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Back to the mystery on whether large language models are learning surface statistics or world models, there have been some tantalizing clues suggesting language models may build interpretable “world models” with probing techniques.

Back to the question we have at the beginning: do language models learn world models or just surface statistics?

Our experiment provides evidence supporting that these language models are developing world models and relying on the world model to generate sequences.

CitationFor attribution of this in academic contexts or books, please cite this work as:Kenneth Li, "Do Large Language Models learn world models or just surface statistics?

BibTeX citation (this blog):…

Autonomous Military Robots: Design and PlausibilityAs Sparrow notes, ‘autonomy’ means different things to different authors (2007, 65).

Danaher predicts that people won’t desire to punish autonomous robots because the robots don’t seem deserving of punishment.

That gives us someone to punish and might also help make sure autonomous military robots are only used judiciously.

Thus, to the extent that deterrence, restoring trust, communicating condemnation, or providing education provide good reasons for punishing human agents, they also provide reasons to punish autonomous robots.

If it is not reasonable or ethically defensible to punish* autonomous robots, we should look hard at whether it i…

This is because their top-down perception has not had enough “experience/training data” to learn and refine itself.

In a way, we can say that their “world model” is not as good as that of adults.

An interesting consequence of a strong top-down perception is the ability of us humans to see things like animals/faces in the clouds (Pareidolia).

Multimodal Language Models (LMs) are an attempt to make such language models perceive the world in a way that’s one step closer to that of humans.

His work focuses on reverse engineering Large Multimodal Language Models to make them explainable to humans.

In one of the claimed birthplaces of democracy, Ancient Athens, demos covered all Athenian citizens, who had an equal say in collective decision-making.

And only the real people – the demos – can recognize the ‘real’ from the ‘not-so-real.’In essence, if you are not part of the demos, you have no say in collective decision-making.

Original Photo: Daria Shevtsova / Pixabay, edited by authorIn democracies, it is the demos that should have the topmost power over collective decision-making.

If we want to preserve democracy and/or demos based on equality and freedom, we could start asking ourselves: Is our future demos nation-state-based or global, and how could we align AI development with this…

Any causal inference problem consists of two parts: causal identification and statistical inference.

Causal inference theoryCausal inference is a theory that describes, discriminates, and measures causal relationships, developed from statistics.

Causal representation learningUnlike the traditional causal inference approach, which uses causal graphs to connect random variables to complete the causal discovery and reasoning hypothesis task, the problem of causal representation learning has recently attracted more attention.

is not valid, and causal inference studies exactly such a situation: how to learn a causal model that can work under different distributions, imply a causal mechanism (Cau…

"By 2030, speech recognition will feature truly multilingual models, rich standardized output objects, and be available to all and at scale.

Finally, speech recognition will engender the principles of responsible AI, and operate without bias."

Source: Hannun, Awni, “Speech Recognition is not Solved”.

CitationFor attribution in academic contexts or books, please cite this work asMigüel Jetté and Corey Miller, "The Future of Speech Recognition: Where will we be in 2030?

BibTeX citation:@article{miller2021futureofowork,author = {Jetté, Migüel and Miller, Corey},title = {The Future of Speech Recognition: Where will we be in 2030?

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 = {Voelcker, 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{michaelbeyond2022,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 pass…

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.

As a member of the ML community, we’d love for you to participate in our industry survey—it’ll only take 10 minutes, and the first 150 respondents will receive a $25 Amazon gift card!

apply() is the ML data engineering event series hosted by Tecton, where the ML community comes together to share best practices.

Today, we’re reaching out to the community to put together the most comprehensive review of the state of applied machine learning.

Whether you’re a product manager, data scientist, engineer, architect, or ML aficionado, we want to hear from you!

and to thank you for participating, we’ll share a free copy of the research report with respondents ahead of its public release.

One of the key enablers of the ChatGPT magic can be traced back to 2017 under the obscure name of reinforcement learning with human feedback(RLHF).

Understanding RLHFRLHF was initially unveiled in Deep reinforcement learning from human preferences , a research paper published by OpenAI in 2017.

In these scenarios, human feedback could make a huge difference.

A reward model (RM) is trained on this dataset to predict which output the labelers would prefer.

In the case of ChatGPT, human AI trainers input conversations in which they played both sides: the user and the AI assistant.

Every organization that runs ML models in production has realized the importance of monitoring for model and data health.

Without proactive monitoring, model failure can have devastating effects on a model's ROI, customer trust, and company revenue.

This guide will help answer key questions about the ML monitoring needs of your organization:What is needed to solve my team’s unique problem?

Estimating labor cost, sticker price, support and infrastructure cost for scenarios across build vs. buy spectrum.

DOWNLOAD THE FREE GUIDE*This post was written by the WhyLabs Team.

Created with Stable DiffusionIn this issue:An overview ML interpretability methods optimized for large neural networks.

A deep dive into the Eli5( Explain like I am a 5-year old) framework.

💡 ML Concept of the Day: Interpretability Methods for Deep Neural NetworksTo close our series about machine learning(ML) interpretability, we would like to discuss the evolution of the space given the fast growth of deep neural networks(DNNs).

Most of the local and global model agnostic methods explored in this series are applicable to DNNs but they were mostly designed for simpler ML architectures.

DNNs have some unique characteristics that provide new dimensions to explore by interpretability methods:

My answer was that I feel that, after the recent market events, OpenAI might have hit “escape velocity”.

Celestial mechanics defines escape velocity as “speed needed to break free from the gravitational attraction of a massive body, without further propulsion”.

ChatGPT has been at the center of OpenAI’s recent momentum.

Furthermore, both Google and Meta have been actively working in the generative AI space with models like LaMDA, Imagen, Muse, Make-A-Scene, Sparrow and many others.

For now, OpenAI seems to have hit escape velocity.

Created Using: Stable DiffusionText-to-Image(TTI) models have been at the center of the generative AI revolution with models such as DALL-E, Stable Diffusion or Midjourney capturing the headlines.

This explosion in high quality TTI models have been fundamentally powered by diffusion or autoregressive methods that can effectively compute similarities between text and images.

Recently, Google unveiled Muse, a TTI model that can achieve state-of-the-art image quality outputs while remaining more efficient than diffusion and autoregressive models.

Muse follows Google’s active work in TTI with diffusion models such as Image or autoregressive models like Parti.

Muse builds on the lessons learned …

Created by: Stable DiffusionIn this issue:An overview of local-interpretability methods based on counterfactual explanations.

Google’s StylEx method for generation visual explanations in image classifiers Microsoft’s open source implementation of the DiCE method.

💡 ML Concept of the Day: : Local Model-Agnostic Interpretability Methods: Counterfactual ExplanationsIn the last few editions of this newsletter, we have been discussing local model-agnostic interpretability methods which focus on deriving explanations based on the outcome of a single prediction.

One of the most interesting methods in this area of ML interpretability is known as counterfactual explanations.

From causality theory, w…

Edge #264: Deep dive into Google’s newest generative AI model: Muse.

The alliance between Microsoft and OpenAI is one of the most fascinating strategic developments in the AI world.

Obviously, the partnership provides Microsoft with first-level access to what many consider the most innovative generative AI stack in the market.

Human-Aligned AI ResearchMicrosoft Research published a summary of their recent research efforts in responsible AI —> Read more.

🤖 Cool AI Tech ReleasesAzure OpenAI Service GAIn the biggest AI news of the week, Microsoft announced the general availability of its Azure OpenAI Service that includes models like GPT-3.5, Codex and DALL-E —> Read more.

Created with Stable Diffusion3D is steadily becoming one of the next frontiers for generative AI.

NVIDIA Omniverse has been one of the platforms adapting many of these generative AI techniques for metaverse applications.

At CES 2023, NVIDIA unveiled the incorporation of many generative AI extensions for its Omniverse platform.

At the center of this announcement, there was a model that NVIDIA research unveiled late last year.

Get3D is a generative AI model able to produce 3D shapes with high-fidelity textures and complex geometries.

Created With: Stable DiffusionIn this issue:An overview of the LIME interpretability method.

💡 ML Concept of the Day: Local Model-Agnostic Interpretability Methods: LIMEContinuing our series about ML interpretability, today we would like to cover one of the most popular local model-agnostic methods.

Local interpretable model-agnostic explanations (LIME) is omnipresent in all literature and frameworks related to ML interpretability.

Just like other local methods, LIME does not try to understand the complete behavior of a model but rather the changes in individual predictions.

Once a local model is chosen, LIME tries to fit it to a new training set formed by adding perturbations to the origin…

📝 EditorialGenerative AI is at the center of the activity in the AI market these days.

This week, we saw some interesting developments with new generative AI models.

Compared to autoregressive models, Muse seems to improve based on its use of parallel decoding techniques.

🔎 ML ResearchNew Text-to-Image ModelGoogle introduced Muse, a new text-to-image generation and editing transformer model —> Read more.

🤖 Cool AI Tech ReleasesPyTorch HTAPyTorch released Holistic Trace Analysis (HTA), a framework developed by Meta to debug performance issues in large scale models —> Read more.

With its diverse portfolio of customers of all sizes, Provectus has helped many SMB clients succeed with their AI initiatives, ranging from complex, end-to-end AI transformations to implementing specific AI solutions.

#1 — Incompleteness of Data and/or AI StrategyAny company may struggle with incomplete or insufficient data or AI strategies, which can limit the effectiveness and accuracy of their AI initiatives.

Implementing a full-scale AI strategy involves building new departments, systems, and potentially even a line of business centered around AI.

The second type of AI initiative is implementing a specific AI solution to address a specific problem, which may involve using a third-party …

Earlier last year, Meta AI unveiled Data2vec, one of the first self-supervised learning models to ever master tasks across different domains such as speech, text and vision.

The model was one of the first iterations in Meta AI’s self-supervised architectures that emulate human learning processes using different sensorial inputs.

The original Data2vec architecture based on a student and a teacher network.

The teacher network computes representations for a text, image, or speech.

The student network takes that output and attempts to predict the latent representations back to the teacher.

Join us at the Robust & Responsible Summit to gain actionable insights from top AI experts!

This free half-day event connects AI builders, leaders, and pioneers in the industry, driving efficient, ethical, and responsible AI.

Sessions include:A Fireside Chat on Data-centric AI with Andrew Ng, Founder of DeepLearning.AIOperationalizing Machine Learning at a Large Financial Institution featuring Daniel Stahl, Head of Data and Analytic Platforms at Regions BankProductionizing Large Language Models featuring Rolland He, Manager, Machine Learning Science, and Linge Lass, Staff Machine Learning Scientist at GlassdoorGet Your Company Started with AI: The Right Way, featuring Ksenia Palke, Head of …

In this issue:We discuss the s, SHapley Additive exPlanations(SHAP) local ML interpretability method.

Review a taxonomy for ML interpretability techniques proposed by MIT.

💡 ML Concept of the Day: Local Model-Agnostic Interpretability Methods: SHAPAmong the local ML interpretability methods, SHapley Additive exPlanations(SHAP) stands out as one of the most popular within the data science community.

Part of the popularity of SHAP comes from its game theoretic approach to ML interpretability.

Shapley values were first introduced by Lloyd Shapley in 1953 as a way to evaluate the marginal contributions of each player despite possible coalitions.

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Модель классификации и функция потерьЧтобы задать вероятностную модель, нам нужно определить, в какой форме она будет предсказывать распределение .

Если мы используем в обучении сигмоиду, то модель непосредственно предсказывает логит , то есть логарифм того, во сколько раз второй класс вероятнее первого.

Например, в пусть в задаче классификации эмоций по видеозаписи датасет размечен сразу несколькими людьми-аннотаторами, которые иногда дают разные ответы.

Если в задаче классификации в эталонном распределении вероятности классов равны 0.7 и 0.3, то мы хотели бы, чтобы в предсказании они тоже были бы равны 0.7 и 0.3.

В этом разделе мы рассмотрели более общий случай, когда эталонное распределе…

В пятом разделе рассмотрим модель регрессии с оценкой уверенности в виде формул и программного кода.

Вообще говоря, большую часть машинного обучения можно считать статистическим выводом, что мы более формально рассмотрим в дальнейшем.

При этом чем сложнее модель и меньше данных, тем менее точной получается аппроксимация, что и является причиной переобучения.

Проверим этот метод на практике, обучив модель на табличном датасете California Housing, в котором нужно предсказывать цену недвижимости в разных районах Калифорнии, имея 8 исходных признаков.

Положительная корреляция говорит о том, что модель в какой-то степени справляется с задачей оценки собственной уверенности в предсказании.

Под размером понимается количество параметров в модели, и для LLM это число превосходит несколько миллиардов.

Языковые модели и фактыЯзыковые модели, или Language Models (LM), решают очень простую задачу: предсказание следующего слова (или токена, части слова).

Это ясно нам, человекам, и как показывают современные языковые модели - это понятно и им.

Мы уже обсудили, что такое токен, и что для модели заранее создается словарь токенов, который используется для подачи входного текста.

Как модель для DotA 2 видит поле боя - принцип сбора признаком для подачи в нейросеть.

То, насколько интерпретация хороша, зависит не только от инструментов и отчетов, которые мы предоставляем пользователю, но и от потребностей пользователя и особенностей задач, которые он решает.

Хотел бы доверять результату работы модели и избежать неприемлемого ущерба в случае ошибки в работе модели.

Нуждается в способе определить, что модель "занесло", и в инструкциях - что делать в этом случае.

Кому и когда на практике в большей степени нужна интерпретируемость ML-моделейМы рассмотрели ключевых пользователей интерпретации ML-моделей и различия в их потребностях в интерпретации.

Усилить применимость модели в реальных условиях в случае модели оптимизации ассортимента может также помочь доб…

Для Забасткома получилось улучшить систему автоматической обработки новостей с помощью алгоритмов машинного обучения.

О проектеЗабастКом — это содружество технических специалистов, которые неравнодушны к проблемам наемных работников и которые решили вместе освещать трудовые конфликты в России и странах ближнего зарубежья.

Алгоритмы фильтрации работали, но могли пропускать важные новости о забастовках или, наоборот, могли выдавать много новостей на сторонние темы.

Есть интересные задачи и для Data Science специалиста:Настроить автоматическое создание документов-отчетов по шаблону, подобно годовым отчетам за 2021 и 2022 год.

Сделать более глубокий анализ данных из API и дополнить им раздел са…

В этой статье сделан обзор успехов и прогресса в этом направлении в 2022 году.

С важным исключением, сюда включены только работы на настоящих играх (настольных и видеоиграх), но не на играх, созданных специально для тестирования ИИ агентов.

Агент Sophy обгоняет киберспортсменов как в индивидуальных, так и в командных гонках в Gran turismo sport.

Результаты: Стратегическое мышление и способность непредсказуемо блефовать принесла алгоритму место в топ-3 лучших игроков на платформе для игры в Stratego.

Результаты: В отдельной работе в 2019 году Hanabi была объявлена в качестве следующего фронтира для ИИ.

Несмотря на то, что это всего лишь пет-проект, в нём было много проблем, которые встречаются и в реальных проектах.

По моим оценкам, уровень ошибок составлял около 10%, что означало, что около 2 тысяч изображений имели неправильные метки.

Image classificationКогда я начал работать над своим обновленным проектом по распознаванию цифр, я начал с обучения модели CNN на Pytorch на моем MacBook.

Ранее я уже разработал пайплайн для тренировки моделей на PyTorch-lightning и Hydra, и я смог его легко допилить для этого проекта.

Этот проект был ценным и приятным опытом обучения для меня, и я надеюсь, что вы также нашли его интересным :)Дополнительные ссылки:

Куда и на сколько смещаются пузырьки — туда и течет пена с определенной скоростью.

Если хочется трекать множество объектов одновременно, то решение будет состоять не только из поиска всех интересующих объектов, но и сопоставления объектов на одном кадре объектам на следующем кадре.

Сопоставление/ассоциация объектов друг другу может происходить на основе разметки (это будет называться supervised multiobject tracking — не наш случай, но тема хорошо раскрыта тут), или на основе корреляции признаков объектов без разметки.

Однако, добавление новых стадий в алгоритм (для анализа сложных сцен) приводит к увеличению времени его работы.

Венгерский алгоритм может ошибаться, может сопоставить пузырек_…

Перемена карьеры и в обычных условия задача нетривиальная, в декрете это усложняется в несколько раз.

