs/2201.06578

GradTail: https://arxiv.org/abs/2201.05938

CLIP…

Three false proofs, and what lessons they teach.

New notebooks: https://store.dftba.com/collections/3blue1brown/products/mathematical-quotebook-notebook

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Time stamps:

0:00 - Fake sphere proof

1:39 - Fake pi = 4 proof

5:16 - Fake proof that all triangles are isosceles

9:54 - Sphere "proof" explanation

15:09 - pi = 4 "proof" explanation

16:57 - Triangle "proof" explanation and conclusion ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://github.com/3b1b/…

Mailing-list: https://summerofmathexposition.substack.com/p/the-summer-of-math-exposition-is?s=r

Find collaborators here: https://github.com/leios/SoME_Topics/

Join the discord: https://discord.gg/dsp3zgB4qQ

Submission form: https://forms.gle/sNqosxqwCW2EjPVu5

Last year’s results: https://3b1b.co/blog/some1-results ------------------ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe: http://3b1b.co/subscribe Various social media stuffs:

Website: https://www.3blue1brown.com

Twitter: https://tw…

Generating functions, as applied to a hard puzzle used for IMO training.

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Books mentioned 102 Combinatorial problems, by Titu Andreescu and Zuming Feng

https://amzn.to/3wAPoNq Generatingfunctionology by Herbert Wilf

https://amzn.to/3sPJ8Al Visualizing the Riemann zeta function

https://youtu.be/sD0NjbwqlYw Fourier series

https://youtu.be/r6sGWTCMz2k Timestamps

0:00 - Puzzle statement and motivation

4:31 - Simpler example

6:51 - The generating function

11:52 - Evaluation tricks

17:24 - Roots of unity

26:31 - Recap and final trick

30:13 - Takeaways -----------------…

A slight correction to the previous video, with some more details about how the best first word was chosen.

Special thanks to these supporters: https://3b1b.co/lessons/wordle#thanks

Help fund future projects: https://www.patreon.com/3blue1brown​

An equally valuable form of support is to simply share the videos. Contents:

0:00 - The Bug

3:31 - How the best first guess is chosen

8:54 - Does this ruin the game? Nice post by Jonathan Olson on optimal wordle algorithms:

https://jonathanolson.net/experiments/optimal-wordle-solutions ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://gi…

An excuse to teach a lesson on information theory and entropy.

Help fund future projects: https://www.patreon.com/3blue1brown​

Special thanks to these supporters: https://3b1b.co/thanks

An equally valuable form of support is to simply share the videos. Contents:

0:00 - What is Wordle?

2:43 - Initial ideas

8:04 - Information theory basics

18:15 - Incorporating word frequencies

27:49 - Final performance Original wordle site:

https://www.powerlanguage.co.uk/wordle/ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ Shannon and von Neumann artwork by Kurt Bruns. Code for this video:

https://github.com/3b1b/videos/blob/master/_2022/wordle.py These animations are largely made using a c…

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 in this video. 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

https://mattferraro.dev/posts/caustics-engineering The Beauty of Bézier Curves

https://youtu.be/aVwxzDHniEw What Is The Most Complicated Lock…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "MegaPortraits: One-shot Megapixel Neural Head Avatars" is available here:

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❤️ Come work for Weights & Biases! Check out open roles at https://wandb.me/jobs

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "Learning to Play Minecraft with Video PreTraining (VPT)" is available here:

https://openai.com/blog/vpt/ ❤️ 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 Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Hierarchical Text-Conditional Image Generation with CLIP Latents" is available here:

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- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Ladybug inpainting: https://www.reddit.com/r/dalle2/comments/veznq2/using_dalles_inpainting_feature_to_fix_up_my/ Michelangelo: https://twitter.com/FLKDayton/status/1543261364315193346 Mona Lisa: https://www.reddit.com/r/dalle2/comments/venhn1/modern_day_mona_lisa_composite_zoomout_video/ Mo…

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Rearchitecting Spatiotemporal Resampling for Production" is available here:

https://research.nvidia.com/publication/2021-07_Rearchitecting-Spatiotemporal-Resampling 📝 Our paper with the spheres scene that took 3 weeks is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/ The denoiser: https://developer.nvidia.com/nvidia-rt-denoiser ❤️ 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 Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Google Parti: Pathways Autoregressive Text-to-Image model" is available here:

https://parti.research.google/ 4 of my favorite prompts from the video (add these to benchmarks if you feel like it):

- surprised scholars looking at a magical parchment emitting magic dust high detail digital art disney style

- scholar delighted by a very long disintegrating magical parchment with sparks and smoke coming out of it fantasy digital art disney style

- stern looking fox in a labcoat, casting a magic spell, digital art

- shiny cybertronic robot frog with leds studio lighting high detail digital art 📝 T…

❤️ If you wish to support us and watch these videos in early access, check this out:

- https://www.patreon.com/TwoMinutePapers 📝 The paper "MineDojo - Building Open-Ended Embodied Agents with Internet-Scale Knowledge" is available here:

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Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Ivo Galic, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Michael Albrecht, Michael T…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers If everything goes well, this will be my GTC talk:

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❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here: http://wandb.me/robotic-telekinesis 📝 The paper "Robotic Telekinesis: Learning a Robotic Hand Imitator by Watching Humans on Youtube" is available here:

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https://nv-tlabs.github.io/ASE/ 📝 Our material synthesis paper with the latent space is available here:

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❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Differentiable Signed Distance Function Rendering" is available here:

http://rgl.epfl.ch/publications/Vicini2022SDF 📝 Our works on differentiable material synthesis and neural rendering are available here (with code): https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/

https://users.cg.tuwien.ac.at/zsolnai/gfx/photorealistic-material-editing/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers

- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join Thank you Gordon Hanzmann-Johnson for catching an issue w…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here (Thank you Soumik!): http://wandb.me/styleGAN-NADA 📝 The paper "StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators" is available here:

https://stylegan-nada.github.io/ ❤️ 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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Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, …

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers ❤️ Their mentioned post is available here (Thank you Soumik Rakshit!): https://wandb.me/modern-evolution 📝 The paper "Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts" is available here:

https://es-clip.github.io/ 🧑‍🎨 My previous genetic algorithm implementation for the Mona Lisa problem (+ some explanation in the video below):

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❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 NVIDIA's paper "Fast Volume Rendering with Spatiotemporal Reservoir Resampling" is available here:

https://dqlin.xyz/pubs/2021-sa-VOR/

https://graphics.cs.utah.edu/research/projects/volumetric-restir/

https://research.nvidia.com/publication/2021-11_Fast-Volume-Rendering 🔆 The free light transport course is available here. You'll love it! https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ Volumetric path tracer by michael0884: https://www.shadertoy.com/view/NtXSR4 ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/…

Во многих задачах исследователи работают с небольшими фрагментами кода — отдельными методами, реже — с файлами. Но чтобы найти качественное решение, зачастую требуется выйти за пределы небольших кусков кода и использовать глобальную информацию о модуле или проекте. Мы поговорим о различных способах использования информации о контексте в ML моделях и о том, на что нужно обращать внимание для честной оценки их качества. Докладчик: Егор Богомолов Материалы: https://arxiv.org/pdf/2201.10705.pdf

Мы разберем методы генерации SQL запросов из описания на естественном языке и немного поговорим о более широком применении их к генерации DSL кода. Мы обсудим почему обучение обучение моделей для DSL может отличаться от моделей генерации кода, текущие подходы к решению задачи на базе лидерборда для Spider датасета и их ограничения. Мы представим более масштабируемый подход к генерации SQL и наши текущие результаты. Докладчик: Денис Литвинов

В настоящее время Reinforcement Learning (RL) применяется для решения ряда задач оптимизации в области компиляторов, таких как конфигурация флагов компиляции, выбор оптимального порядка выполнения инструкций и многие другие. Однако, подобрать оптимальный RL-алгоритм бывает сложно, так как он зависит от контекста конкретной задачи. Более того, разработчики компиляторов зачастую могут быть не вовлечены в область RL, что еще сильнее осложняет решение данной задачи. В работе Automating Reinforcement Learning Architecture Design for Code Optimization авторы предлагают инструмент Supersonic, позволяющий автоматически подбирать оптимальный RL-алгоритм для решения оптимизационных задач в компилятор…

Несмотря на то, что многие из последних достижений в области машинного обучения связаны с глубоким обучением с подкреплением, Deep RL алгоритмы остаются ненадёжными (по сравнению с классическими моделями глубокого обучения) и трудновоспроизводимыми (с точки зрения результата). Авторы статьи связывают описанные недостатки с проблемой отсутствия понимания того как внутренние механизмы, используемые в RL алгоритмах, влияют на поведение агента по отдельности и вместе взятые. На семинаре мы поговорим о поднятой авторами проблеме на примере алгоритмов Trust Region Policy Optimization (TRPO) и Proximal Policy Optimization (PPO), рассмотрим эксперименты по оценке влияния составных частей этих алгор…

Зачастую в прикладных задачах собрать достаточно большой, подходящим образом размеченный датасет для обучения модели не представляется возможным. Популярным решением в такой ситуации является Self-Supervised Learning. В рамках этого подхода модель сначала предобучают на синтетической, искусственно выдуманной задаче, выборку для которой автоматически формируют из неразмеченных данных. Примерами таких синтетических задач являются восстановление маскированных токенов в NLP (этот же подход используется и в некоторых моделях для работы с кодом), восстановление фрагментов или удаление искусственного шума при работе с картинками, восстановление последовательности кадров при работе с видео и т.д.. …

Многие из самых захватывающих новых прорывов в области искусственного интеллекта произошли благодаря двум недавним инновациям: самоконтролируемое обучение, который позволяет машинам учиться на случайных немаркированных примерах, а также Трансформеры, которые позволяют моделям ИИ выборочно сосредотачиваться на определенных частях своего ввода и, таким образом, рассуждать более эффективно. На семинара будет разобрана новая статья "Emerging Properties in Self-Supervised Vision Transforms", в которой авторы используются ранее упомянутые техники для решения задач компьютерного зрения. Докладчик: Ольга Лавриченко.

Существующие фреймворки для генерации изображений могут обуславливаться на пользовательский ввод в одной модальности — например, на текст, эскиз, маску сегментации или пример изображения со стилем. При этом, такие подходы не используют доступные мультимодальные данные. Авторы данной статьи предлагают Product-of-Experts Generative Adversarial Networks (PoE-GAN) фреймворк, который позволяет синтезировать изображение на основе условий в нескольких модальностях или любом их подмножестве, а также осуществлять безусловную генерацию. Данная модель также превосходит другие подходы в условиях унимодальной условной генерации. Докладчик: Дарья Евсикова.

Трансформеры уже давно господствуют во многих задачах NLP. И если с задачами где длина последовательности относительно мала (не более 512 токенов) проблем не возникает, то с обработкой больших текстов не все так ясно. Проблема в том, что потребление памяти увеличивается квадратично с ростом обрабатываемой последовательности. Существуют различные подходы к решению проблемы, например, можно линеаризовать softmax в модуле внимания, снизив асимптотику до O(N) (linear transformers); или же исследовать разреженность (BigBird). В свою очередь, авторы статьи продолжают идеи sliding-window и Transformer-XL. Поэтому на семинаре поговорим об этих подходах и архитектуре Block-Recurrent Transformer. Док…

Мы расскажем про исследование, посвященное дообучению ML4SE моделей под конкретный проект. В то время как большинство исследователей обучает и тестирует модели на непересекающихся наборах проектов, мы задались вопросом: “А что будет, если показать модели данные из целевого проекта?“. Мы поговорим об особенностях оценки качества проектно-дообученных моделей и презентуем полученные результаты для трех моделей в задаче предсказания имен методов.

Докладчик – Егор Богомолов

На семинаре мы поговорим о нашей работе в области предварительной тренировки векторных представлений графовых нейронных сетей (GNN) для исходного кода. Качество векторов мы оцениваем с помощью задачи предсказания типов для языка с динамической типизацией Python. Для предварительной тренировки используется задача предсказания имён. По результатам наших экспериментов векторные представления GNN позволяют достичь точности классификации типов, сравнимой с CodeBERT. Вдобавок, объединение CodeBERT и GNN векторов в гибридную модель позволяет улучшить точность классификации типов. При этом, улучшения достигаются даже после тренировки GNN модели в течение всего одной эпохи, что намного меньше чем тр…

В крупных компаниях, где весь код лежит в едином репозитории, очень важно уметь оперативно локализовать баг. Задача усложняется, когда отельные файлы состоят из сотен строк, а проблема выявляется на этапе End-to-End тестирования или в продакшене. В такой ситуации необходимо автоматическое решение, которое способно быстро найти ломающий коммит, несмотря на то, что сообщения об ошибке зачастую трудночитаемые и содержат большой объём информации. На этом семинаре мы разберём статью от Facebook (https://arxiv.org/pdf/2010.09977.pdf), в которой авторы предлагают эффективный unsupervised алгоритм локализации бага к коммиту, использующий методы информационного поиска. Описанный алгоритм приспособле…

Разработка программного обеспечения сопряжена с поиском и исправлением ошибок. В программной инженерии уже давно изучаются и описываются запахи кода – формальные признаки, индицирующие о возможном наличии проблем. Примерами запахов кода могут быть завистливая функция (метод обращается к данным чужого класса чаще, чем к данным собственного) или параллельная иерархия (ситуация, когда при создании нового класса в одной иерархии классов вам почти всегда приходится создавать парный к нему класс в другой иерархии). Для каждого запаха кода описаны потенциальные пути исправления, часто сводящиеся к какому-то рефакторингу.

Однако, проекты, связанные с машинным обучением, обладают особой спецификой и…

Трансформер - очень хорошая модель для понимания текста, однако она не эффективна из-за квадратичной асимптотической сложности по длине входящей последовательности. Хотя существует множество методов ускорения трансформера, они все еще недостаточно эффективны на длинных последовательностях. Авторы статьи предлагают Fastformer, эффективную модель трансформера, основанную на аддитивном внимании (additive attention). На семинаре мы вспомним, как работают трансформеры, познакомимся с additive attention и Fastformer и посмотрим, как он справляется с различными задачами. Докладчик: Тимур Хабибуллин

Задачи обработки естественного языка, такие как машинный перевод, ответы на вопросы и обобщения текстов, как правило решаются с помощью обучения с учителем на специально подобранных под конкретное задание датасетах. Авторы статьи показывают, что можно обучить модель, которая будет способна решать различные задачи с минимальным количеством обучения с учителем, используя для этого датасет Webtext, состоящий из миллионов различных веб-страниц. На семинаре мы обсудим, как модель справляется с заданиями различной специфики и сравним результаты авторов с результатами state-of-the art моделей. Докладчики: Маргарита Чудова

Я расскажу про процесс создания командой AI Team системы автодополнения для языка R. Будет рассказано о том, с какими трудностями можно столкнуться при разработке и внедрении системы автодополнения, основанной на нейросетях. Также мы рассмотрим некоторые нерешённые исследовательские проблемы в области нейросетевого автодополнения и обсудим возможные способы их решения. Большая часть рассказа будет основана на статье Time-Efficient Code Completion Model for the R Programming Language (https://aclanthology.org/2021.nlp4prog-1.4/), опубликованной на воркшопе NLP4prog 2021 (https://nlp4prog.github.io/2021/) конференции ACL. Докладчики: Артем Попов.

Дерево отрезков. Задача RSQ (range sum query). Задачи LCA (least common ancestor) и RMQ (range minimum query). Решение RMQ с помощью sparse table. Сведение LCA к RMQ (алгоритм Фарах-Колтона-Бендера). Сведение RMQ к LCA. Задача LA (level ancestors). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Кратчайшие пути в графах. Оценки расстояний и их релаксация. Алгоритмы Беллмана-Форда, Флойда и Дийкстры. Потенциалы. Критерий консервативности длин в терминах наличия допустимых потенциалов. Нахождение допустимых потенциалов с помощью алгоритма Беллмана-Форда. Алгоритм Джонсона. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Остовы минимального веса. Лемма о минимальном ребре в разрезе. Алгоритмы Краскала и Прима. Структура DSU (disjoint set union) Реализация с использованием леса. Ранги вершин, эвристика ранга. Логарифмическая оценка ранга через количество элементов. Эвристика сжатия путей. Оценка учетной стоимости операций (без доказательства). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Splay-деревья. Операция splay: zig, zig-zig и zig-zag шаги. Реализация операций вставки, удаления, слияния и разделения для splay-деревьев. Обход в глубину. Топологическая сортировка Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Хеш-функции. Коллизии. Разрешение коллизий методом цепочек. Гипотеза простого равномерного хеширования, оценка средней длины цепочки хеш-функции. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Модель разрешающих деревьев. Нижняя оценка на число сравнений при сортировке и поиске в модели разрешающих деревьев.