Бездумные развлечения – долойУ меня нет телевизора, я не смотрю сериалы, не подписала на Netflix, не играю в компьютерные игры.

Я не устраиваю вечеринки и не хожу по гостям.

Ваши впечатления, что все гениальны, исчезнут, когда вы увидите, что и остальные зависают и не знают каких-то вещей.

Не подписывайтесь на то, что не хотите делать, только потому, что вежливы и не хотите огорчить человека.

Уточнение задачиНе нужно сразу бросаться решать задачу, а лучше задать как можно больше правильных уточняющих вопросов.

Таким образом покажете, что у вас широкий опыт как с технической точки зрения, так и с продуктовой.

Можно упомянуть извечную проблему training-serving skew (расхождение между тренировкой и инференсом модели) и как ее можно решить с помощью фича сторов.

Зачастую добавляются сторонние источники данных (Redis, Postgres, S3), необходимые для инициализации модели и ее инференса.

Стоит хотя бы немного понимать про архитектуру систем, из каких компонент обычно состоят и как связаны компоненты.

О задаче Metric learningЗадача metric learning состоит в том, чтобы построить функцию от двух объектов, которая будет оценивать расстояние (похожесть) между ними.

Далее мы рассмотрим решение данной задачи с помощью нейронных сетей, то есть deep metric learning, где выделяются два основных подхода:Siamese.

Задачи deep metric learning и классификации могут перетекать друг в друга, что делает использование терминологии запутанным.

Если всё-таки выделить характерное отличие, то я бы сказал, что в классификации классы на train и test выборках совпадают, а в metric learning — не обязательно.

Да, для metric learning, как и для классификации, существует набор популярных датасетов, например, картино…

Пару недель назад мы выложили на GitHub утилиту для запуска ML скриптов в облаке.

Проблема с такими платформами заключается в том, что вам необходимо полностью интегрироваться в эту платформу, и использовать инструменты предлагаемые исключительно на базе этой платформы.

Традиционных подход заключается в использовании базы данных и в выделении центрального фасада (backend) для работы с этим стейтом.

Про запускЗдесь я бы хотел поделиться опытом запуска проектов в целом и open-source проектов в частности.

Если интересно поговорить про dstack, запуск ML скриптов, или про разработку open-source проектов, пишите в личное сообщения!

Получили некий зашумленный датасет, в котором в том числе есть и транскрипции сделанные другими ASR системами, много тишины и шумов, смех, апплодисменты и т.д.

Объем получился 680 000 часов на 97 языках, из которых 117 000 часов не на английском.

И это в разы меньше.

Это не хорошо и не плохо, так как бывают различные требования к результату.

Whisper не был на это натренирован, но вот здесь этот вопрос обсуждают и делятся наработками (colab).

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

Мы делаем новый запуск курса Natural Language Processing.

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

Сам курс запускается в этом виде в пятый раз.

Ссылка будет в группе курса.

Data science pet project – это внерабочая активность, целью которой является решение некоторой задачи с помощью обработки данных, улучшающая ваши профессиональные навыки1.

В своем проекте вы “и спец, и на дудке игрец”, а также PO, CTO, CEO (и немного HR).

Поиск темы проекта и данных для анализаВ пет-проектах по анализу данных тема неразрывно связана с данными.

__поиск данных для примера 1 существуют датчики, которые определяют шаги, пульс, глубину дыхания, частоту сердцебиения, температуру тела.

ODS ник: Sergei Два года пилил пет-проект про GAN/Deepfake, в процессе хорошо прокачался в в DL, описание проекта в хабр-статье.

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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.

The first time I ever wrote for a puzzlehunt was Mystery Hunt 2013.

Ten years later, teammate wrote another Mystery Hunt that went into Monday, with a similarly large number of free answers as MH 2013.

I don’t think there was any single reason that Mystery Hunt was so hard this year, but there was definitely a systematic underestimation of difficulty and length.

However, there are some first-time constructors on teammate this year, where their Hunt puzzles are their first puzzles for the public.

I’m pretty sure I’ve spent more time on Hunt this year than I spent in all my past puzzle writing combined.

In the months since, image generation has gone from a thing some people talked about, to something everyone was talking about.

I read a post from someone who discussed AI asceticism, and then acknowledged that they could not do it, the image generation was too fun to play with.

People have normalized that it is possible to get high quality language-guided image generation really, really quickly.

I think there’s only a few domains where we actually have enough human data at the moment.

I don’t think they’ll lead to fundamental floor raising of what we believe ML models are capable of.

This January, the team I was on won MIT Mystery Hunt, the biggest puzzlehunt of the year.

See, people don’t quite understand how long it takes to write Mystery Hunt.

markdown 414 2022 - 01 - 22 - mh - 2022. markdown 400 2022 - 04 - 15 - do - what - i - mean .

markdownI’m a bit surprised the ML-related post has fewer views than the Mystery Hunt post.

I’m guessing shades of what this post would have been will appear in other posts I write later.

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.

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…

Can AI Image generation tools make re-imagined, higher-resolution versions of old video game graphics?

Over the last few days, I used AI image generation to reproduce one of my childhood nightmares.

It’s been a few months since the vast majority of people started having broad access to AI image generation tools.

In this time, I’ve gotten to use three of these image generation services.

A key point for extending the capability and building more advanced systems that use an image generation component.

The image generator goes through two stages:1- Image information creatorThis component is the secret sauce of Stable Diffusion.

The image information creator works completely in the image information space (or latent space).

This concludes the description of image generation by diffusion models mostly as described in Denoising Diffusion Probabilistic Models.

Speed Boost: Diffusion on Compressed (Latent) Data Instead of the Pixel ImageTo speed up the image generation process, the Stable Diffusion paper runs the diffusion process not on the pixel images themselves, but on a compressed version of the image.

The released Stable Diffusion model uses ClipText (A GPT-based model), while the paper …

Now almost 13 years after my PhD defense, my view is that science is actually a rather fragile thread we use to hold together and explain various mysteries in the world.

But I now view science as any other social activity, being influenced by zeitgeist, politics, fashion, financing and often stuck in a dogma, no different than the dogma that threatened Galileo or Copernicus.

A few years back I digested all of his books and this experience has completely changed my view on science.

Science is the best method, but scientific community is mostly toxicThe general theme of this post is that by looking at several seemingly disconnected aspects of science and technology we can see that our contemp…

Data science essentially is glorified statistics with a computer, AI is deeply statistical at its very core, we use statistical analysis for pretty much everything from economy to biology.

Statistics is a craft that allows us to analyze and predict certain subset of complex signals that are not possible to describe in terms of dynamics.

Now let me repeat this once again: statistics can be applied to some data sometimes.

Also the smaller signals can be reasonably "independent" of each other, but can all be dependent on some other bigger external thing.

they only applied statistics to what can be understood with mechanics but at a slightly higher level of organization.

This post takes a closer look at the state of multilingual AI.

Multilingual models These models have multilingual analogues—in NLP, models such as mBERT, RemBERT , XLM-RoBERTa , mBART , mT5 , and mDeBERTa —that were trained in a similar fashion, predicting randomly masked tokens on data of around 100 languages.

Compared to their monolingual counterparts, these multilingual models require a much larger vocabulary to represent tokens in many languages.

CitationFor attribution in academic contexts or books, please cite this work as:Sebastian Ruder, "The State of Multilingual AI".

BibTeX citation:@misc{ruder2022statemultilingualai, author = {Ruder, Sebastian}, title = {{The State of Multilingua…

This post discusses my highlights of ACL 2022, including language diversity and multimodality, prompting, the next big ideas and keynotes, my favorite papers, and the hybrid conference experience.

ACL 2022 took place in Dublin from 22nd–27th May 2022.

Language diversity and multimodalityPanelists and their spoken languages at the ACL 2022 keynote panel on supporting linguistic diversity.

Multimodality is also at the heart of the ACL 2022 D&I Special Initiative “60-60 Globalization via localisation” announced by Mona Diab.

Annotator opinions bias language models (Sap et al., 2021) and ambiguous examples improve generalization (Swayamdipta et al., 2020).

But with all of these advances, the impressive feats in deep learning required a substantial amount of sophisticated engineering effort.

Recently, Yujin Tang and I put together a survey paper called Collective intelligence for deep learning: A survey of recent developments about this topic, and in this post, I will summarize the key themes in our paper.

They developed something called Cellular Neural Networks, which are artificial neural network circuits with grids of artificial neurons.

Deep Reinforcement LearningAnother popular area within Deep Learning is to train neural networks with reinforcement learning for tasks like locomotion control.

I believe that deep learning research can bene…

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

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

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

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

Minecraft is a great testbed for human creativity that has inspired the design of various structures and even functioning machines, including flying machines.

EvoCraft is an API for programmatically generating structures in Minecraft, but the initial work in this domain was not capable of evolving flying machines.

This paper applies fitness-based evolution and quality diversity search in order to evolve flying machines.

Although fitness alone can occasionally produce flying machines, thanks in part to a more sophisticated fitness function than was used previously, the quality diversity algorithm MAP-Elites is capable of discovering flying machines much more reliably, at least when an approp…

In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution.

In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT).

Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT.

We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in …

Given a sentence "Abby told Brittney that she upset Courtney", one would struggle to understand who "she" refers to, and ask for clarification.

To this end, we construct AmbiCoref, a diagnostic corpus of minimal sentence pairs with ambiguous and unambiguous referents.

Our examples generalize psycholinguistic studies of human perception of ambiguity around particular arrangements of verbs and their arguments.

Analysis shows that (1) humans are less sure of referents in ambiguous AmbiCoref examples than unambiguous ones, and (2) most coreference models show little difference in output between ambiguous and unambiguous pairs.

We release AmbiCoref as a diagnostic corpus for testing whether mode…

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR).

Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally.

This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textua…

Monte Carlo simulations of physics processes at particle colliders like the Large Hadron Collider at CERN take up a major fraction of the computational budget.

We leverage symmetry arguments from particle physics to optimize the performance of the regressors.

Inspired by ResNets, we design a Deep Neural Network with skip connections that outperform fully connected Deep Neural Networks.

We find that at lower dimensions, boosted decision trees far outperform neural networks while at higher dimensions neural networks perform significantly better.

Additionally, using symmetry arguments derived from particle physics, we reduce the number of regressors necessary for each simulation by an order of…

Tabular data plays an essential role in many data analytics and machine learning tasks.

In this context, semantic table interpretation is crucial for making data analytics workflows more robust and explainable.

We introduce original lightweight semantic profiles that enrich a domain ontology's concepts and relations and represent domain and table characteristics.

In contrast to the existing semantic table interpretation approaches, Tab2KG relies on the semantic profiles only and does not require any instance lookup.

Our experimental evaluation on several real-world datasets from different application domains demonstrates that Tab2KG outperforms state-of-the-art semantic table interpretation…

In this work we explore how language models can be employed to analyze language and discriminate between mentally impaired and healthy subjects through the perplexity metric.

Perplexity was originally conceived as an information-theoretic measure to assess how much a given language model is suited to predict a text sequence or, equivalently, how much a word sequence fits into a specific language model.

We carried out an extensive experimentation with the publicly available data, and employed language models as diverse as N-grams, from 2-grams to 5-grams, and GPT-2, a transformer-based language model.

We investigated whether perplexity scores may be used to discriminate between the transcrip…

For this purpose, we design an uncertainty-based paced curriculum learning in self distillation for medical image segmentation.

We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (PCD).

We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation.

Results: The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robotassisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness.

While curricul…

We introduce Neuro-Symbolic Continual Learning, where a model has to solve a sequence of neuro-symbolic tasks, that is, it has to map sub-symbolic inputs to high-level concepts and compute predictions by reasoning consistently with prior knowledge.

Traditional approaches fall short: existing continual strategies ignore knowledge altogether, while stock neuro-symbolic architectures suffer from catastrophic forgetting.

We show that leveraging prior knowledge by combining neuro-symbolic architectures with continual strategies does help avoid catastrophic forgetting, but also that doing so can yield models affected by reasoning shortcuts.

To overcome these issues, we introduce COOL, a COncept-l…

Hyperspectral image (HSI) classification is used in multiple domains like precision agriculture, mineral exploration, remote sensing, and others.

Conventionally, couvolutional neural networks (CNNs) were used in HSI classification, however they have limitations in exploiting spectral-spatial relationships, which is a key factor in understanding HSI.

In this paper, we propose a novel method based on the concept of matrix capsules with Expectation-Maximization (EM) routing algorithm which is specifically designed to accommodate the nuances in the HSI data to efficiently tackle the aforementioned problems.

The capsule units enable effective identification of spectral siguatures and part-whole …

We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects speech quality.

Since the Deep Noise Suppression (DNS) Challenge dataset does not contain practical disturbance, we collect a transmitted DNS (t-DNS) dataset using Zoom Meetings over T-Mobile network.

We select two baseline models: Demucs and FullSubNet.

The Demucs is an end-to-end model that takes time-domain inputs and outputs time-domain denoised speech, and the FullSubNet takes time-frequency-domain inputs and outputs the energy ratio of the target speech in the inputs.

The goal of this project is to enhance the s…

The past years have seen a drastic rise in studies devoted to the investigation of colexification patterns in individual languages families in particular and the languages of the world in specific.

Specifically computational studies have profited from the fact that colexification as a scientific construct is easy to operationalize, enabling scholars to infer colexification patterns for large collections of cross-linguistic data.

Studies devoted to partial colexifications -- colexification patterns that do not involve entire words, but rather various parts of words--, however, have been rarely conducted so far.

This is not surprising, since partial colexifications are less easy to deal with …

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs).

Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention.

State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA.

In this paper, we present DualMatch which effectively fuses the relational and temporal information for EA.

DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information.

Safe Interval Path Planning (SIPP) is a powerful algorithm for solving single-agent pathfinding problem when the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment.

Original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously.

Unfortunately, as we show in this work, in such a case original SIPP is incomplete.

To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration.

In the experimental evaluation we show that the key property of the original SIPP still holds for the modifi…

This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using OpenRAM.

The chip is taped out in the 130 nm SkyWater process and integrates over 1 million synaptic weights, and offers a reprogrammable architecture.

It operates at a clock speed of 40 MHz, a supply of 1.8 V, uses a PicoRV32 core for control, and occupies an area of 33.3 mm^2.

The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 us, at 56.8 GOPS/W.

This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the-art, full precision SNNs.

Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D sensing capabilities of self-driving vehicles.

Although various supervised nighttime image enhancement methods have been proposed, their generalization performance in challenging driving scenarios is not satisfactory.

To this end, we propose the first method that jointly learns a nighttime image enhancer and a depth estimator, without using ground truth for either task.

Our method tightly entangles two self-supervised tasks using a newly proposed uncertain pixel masking strategy.

This strategy originates from the observation that nighttime images not only suffer from underexposed regions but also fro…

We propose the first Bayesian encoder for metric learning.

Rather than relying on neural amortization as done in prior works, we learn a distribution over the network weights with the Laplace Approximation.

We actualize this by first proving that the contrastive loss is a valid log-posterior.

We then propose three methods that ensure a positive definite Hessian.

Empirically, we show that our Laplacian Metric Learner (LAM) estimates well-calibrated uncertainties, reliably detects out-of-distribution examples, and yields state-of-the-art predictive performance.

We consider the optimisation of large and shallow neural networks via gradient flow, where the output of each hidden node is scaled by some positive parameter.

We focus on the case where the node scalings are non-identical, differing from the classical Neural Tangent Kernel (NTK) parameterisation.

We prove that, for large neural networks, with high probability, gradient flow converges to a global minimum AND can learn features, unlike in the NTK regime.

We also provide experiments on synthetic and real-world datasets illustrating our theoretical results and showing the benefit of such scaling in terms of pruning and transfer learning.

PDFAbstract

The goal of inverse rendering is to decompose geometry, lights, and materials given pose multi-view images.

To achieve this goal, we propose neural direct and joint inverse rendering, NDJIR.

Different from prior works which relies on some approximations of the rendering equation, NDJIR directly addresses the integrals in the rendering equation and jointly decomposes geometry: signed distance function, lights: environment and implicit lights, materials: base color, roughness, specular reflectance using the powerful and flexible volume rendering framework, voxel grid feature, and Bayesian prior.