Сортировка слиянием (Merge-Sort). Top-down и bottom-up подходы. Сортировка слиянием во внешней памяти. Inplace Merge-Sort. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Задача о длиннейшой возрастающей подпоследовательности. Алгоритмы сортировки Heap-Sort и Intro-Sort. Частичная сортировка с помощью кучи и поиска порядковой статистики. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Построение совершенной хеш-функции методом двухуровненого хеширования. Построение совершенной хеш-функции методом ациклических графов. Фильтр Блюма (Bloom filter). Оценка вероятности ложноположительного срабатывания. Count-min sketch. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Сильно связанные компоненты. Точки сочленения: определение и нахождение с помощью обхода в глубину. Мосты. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Реализация очереди на паре стеков с константной учетной сложностью. Динамические минимумы-максимумы в стеках и очередях. Персистентные структуры данных. Виды персистентности. Модель вычислений Pointer Machine. Персистентные стеки и очереди. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Алгоритм Misra-Gries.

Деревья поиска. Вставка и удаление элементов. Inorder-обход дерева. Красно черные деревья: определение и основные свойства. Реализация операций вставки для красно-черного дерева. Дучи (treaps). Единственность дучи для заданного набора различных ключей и приоритетов. Логарифмическая оценка матожидания высоты дучи. Операции слияния и разделения для дуч. Операции вставки и удаления элементов для дуч. Подробнее о поступлении в Школу анализа данных: https://academy.yandex.ru/dataschool

Быстрая сортировка (Quick-Sort). Способы выбора разделяющего элемента. Элиминация хвостовой рекурсии. Порядковые статистики. Рандомизированный алгоритм Quick-Select. Детермининированный алгоритм поиска (метод "медианы медиан").

Бинарный поиск. Galloping.

Линейное по времени слияние упорядоченных последовательностей. Оптимальное по числу сравнений слияние упорядоченных последовательностей.

Задача о длиннейшей возврастающей подпоследовательности. Динамическое программирование. O(n log n)-алгоритм. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Дерево отрезков. Задача RSQ (range sum query). Задачи LCA (least common ancestor) и RMQ (range minimum query). Решение RMQ с помощью sparse table. Сведение LCA к RMQ (алгоритм Фарах-Колтона-Бендера). Сведение RMQ к LCA. Задача LA (level ancestors). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Сергей Кастрюлин | Research Scientist, Philips Research Russia & Skoltech Возможна ли автоматическая оценка качества МРТ-снимков без участия врачей? О различных метриках и более интересных вариантах рассказывает Сергей Кастрюлин Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Александр Нестеров | Руководитель направления по исследованию данных, Сбер

NeuralSympCheck: Нейросетевая модель уточнения симптомов и постановки диагноза с использованием принципа логической регуляризации Может ли нейронная сеть не только определить самый вероятный диагноз по симптомам пациента, но и задать ему уточняющие вопросы? Александр рассказывает об опыте создания новой архитектуры такого симптом-чекера Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Александр Золотарев и Никита Кубраков | Сколтех Научный доклад о нетипичном применении Data Science в кардиологии Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Екатерина Кондратьева | PhD Student, Сколтех; Researcher, AIRI Флюорография, маммография, КТ лёгких и мозга... Уже слышали! А есть в области medical imaging что-то более интересное? Конечно! О менее популярных, но очень интересных областях применения ML вы узнаете из доклада Кати Кондратьевой Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Олег Рогов | Research Scientist, Сколтех Олег рассказывает о двух нестандартных кейсах применения технологий машинного обучения в медицине - визуализация вен и автоматическая генерация текстов заключений по снимкам Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Никита Николаев | Marketing Director, Цельс В индустрии медтеха есть много громких случаев провалов раздутых стартапов. Никита Николаев рассказывает о самых ярких и даёт советы стартаперам Таймкоды:

- 2:02 - кейсы провалов

- 18:25 - главные причины краха

- 19:53 - рецепты успеха

- 22:24 - практика Цельса

- 25:16 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Мария Гарец | ML Lead, Цельс Маша Гарец рассказывает о методах умного выбора данных на разметку. Размечаем меньше - прирост метрик больше: сплошная экономия. Таймкоды:

- 3:07 - uncertainty sampling

- 5:38 - diversity sampling

- 8:44 - random sampling

- 9:42 - случаи из практики

- 17:42 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Антон Краснов | Data Annotation Lead, Цельс Без врачей в ML в медицине далеко не уедешь. Как же выстроить систему отбора и контроля качества их работы? Рассказывает руководитель команды разметки в Цельсе - Антон Краснов Таймкоды:

- 4:42 - как искать врачей-разметчиков?

- 6:15 - входной контроль

- 9:30 - выход на промышленные масштабы

- 18:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Николай Филатов | ML Lead, Цельс Домен медицинских данных отличается особенно шумной и грязной разметкой. Какие есть способы автоматического и ручного контроля её качества? Об этом в докладе Коли Филатова Таймкоды:

- 3:06 - этап постановки задачи

- 7:30 - этап очистки данных

- 15:13 - этап разметки данных

- 16:15 - заключение и рекомендации

- 17:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Евгений Никитин | Head of AI, Цельс

Медицинские данные - это боль. Но только преодолевая боль, мы становимся сильнее. Этот доклад - обзор ключевых компонентов процесса обеспечения качества медицинских данных. Таймкоды:

- 1:02 - почему медицинские данные - это боль

- 12:12 - что такое качество данных?

- 16:32 - путь к качеству медицинских данных

- 26:34 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Антон Голубев | Data Platform Lead, Цельс Как выстроить работу с разнородными медицинскими данными с технической точки зрения? Какие инструменты использовать? Антон отвечает на эти вопросы на основе истории проекта Цельс.Маммография Таймкоды:

- 1:44 - история данных на проекте Маммография

- 8:59 - дата-платформа (хранение данных, ETL, дата-продукты)

- 24:57 - что дальше?

- 26:45 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Наши соц.сети

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

Ссылка на доклад: https://youtu.be/mt-KIFEEhyk

Спикер: Дмитрий Колодезев Полезные ссылки

Мероприятие Data Fest Online 3.0: https://ods.ai/events/datafestonline2022

Трек Reliable ML: https://ods.ai/tracks/reliable-ml-df2022

Telegram канал Reliable ML: https://t.me/reliable_ml Наши соц.сети

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

Ссылка на доклад: https://youtu.be/mt-KIFEEhyk

Спикер: Наталья Тоганова Полезные ссылки

Мероприятие Data Fest Online 3.0: https://ods.ai/events/datafestonline2022

Трек Reliable ML: https://ods.ai/tracks/reliable-ml-df2022

Telegram канал Reliable ML: https://t.me/reliable_ml Наши соц.сети

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Twitter: https://twitter.com/NewsOds

00:00 - 2:32 Вступление

2:32 - 9:29 Краткая история трансформеров

9:29 - 11:48 Тренд на доступные языковые модели

11:48 - 15:37 Что внутри модели?

15:37 - 17:49 Language Models 101

17:49 - 20:57 KNN and ANN 101

20:57 - 24:29 Datastore and hidden states

24:29 - 30:09 kNN-LM pipeline

30:09 - 34:05 Перплексия и датасет

34:05 - 44:35 Метрики и выводы из них Ссылка на презентацию: https://1drv.ms/p/s!AlnN0aqNwShslQucZAy9R5VeGRRt?e=Gc3dgp

Try catching cheaters yourself: https://primerlearning.org/ Support these videos on Patreon: https://www.patreon.com/primerlearning

Plush blobs and other stuff: https://store.dftba.com/collections/primer Binomial probability example (the whole section on Khan Academy may be helpful)

https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/binomial-random-variables/v/probability-of-making-2-shots-in-6-attempts For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Made with Unity and Manim

https://github.com/Helpsypoo/PrimerUnity

https://www.manim.community/ Made possible by support through Patreon:…

Play at primerlearning.org

Or on Google Play: https://play.google.com/store/apps/details?id=com.Primer.CatchtheCheaters For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning Made with Unity

https://github.com/Helpsypoo/PrimerUnity Made possible by support through Patreon:

Anthony Eufemio

Jon Mundle

Spline

Zachariah Richard Fournier

Vladimir Duchenchuk

Roy & BreAnna Steves

Shayn Osborn

Jeremy

Guguke

Anders Fjeldvær

Luc Cedric R.

Erik Broeders

Kairui Wang

Sean Barker

Eric Helps

Stevie Hr…

Duncan Trussell is a comedian, host of The Duncan Trussell Family Hour podcast, and co-creator of The Midnight Gospel.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.com/lex– Calm: https://calm.com/lex to get 40% off premium– SimpliSafe: https://simplisafe.com/lex– NetSuite: http://netsuite.com/lex to get free product tour– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Duncan’s Twitter: https://twitter.com/duncantrussellDuncan’s Instagram: https://instagram.com/duncantrussellThe Duncan Trussell Family Hour: https://duncantrussell.comThe Midnight Gospel: https://netflix.com/themidnightgospelBooks mentioned:Superintelligence: https://amzn.to/3Q…

Magatte Wade is an entrepreneur with a passion for creating positive change in Africa through economic freedom.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Audible: https://audible.com/lex to get 30-day free trial– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Magatte’s Twitter: https://twitter.com/magattewMagatte’s Instagram: https://instagram.com/magattewMagatte’s Facebook: https://facebook.com/themagattewadeMagatte’s Website: https://magattewade.comWebsites mentioned:Austin housing project: https://texansforreasonables…

Andrew Bustamante is a former CIA covert intelligence officer.

Check out his work and podcast at https://everydayspy.com Please support this podcast by checking out our sponsors:– Wealthfront: https://wealthfront.com/LEX to get $50 sign-up bonus– LMNT: https://drinkLMNT.com/lex to get free sample pack– BetterHelp: https://betterhelp.com/lex to get 10% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months free– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Everyday Spy: https://everydayspy.com/quizEveryday Spy Podcast: https://everydayspy.com/podcastAndrew’s Twitter: https://twitter.com/everydayspyAndrew’s Instagram: https://instagram.com/everydayspyPODCAST IN…

John Carmack is a legendary programmer, co-founder of id Software, and lead programmer of many revolutionary video games including Wolfenstein 3D, Doom, Quake, and the Commander Keen series.

He is also the founder of Armadillo Aerospace, and for many years the CTO of Oculus VR.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 credit– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish o…

Lt. Ryan Graves is a former Navy fighter pilot, who has worked on advanced research and development programs for DARPA, Office of Naval Research, and Air Force Research Labs on topics of multi-agent collaborative autonomy, AI-assisted air-to-air combat, and manned-unmanned teaming technologies.

Ryan and people in his squadron detected and engaged with UFOs on multiple occasions, and he has been one of the few people willing to speak publicly about these experiences.

Please support this podcast by checking out our sponsors:– GiveWell: https://www.givewell.org/ and use code LEX– Notion: https://notion.com/startups to get up to $1000 off team plan– Magic Spoon: https://magicspoon.com/lex and u…

Brian Armstrong is the CEO of Coinbase.

Please support this podcast by checking out our sponsors:– Audible: https://audible.com/lex– Skiff: https://skiff.org/lex– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– Fundrise: https://fundrise.com/lex– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oilEPISODE LINKS:Brian’s Twitter: https://twitter.com/brian_armstrongBrian’s Instagram: https://www.instagram.com/brian_armstrongCoinbase’s Website: https://www.coinbase.comResearchHub: https://www.researchhub.comNewLimit: https://www.newlimit.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZI…

Oriol Vinyals is the Research Director and Deep Learning Lead at DeepMind.