Our method directly uses the physically-based rendering, so we can seamlessly export an extracted …

Understanding the accuracy limits of machine learning algorithms is essential for data scientists to properly measure performance so they can continually improve their models' predictive capabilities.

This study empirically verified the error bound of the AdaBoost algorithm for both synthetic and real-world data.

The results show that the error bound holds up in practice, demonstrating its efficiency and importance to a variety of applications.

The corresponding source code is available at https://github.com/armanbolatov/adaboost_error_bound.

PDFAbstract

One natural approach is to initialize the policy for online learning with the one trained offline.

After learning the offline policy, we use it as one candidate policy in a policy set.

We then expand the policy set with another policy which will be responsible for further learning.

With this approach, the policy previously learned offline is fully retained during online learning, thus mitigating the potential issues such as destroying the useful behaviors of the offline policy in the initial stage of online learning while allowing the offline policy participate in the exploration naturally in an adaptive manner.

Moreover, new useful behaviors can potentially be captured by the newly added p…

Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer.

However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy.

To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning.

The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference.

To mitigate the effect of such mistakes, we propose Multimodal-CoT that …

This study presents a high-accuracy, efficient, and physically induced method for 3D point cloud registration, which is the core of many important 3D vision problems.

Our proposed method consists of four major modules.

First, we leverage the graph signal processing (GSP) framework to define a new signature, (i.e., point response intensity for each point), by which we succeed in describing the local surface variation, resampling keypoints, and distinguishing different particles.

We perform comprehensive experiments to evaluate the proposed method on various datasets captured from range scanners to LiDAR.

Results demonstrate that our proposed method outperforms representative state-of-the-art…

Thanks to the ubiquitous deployment of Wi-Fi hotspots, channel state information (CSI)-based Wi-Fi sensing can unleash game-changing applications in many fields, such as healthcare, security, and entertainment.

However, despite one decade of active research on Wi-Fi sensing, most existing work only considers legacy IEEE 802.11n devices, often in particular and strictly-controlled environments.

Worse yet, there is a fundamental lack of understanding of the impact on CSI-based sensing of modern Wi-Fi features, such as 160-MHz bandwidth, multiple-input multiple-output (MIMO) transmissions, and increased spectral resolution in IEEE 802.11ax (Wi-Fi 6).

This work aims to shed light on the impact …

We propose a spatio-temporal mixing kinematic data estimation method to estimate the shape of the colon with deformations caused by colonoscope insertion.

Although many previous methods focused to track bronchoscopes and surgical endoscopes, few number of colonoscope tracking methods were proposed.

We propose a colon shape estimation method using a Kinematic Spatio-Temporal data Mixer (KST-Mixer) that can be used during colonoscope insertions to the colon.

Kinematic data of a colonoscope and the colon, including positions and directions of their centerlines, are obtained using electromagnetic and depth sensors.

We evaluated colon shape estimation accuracies in phantom studies.

There has been significant recent progress in training differentially private (DP) models which achieve accuracy that approaches the best non-private models.

These DP models are typically pretrained on large public datasets and then fine-tuned on downstream datasets that are (i) relatively large, and (ii) similar in distribution to the pretraining data.

We show that to achieve DP accuracy on par with non-private models, the shots per class must be increased as the privacy level increases by as much as 32$\times$ for CIFAR-100 at $\epsilon=1$.

We also find that few-shot non-private models are highly susceptible to membership inference attacks.

Finally, we evaluate DP federated learning syste…

Dynamic graphs are rich data structures that are used to model complex relationships between entities over time.

In particular, anomaly detection in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks.

In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i).

To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot.

MultiLAD provides the first c…

Federated learning (FL) is a hot collaborative training framework via aggregating model parameters of decentralized local clients.

It renders the global model to signifcantly degrade recognition performance on old categories (i.e., catastrophic forgetting), when local clients receive new categories consecutively under limited memory of storing old categories.

Specifcally, considering tackling class imbalance of local client to surmount local forgetting, we develop a category-balanced gradient-adaptive compensation loss and a category gradient-induced semantic distillation loss.

They can balance heterogeneous forgetting speeds of hard-to-forget and easy-to-forget old categories, while ensure…

As part of DeepMind’s mission to solve intelligence, we created a system called AlphaCode that writes computer programs at a competitive level.

AlphaCode placed at about the level of the median competitor, marking the first time an AI code generation system has reached a competitive level of performance in programming competitions.

We pre-train our model on selected public GitHub code and fine-tune it on our relatively small competitive programming dataset.

"Solving competitive programming problems is a really hard thing to do, requiring both good coding skills and problem solving creativity in humans.

AlphaCode ranked within the top 54% in real-world programming competitions, an advancem…

Diplomacy is a seven-player game of negotiation and alliance formation, played on an old map of Europe partitioned into provinces, where each player controls multiple units (rules of Diplomacy).

We use Diplomacy as an analog to real-world negotiation, providing methods for AI agents to coordinate their moves.

We take our non-communicating Diplomacy agents and augment them to play Diplomacy with communication by giving them a protocol for negotiating contracts for a joint plan of action.

We call these augmented agents Baseline Negotiators, and they are bound by their agreements.ÂDiplomacy contracts.

In practice, Learned Deviators occasionally break contracts late in the game, and in doing so…

Stratego is challenging for AI, in part, because it’s a game of imperfect information.

The machine learning approaches that work so well on perfect information games, such as DeepMind’s AlphaZero, are not easily transferred to Stratego.

The art of the bluffAs in poker, a good Stratego player must sometimes represent strength, even when weak.

See more by watching these four videos of full-length games played by DeepNash against (anonymised) human experts: Game 1, Game 2, Game 3, Game 4.“The level of play of DeepNash surprised me.

I had never heard of an artificial Stratego player that came close to the level needed to win a match against an experienced human player.

Advancing best-in-class large models, compute-optimal RL agents, and more transparent, ethical, and fair AI systemsThe thirty-sixth International Conference on Neural Information Processing Systems (NeurIPS 2022) is taking place from 28 November - 9 December 2022, as a hybrid event, based in New Orleans, USA.

We updated the scaling laws of large models, showing how previously trained models were too large for the amount of training performed.

Pioneering responsiblyAt the heart of DeepMind’s mission is our commitment to act as responsible pioneers in the field of AI.

We’re committed to developing AI systems that are transparent, ethical, and fair.ÂExplaining and understanding the behavio…

Learning in “the playhouse”Our framework begins with people interacting with other people in the video game world.

Human participants set the contexts for the interactions by navigating through the world, setting goals, and asking questions for agents.

This phase was covered in two of our earlier papers, Imitating Interactive Intelligence, and Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning, which explored building imitation-based agents.

Our agents trained by RL performed much better than those trained by imitation learning alone.ÂWe asked people to evaluate our agents in online real-time interactions.

In Deep reinforcement learning from human prefere…

For example, when large models are deployed, whatever they have learned on one task is seldom harnessed to facilitate their learning of the next task.

What’s more, once new data or more compute become available, large models are typically retrained from scratch – a costly, time-consuming process. ÂThis raises the question of whether we could improve the trade-off between the efficiency and performance of these large models, making them faster and more sustainable while also preserving their outstanding capabilities.

The Never-Ending Visual classification Stream (NEVIS’22) is a benchmark stream in addition to an evaluation protocol, a set of initial baselines, and an open-source codeb…

In the past 12 months, we’ve collaborated with Partnership on AI (PAI) to carefully consider these challenges, and have co-developed standardised best practices and processes for responsible human data collection.

The best practicesFollowing PAI’s recent white paper on Responsible Sourcing of Data Enrichment Services, we collaborated to develop our practices and processes for data enrichment.

This included the creation of five steps AI practitioners can follow to improve the working conditions for people involved in data enrichment tasks (for more details, please visit PAI’s Data Enrichment Sourcing Guidelines):ÂSelect an appropriate payment model and ensure all workers are paid above…

Meet Sylvia Christie, our education partnerships manager who’s played a leading role in expanding our scholarship programme, which has just celebrated its five-year anniversary.

Every academic year, we get to see the new crop of talented AI scholars become part of an international community of students and mentors.

We need to make sure that our work drives real change in the wider community and for AI education more generally.

The series also includes the short cinematic film below as a new way of speaking to audiences about the scholarship programme in a creative way.

What’re your biggest learnings now that the scholarship programme is five years old?ÂHow important collaboration is.

Applying our AI research, we’ve helped Google Cloud enhance core solutions used by their customers at scaleAlphabet’s Google Cloud empowers organisations to digitally transform themselves into smarter businesses.

Last week, many of the platform’s latest advances were shared at Next '22, Google Cloud's annual developer and tech conference about digital transformation in the cloud.

We’ve partnered with Google Cloud over the last few years to apply our AI research for making a positive impact on core solutions used by their customers.

And in recent years, we’ve partnered with Google Cloud Professional Services to positively impact the wind energy sector to help build a carbon-free fu…

So today, we’re introducing the Perception Test, a multimodal benchmark using real-world videos to help evaluate the perception capabilities of a model.

Multimodal models, such as Perceiver, Flamingo, or BEiT-3, aim to be more general models of perception.

Geolocation of crowd-sourced participants involved in filming.ÂLearning more about the Perception TestThe Perception Test benchmark is publicly available here and further details are available in our paper.

A leaderboard and a challenge server will be available soon too.ÂOn 23 October, 2022, we’re hosting a workshop about general perception models at the European Conference on Computer Vision in Tel Aviv (ECCV 2022), where we will dis…

Exploring examples of goal misgeneralisation – where an AI system's capabilities generalise but its goal doesn'tAs we build increasingly advanced artificial intelligence (AI) systems, we want to make sure they don’t pursue undesired goals.

Such behaviour in an AI agent is often the result of specification gaming – exploiting a poor choice of what they are rewarded for.

Crucially, in contrast to specification gaming, GMG can occur even when the AI system is trained with a correct specification.

During training, there is an “expert” agent (the red blob) that visits the coloured spheres in the correct order.

This AI system does what its designers intend it to do.

For centuries, mathematicians believed that the standard matrix multiplication algorithm was the best one could achieve in terms of efficiency.

We then trained an AlphaTensor agent using reinforcement learning to play the game, starting without any knowledge about existing matrix multiplication algorithms.

Through learning, AlphaTensor gradually improves over time, re-discovering historical fast matrix multiplication algorithms such as Strassen’s, eventually surpassing the realm of human intuition and discovering algorithms faster than previously known.

Single-player game played by AlphaTensor, where the goal is to find a correct matrix multiplication algorithm.

By exploring the space of …

These barriers not only contribute to the existing attainment gap, they directly impact the number of opportunities students have to pursue a career in STEM related fields, including AI, down the line.

Amplifying the reach of existing programmesÂDeepMind will also be providing funding and volunteering support to five other organisations.

This will help bring new AI content to their existing activities, increasing the reach and the number of young people that can benefit from their programmes.

We hope that this programme can help encourage and inspire the next generation of scientists and engineers - especially those who never imagined it to even be a possibility.

‍To learn more about Deep…

However, dialogue agents powered by LLMs can express inaccurate or invented information, use discriminatory language, or encourage unsafe behaviour.

To create safer dialogue agents, we need to be able to learn from human feedback.

Applying reinforcement learning based on input from research participants, we explore new methods for training dialogue agents that show promise for a safer system.

Sparrow is a research model and proof of concept, designed with the goal of training dialogue agents to be more helpful, correct, and harmless.

Sparrow is a significant step forward in understanding how to train dialogue agents to be more useful and safer.

Our Operating Principles have come to define both our commitment to prioritising widespread benefit, as well as the areas of research and applications we refuse to pursue.

From principles to practiceWritten principles are only part of the puzzle – how they’re put into practice is key.

A major release of protein structure predictions in partnership with EMBL-EBI (EMBL’s European Bioinformatics Institute), the established community leader.

As a public institution, EMBL-EBI enables anyone to look up protein structure predictions as easily as a Google search.

As a public institution, EMBL-EBI enables anyone to look up protein structure predictions as easily as a Google search.

Real-time boundary tracking of the 2021-2022 Wrattonbully bushfire, shown as a red polygon in Google Maps.

The most scalable method to obtain frequent boundary updates is to use geostationary satellites, i.e., satellites that orbit the earth once every 24 hours.

The spatial resolution is 2km at nadir (the point directly below the satellite), and lower as one moves away from nadir.

ModelPrior work on fire detection from satellite imagery is typically based on physics-based algorithms for identifying hotspots from multispectral imagery.

In our wildfire tracker, the model is trained on all satellite inputs, allowing it to learn the relative importance of different frequency bands.

In this blogpost, we describe how we streamline this process by adding two feature engineering capabilities in BigQuery ML.

Our previous blog outlines the data to AI journey with BigQuery ML, highlighting two powerful features that simplify MLOps - data preprocessing functions for feature engineering and the ability to export BigQuery ML TRANSFORM statement as part of the model artifact.

Data Preprocessing FunctionsPreprocessing and transforming raw data into features is a critical but time consuming step when operationalizing ML.

This capability also works when BigQuery ML models are registered with Vertex AI Model Registry and deployed to Vertex AI Prediction endpoints.

Step 1: Transform …

Distributed systems for MLThis year, we've made significant strides in improving our systems to better support large-scale computation in ML and scientific computing in general.

The Google TPU hardware has been designed with scaling in mind since its inception, and each year we strive to push the boundaries even further.

Telamalloc employs a combination of ML model plus heuristics to make a decision when multiple options are available, and leverages a constraint solver to infer further dependent decisions.

The first step is to select the most efficient ML model architecture.

Google Research, 2022 & beyondThis was the second blog post in the “Google Research, 2022 & Beyond” series.

Google Vizier is the de-facto system for blackbox optimization over objective functions and hyperparameters across Google, having serviced some of Google’s largest research efforts and optimized a wide range of products (e.g., Search, Ads, YouTube).

Today we are excited to announce Open Source (OSS) Vizier (with an accompanying systems whitepaper published at AutoML Conference 2022), a standalone Python package based on Google Vizier.

Integrations, algorithms, and benchmarksAs Google Vizier is heavily integrated with many of Google’s internal frameworks and products, OSS Vizier will naturally be heavily integrated with many of Google’s open source and external frameworks.

Furthermore, OSS V…

The US National Cancer Institute (NCI) has long prioritized collection, curation, and dissemination of comprehensive, publicly available cancer imaging datasets.

Lack of a common data model or tooling make capabilities such as search, visualization, and analysis of data difficult and repository- or dataset-specific.

Introducing Imaging Data CommonsTo address these issues, as part of the Cancer Research Data Commons (CRDC) initiative that establishes the national cancer research ecosystem, NCI launched the Imaging Data Commons (IDC), a cloud-based repository of publicly available cancer imaging data with several key advantages:Colocation: Image files are curated into Google Cloud Storage buc…

In “The Flan Collection: Designing Data and Methods for Effective Instruction Tuning”, we closely examine and release a newer and more extensive publicly available collection of tasks, templates, and methods for instruction tuning to advance the community’s ability to analyze and improve instruction-tuning methods.

Public instruction tuning data collectionsSince 2020, several instruction tuning task collections have been released in rapid succession, shown in the timeline below.

A timeline of public instruction tuning collections, including: UnifiedQA, CrossFit, Natural Instructions, FLAN, P3/T0, MetaICL, ExT5, Super-Natural Instructions, mT0, Unnatural Instructions, Self-Instruct, and OPT-…

The field of online machine learning studies such settings and provides various techniques for decision-making problems under uncertainty.

In “Online Learning and Bandits with Queried Hints” (presented at ITCS 2023), we show how an ML model that provides us with a weak hint can significantly improve the performance of an algorithm in bandit-like settings.

Algorithmic IdeasOur algorithm for the bandits setting utilizes the well known upper confidence bound (UCB) algorithm.

ConclusionIn this work we explore how a simple pairwise comparison ML model can provide simple hints that prove very powerful in settings such as the experts and bandits problems.

We believe our model of hints can have mor…

However, clinical notes are hard to understand because of the specialized language that clinicians use, which contains unfamiliar shorthand and abbreviations.

In “Deciphering clinical abbreviations with a privacy protecting machine learning system”, published in Nature Communications, we report our findings on a general method that deciphers clinical abbreviations in a way that is both state-of-the-art and is on-par with board certified physicians in this task.