Language Models are Few-Shot Learners: https://arxiv.org/abs/2005.141654.

Emergent Abilities of Large Language Models: https://arxiv.org/abs/2206.076825.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(05:18) – AI(20:14) – Weights(26:33) – Gato(1:01:22) – Meta learning(1:15:21) – Neural networks(1:37:46) – Emergence(1:44:30) – AI sentience(2:08:27) – AGI

Lord Martin Rees is a cosmologist and astrophysicist at Cambridge University and co-founder of the Centre for the Study of Existential Risk.

Please support this podcast by checking out our sponsors:– Lambda: https://lambdalabs.com/lex– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 credit– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Martin’s Twitter: https://twitter.com/lordmartinreesMartin’s Website: https://www.martinrees.ukMartin’s Books:If Science is to Save Us: https://amzn.to/3yXRqscThe End of Astronauts: https://amzn.to/…

Robert Barron is a bishop and founder of Word on Fire Catholic Ministries.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– BetterHelp: https://betterhelp.com/lex to get 10% off– Notion: https://notion.com/startups to get up to $1000 off team plan– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savingsEPISODE LINKS:Robert’s Instagram: https://instagram.com/bishopbarronRobert’s Twitter: https://twitter.com/BishopBarronWord on Fire’s Instagram: https://instagram.com/wordonfire_catholicministriesWord on Fire’s…

Steve Keen is a heterodox economist and author.

Please support this podcast by checking out our sponsors:– Weights & Biases: https://lexfridman.com/wnb– Skiff: https://skiff.org/lex– Indeed: https://indeed.com/lex to get $75 credit– NetSuite: http://netsuite.com/lex to get free product tour– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Steve’s Twitter: https://twitter.com/profstevekeenThe New Economics (book): https://amzn.to/3zb4eg4PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube…

Richard Haier is a psychologist specializing in the science of human intelligence.

Child IQ and survival to 79: https://ncbi.nlm.nih.gov/pmc/articles/PMC5491698/2.

The Bell Curve: https://amzn.to/3Ng4RJe6.

On some podcast players you should be able to click the timestamp to jump to that time.

(00:00) – Introduction(08:06) – Measuring human intelligence(22:34) – IQ tests(45:23) – College entrance exams(53:59) – Genetics(59:58) – Enhancing intelligence(1:07:27) – The Bell Curve(1:19:58) – Race differences(1:39:11) – Bell curve criticisms(1:48:21) – Intelligence and life success(1:57:57) – Flynn effect(2:02:49) – Nature vs nuture(2:29:42) – Testing artificial intelligence(2:41:46) – Advice(2:4…

Jack Barsky is a former KGB spy and author of “Deep Undercover: My Secret Life and Tangled Allegiances as a KGB Spy in America”.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex to get 20% off– Notion: https://notion.com/startups to get up to $1000 off team plan– BetterHelp: https://betterhelp.com/lex to get 10% off– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oilEPISODE LINKS:Jack’s Twitter: https://twitter.com/deepcoverbarskyJack’s Website: https://jackbarsky.comDeep Undercover (book): https://amzn.to/39XMTgGThe Agen…

Joe Rogan is a comedian, UFC commentator, and host of the Joe Rogan Experience.

Please support this podcast by checking out our sponsors:– Theragun: https://therabody.com/lex– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months freeEPISODE LINKS:Joe’s Instagram: https://instagram.com/joeroganJoe’s Twitter: https://twitter.com/joeroganJRE (Spotify): https://open.spotify.com/show/4rOoJ6Egrf8K2IrywzwOMkJRE (YouTube): https://youtube.…

Demis Hassabis is the CEO and co-founder of DeepMind.

Please support this podcast by checking out our sponsors:– Mailgun: https://lexfridman.com/mailgun– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Indeed: https://indeed.com/lex to get $75 credit– Magic Spoon: https://magicspoon.com/lex and use code LEX to get $5 offEPISODE LINKS:Demis’s Twitter: https://twitter.com/demishassabisDeepMind’s Twitter: https://twitter.com/DeepMindDeepMind’s Instagram: https://instagram.com/deepmindDeepMind’s Website: https://deepmind.comPlasma control paper: https://nature.com/articles/s41586-021-04301-9Quantum simulation paper: https://…

Susan Cain is the author of Quiet: The Power of Introverts in a World That Can’t Stop Talking, and Bittersweet: How Sorrow and Longing Make Us Whole.

Please support this podcast by checking out our sponsors:– Brave: https://brave.com/lex– Skiff: https://skiff.org/lex to get early access– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Susan’s Twitter: https://twitter.com/susancainSusan’s Instagram: https://instagram.com/susancainauthorSusan’s Website: https://susancain.netBittersweet (book): https://amzn…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary L. Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and communities together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

They also discuss how critical thinkers and makers from social movements…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

In this episode of the series, Dr. Gray talks with Dr. Tawanna Dillahunt, Ass…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

Together, they explore Patton’s learnings about the challenges of using AI in…

In this episode, we talk with Kayo Yin, an incoming PhD at Berkeley, and Malihe Alikhani, an assistant professor at the University of Pittsburgh, about opportunities for the NLP community to contribute to Sign Language P…

This episode is the third in our current series on PhD applications.

In this episode, we talk about what the PhD application process looks like after applications are submitted.

We start with a general overview of the t…

AdWords with Unknown BudgetsIn today’s episode, we are joined by Rajan Udwani, an Assistant Professor at the University of California Berkeley.

Rajan began by discussing how the tools for operations research vary based on the optimization problem.

He then delved into the optimization problem for AdWords.

Rajan explained the approaches to modelling the problem of ad allocation.

Concluding, Rajan discussed two other ideas (throttling and bid scaling) that can better optimize ad allocation.

Piotr discusses common MLOps activities and how data science teams can take advantage of Neptune.ai for better experiment tracking.

He also mentioned when beginners are advised to start using machine learning tools.

Piotr also gave some advice on key activities that should be done by machine learning specialists during machine learning development.

He then talked about the short-term and long-term benefits of experiment tracking and model registry for machine learning developers.

You can read more about how to use the Neptune.ai platform from their blog page or learn more about the platform from their documentation.

Affiliate Marketing RabbitholeAffiliate marketing creates an opportunity for marketers to gain a commission by promoting a product or service.

Skeptoid podcast is a weekly podcast focused on conversations around skepticism and pseudoscience.

Today, Brian shares his personal story about affiliate marketing and the case he had with eBay between 2008 and 2014.

He was involved in affiliate marketing from 2002 to 2006.

Brian explained how affiliate marketing works in simple terms, and how people make money off it.

Monetization of YouTube Conspiracy TheoristsToday, Cameron Ballard joins us to discuss his research paper titled, Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories.

After collecting and analysing the data, Cameron discussed some observations he found.

In other words, ads likely to be financial scams or ads with fake promises take advantage of conspiracy videos for more reach.

Going forward, Cameron discussed how YouTube and other stakeholders can take action to forestall the advancement of predatory or scam ads.

Wrapping up, Cameron spoke about the Raditube project.

He discusses a study and his coauthors titled, What Makes a “Bad” Ad?

Eric began by explaining what a bad ad is.

Afterwards, he discussed how bad ads find their way to social media or web pages despite the policies provided by advertising platforms.

Eric and his team crawled an enormous array of websites, including those for misinformation and political ads.

To analyze the data, Eric used a clustering technique called population label distribution learning.

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

Fraud Detection in Crowdfunding CampaignsOn the show today, we are joined by Beatrice Perez.

She discusses her study titled I call BS: Fraud Detection in Crowdfunding Campaigns.

Machine learning has largely been used for bank fraud detection but finds sparse application in detecting fraudulent campaigns on crowdfunding platforms.

She also explained the data collection process of retrieving properties of various crowdfunding campaigns.

Rounding up, she gave some advice on how users can spot a potential fraudulent campaign on crowdfunding platforms.

Artificial Intelligence and Auction DesignMartino Banchio, a PhD student at the Stanford Graduate School of Business, joins us to discuss his study on the intersection of artificial intelligence and economics.

He speaks to us about his findings from his research titled, Artificial Intelligence and Auction Design.

He particularly discussed how the knowledge of game theory is key to model interactions between economic agents and decision agents.

He went deeper into what game theory was using chess as an example, and how it is applied in modelling economic problems.

Trust issues can cause collusive agreements to fail, but Martino discussed how reward-punishment schemes can sustain low bids.

Max’s research focuses on tracking website cookie dialogues.

He joins us to discuss the efforts of regulatory standards in the ad tech industry.

There is a need for standards to be put in place to regulate the activities of ad tech.

Max mentioned the ad tech standards that have been used over the years.

He rounded up by sharing his thoughts on what the ad tech space should look like ideally.

Neural Architecture Search for CTR PredictionWe are joined by Ravi Krishna, an AI Scientist.

He joins us to discuss his recent work on the implementation of a differentiable NAS framework for ads CTR prediction.

Ravi began with an explanation of what CTR (click-through rate) is about and why it is a vital AI problem today.

He advanced to the NAS (neural architecture search) side of his research - explaining what NAS is, why it is important, how the framework was built and the commensurate result.

His NAS framework could reduce the embedded table compression 15.1X whilst only increasing the loss from 0.4442 to 0.4454.

Algorithmic PPC ManagementEffectively managing a large budget of pay per click advertising demands software solutions.

When spending multi-million dollar budgets on hundreds of thousands of keywords, an effective algorithmic strategy is required to optimize marketing objectives.

In this episode, Nathan Janos joins us to share insights from his work in the ad tech industry.

Increasingly, people get most if not all of the information they consume online. Alongside the web sites, videos, apps, and other destinations, we’re consistently served advertisements alongside the organic content we search for or discover. Targetted ads make it possible for you to discover relevant new products you might otherwise not have heard about. Targetting can also open a pandora’s box of ethical considerations. Online advertising is a complex network of automated systems. Algorithms controlling algorithms controlling what we see. This season of Data Skeptic will focus on the applications of data science to digital advertising technology. In this first episode in particular, Kyle s…

Our mobile phones generate an incredible amount of data inbound and outbound. In today’s episode, Nishant Kishore, a PhD graduate of Harvard University in Infectious Disease Epidemiology, explains how mobility data from mobile phones can be captured and analysed to understand the spread of infectious diseases.

The pandemic changed how we lived. And this had a ripple effect on the performance of machine learning models. Ravi Parikh joins us today to discuss how the pandemic has affected the performance of machine learning models in clinical care and some actionable steps to fix it.

This week, Sarah Catanzaro, General Partner at Amplify Partners joins Jon for an episode that dives into the venture capital side of data science.

Learn how to fund your data science business idea, take note of what star…

Rest and relaxation await as Steve Fazzari joins us this week for a special edition of the podcast!

Tune in for a rejuvenating session of Yoga Nidra led beautifully by the expert.

Additional materials: www.superdatascie…

This week, Mikiko Bazeley, Senior Software Engineer at Mailchimp joins the podcast to share her in-depth knowledge of MLOps: Machine Learning Operations.

Tune in to hear her discuss what it entails, why it's so critical …

Ben Taylor makes a fourth appearance on Five-Minute Friday to discuss the best ways to introduce STEM to children.

Tune in to hear the many ways in which he thinks STEM education will evolve in the future.