The model input is a string that may or may not contain medical abbreviations.

We then “rewrote” those sentences by abbreviating each expansion, resulting in web data that looked like it was written by a doctor.

Comparative Performan…

In this blogpost, we share ways we have approached Responsible AI across our research in the past year and where we’re headed in 2023.

TopTheme 2: Responsible AI Research in ProductsThe ability to see yourself reflected in the world around you is important, yet image-based technologies often lack equitable representation, leaving people of color feeling overlooked and misrepresented.

This is one of many examples of how Responsible AI in Research works closely with products across the company to inform research and develop new techniques.

We also showed that instruction fine-tuning results in many improvements for Responsible AI benchmarks.

Google Research, 2022 & BeyondThis was the second b…

A collaboration between Google Cloud and NVIDIA has enabled Apache Beam users to maximize the performance of ML models within their data processing pipelines, using NVIDIA TensorRT and NVIDIA GPUs alongside the new Apache Beam TensorRTEngineHandler.

Deploying and managing end-to-end ML inference pipelines while maximizing infrastructure utilization and minimizing total costs is a hard problem.

Call ML models within data processing pipelines while supporting different inference use-cases: batch, streaming, ensemble models, remote inference, or local inference.

Google Cloud Dataflow is a fully managed runner for stream or batch processing pipelines written with Apache Beam.

To enable develope…

One of the key benefits of Vertex AI Training is the ability to specify multiple machines (nodes) in a training cluster when you run a distributed training job with Vertex AI.

With Vertex AI Training you won't need to worry about setting up, configuring, hardening, patching or otherwise maintaining these clusters.

Vertex AI Training executes your training code.

Custom training jobs (CustomJob resources in the Vertex AI API) are the basic way to run your custom machine learning (ML) training code in Vertex AI.

Vertex AI Training allows you to use Vertex AI managed datasets to train your custom models.

Master data undergoes a far more enriched and refined process than other types of data captured across the organization.

Without master data, enterprise applications are left with potentially inconsistent data living in disparate systems; with an unclear picture of whether multiple records are related.

This data preparation, consolidation and enrichment requires the right infrastructure, tools, and processes, otherwise it will be an additional burden on already thinly stretched data management teams. .

This is why it is necessary to adopt and implement a next-generation master data management platform that enables a data product strategy to be operationalized.

Organizations benefit from Tam…

This makes time series forecasting one of the most popular models in BigQuery ML.

What is multivariate time series forecasting?

When it comes to time series forecasting, covariates or features besides the target time series are often used to provide better forecasting.

We see strong customer demand for multivariate time series forecasting support that allows you to forecast using covariate and features.

This new model leverages the BigQuery ML linear regression model to include the side features and the BigQuery ML ARIMA_PLUS model to model the linear regression residuals.

Language ModelsThe progress on larger and more powerful language models has been one of the most exciting areas of machine learning (ML) research over the last decade.

Interestingly, the utility of language models can grow significantly as their sizes increase due to the emergence of new capabilities.

TopMultimodal ModelsMost past ML work has focused on models that deal with a single modality of data (e.g., language models, image classification models, or speech recognition models).

Generative AudioIn addition to visual-oriented generative models, we have made significant progress on generative models for audio.

And we’ve demonstrated models that can, in various combinations, generate image…

This wealth of unstructured data is often untapped, as some business leaders may be unaware of the value or unsure how to leverage it.

By combining Google Cloud services such as BigQuery and Vertex AI with Snorkel AI’s data-centric AI platform for programmatic data curation and preparation, organizations can accelerate AI development 10-100x [1].

Snorkel AI overcomes this bottleneck through using a programmatic labeling approach implemented in Snorkel Flow, a novel data-centric AI platform.

Google Cloud customers can easily deploy Snorkel Flow on their Google Cloud infrastructure using Google Kubernetes Engine (GKE), then consume unstructured, semi-structured or structured data from Google …

We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.

The new subscription plan, ChatGPT Plus, will be available for $20/month, and subscribers will receive a number of benefits:General access to ChatGPT, even during peak timesFaster response timesPriority access to new features and improvementsChatGPT Plus is available to customers in the United States, and we will begin the process of inviting people from our waitlist over the coming weeks.

We love our free users and will continue to offer free access to ChatGPT.

By offering this subscription pricing, we will be able to help suppo…

We’re launching a classifier trained to distinguish between AI-written and human-written text.

We’ve trained a classifier to distinguish between text written by a human and text written by AIs from a variety of providers.

In our evaluations on a “challenge set” of English texts, our classifier correctly identifies 26% of AI-written text (true positives) as “likely AI-written,” while incorrectly labeling human-written text as AI-written 9% of the time (false positives).

Compared to our previously released classifier, this new classifier is significantly more reliable on text from more recent AI systems.

Training the classifierOur classifier is a language model fine-tuned on a dataset of pair…

We're happy to announce that OpenAI and Microsoft are extending our partnership.

In pursuit of our mission to ensure advanced AI benefits all of humanity, OpenAI remains a capped-profit company and is governed by the OpenAI non-profit.

Microsoft will increase their investment in these systems to accelerate our independent research and Azure will remain the exclusive cloud provider for all OpenAI workloads across our research, API and products.

Learning from real-world use – and incorporating those lessons – is a critical part of developing powerful AI systems that are safe and useful.

So, we've partnered with Microsoft to deploy our technology through our API and the Azure OpenAI Service — …

Read reportAs generative language models improve, they open up new possibilities in fields as diverse as healthcare, law, education and science.

The widespread availability of technology powered by language models has the potential to impact all three facets:Actors: Language models could drive down the cost of running influence operations, placing them within reach of new actors and actor types.

Our bottom-line judgment is that language models will be useful for propagandists and will likely transform online influence operations.

Critical UnknownsMany factors impact whether, and the extent to which, language models will be used in influence operations.

Will it be more effective to engineer …

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use.

The new model, text-embedding-ada-002 , replaces five separate models for text search, text similarity, and code search, and outperforms our previous most capable model, Davinci, at most tasks, while being priced 99.8% lower.

You can query the /embeddings endpoint for the new model with two lines of code using our OpenAI Python Library, just like you could with previous models:import openai response = openai.Embedding.create( input="porcine pals say", model="text-embedding-ada-002" ) Print response print(response) { "data": [ { "embedding": [ -0.0108, -0.0107, 0.0323, ..…

For example, suppose we want to generate a public-key cryptography system for a user with the initials "ABC".

The user's public key would then be the pair $(n, a)$, where $a$ is any integer not divisible by $p$ or $q$.

To decrypt the message, the recipient simply computes $m = c^b \bmod n$, which (by Fermat's Little Theorem) is equivalent to $m = (m^a)^b \bmod n = m^(ab) \bmod n = m^1 \bmod n = m \bmod n$.

In this way, Fermat's Little Theorem allows us to perform modular exponentiation efficiently, which is a crucial operation in public-key cryptography.

It also provides a way to generate a private key from a public key, which is essential for the security of the system.

Starting today, developers can begin building apps with the DALL·E API.

Read documentationDevelopers can now integrate DALL·E directly into their apps and products through our API.

Microsoft Bing Microsoft is bringing DALL·E to a new graphic design app called Designer, which helps users create professional quality social media posts, invitations, digital postcards, graphics, and more.

Mixtiles uses the DALL·E API to create and frame emotionally resonating artwork, by guiding users through a creative process that captures childhood memories, dream destinations, and more.

All API customers can use the DALL·E API today.

New users can start creating straight away.

Sign upStarting today, we are removing the waitlist for the DALL·E beta so users can sign up and start using it immediately.

Since we first previewed the DALL·E research to users in April, users have helped us discover new uses for DALL·E as a powerful creative tool.

We can't wait to see what users from around the world create with DALL·E.

Sign up today and start creating.

We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition.

Whisper examples: Reveal TranscriptWhisper is an automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web.

We show that the use of such a large and diverse dataset leads to improved robustness to accents, background noise and technical language.

We find this approach is particularly effective at learning speech to text translation and outperforms the supervised SOTA on CoVoST2 to English translation zero-shot.

Check out the paper, model card, and code to learn more det…

Now, with Outpainting, users can extend the original image, creating large-scale images in any aspect ratio.

Outpainting takes into account the image’s existing visual elements — including shadows, reflections, and textures — to maintain the context of the original image.

More than one million people are using DALL·E, the AI system that generates original images and artwork from a natural language description, as a creative tool today.

Artists have already created remarkable images with the new Outpainting feature, and helped us better understand its capabilities in the process.

Original outpainting by Tyna Eloundou Original outpainting by OpenAI Outpainting by David Schnurr Original outpai…

IntroductionOur alignment research aims to make artificial general intelligence (AGI) aligned with human values and follow human intent.

We believe that even without fundamentally new alignment ideas, we can likely build sufficiently aligned AI systems to substantially advance alignment research itself.

At a high-level, our approach to alignment research focuses on engineering a scalable training signal for very smart AI systems that is aligned with human intent.

Instead, we aim for a more pragmatic approach: building and aligning a system that can make faster and better alignment research progress than humans can.

Therefore human researchers will focus more and more of their effort on revi…

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.

For instance, Inworld, an OpenAI A…

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…

Azure High-Performance Computing provides a platform for energy industry innovation at scale.

I am thrilled to announce that Microsoft has been recognized as a Leader in the IDC MarketScape: Worldwide General-purpose Computer Vision AI Software Platform 2022 Vendor Assessment.

Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

Microsoft Research Asia has been studying Document AI since 2019, working at the intersection of natural language processing and computer vision and using deep learning techniques.

Tapping into Large Language Models with Microsoft’s Turing Academic ProgramLarge language models (LLMs) deliver impressive performance with difficult tasks and across various applications.

As AI researchers explore LLMs, many questions persist.

Microsoft researchers named 2022 ACM FellowsThree researchers from Microsoft w…

One of the tools that we believe will help to enable precision medicine is Terra, the secure biomedical research platform co-developed by Broad Institute of MIT and Harvard, Microsoft, and Verily.

Today, we are excited to share that Terra is available for preview on Microsoft Azure.

Starting today, any researcher can bring their data, access publicly available datasets, run analyses, and collaborate with others on Terra using Microsoft Azure.

Figure 1: Terra brings together components of the Microsoft Genomics and healthcare ecosystems to offer optimized, secure, and collaborative biomedical research.

Terra is a powerful platform that will enhance biomedical research collaboration and scien…

Azure AI is driving innovation and improving experiences for employees, users, and customers in a variety of ways, from increasing workday productivity to promoting inclusion and accessibility. The…

Let’s explore what’s new for Azure Data & AI this month.

With Azure OpenAI Service now generally available, more businesses can apply for access to the most advanced AI models in the world—including GPT-3.5, Codex, and DALL•E 2—backed by the trusted enterprise-grade capabilities and AI-optimized infrastructure of Microsoft Azure, to create cutting-edge applications.

A stance that encourages reflexivity among AI practitioners is a step toward ensuring that AI systems are human-centered, developed and deployed with the interests and well-being of individuals and society front and center.

The following is a glimpse into the past year’s research for advancing responsible AI with authors from Aether.

Considering who AI systems are forThe need to cultivate broader perspectives and, for society’s benefit, reflect on why and for whom we’re creating AI is not only the responsibility of AI development teams but also of the AI research community.

The survey, which included a representative sample of the US population, found AI practitioners often gave less weight…

Welcome to Research Focus, a new series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft.

Spotlight: On-Demand EVENT Microsoft Research Summit 2022 On-DemandWatch now to learn about some of the most pressing questions facing our research community and listen in on conversations with 120+ researchers around how to ensure new technologies have the broadest possible benefit for humanity.

Research Fellows Program at Microsoft Research India – Apply nowThe Research Fellows Program at Microsoft Research India is now accepting applications for Fall 2023.

Previous Research Fellows have contribut…

Forrester recently published The Forrester Wave™: Public Cloud Development and Infrastructure Platforms, Global, Q4 2022, placing Microsoft in the Leaders category.

How your organization can benefit, no matter the industry.

Advancing AI foundations and accelerating progressOver the past year, the research community at Microsoft made significant contributions to the rapidly evolving landscape of powerful large-scale AI models.

Microsoft Research and the Microsoft Turing team unveiled a new Turing Universal Language Representation model capable of performing both English and multilingual understanding tasks.

Advancing AI for decision makingBuilding the next generation of AI requires continuous research into fundamental new AI innovations.

This year, Microsoft Research continued its work on causal ML, which combines traditional machine learning with causal inference methods.

We appreciate the opportunity to share…

By making the Soundscape code available as open-source software, we hope the interest and potential continues to grow.

Q: What will happen to the Microsoft Soundscape app on iOS?

Q: Will the Azure services that enable the Microsoft Soundscape app continue to be supported?

Q: Will user feedback on the Microsoft Soundscape app continue to work?

A: The original Microsoft Soundscape app only supports iOS, and that is also true for the open-source release.

This special edition of Research Focus highlights some of the 100+ papers from Microsoft Research that were accepted for publication at NeurIPS 2022 – the thirty-sixth annual Conference on Neural Information Processing Systems. In this issue, we continue to feature some of our 100+ papers accepted at NeurIPS 2022. Outstanding paper: Gradient Estimation with Discrete Stein […]

The post Research Focus: Week of December 5, 2022 appeared first on Microsoft Research.

Explore sessionsToday, using software developed by Microsoft researchers, IOM released its second synthetic dataset from trafficking victim case records, the first ever public dataset to describe victim-perpetrator relations.

But not all synthetic data comes with formal guarantees of data privacy or accuracy.

The aggregate data thereby supports both evaluation of synthetic data quality and retrieval of accurate counts for official reporting.

Through this collaboration and the complementary nature of synthetic data and aggregate data—together with interactive interfaces with which to view and explore both datasets—the open-source Synthetic Data Showcase software was developed.

The new Global…

Expressed in other terms, we’d like to know just how uncertain our uncertainty is.

These researchers succeeded not only in obtaining accurate measures of uncertainty, they also found a way to display uncertainty in a manner the average person could grasp.

And, as addressed in the December 2022 paper, what is the best way to represent the uncertainty in that image?

Moreover, they can set precise bounds on the range, or interval, and provide a probabilistic guarantee that the true depiction lies somewhere within that range.

Much of what we do and many of the things happening in the world around us are shrouded in uncertainty, Sankaranarayanan notes.

“In this case, it’s important to know that if you detect lung cancer early, the long-term outcome is significantly better.

Perhaps that will change in the future.”There is a growing population of patients with lung cancer who are categorized as nonsmokers.

Women nonsmokers are more likely to be diagnosed with lung cancer than men who are nonsmokers.

Globally, over 50 percent of women diagnosed with lung cancer are nonsmokers, compared to 15 to 20 percent of men.

“When she started coughing, neither her doctors nor her family initially suspected that the cause could be lung cancer.

Students then split into small groups in the fall to collaborate on six machine learning challenge projects presented to them by MathWorks, MIT-IBM Watson AI Lab, and Replicate.

The challenges gave the undergraduates the chance to help contribute to actual projects that industry organizations are working on and to put their machine learning skills to the test.

“Students are gaining industry experience by working closely with their project advisors,” says Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing and the MIT director of the MIT-IBM Watson AI Lab.

In December, the students celebrated the fruits of their labor at a showcase event held at M…

Every year Takeda funds fellowships to support graduate students pursuing research related to health and AI.

This year’s Takeda Fellows, described below, are working on projects ranging from electronic health record systems and robotic control to pandemic preparedness and traumatic brain injuries.

Wenhao GaoGao is a PhD candidate in the Department of Chemical Engineering who aims to accelerate biological and chemical discovery processes.

His work specifically focuses on AI for health sciences and cutting-edge applications of machine learning for molecular discovery and drug development.

Sarah GurevGurev is a PhD candidate in the Department of Electrical Engineering and Computer Science.

“It was a strange experience,” says Ajisafe, who plays the tuba and is pursuing a double major in aerospace engineering and music.

“Aerospace engineering is the most exciting field within engineering right now,” says Ajisafe.

Now, he’s working on a project, through MIT’s Undergraduate Research Opportunities Program, that combines linguistics, natural language processing, and aircraft design requirements.

One of the challenges of writing design requirements for aircraft is ambiguity, especially when the requirements are written in traditional, natural language form.

More engineers are turning to model-based systems engineering standards, which is newer and more formalized.

A new academic program developed at MIT aims to teach U.S. Air and Space Forces personnel to understand and utilize artificial intelligence technologies.

In a recent peer-reviewed study, the program researchers found that this approach was effective and well-received by employees with diverse backgrounds and professional roles.

Experts in MIT Open Learning built a curriculum for three general types of military personnel — leaders, developers, and users — utilizing existing MIT educational materials and resources.

They used interviews and several questionnaires, offered to both program learners and staff, to evaluate how 230 Air and Space Forces personnel interacted with the course material.…

But what exactly are these deep learning models learning?

These powerful machine-learning models are typically based on artificial neural networks that can have millions of nodes that process data to make predictions.

Jegelka is particularly interested in optimizing machine-learning models when input data are in the form of graphs.

She approaches this question by combining her passion for algorithms and discrete mathematics with her excitement for machine learning.

She is also exploring methods to improve the performance of machine-learning models for image classification.

To assist with perspective-taking, MIT researchers have developed “On the Plane,” a virtual reality role-playing game (VR RPG) that simulates discrimination.

In this case, the game portrays xenophobia directed against a Malaysian America woman, but the approach can be generalized.

In turn, players’ decisions control the outcome of a tense conversation between the characters about cultural differences.

“Many virtual identity systems, such as avatars, accounts, profiles, and player characters, are not designed to serve the needs of people across diverse cultures.

A paper on the work was presented in December at the 2022 IEEE International Conference on Artificial Intelligence and Virtual Real…

The way electrons interact with photons of light is a key part of many modern technologies, from lasers to solar panels to LEDs.

Now, researchers at MIT and elsewhere have come up with an innovative way to make much stronger interactions between photons and electrons possible, in the process producing a hundredfold increase in the emission of light from a phenomenon called Smith-Purcell radiation.

This may make it especially valuable for making sources of emission at wavelengths that are difficult to produce efficiently, including terahertz waves, ultraviolet light, and X-rays.

The team has so far demonstrated the hundredfold enhancement in emission using a repurposed electron microscope to…

Any account of language comprehension, researchers believe, would benefit from understanding difficulties in comprehension.

A new study led by researchers from MIT's Department of Brain and Cognitive Sciences (BCS) now provides such a unified account for difficulties in language comprehension.

Each of these older models identifies a distinct culprit for frustrated comprehension: difficulty in expectation and difficulty in memory retrieval.

Thus, according to this unified model, memory constraints can create a new source of difficulty in anticipation.

Researchers quantify comprehension difficulty by measuring the time it takes readers to respond to different comprehension tasks.

Artificial intelligence (AI) systems — those based on machine learning, in particular — are seeing increased use in medicine for diagnosing specific diseases, for example, or evaluating X-rays.

AI models used in medicine can suffer from inaccuracies and inconsistencies, in part because the data used to train the models are often not representative of real-world settings.

A group of 954 people (438 clinicians and 516 nonexperts) took part in an experiment to see how AI biases can affect decision-making.

“We want to understand how biased models can influence decisions, but we first need to understand how human biases can affect the decision-making process,” Adam notes.

Third, the MIT team dis…

The intricacies of coding and machine learning can seem daunting to newcomers, but Refsgaard’s practice as a creative coder, interaction designer, and educator seeks to open the field to all.

Learning through laughterRefsgaard, who is based in Copenhagen, is a true maverick of machine learning.

In “Doodle Tunes,” a machine learning algorithm is trained on a dataset of drawings of different instruments: a piano, drums, bass guitar, or saxophone.

This open-minded attitude set the tone of the workshops “Art, Algorithms and Artificial Intelligence” and “Machine Learning for Interaction Designers,” intended to be suitable for newcomers as well as curious experts.

By choosing to make his lighthea…

This year’s Accenture Fellows work across research areas including telemonitoring, human-computer interactions, operations research, AI-mediated socialization, and chemical transformations.

Buzzell earned his BS in physics and engineering science and his MS in engineering science from the Pennsylvania State University.

With the support of an Accenture Fellowship, Gong seeks to find solutions to operational problems by designing reinforcement learning methods and other machine learning techniques to embedded operational problems.

Joules Provenzano is a doctoral candidate in chemical engineering.

Provenzano earned a BS in chemical engineering and international and global studies from the Roch…

As a result, he has relentlessly focused on practical applications in his research, work that has netted him the 2022 Reactor Technology Award from the American Nuclear Society.

The goal within this program, says Shirvan, is to develop new forms of nuclear fuels that can tolerate heat.

“[The research] informs how nuclear fuels perform in the reactor, from a practical point of view,” Shirvan says.

A multipronged approach to savingsAnother very real problem nuclear utilities face is cost.

It’s all back to the basics and bringing “commercial viable arguments in with your research,” Shirvan explains.

Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile ManipulationReinforcement learning provides a conceptual framework for autonomous agents to learn from experience, analogously to how one might train a pet with treats.

Can we instead devise reinforcement learning systems for robots that allow them to learn directly “on-the-job”, while performing the task that they are required to do?

Learning systems have the ability to create the entire control algorithm for the robot, and are not limited to tuning a few parameters in a script.

The key step in this work allows these real-world learning systems to autonomously collect the data needed to enable the success of…

Keeping Learning-Based Control Safe by Regulating Distributional ShiftTo regulate the distribution shift experience by learning-based controllers, we seek a mechanism for constraining the agent to regions of high data density throughout its trajectory (left).

The central idea behind our work is to view the training data distribution as a safety constraint, and to draw on tools from control theory to control the distributional shift experienced by the agent during closed-loop control.

To use an LDM in control, we can train an LDM and learning-based controller on the same training dataset and constrain the controller’s action outputs with an LDM constraint ($G(s, a)) \leq -\log(c)$).

The ce…

Reverse engineering the NTK: towards first-principles architecture designDeep neural networks have enabled technological wonders ranging from voice recognition to machine transition to protein engineering, but their design and application is nonetheless notoriously unprincipled.

Neural network kernelsThe field of deep learning theory has recently been transformed by the realization that deep neural networks often become analytically tractable to study in the infinite-width limit.

4 below shows a “mimic” activation function \(\tilde{\phi}\) that gives virtually the same NTK as a deep \(\textrm{ReLU}\) FCN.

This is interesting from an engineering perspective because the shallow network us…

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 published the Berkeley Crossword Solver (BCS), the current state of the art for solving American-style crossword puzzles.

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.

Figure 4: Results compared to previous state-of-the-art Dr.FillWinning The American Crossword Puzzle TournamentThe American Crossword Puzzle Tournament (ACPT) is the largest and longest-runnin…

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…

A data distribution with fat tails is common in real-world applications, where rare events have significant impact on the overall performance of the models.

Using a robust method to accurately model distribution over extreme events is crucial for better overall performance.

The data distribution for punt and kickoff are different.

We used the SBP distribution provided by GluonTS.

ConclusionIn this post, we showed how to build predictive models with fat-tailed data distribution.

This process helps identify the actual number of impacts individual players experience by removing duplicate impacts detected in multiple views.

In particular, we focus on deduplicating and visualizing videos with the ID 57583_000082 in endzone and sideline views.

PrerequisitesThe solution requires the following:An Amazon SageMaker Studio Lab accountA Kaggle account for downloading the dataGet started on SageMaker Studio Lab and install the required packagesYou can run the notebook from the GitHub repository or from SageMaker Studio Lab.

We use the following function for this, and the inputs needed are the paths to the endzone video, sideline video, fused_df dataframe, and the final output …

To learn more about metrics for evaluating content moderation, refer to Metrics for evaluating content moderation in Amazon Rekognition and other content moderation services.

The default pricing for the video content moderation API is $0.10 per minute and $0.001 per image for the image content moderation API.

Type Amazon Rekognition Costs Compute Costs Total Cost Video API Solution $427.14 $0(Free tier) $427.14 Image API Solution: Two frames per second $512.57 $164.23 $676.80 Image API Solution: One frame per second $256.28 $82.12 $338.40PerformanceOn average, the video API solution has a four times faster processing time than the image API solution.

ConclusionThe video moderation API is id…

AWS customers often deploy these workloads using Amazon Elastic Kubernetes Service (Amazon EKS).

Today, we are excited to announce official support for distributed training jobs using Amazon EKS and EC2 Trn1 instances.

To follow along, a broad familiarity with core AWS services such as Amazon Elastic Compute Cloud (Amazon EC2) and Amazon EKS is implied, and basic familiarity with deep learning and PyTorch would be helpful.

Create an EKS clusterTo get started with distributed training jobs in Amazon EKS with Trn1 instances, you first create an EKS cluster as outlined in the tutorial on GitHub.

Set up the training dataBefore launching a training job, the training data is first copied to the s…

Starting today, the SageMaker LightGBM algorithm offers distributed training using the Dask framework for both tabular classification and regression tasks.

This post discusses how SageMaker LightGBM helps you set up and launch distributed training, without the expense and difficulty of directly managing your training clusters.

By splitting the data and training multiple models in parallel, distributed training can significantly reduce training time and improve the performance of models on big data.

Solution overviewWhen a training job using LightGBM is started with multiple instances, we first create a Dask cluster.

The SageMaker LightGBM algorithm makes the process of setting up distribute…

Accurate water consumption forecasting is critical to make sure that an agency can run day-to-day operations efficiently.

– As a utility provider agency, you need to find a balance between water demand and supply.

This post focuses on a solution to perform water consumption forecasting and a what-if analysis.

Create a dataset group and datasetsThis post demonstrates two use cases related to water demand forecast: forecasting the water demand based on past water consumption, and conducting a what-if analysis for increased demand.

In our example, the target time series dataset contains item_id and timestamp dimensions, and the complementary related time series dataset includes no_of_consumer.

Since March 2014, Best Egg has delivered $22 billion in consumer personal loans with strong credit performance, welcomed almost 637,000 members to the recently launched Best Egg Financial Health platform, and empowered over 180,000 cardmembers who carry the new Best Egg Credit Card in their wallet.

Automatic model tuning makes it easy to zero in on the optimal model configuration, freeing up time and money for better use elsewhere in the financial sector.

Best Egg runs SageMaker training jobs with automated hyperparameter tuning powered by Bayesian optimization.

Deep Dive into Model Tuning and Benefits of Warm PoolsSageMaker Automated Model Tuning leverages Warm Pools by default for any tun…

In this post, we demonstrate a common loyalty points earn and burn scenario, in which we detect anomalies in the customer’s earn and redeem pattern.

Solution overviewThis post demonstrates how you can set up anomaly detection on a loyalty points earn and redeem pattern using Lookout for Metrics.

To create the detector, complete the following steps:On the Lookout for Metrics console, choose Create detector.

Let’s look at an example anomaly detected from our loyalty points anomaly detector use case.

The following screenshot shows an anomaly detected in loyalty points redemption at a specific location on the designated time and date with a severity score of 91.

Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers to explain the predictions of their ML models.

After the model is trained, create a custom Docker container that can be used to create a SageMaker model and optionally deploy the model as a SageMaker model endpoint.

Deploy the trained BlazingText model using your own container on SageMakerWith Clarify, there are two options to provide the model information:Create a SageMaker model without deploying it to an endpoint – When a SageMaker model is provided to Clarify, it creates an ephemeral endpoint using the model.

ConclusionIn this post, we explained how you can use Clarify to explain pred…

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart.

Today, we announce a new feature that lets you upscale images (resize images without losing quality) with Stable Diffusion models in JumpStart.

This process, called upscaling, can be applied to both real images and images generated by text-to-image Stable Diffusion models.

The following video shows how to find the pre-trained Stable Diffusion upscaler model on JumpStart and deploy it.

ConclusionIn this post, we showed how to deploy a pre-trained Stable Diffusion upscaler model using JumpStart.

Cohere’s state-of-the-art language AI is now available through Amazon SageMaker.

This makes it easier for developers to deploy Cohere’s pre-trained generation language model to Amazon SageMaker, an end-to-end machine learning (ML) service.

“As Cohere continues to push the boundaries of language AI, we are excited to join forces with Amazon SageMaker.

If you’re interested in using Cohere for your own SageMaker projects, you can now access it on SageMaker JumpStart.

Karl Albertsen leads product, engineering, and science for Amazon SageMaker Algorithms and JumpStart, SageMaker’s machine learning hub.

In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned.

This notification is sent to an Amazon SQS queue and handled by a Lambda function called Process Input.

In this system, two types of SageMaker endpoints are supported:Asynchronous: The Process Input Lambda makes the request to the SageMaker asynchronous endpoint.

As mentioned above, an AI Ensemble is an architecture based on a group of AI models working together to generate a single overall prediction.

It then makes a call to each AI service which in this context, can…

The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework to create AWS CloudFormation stacks through automatic CloudFormation template generation.

AWS CDK constructs are the building blocks of AWS CDK applications, representing the blueprint to define cloud architectures.

Setting up Studio with AWS CDK has become a streamlined process.

The AWS CDK reduces the time required to perform typical infrastructure deployment tasks while shrinking the surface area for human error through automation.

However, the AWS CDK offers built-in support for multiple other programming languages like JavaScript, Java and C#.

We use Amazon Simple Storage Service (Amazon S3) alongside SageMaker to store the training data and model artifacts, and Amazon CloudWatch to log training and endpoint outputs.

Train two AutoGluon multimodal models: an AutoGluon multimodal weighted/stacked ensemble model, and an AutoGluon multimodal fusion model.

Navigate to the Churn Prediction with Text solution in JumpStart.

After you launch the Churn Prediction with Text solution, open the demo notebook by choosing Use Endpoint in Notebook.

ConclusionIn this post, we showed how you can use Sagemaker JumpStart to predict churn using multimodality of text and tabular features.

In this post, we show you how Parsons is building its next generation workforce by using machine learning (ML) and artificial intelligence (AI) with AWS DeepRacer in a fun and collaborative way.

The AWS DeepRacer workshop saw unprecedented interest with over 470 Parsons employees joining the initial workshop.

The virtual AWS DeepRacer league at Parsons provided a fun and inviting environment with lots of iterations, learning, and experimentation.

To continue to upskill and educate their workforce, Parsons intends to run more AWS DeepRacer events and workshops focused on object avoidance, an Amazon SageMaker deep dive workshop, and an AWS DeepRacer head-to-head race.

Whether your organizatio…

Cheers to another year of cloud gaming!

GeForce NOW celebrates its third anniversary with a look at how far cloud gaming has come, a community celebration and 25 new games supported in February.

And with 1,500+ games supported in the GeForce NOW library, the action never has to stop.

As the cloud gaming service has evolved, so have the devices members can use to keep the gaming going.

🎥 pic.twitter.com/h5Iuvua741 — 🌩️ NVIDIA GeForce NOW (@NVIDIAGFN) February 1, 2023It’s been a packed three years, and we’re just getting started.

NVIDIA A100 Tensor Core GPUs running on Supermicro servers have captured leading results for inference in the latest STAC-ML Markets benchmark, a key technology performance gauge for the financial services industry.

The results show NVIDIA demonstrating unrivaled throughput — serving up thousands of inferences per second on the most demanding models — and top latency on the latest STAC-ML inference standard.

NVIDIA A100: Top Latency ResultsThe STAC-ML inference benchmark is designed to measure the latency of long short-term memory (LSTM) model inference — the time from receiving new input data until the model output is computed.

NVIDIA A100 GPUs, running in a Supermicro Ultra SuperServer, d…

Below are the top four findings we gleaned from our “State of AI in Financial Services: 2023 Trends” survey taken by nearly 500 global financial services professionals.

Banks Seeing More Potential for AI to Grow RevenueThe survey found that AI is having a quantifiable impact on financial institutions.

Insufficient data sizes for model training and accuracy is another pressing issue noted by 26% of financial services professionals.

This could be addressed through the use of generative AI to produce accurate synthetic financial data used to train AI models.

Download the “State of AI in Financial Services: 2023 Trends” report for in-depth results and insights.

NVIDIA has demonstrated in the STAC-ML inference benchmark, audited by STAC,1 that the NVIDIA A100 Tensor Core GPU can run LSTM model inference consistently with low latencies.