Additional m…

Miles Brundage, Head of Policy Research at OpenAI, joins Jon Krohn this week to discuss AI model production, policy, safety, and alignment.

Tune in to hear him speak on GPT-3, DALL-E, Codex, and CLIP as well.

Ben Taylor returns for a third Five-Minute Friday episode!

This week, he looks ahead and digs into what we can expect from the A.I.

platforms of the future.

Additional materials: www.superdatascience.com/596

Tune in as Joe Reis and Matt Housley, co-founders of Ternary Data and co-authors of the book “Fundamentals of Data Engineering” join Jon Krohn to discuss major undercurrents across the data engineering lifecycle, and the…

This week, Jon Krohn and A.I.

industry veteran Ben Taylor discuss the driving factors that push CEOs to prioritize A.I.

over other technologies.

Additional materials: www.superdatascience.com/594

Jon welcomes Professor Philip Bourne, Founding Dean of the School of Data Science at the University of Virginia to discuss his biomedical data science research, the importance of open-source and open-access within the in…

In this episode, Jon Krohn welcomes A.I.

industry veteran Ben Taylor to discuss how to sell multimillion dollar A.I.

Tune in to hear why trust and proof of value are some of the critical steps in his sales pro…

Mars Buttfield-Addison, PhD Candidate at the University of Tasmania, joins Jon Krohn for a high-energy episode covering everything from Machine Learning simulations to Swift, space junk, and more!

In this episode you wi…

In this episode, Jon continues his two-part series on artificial general intelligence (AGI) and why we are unlikely to realize it anytime soon.

Listen in as Jon reviews Meta's Yann LeCun's seven-part perspective on the t…

Hilary Mason, Co-Founder and CEO of Hidden Door, joins Jon Krohn for a live discussion that explores narrative A.I., emerging ML techniques, and how her OSEMN data science process developed.

In this episode you will lea…

In this episode, Jon kicks off a two-part series that sees him explore the popular topic of artificial general intelligence and why it might–or might not–be only a few years away.

Listen in as Jon explains the several re…

Mark Freeman, Senior Data Scientist at Humu, joins Jon Krohn to talk about all things data engineering and offers listeners some critical tips for their data science career journey – from what it takes to get promoted to…

June 3, 2022 podcastThat deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement.

Generalist agents have great properties that can overcome some of the limitations of single-task deep learning models.

Be aware, we are still far from AGI, though.

So what are generalist agents?

Referenceshttps://arxiv.org/pdf/2205.06175

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May 16, 2022 podcastIn this episode I speak with Matt Swalley, Chief Business Officer of Omneky, an AI platform that generates, analyzes and optimizes personalized ad creatives at scale.

We speak about the way AI is used for generating customized recommendation and creating experiences with data aggregation and analytics.

respecting the privacy of individuals.

LinksGrow your business with personalized ads https://www.omneky.com/Data Science at Home Podcast (Live) https://www.twitch.tv/datascienceathome

May 16, 2022 podcastLet’s take a break and think about the state of AI in 2022.

In this episode I summarize the long report from the Stanford Institute for Human-Centered Artificial Intelligence (HAI)Enjoy!

If you want a new interactive experience, I am scheduling hands-on session on TwitchFeel free to drop by when there is a live session, and interact with me.

I’ll see you there!

Referenceshttps://spectrum.ieee.org/artificial-intelligence-indexhttps://www.twitch.tv/datascienceathome

May 16, 2022 podcastIn this episode I have a conversation with, Itai Bar-Sinai, CPO & Cofounder of Mona.

Why is AI monitoring so different from monitoring classic software?

How to reduce the gap between data science and business?

What is the role of MLOps in the data monitoring field?

With over 10 years of experience with AI and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry.

April 21, 2022 podcastI am with Ander Steele, data scientist and mathematician with a passion for privacy and Shannon Bayatpur, product manager with a background in technical writing and computer science, from Tonic.ai.

We speak about data.

But all we say is authentic.

LinksTonic websiteCareer pageNeural networks for synthetic data

April 21, 2022 podcastIn this episode my friend and I speak about AI, batteries and automotive.

Dennis Berner, founder of Digitlabs has been operating in the field of automotive and batteries for a long time.

His point of views are absolutely a must to listen to.

Below a list of the links he mentioned in the show.

April 21, 2022 podcastThis is one episode where passion for math, statistics and computers are merged.

I have a very interesting conversation with Ravin, data scientist at Google where he uses data to inform decisions.

All opinions in this episode are his own and none of the companies he has worked for are represented.

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.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

We speak about machine learning applied to spatial data, spatial SQL and GIS (Geographic Information System).

Just tell them you came through Data Science at Home podcast.

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.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

ReferencesCarto https://carto.comSpatial Feature Engineering: https://geographicdata.science/book/intro.htmlCARTO Blog: https://carto.com/blog/Spati…

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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.

Get secure and private access to the internet by surfing nordvpn.com/DATASCIENCE or use coupon code DATASCIENCE and get a massive discount.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and pre…

April 1, 2022 podcastIn this episode I speak about AI and cloud automation with Leon Kuperman, co-founder and CTO at CAST AI.

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 higher transparency and disintermediation, and build the statistical models that will support your business.

Get secure and private access to the internet by surfing nordvpn.com/…

February 3, 2022 podcastThis is the last episode of the series “Embedded ML” and I made it for the bravest 🙂I speak about machine learning compiler optimization to a much greater detail.

Enjoy the episode!

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 higher transparency and disintermediation, and build the statistical models that will support your…

January 25, 2022 podcastIn this episode I speak about machine learning compilers, the most important tools to bridge the gap between high level frontends, ML backends and hardware target architectures.

There are several compilers one can choose.

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 higher transparency and disintermediation, and build the s…

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…