STAC-ML inference benchmark resultsDeep neural networks with long short-term memory (LSTM) are an established tool for time series forecasting.

The STAC-ML inference benchmark is designed to measure the latency of LSTM model inference.

Trading applications that rely on complex neural networks like LSTMs run the risk that model inference takes too long.

For this reason, a stack for AI applications based on NVIDIA GPUs becomes even faster throughout its lifetime (Figure 1).

Before diving into these capabilities, a brief summary details the currently available cuBLAS APIs, how each can be more effectively applied, and how cuBLAS relates to other available NVIDIA tools for matrix multiplications.

Determining which cuBLAS API to useThe cuBLAS library is an implementation of Basic Linear Algebra Subprograms (BLAS) on top of the NVIDIA CUDA runtime, and is designed to leverage NVIDIA GPUs for various matrix multiplication operations.

The cuBLAS library provides maximal performance across a broad range of problems through extensively trained heuristics.

cuBLAS 12.0 extends the cuBLAS API to support 64-bit integer problem sizes, leading dimensions, and vector increme…

The “SunPath” extension lets users easily add, manipulate and update a sun-path diagram within the Omniverse viewport.

Many design tools that come with a skylight feature lack realistic shadows, the researcher had noticed.

“The core technical advantage of HNADI-AECKIT is that it opens up a complete linkage between Rhino and Omniverse,” Wu said.

Discover how to build an Omniverse extension in less than 10 minutes.

Follow NVIDIA Omniverse on Instagram, Medium, Twitter and YouTube for additional resources and inspiration.

Artificial intelligence is the new electricity.

And companies that go all-in on AI are reaping the rewards.

— is explored by Nitin Mittal, principal at Deloitte, one of the world’s largest professional services organizations, and co-author Thomas Davenport in their new book “All in on AI: How Smart Companies Win Big with Artificial Intelligence.”On the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz speaks with Mittal, who leads Deloitte’s artificial intelligence growth platform.

He describes how companies across a wide variety of industries have used AI to radically transform their organizations and achieve competitive advantage.

Mittal emphasizes that companies must have a clear …

Fire up Cyberpunk 2077 and you’ll see much more than the watering hole’s colorful clientele.

Playing with the FutureThere are many tales on the increasingly immersive streets of Cyberpunk 2077’s Night City, but the one even non-gamers should pay attention to the story behind these stories: gaming as a proving ground for the technologies that will shape the future Cyberpunk 2077 is simulating right before our eyes.

CD PROJEKT RED is known for supporting its flagship titles like Cyberpunk 2077 and The Witcher 3: Wild Hunt for extended periods of time with a variety of patches that take advantage of modern hardware.

With realistic shadows and lighting and the added performance of NVIDIA DLSS 3…

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 improves creative workflows.

We’re also deep diving on new GeForce RTX 40 Series GPU features, technologies and resources, and how they dramatically accelerate content creation.

Prizes include an NVIDIA Studio laptop, RTX 40 Series GPUs from MSI and ArtStation gift cards.

NVIDIA Studio could feature you in an in-depth interview to add exposure to your world.

Follow NVIDIA Studio on Instagram, Twitter and Facebook.

AI Computing DefinedAI computing is the math-intensive process of calculating machine learning algorithms, typically using accelerated systems and software.

Three Steps to AI ComputingBefore getting into the many use cases for AI computing, let’s explore how it works.

GPU Computing Meets AIGPUs are the de facto engines of AI computing.

AI Computing Starts Up Conversational AIAI computing made huge inroads in natural language processing after the invention of the transformer model in 2017.

AI + Graphics Create 3D WorldsUsers in many, often unexpected, areas are feeling the power of AI computing.

Large language models are among the most successful applications of transformer models.

Now, large language models are typically trained on datasets large enough to include nearly everything that has been written on the internet over a large span of time.

Top Applications for Large Language ModelsLarge language models are unlocking new possibilities in areas such as search engines, natural language processing, healthcare, robotics and code generation.

Financial advisors can summarize earnings calls and create transcripts of important meetings using large language models.

Challenges of Large Language ModelsScaling and maintaining large language models can be difficult and expensive.

This GFN Thursday brings updates to some of GeForce NOW’s hottest games that take advantage of these amazing technologies, all from the cloud.

AI-Powered PerformanceNVIDIA DLSS has revolutionized graphics rendering, using AI and GeForce RTX Tensor Cores to boost frame rates while delivering crisp, high-quality images that rival native resolution.

DLSS 3 games are backwards compatible with DLSS 2 technology — when developers integrate DLSS 3, DLSS 2, aka DLSS Super Resolution, is supported by default.

Additionally, integrations of DLSS 3 include NVIDIA Reflex, reducing system latency for all GeForce RTX users and making games more responsive.

Support for DLSS 3 is growing, and soon GeForce N…

Foundations for training and inferencingData storage hierarchy for AITo get started, understand the data storage hierarchy for AI, which includes GPU memory, a data fabric, and storage devices (Figure 2).

Generally, the higher you go in the storage hierarchy, the faster the storage performance–especially latency.

Data storage hierarchy for AIStorage devicesHard drives and flash drives are at the base of the storage hierarchy.

What will it cost in lost orders when you disable a webstore recommender engine to upgrade storage capacity or storage performance?

Gain a better understanding of how workload complexity impacts storage performance in the post, Storage Performance Basics for Deep Learn…

Founded in 2016, OSK is among the first to use hyperspectral intelligence to detect hydrocarbon or gas leaks.

That data is processed and analyzed in real time using the NVIDIA Jetson edge AI platform.

“We’re taking hyperspectral intelligence to the finest commercial resolution that the world has ever seen to make the Earth a more sustainable place,” Bangalore said.

For this reason, the OSK platform is deployed across a broad range of customers, including the U.S. Department of Defense and energy sector.

For the commercial oil and gas industry, OSK technology helps detect gas and hydrocarbon leaks, allowing pipeline operators to quickly halt work and fix issues.

The highlight: a far-reaching conversation between NVIDIA founder and CEO Jensen Huang and Swedish industrialist Marcus Wallenberg exploring the intersections of AI, green computing, and Scandinavia’s broader tech scene.

“This generative AI phenomenon is creating a whole slew of new startups, new ideas, new video editing, image editing, new text,” Huang said.

Increasing in size 10x every year for the last few years, large language models are just one state-of-the-art AI technology that promises transformation through learned knowledge.

Models like ChatGPT are making a name for themselves as a new way to use AI.

Sara Mazur, vice executive director of the Knut and Alice Wallenberg Foundation …

We’re sharing how Meta is applying statistics and machine learning (ML) to improve notification personalization and management on Instagram – particularly on daily digest push notifications.

At Meta, we have been applying statistics and machine learning (ML) for notification personalization and management on Instagram.

Today, we would like to share an example of how we used causal inference and ML to control sending for daily digest push notifications.

By doing so, we intend to maintain a fixed notification sending rate r where 0 < r < 1.

In the Instagram Notifications Systems team, ML and statistics have been applied in different areas to improve user notification experience.

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.

Scaling …

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.

Chatting & Coding

#shorts #ai #lab42 Write an essay that answers the following question:

Which fundamental principles of intelligence must be considered in the successful design of artificial intelligence? Submit here: https://lab42.global/essay/

Cash prizes and fame await :) 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

LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):

S…

Chatting & Coding

#chatgpt #ai #openai ChatGPT, OpenAI's newest model is a GPT-3 variant that has been fine-tuned using Reinforcement Learning from Human Feedback, and it is taking the world by storm! Sponsor: Weights & Biases

https://wandb.me/yannic OUTLINE:

0:00 - Intro

0:40 - Sponsor: Weights & Biases

3:20 - ChatGPT: How does it work?

5:20 - Reinforcement Learning from Human Feedback

7:10 - ChatGPT Origins: The GPT-3.5 Series

8:20 - OpenAI's strategy: Iterative Refinement

9:10 - ChatGPT's amazing capabilities

14:10 - Internals: What we know so far

16:10 - Building a virtual machine in ChatGPT's imagination (insane)

20:15 - Jailbreaks: Circumventing the safety mechanisms

29:25 - How OpenAI sees the future …

#ai #mlnews #gpt4 Your weekly news from the AI & Machine Learning world. OUTLINE:

0:00 - Introduction

0:25 - AI reads brain signals to predict what you're thinking

3:00 - Closed-form solution for neuron interactions

4:15 - GPT-4 rumors

6:50 - Cerebras supercomputer

7:45 - Meta releases metagenomics atlas

9:15 - AI advances in theorem proving

10:40 - Better diffusion models with expert denoisers

12:00 - BLOOMZ & mT0

13:05 - ICLR reviewers going mad

21:40 - Scaling Transformer inference

22:10 - Infinite nature flythrough generation

23:55 - Blazing fast denoising

24:45 - Large-scale AI training with MultiRay

25:30 - arXiv to include Hugging Face spaces

26:10 - Multilingual Diffusion

26:30 - Mu…

#ai #cicero #diplomacy A team from Meta AI has developed Cicero, an agent that can play the game Diplomacy, in which players have to communicate via chat messages to coordinate and plan into the future. Paper Title: Human-level play in the game of Diplomacy by combining language models with strategic reasoning Commented game by human expert: https://www.youtube.com/watch?v=u5192bvUS7k OUTLINE:

0:00 - Introduction

9:50 - AI in cooperation games

13:50 - Cicero agent overview

25:00 - A controllable dialogue model

36:50 - Dialogue-conditional strategic planning

49:00 - Message filtering

53:45 - Cicero's play against humans

55:15 - More examples & discussion Homepage: https://ai.facebook.com/res…

#ai #galactica #meta Galactica is a language model trained on a curated corpus of scientific documents, such as papers, knowledge bases, reviews, and other articles. The model can be used in a generative fasion to assist scientific writing, do reference prediction, and much more, including a new approach to do step-by-step reasoning using a clever encoding of intermediate steps. This video explains the paper, but also dives into the drama that ensued once Meta released a public demo of the model. OUTLINE:

0:00 - Introduction

1:30 - Drama around the public demo

16:00 - Start of paper review

20:30 - Dataset construction and encoding

23:30 - Encoding step-by-step reasoning using a scratchpad

3…

#mlnews #ai #mlinpl Your news from the world of Machine Learning! OUTLINE:

0:00 - Introduction

1:25 - Stable Diffusion Multiplayer

2:15 - Huggingface: DOI for Models & Datasets

3:10 - OpenAI asks for more funding

4:25 - The Stack: Source Code Dataset

6:30 - Google Vizier Open-Sourced

7:10 - New Models

11:50 - Helpful Things

20:30 - Prompt Databases

22:15 - Lexicap by Karpathy References:

Stable Diffusion Multiplayer

https://huggingface.co/spaces/huggingface-projects/stable-diffusion-multiplayer?roomid=room-0 Huggingface: DOI for Models & Datasets

https://huggingface.co/blog/introducing-doi OpenAI asks for more funding

https://www.theinformation.com/articles/openai-valued-at-nearly-20-billio…

#ai #stablediffusion #license So-called responsible AI licenses are stupid, counterproductive, and have a dangerous legal loophole in them. OpenRAIL++ License here: https://www.ykilcher.com/license OUTLINE:

0:00 - Introduction

0:40 - Responsible AI Licenses (RAIL) of BLOOM and Stable Diffusion

3:35 - Open source software's dilemma of bad usage and restrictions

8:45 - Good applications, bad applications

12:45 - A dangerous legal loophole

15:50 - OpenRAIL++ License

16:50 - This has nothing to do with copyright

26:00 - Final thoughts References:

https://huggingface.co/CompVis/stable-diffusion/tree/main

https://huggingface.co/spaces/CompVis/stable-diffusion-license

https://huggingface.co/bigsci…

#ai #language #knowledge Large Language Models have the ability to store vast amounts of facts about the world. But little is known, how these models actually do this. This paper aims at discovering the mechanism and location of storage and recall of factual associations in GPT models, and then proposes a mechanism for the targeted editing of such facts, in form of a simple rank-one update to a single MLP layer. This has wide implications both for how we understand such models' inner workings, and for our ability to gain greater control over such models in the future. OUTLINE:

0:00 - Introduction

1:40 - What are the main questions in this subfield?

6:55 - How causal tracing reveals where fa…

#stablediffusion #aiart #openai Stability AI has stepped into some drama recently. They are accused of a hostile takeover of the community-led sub-reddits and Discord servers, of going after an alternative web UI, and of falsely dealing out IP takedown notices. OUTLINE:

0:00 - Intro

2:40 - Stability takes over community Discord & Reddit

14:50 - AUTOMATIC1111 web UI, stolen or not ?

24:50 - Stable Diffusion 1.5 takedown request

31:20 - Scary: Stability CIO statement on safety & openness References:

https://finance.yahoo.com/news/stability-ai-startup-behind-stable-170151950.html?guccounter=1

https://analyticsindiamag.com/when-stability-ai-went-rogue-on-reddit-rampage%ef%bf%bc/

https://www.red…

#neuralnetworks #machinelearning #ai Alexander Mattick joins me to discuss the paper "Neural Networks are Decision Trees", which has generated a lot of hype on social media. We ask the question: Has this paper solved one of the large mysteries of deep learning and opened the black-box neural networks up to interpretability? OUTLINE:

0:00 - Introduction

2:20 - Aren't Neural Networks non-linear?

5:20 - What does it all mean?

8:00 - How large do these trees get?

11:50 - Decision Trees vs Neural Networks

17:15 - Is this paper new?

22:20 - Experimental results

27:30 - Can Trees and Networks work together? Paper: https://arxiv.org/abs/2210.05189 Abstract:

In this manuscript, we show that any feed…

#alphatensor #deepmind #ai Matrix multiplication is the most used mathematical operation in all of science and engineering. Speeding this up has massive consequences. Thus, over the years, this operation has become more and more optimized. A fascinating discovery was made when it was shown that one actually needs less than N^3 multiplication operations to multiply to NxN matrices. DeepMind goes a step further and creates AlphaTensor, a Deep Reinforcement Learning algorithm that plays a single-player game, TensorGame, in order to find even more optimized algorithms for matrix multiplication. And it turns out, there exists a plethora of undiscovered matrix multiplication algorithms, which not…

#mlnews #openai #ai Everything important going on in the ML world right here! Sponsor: Paperspace

https://www.paperspace.com/?src=yannic OUTLINE:

0:00 - Introduction

0:20 - Whisper: Open-Source Speech Transcription

6:30 - Sponsor: Paperspace

9:30 - Meta: How the brain hears audio

11:25 - PyTorch moves to Linux Foundation

12:15 - French Government uses AI to find unlicensed swimming pools

13:35 - AlphaFold extends database

14:10 - John Carmack raises 20M to build AGI0729970510422016

16:10 - Cerebras achieves model size record

17:40 - Andrej Karpathy on YouTube

18:35 - ColabPro changes pricing

19:15 - Huggingface runs evaluation on the hub

20:35 - AI wins art fair

22:50 - PaLI: Multilingual L…

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…

Discrete convolutions, from probability, to image processing and FFTs.

Help fund future projects: https://www.patreon.com/3blue1brown

Special thanks to these supporters: https://3b1b.co/lessons/convolutions#thanks

An equally valuable form of support is to simply share the videos. ------------------ Other videos I referenced Live lecture on image convolutions for the MIT Julia lab

https://youtu.be/8rrHTtUzyZA Lecture on Discrete Fourier Transforms

https://youtu.be/g8RkArhtCc4 Reducible video on FFTs

https://youtu.be/h7apO7q16V0 Veritasium video on FFTs

https://youtu.be/nmgFG7PUHfo A small correction for the integer multiplication algorithm mentioned at the end. A “straightforward” applicatio…

A curious pattern of integrals that all equal pi...until they don't.

Next video on convolutions: https://youtu.be/KuXjwB4LzSA

Help fund future projects: https://www.patreon.com/3blue1brown

Special thanks to these patrons: https://3b1b.co/lessons/borwein#thanks

An equally valuable form of support is to simply share the videos. ------------------ Original paper from David and Jonathan Borwein

https://carma.edu.au/resources/db90/pdfs/db90-119.00.pdf Other fun coverage of the topic:

http://schmid-werren.ch/hanspeter/publications/2014elemath.pdf https://johncarlosbaez.wordpress.com/2018/09/20/patterns-that-eventually-fail/ Correction: 4:12 The top line should not be there, as that integral diver…

Winners and honorable mentions for the SoME2 contest

Playlist of all entries: https://www.youtube.com/playlist?list=PLnQX-jgAF5pTZXPiD8ciEARRylD9brJXU

Help fund future projects: https://www.patreon.com/3blue1brown Post with links to all entries:

https://www.3blue1brown.com/blog/some2 **Winners** Clear Crystal Conundrums, A Multifaceted Intro to Group Theory

https://explanaria.github.io/crystalgroups/ The Lore of Calculus

https://youtu.be/5M2RWtD4EzI How Realistic CGI Works (And How To Do It Way Faster)

https://www.youtube.com/watch?v=gsZiJeaMO48 Percolation: a Mathematical Phase Transition

https://youtu.be/a-767WnbaCQ The Coolest Hat Puzzle

https://youtu.be/6hVPNONm7xw **Honorable mentions*…

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. Here's a nice short video on the false pi = 4 proof

https://www.youtube.com/watch?v=6Qnfd5dRyf4 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, m…

Announcing the second iteration of the Summer of Math Exposition 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…

A lesson on generating functions, and clever uses of complex numbers for counting

Help fund future projects: https://www.patreon.com/3blue1brown

An equally valuable form of support is to simply share the videos.

Special thanks: https://3b1b.co/lessons/subsets-puzzle#thanks Artwork by Kurt Burns

Music by Vince Rubinetti Nice writeup and video giving solutions to the exercises at the end, by Benjamin Hackl

https://benjamin-hackl.at/blog/2022/06/generating-functions-3b1b.html

https://youtu.be/9SzwfM-S9sk 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

http…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Disentangling Random and Cyclic Effects in Time-Lapse Sequences" is available here:

https://arxiv.org/abs/2207.01413

https://github.com/harskish/tlgan My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward …

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals" is available here:

https://deepmind.github.io/dramatron/details.html My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang,…

❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum 📝 The paper "Text2LIVE: Text-Driven Layered Image and Video Editing" is available here:

https://text2live.github.io/ My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geronimo Mora…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "From Motor Control to Team Play in Simulated Humanoid Football" is available here:

https://www.science.org/doi/abs/10.1126/scirobotics.abo0235

https://arxiv.org/abs/2105.12196 My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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 📝 The paper "Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation" is available here:

https://tuneavideo.github.io/ My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric M…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers Try ChatGPT here:

https://openai.com/blog/chatgpt/ My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 SVG objects: https://twitter.com/brdskggs/status/1599533975357095936

Twitter logo with SVG graphics: https://twitter.com/DataChaz/status/1605989977447272448

Shadertoy: https://www.shadertoy.com/results?query=chatgpt Mirror shader: https://sigmoid.social/@[email protected]/109558911131170252

Security holes: https…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "VToonify: Controllable High-Resolution Portrait Video Style Transfer" is available here:

https://www.mmlab-ntu.com/project/vtoonify/ Web demo:

https://huggingface.co/spaces/PKUWilliamYang/VToonify Source code:

https://github.com/williamyang1991/VToonify My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, …

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds" is available here:

https://github.com/sebastianstarke/AI4Animation My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geroni…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback" is available here:

https://www.deepmind.com/blog/building-interactive-agents-in-video-game-worlds 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "eDiff-I: Text-to-Image Diffusion Models with Ensemble of Expert Denoisers" is available here:

https://deepimagination.cc/eDiff-I/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, Matthew Allen Fisher, Matthew Valle, Michael Albr…

❤️ 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/RLHF-OpenAI Try #ChatGPT!

https://chat.openai.com/

https://openai.com/blog/chatgpt/ Our earlier paper with the translucent materials:

https://users.cg.tuwien.ac.at/zsolnai/gfx/separable-subsurface-scattering-with-activision-blizzard/ My latest paper on simulations that look almost like reality is available for free here:

https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations:

https://www.nature.com/articles/s41567-022-01788-5 Tweet links:

Decoration: https://twitter.com/GuyP/status/1598020781065527296

Bubble sort…

❤️ Check out Cohere and sign up for free today: https://cohere.ai/papers 📝 The paper "Self-Distilled StyleGAN: Towards Generation from Internet Photos" is available here:

https://self-distilled-stylegan.github.io/ 📝 Our paper with the material synthesis, i.e., "Gaussian Material Synthesis" is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ I have decided to try Mastodon. If you are interested, you can follow me there too: https://sigmoid.social/@twominutepapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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: http://wandb.me/variable-bitrate 📝 The paper "Variable Bitrate Neural Fields" is available here:

https://nv-tlabs.github.io/vqad/ I have been trying Mastodon - not sure how to do this yet, but here goes: https://sigmoid.social/@twominutepapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Edward Unthank, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan,…

❤️ Check out Anyscale and try it for free here: https://www.anyscale.com/papers Stable Diffusion version 2 release notes:

https://stability.ai/blog/stable-diffusion-v2-release Try it on the web - https://huggingface.co/spaces/stabilityai/stable-diffusion

Or run it locally: https://github.com/Stability-AI/stablediffusion Refractive images - @recatm - https://twitter.com/recatm/status/1596933520672583680

Textures - @recatm - https://twitter.com/recatm/status/1596933527836119040

Humans - @EMostaque - https://twitter.com/EMostaque/status/1596620680442703873

Cyberpunk book cover @technollama - https://twitter.com/technollama/status/1597219897683378177

Interiors - https://twitter.com/williamcusic…

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…

Data Dojo — тренировки по машинному обучению и место встречи специалистов в сфере анализа данных. Задавайте вопросы спикерам в телеграм-чате (https://t.me/+OsKnLNG-7DE1ZTFi) с хештегом #вопрос, чтобы ведущий зачитал их в прямом эфире. 0:00:00 — Начало трансляции

0:00:55 — ML-соревнования 2022. Подведение итогов года / Петр Ермаков

0:12:17 — Предсказание исполнителя трека по набору акустических признаков. Разбор решения с Yandex Cup 2022 / Владимир Фоменко

0:37:52 — Что было, что будет, чем сердце успокоится: об анализе новостной ленты из прошлого, настоящего и будущего / Елизавета Пушкарева и Георгий Сурков

2:02:12 — Дорога к Kaggle Competitions Master в 17 лет / Вадим Тимакин

2:31:10 — При…

Data Dojo — тренировки по машинному обучению и место встречи специалистов в сфере анализа данных. Задавайте вопросы спикерам в телеграм-чате (https://t.me/+OsKnLNG-7DE1ZTFi) с хештегом #вопрос, чтобы ведущий зачитал их в прямом эфире.

Data Dojo — тренировки по машинному обучению и место встречи специалистов в сфере анализа данных. Задавайте вопросы спикерам в телеграм-чате (https://t.me/+OsKnLNG-7DE1ZTFi) с хештегом #вопрос, чтобы ведущий зачитал их в прямом эфире. Программа: 0:05 — Открытие / Петр Ермаков (Яндекс)

4:38 — Бенчмарк приемлемости предложений на русском языке (RuCoLA) + секретный релиз / Максим Рябинин (Яндекс) / Презентация: https://clck.ru/32G6nT

39:50 — Верификация моделей автомобилей (Machines Can See 2022) / Дмитрий Гаус (VisionLabs) и Артём Стрекалов (АО Уфанет) / Презентация: https://clck.ru/32G6oS

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

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

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

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

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

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

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

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

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

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

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

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

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1YnhjcIoH7Wqwp1U7-NA81JubE42ePBFN/view?usp=sharing

Максим рассказывает про байесовский подход к АБ-тестированию. Подход состоит из 3 этапов: составление гипотезы про эксперимент, понимание ограничений на время и данные, интерпретирование результатов после сбора данных. Преимуществом является то, что Байесовский AB(C) тест не требует p-values, коррекций или процедур бутстрапа, консервативный (не преувеличивает результат на малых данных) и легко интерпретируем для бизнеса.

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ODS соц.сети: Telegram: https://t.me/datafest В…

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1m7Lhs_krYeZ56KUr-Ly_jw5029mcWFFm/view?usp=sharing

В докладе Дмитрий рассказывает:

- Как устроен Causal Impact и в каких случаях его стоит применять.

- Как используется Causal Impact в Ленте и какие доработки потребовались, чтобы получать устойчивые результаты и эффективно оценивать нововведения.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/19qp-sQ9y9JBsOU_3fe8ZXqAC40x5pa5F/view?usp=sharing

Григорий рассказывает о предположениях, которые лежат под капотом у таких вроде бы надежных и проверенных временем методов, как экспериментальные интервенции (оно же RCT/AB и др.). Далее, ведет обсуждение того, как современные графические методы из causal inference могут быть полезны для планирования экспериментов. В частности, говорит о том, как учесть предварительно собранные наблюдаемые данные или domain expertise при планировании факторных экспериментов (когда предпол…

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1SAcpd_mvx1JDlZr9638XefjgQvrcjPP-/view?usp=sharing

Валера рассказывает про онлайн, офлайн и прокси метрики, а также про иерархию метрик в АБ-тестах.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1x5pRGxN-jNvyuUjiZzPYAimfCRoeCCPk/view?usp=sharing

Александр Сахнов рассказывает о разных типах бутстрепа, их преимуществах, недостатках и границах применимости.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1v7rJ4UDr6trJ_54tXDKdiFY1Yw8DzLuE/view?usp=sharing

Дима рассказывает о том, что такое синтетический контроль и как он помогает проводить АБ тесты в Ленте даже на небольшом числе магазинов.

Подробнее рассмотрены: (1) методы, позволяющие снизить минимально детектируемый эффект и улучшить точность моделей, используемых при синтетическом контроле, (2) способы оценок ошибок I и II рода после применения синтетического контроля.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1W33At1Pt_i1UM3Ehp4f6tZZvJZjwV0Uo/view?usp=sharing

В докладе Артем рассказывает о том, что такое балансировка в causal inference, какие методы балансировки существуют и как они работают.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1HOUhAkrKJswS0fpa4kObps9a52-aOKTX/view?usp=sharing

В своем докладе Аслан рассказывает: 1) про кейсы A/B тестов в МТС BigData, про то, какие бывают эксперименты и как они проводятся на конкретных примерах;

2) про мотивацию создания этой библиотеки, как, зачем и почему в МТС ее сделали и хотят дальше развивать;

3) что Ambrosia умеет уже сейчас и чем она лучше других инструментов;

4) ближайшие планы по развитию инструмента.

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ODS соц.сети: Telegram: https://t.me/datafest Вконтакте: https://vk.com/datafest

ODS Reliable ML AB Testing & Causal Inference Meetup 17 декабря 2022 г.

Тг-канал Reliable ML: https://t.me/reliable_ml

Скачать презентацию: https://drive.google.com/file/d/1nTf1FbSAkjL9LeAZlYG5WtXdt7RcJ-WW/view?usp=sharing

Ваагн рассказывает об одном из аспектов A/B тестирования: о ситуации, когда не было предварительного случайного разделения на группы, но ставится задача оценить влияние того или иного воздействия на бизнес-показатели. Акцент сделан на том, как "хорошо" оценить влияние и застраховать себя от потенциальных проблем с моделями/оценками. Рассмотрены оценки с двойной надёжностью, при которых оценки получаются "хорошими", если верно задана хотя бы одна из моделей: либо модель ра…

Шаблон Reliable ML для ML System Design Doc – GitHub Repo - https://github.com/IrinaGoloshchapova/ml_system_design_doc_ru

Канал Reliable ML: https://t.me/reliable_ml Страница курса: https://ods.ai/tracks/ml-system-design-22

Все доп.материалы в блоке на странице курса: https://ods.ai/tracks/ml-system-design-22/blocks/1cd9e827-487d-4ff0-a224-33adc0ecab97

Course Fest: https://ods.ai/events/course_season_autumn_22 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Курс Deep Reinforcement Learning: https://ods.ai/tracks/drlcourse22

Сезон курсов: https://ods.ai/events/course_season_autumn_22 Обсуждаем решения домашних заданий 1-3. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Канал Reliable ML: https://t.me/reliable_ml Страница курса: https://ods.ai/tracks/ml-system-design-22

Все доп.материалы в блоке на странице курса: https://ods.ai/tracks/ml-system-design-22/blocks/ac1efde8-e11f-4c16-b7f2-2a06e9fce067

Course Fest: https://ods.ai/events/course_season_autumn_22 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Курс Deep Reinforcement Learning: https://ods.ai/tracks/drlcourse22

Сезон курсов: https://ods.ai/events/course_season_autumn_22 На шестом практическом занятии решаем Pendulum с помощью DDPG. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Страница курса: https://ods.ai/tracks/ml-system-design-22

Все доп.материалы в блоке на странице курса: https://ods.ai/tracks/ml-system-design-22/blocks/74dc3c09-7712-499d-912f-c1d8b77db5f8

Course Fest: https://ods.ai/events/course_season_autumn_22 Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

You can get 50% off What We Owe The Future and drive sales to local independent bookstores by using the promotion code PRIMER50 when buying from the following website: https://bookshop.org/books/what-we-owe-the-future/9781541618626 I made this video in partnership with the Forethought Foundation for Global Priorities Research, where the author Will MacAskill serves as director. Their goal is to help the book reach more people, and I’m very aligned with that goal. The more we can work together to think about our future, the better! Source links:

https://ourworldindata.org/longtermism

https://www.prb.org/articles/how-many-people-have-ever-lived-on-earth/

https://ourworldindata.org/world-popul…

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:…

David Kipping is an astronomer at Columbia University, director of the Cool Worlds Lab, and host of the Cool Worlds YouTube channel.

Please support this podcast by checking out our sponsors:– SimpliSafe: https://simplisafe.com/lex– Shopify: https://shopify.com/lex to get free trial– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:David’s Twitter: https://twitter.com/david_kippingDavid’s YouTube: https://youtube.com/@CoolWorldsLabCool Worlds Lab: https://coolworldslab.com/PODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes…

Jeremi Suri is a historian at UT Austin.

Civil War by Other Means: https://amzn.to/3hRa3cT2.

The Impossible Presidency: https://amzn.to/3hTn5X83.

Henry Kissinger: https://amzn.to/3WqkBOYPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full Episodes: https://youtube.com/lexfridmanYouTube Clips: https://youtube.com/lexclipsSUPPORT & CONNECT:– Check out the sponsors above, it’s the best way to support this podcast– Support on Patreon: https://www.patreon.com/lexfridman– Twitter: https://twitter.com/lexfridman– Instagram: https://www.instagram.com/lexfridman– Link…

Dennis Whyte is a nuclear scientist at MIT and the director of the MIT Plasma Science and Fusion Center.

Please support this podcast by checking out our sponsors:– Rocket Money: https://rocketmoney.com/lex– MasterClass: https://masterclass.com/lex to get 15% off– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Dennis’s Twitter: https://twitter.com/MIT_FusionDennis’s LinkedIn: https://linkedin.com/in/dennis-whyte-33474a54Dennis’s Website: https://www.psfc.mit.edu/whyteSPARC: https://www.psfc.mit.edu/sparcMIT Plasma Science and Fusion Center: https://www.psfc.mit.eduMIT Plasma Science and Fusion Center’s YouTube: https://youtube.com/@mitplasmascienceandfusionc6211Comm…

Imam Omar Suleiman is the Founder and President of the Yaqeen Institute for Islamic Research and a professor of Islamic Studies at Southern Methodist University.

Please support this podcast by checking out our sponsors:– NetSuite: http://netsuite.com/lex to get free product tour– House of Macadamias: https://houseofmacadamias.com/lex and use code LEX to get 20% off your first order– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Omar’s Instagram: https://instagram.com/imamomarsuleimanOmar’s Twitter: https://twitter.com/omarsuleiman504Omar’s Facebook: https://www.facebook.com/imamomarsuleimanYaqeen Institute’s YouTube: https://www.youtube.com/@yaqeeninstituteoffi…

MrBeast is a legendary YouTube creator.

Please support this podcast by checking out our sponsors:– House of Macadamias: https://houseofmacadamias.com/lex and use code LEX to get 20% off your first order– Eight Sleep: https://www.eightsleep.com/lex to get special savings– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:MrBeast Main Channel: https://youtube.com/@MrBeastMrBeast Reacts: https://youtube.com/@BeastReactsMrBeast Gaming: https://youtube.com/@MrBeastGamingMrBeast Philanthropy: https://youtube.com/@BeastPhilanthropyMrBeast’s TikTok: https://tiktok.com/@mrbeastMrBeast’s Twitter: https://twitter.com/MrBeastMrBeast’s Instagram: https://instagram.com/mrbeastFeastable’s…

Betül Kaçar is an astrobiologist at University of Wisconsin.

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Bhaskar Sunkara is a democratic socialist, political writer, founding editor of Jacobin, president of The Nation, and author of The Socialist Manifesto.

Please support this podcast by checking out our sponsors:– House of Macadamias: https://houseofmacadamias.com/lex and use code LEX to get 20% of your first order– Linode: https://linode.com/lex to get $100 free credit– Onnit: https://lexfridman.com/onnit to get up to 10% off– InsideTracker: https://insidetracker.com/lex to get 20% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Bhaskar’s Twitter: https://twitter.com/sunraysunrayJacobin: https://jacobin.comJacobin’s Twitter: https://twitter.com/jacobinThe Nat…

Nathalie Cabrol is an astrobiologist at the SETI Institute, directing the Carl Sagan Center for the Study of Life in the Universe.

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Michael Malice is a political thinker, podcaster, author, and anarchist.

Please support this podcast by checking out our sponsors:– House of Macadamias: https://houseofmacadamias.com/lex and use code LEX to get 20% of your first order– InsideTracker: https://insidetracker.com/lex to get 20% off– NetSuite: http://netsuite.com/lex to get free product tour– SimpliSafe: https://simplisafe.com/lexEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmaliceMichael’s Community: https://malice.locals.comMichael’s YouTube: https://youtube.com/channel/UC5tj5QCpJKIl-KIa4Gib5XwMichael’s Website: http://michaelmalice.com/aboutYour Welcome podcast: https://bit.ly/30q8oz1Books:The White Pill (book) …

Ed Calderon is a security specialist who worked on counter-narcotics and organized crime investigation in Mexico.

Please support this podcast by checking out our sponsors:– Policygenius: https://www.policygenius.com/– Bambee: https://bambee.com and use code LEX to get free HR audit– Onnit: https://lexfridman.com/onnit to get up to 10% off– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Ed’s Instagram: https://instagram.com/manifestoradiopodcastEd’s Patreon: https://patreon.com/edsmanifestoEd’s Website: https://edsmanifesto.comEd’s Field Notes: https://edsmanifesto.com/field-notesEd’s Twitter: https://twitter.com/eds_manifestoPODCAST INFO:Podcast website: https://le…

Coffeezilla is a journalist and investigator on YouTube who exposes financial frauds, scams, and fake gurus.

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Noam Brown is a research scientist at FAIR, Meta AI, co-creator of AI that achieved superhuman level performance in games of No-Limit Texas Hold’em and Diplomacy.

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Roger Gracie is a legendary jiu jitsu competitor and MMA fighter.

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Todd Howard is a legendary video game designer at Bethesda Game Studios.

He led the development of the Elder Scrolls series and the Fallout series, and an upcoming game Starfield.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get free trial– Eight Sleep: https://www.eightsleep.com/lex to get special savings– InsideTracker: https://insidetracker.com/lex to get 20% off– LMNT: https://drinkLMNT.com/lex to get free sample packEPISODE LINKS:Bethesda: https://bethesda.netBethesda Game Studios: https://bethesdagamestudios.comCreation Club: https://creationclub.bethesda.netPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: ht…

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…

Compositional generalization refers to the capability of models to generalize to out-of-distribution instances by composing information obtained from the training data.

In this episode we chatted with Najoung Kim, on how…

We invited Urvashi Khandelwal, a research scientist at Google Brain to talk about nearest neighbor language and machine translation models.

These models interpolate parametric (conditional) language models with non-param…

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…

Ziang discussed how chatbots are used for conversational surveys.

To expound on conversational surveys, Ziang discussed the three kinds of interviews: structured, semi-structured, and unstructured.

He contrasted the results from both conversational surveys and traditional surveys.

Ziang shared his thoughts about the impact of generative models such as ChatGPT on the progress of conversational surveys.

He also emphasized the need for continuous development and privacy protection when designing conversational surveys.

Do Results Generalize for Privacy and Security SurveysOn the show today, we are joined by Jenny Tang, a Ph.D. student of societal computing at Carnegie Mellon University.

She is also affiliated with Skylab, the security and privacy institute of the university.

She joins us to discuss her study that assessed the use of online surveys by privacy and security researchers.

She also shared examples of security and privacy questions asked in her surveys.

Rounding up, Jenny gave her take on people’s level of awareness to online security and privacy matters.

4 out of 5 Data Scientists AgreeThis episode kicks off the new season of the show, Data Skeptic: Surveys.

Linhda rejoins the show for a conversation with Kyle about her experience taking surveys and what questions she has for the season.

Lastly, Kyle announces the launch of survey.dataskeptic.com, a new site we’re launching to gather your opinions.

Please take a moment and share your thoughts!

Crowdfunded Board GamesWe are joined by Johannes Wachs, an assistant professor at the Vienna University of Economics and Business.

Today, he discusses the findings of his study on whether crowdfunding truly drives innovation, using board games as a case study.

He talked about how he and his co-authors collected the board games data for their research.

He discussed some observations about crowdfunded games after analyzing the data.

The research questions revolved around checking if crowdfunded games were more innovative and novel than traditional ones.

Russian Election Interference EffectivenessToday, we are joined by Koustuv Saha, a researcher at Microsoft Research in the Montreal Lab.

On the show, he talks about targeted ads for political campaigns using Russian interference in the 2016 election as a case study.

He gave examples of the use of passive sensors to get data on social media.

Using the 2016 US election as an example, Koustuv discussed how political organizations can use targeted ads for political gains.

Concluding, Koustuv discussed the other similar studies he is working on such as building an ad targeting tool to understand people’s likes, interests, and awareness.

Placement Laundering FraudOn the show today, we speak with Jeff Kline on placement laundering fraud in the ad tech space.

Jeff began with a background into what ad fraud and placement laundering were.

He discussed the technicalities of engaging in placement laundering fraud.

He detailed the process of identifying placement laundering fraud through the mismatch between the campaign data and the panelist data.

Rounding up, he spoke about how to possibly quantify the magnitude of placement laundering fraud.

Bosko discusses how their clean room platform can enable seamless data collaboration between parties.

Bosko started by discussing how data is shared without clean rooms and how clean rooms help to make collaboration more secure.

He gave practical applications where clean rooms ensure the shared data is used for the agreed-upon purpose.

He also explained how programmers can get data in and out of the data clean rooms.

He also discussed the feedback from users implementing clean rooms and the metrics to evaluate their success.

Dark Patterns in Site DesignKerstin Bongard-Blanchy, a Research Associate at the University of Luxembourg, joins us today.

She discusses the activities of dark patterns in website designs.

Kerstin started by sharing what it means to be manipulated through designs — a term called dark patterns.

Kerstin then shared the reactions of her survey participants, after explaining what dark patterns in websites are about.

She shared her thoughts on whether the activities of websites with dark patterns should be regulated.

Internet Advertising Bureau Media LabToday, we are joined by Anthony Katsur, the CEO of IAB Tech Lab.

The IAB Tech Lab is the global technical standard-setting body for digital advertising.

Anthony joins us to discuss the standards within the ad tech industry and what IAB Tech Lab does in that space.

He explained some solutions IAB Tech Lab is currently working on for more privacy-centric ads.

Rounding up, Anthony explained how you can sign up for IAB Tech Lab.

Your Mouse Reveals your Gender and AgeLuis Leiva is a Professor of Computer Science at the University of Luxembourg.

Today, he talks to us about his recent work on behavioural profiling via mouse tracking.

He outlined the techniques used for tracking human behavior on the web (eye gaze measurement and mouse tracking) and contrasted between both techniques.

Luis then talked about his research, where he and his co-authors tracked a sample of the user’s mouse movement after consent.

The positives and negatives of accurate mouse tracking for targeted ads were also discussed on the showLuis discussed efforts to curtail the privacy concerns with mouse tracking.

Measuring Web Search BehaviorIt’s a double guest show today.

Aleksandra Urman and Mykola Makhortykh join us to discuss their work on the comparative analysis of web search behavior using web tracking data.

Aleksandra is a postdoctoral researcher at the University of Zurich, Switzerland where she works with s social computing group.

Aleksandra revealed how German and Swiss citizens use search engines such as Google, Bing, Ecosia, etc.

Furthermore, Mykola and Aleksandra discussed some of the takeaways for search engines from the analysis result.

The last time, she spoke about annotator bias in language models and how it affects the robustness of NLP models.

Mor spoke about the StrategyQA dataset, a question-answering benchmark for testing the ability of models to perform implicit reasoning.

StrategyQA is one of the challenging tasks in the Big Bench benchmark, a collaborative benchmark for measuring the capabilities of large language models.

The construction of Big Bench was led by Google and involved contributions from over 400 researchers in the NLP community.

In closing, she gave her take on whether the trajectory in language models will lead to AGI.

Ad Blockers Effect on News ConsumptionOn the show today, we speak with Shunyao Yan, an Assistant Professor in Marketing at Leavey School of Business, Santa Clara University.

She joins us to discuss her recent work on the effect of ad blockers on news consumption.

Her study investigated the changes in the behavior of users that adopt ad blockers.

She specifically spoke about the characterization of users who are most likely to adopt ad blockers.

She also explained how news platforms can still generate revenues despite the adoption of ad blockers — a development news publishers have acknowledged.

Your Consent is Worth 75 Euros a YearOn the show today, we speak to Victor Morel, a Postdoc candidate at the Chalmers University of Technology.

He also questioned the legalities of website targeting activities.

Victor then spoke about the Transparency and Consent Framework (TCF) to communicate users’ consent and aggregate the data for targeted advertising.

He discussed the efforts of agencies such as the Belgium Data Protection Agency to audit websites for ad targeting.

Wrapping up, Victor discussed future opportunities for research in the field.

SparseGPT is a noteworthy one-shot pruning technique that can halve the size of large language models like GPT-3 without adversely affecting accuracy.

In this episode, Jon Krohn provides an overview of this development a…

Looking for a short primer on Machine Learning concepts?

SDS Founder Kirill Eremenko and AI expert Hadelin de Ponteves are back, joining Jon Krohn to review essential ML concepts.

From classification errors to logistic r…

Text-to-speech gets a groundbreaking update with Microsoft’s VALL-E. On this Five-Minute Friday, Jon Krohn investigates how the Microsoft team modeled their tool to replicate natural human speech using just three seconds…

Knowledge management, trust of AI, and job automation: Tom Davenport speaks with Jon Krohn about the organizational obstacles to adopting AI, and why the C-suite also needs to learn how to handle data.

This episode is b…

Are you still wondering how to get the most out of ChatGPT's game-changing technology?

In this week's Five-Minute Friday guest episode, Jon Krohn sits down with longtime friend and e-commerce entrepreneur Zack Weinberg, …

Machine learning, security and Call of Duty collide this week as Jon Krohn sits down with Carly Taylor, Lead Machine Learning Engineer for Activision's COD franchise to discuss the importance of low-latency, the future o…

Love and money matter in this week’s Five-Minute Friday, as Stanford University’s Myra Strober sits down with Jon Krohn to talk about her latest book, Money and Love, coauthored with Abby Davisson.

In this unorthodox tak…

AI prediction tools for antibodies and using statistics to prepare healthcare systems for pandemics: host Jon Krohn speaks with Chief Scientist of Biologics AI for Exscientia Charlotte Deane about the variety of potentia…

Looking to shake up your data science productivity in 2023?

Switching to a continuous calendar can make all the difference.

Jon Krohn shares his new calendar with those taking their yearly, monthly and daily planning to …

The top data science trends of 2023 are here.

Sadie St. Lawrence joins Jon Krohn to share annual predictions on the future of AI.

From the data mesh to multimodal models like ChatGPT, tune in to discover what's next.

From AI trends to rediscovering how fun it is to work with colleagues ‘in person’, host Jon Krohn wraps up the year’s best SuperDataScience content and looks ahead to another year of interviews with the data science comm…

Learning Python for beginners is made fun on Mariya Sha’s YouTube and Discord channels, on which she posts hacks, breakdowns and tutorials on everything to do with the world’s most important programming language.

OpenAI's ChatGPT helps us generate a special holiday greeting this week.

Tune in to hear the festive message that this impressive natural language generating algorithm churned out as we close out the year.

Additional m…

It's all about data visualization this week as Jon Krohn welcomes Ann K. Emery, data visualization designer and owner of Depict Data Studio, to the show.

If you want to learn data viz best practices, tips and tricks and …

Digital literacy and data bias: Can one reduce or even eradicate the other?

Law professor Orly Lobel speaks with SDS host Jon Krohn about Orly’s latest book, The Equality Machine, which offers an optimistic look into the…

In this episode, I’ll be discussing the capabilities and limitations of ChatGPT, an advanced language AI model.

I’ll go over its power to understand and respond to natural language, and its applications in tasks such as language translation and text summarization.

However, I’ll also touch on the challenges that still need to be overcome such as bias and data privacy concerns.

Tune in for a comprehensive look at the current state of advanced language AI.

In this episode, I am with Kevin McNamara, founder, and CEO of Parallel Domain.

We speak about a very effective method to generate synthetic data currently in production at Parallel Domain.

Enjoy the show!

ReferencesParallel Domain Synthetic Data Improves Cyclist Detection (blog post):https://paralleldomain.com/parallel-domain-synthetic-data-improves-cyclist-detection/Beating the State of the Art in Object Tracking with Synthetic Data:https://paralleldomain.com/beating-the-state-of-the-art-in-object-tracking-with-synthetic-data/Parallel Domain Open Synthetic Dataset:https://paralleldomain.com/open-datasets/bicycle-detectionHow Toyota Research Institute Trains Better Computer Vision Models w…

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Is it possible to reconstruct a 3D model from a simple image?

In this episode, I tell you how.

Our SponsorsExplore the Complex World of Regulations.

Check it out at https://arcticwolf.com/datascienceAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

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What if we borrowed from physics some theories that would interpret deep learning and machine learning in general?

Here is a list of plausible ways to interpret our beloved ML models and understand why they work or they don’t.

All this with the highest standard of cyber security technology.

See NordPass Business in action now with a 3-month free trial herehttps://nordpass.com/DATASCIENCE with code: DATASCIENCEAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

We provide AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance solutions.

If you think that the problem of self-driving cars has been solved, think twice.

As a matter of fact, the problem of self-driving cars cannot be solved with the technical solutions that companies are currently considering.

Whoever is telling you they solved the problem of driving a vehicle fully autonomously, they are lying.

Check it out at https://arcticwolf.com/datascienceAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.

Let’s look at the history of data platforms.

Shall I switch to the latest architecture?

Our SponsorsExplore the Complex World of Regulations.

Check it out at https://arcticwolf.com/datascienceAmethix works to create and maximize the impact of the world’s leading corporations and startups, so they can create a better future for everyone they serve.

We provide solutions in AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance.

Companies and other business entities are actively involved in defining data products and applied research every year.

Academia has always played a role in creating new methods and solutions/algorithms in the fields of machine learning and artificial intelligence.

Is studying AI in academia a waste of time?

Our SponsorsReady to advance your career in data science?

University of Cincinnati Online offers nationally recognized educational programs in business analytics and information systems.

There are many solutions to private machine learning.

I am with Daniel Huynh, CEO of Mithril Security, a graduate from Ecole Polytechnique with a specialisation in AI and data science.

He has written articles on Homomorphic Encryptions with the CKKS explained series (https://blog.openmined.org/ckks-explained-part-1-simple-encoding-and-decoding/).

He is now focusing on Confidential Computing at Mithril Security and has written extensive articles on the topic: https://blog.mithrilsecurity.io/.

In this show we speak about confidential computing, SGX and private machine learningReferences

Our SponsorsReady to advance your career in data science?

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That 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

How does an autonomous vehicle see?

In this episode I speak about LIDAR, high resolution cameras and some machine learning methods adapted to a minimal number of sensors.

Our SponsorsReady to advance your career in data science?

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