We show that anyone can take a dated off-the-shelf open source large language model (LLM) and give it magical ChatGPT-like instruction following ability by training it in less than three hours on one machine, using high-quality training data. Surprisingly, instruction-following does not seem to require the latest or largest models: our model is only 6 billion parameters, compared to 175 billion for GPT-3. We open source the code for our model (Dolly) and show how it can be re-created on Databricks. We believe models like Dolly will help democratize LLMs, transforming them from something very few companies can afford into a commodity every company can own and customize to improve their produ…

K-Planes was released with PyTorch code only and CoBaFa didn't provide code, I implemented both of them in a short repo with CUDA acceleration : https://github.com/loicmagne/tinynerf submitted by /u/Lairv [link] [comments]

I'm looking for ways to generate a couple of different voices that all say the same thing. What I've used before are some amazing models based on TTS but this time I'm not cloning an English voice. It's Swedish. So I'm trying to find out if ther's another way to approach this. Right now it looks like I can't do it without training a new model based on tons of data I don't have. But now I don't need a tts clone that can say whatever I want. I just need it to say one single thing. Is there a way where I could "morph" one voice into another or something like that? Thinking that I'll just make one recording of me saying the things that should be said and then train that with recordings of the o…

We found an LFI/RFI that leads to system takeover and cloud account takeover in MLflow versions <2.2.1. The devs have had it patched for a few weeks now. No user interaction required Unauthenticated Remotely exploitable All configurations vulnerable including fresh install No prerequisite knowledge of the environment required We urge users of MLflow to patch immediately if they have not done so in the past month. https://github.com/protectai/Snaike-MLflow submitted by /u/FlyingTriangle [link] [comments]

Hey! We're creating an open-source training framework focused on evolutionary hyperparameter optimization for RL. This offers a speed up of 10x over other HPO methods! Check it out and please get involved if you would be interested in working on this - any contributions are super valuable. We believe this can change the way we train our models, and democratise access to RL for people and businesses who don't currently have the resources for it! GitHub: https://github.com/AgileRL/AgileRL submitted by /u/nicku_a [link] [comments]

GPT-4 is a multimodal model, which specifically accepts image and text inputs, and emits text outputs. And I just realised: You can layer this over any application, or even combinations of them. You can make a screenshot tool in which you can ask question. This makes literally any current software with an GUI machine-interpretable. A multimodal language model could look at the exact same interface that you are. And thus you don't need advanced integrations anymore. Of course, a custom integration will almost always be better, since you have better acces to underlying data and commands, but the fact that it can immediately work on any program will be just insane. Just a thought I wanted to s…

i have data,that i have split into train and test,when i doing onehotencoding in train data with min_frequency=5 and the same i do in test, their columns arent same,train columns more than test,i cant do fitting in test data.Who can help? submitted by /u/Realistic_Tie_124 [link] [comments]

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

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

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The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods.

Geometric Deep Learning is concerned with exposing these regularities through unified geometric principles that can be applied throughout a broad spectrum of applications.

Kunihiko Fukushima and the neocognitron, an early geometric deep learning architecture and a precursor of the modern convolutional neural networks.

The ‘Erlangen Programme’ of Deep LearningOur historical overview of the geometric foundations of deep learning has now naturally brought us to the blueprint that underpins this book.

The Geometric Deep Learning Blueprint can be used to derive from…

However, there is another side to the AI art process, one that is not talked about enough.

In this article, I will cover why this the case, the debate around artist compensation in AI art, and some possible solutions to the problem.

With that out of the way, let’s move on to the overarching debate about AI art and whether it copies artists.

Let’s connect: https://rb.gy/m5ok2yMy Instagram: https://rb.gy/gmvuy9My Twitter: https://twitter.com/Machine0177681CitationFor attribution of this in academic contexts or books, please cite this work as:Devansh Lnu, "Artists enable AI art - shouldn't they be compensated?

BibTeX citation:@article{Lnu2023aiart,author = {Lnu, Devansh},title = {Artists enabl…

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?

Created Using MidjourneyLarge Language Models (LLMs) have recently taken the world by storm with their remarkable ability to perform new tasks from textual instructions or a few examples.

This ability, known as few-shot learning, was first observed when models were scaled up to a sufficient size.

However, recent research has shown that, for a given compute budget, the best performance is not achieved by the largest models.

Instead, smaller models trained on more data outperform their larger counterparts.

In that context, Meta AI recently published a paper detailing LLaMA, a 65B LLM that is able to outperform GPT-3 across many tasks despite being significantly smaller.

Created Using MidjourneyIn this issue:An overview of vertical federated learningA review of Google’s paper about using federated learning to optimize mobile keyword predictions.

An introduction to Flower’s federated learning framework.

💡 ML Concept of the Day: Understanding Vertical Federated LearningIn the previous edition of this series, we explored horizontal federation learning(HFL) as an architecture in which nodes in a federation shared the same feature space but different sample space.

The natural complement to HFL is a type of federation in which nodes share a similar sample space but different feature space.

This is known in machine learning theory as vertical federated learning(VF…

To remind everyone, MLOps is a set of guiding principles that facilitate ML pipeline optimization and interconnectedness between different ML stages.

Typically, MLOps is divided into three key components – culture, practices, and tools.

The survey’s results point to the fact that the MLOps culture and practices are still lagging behind, while there’s a disproportionate influx of technical solutions.

In addition, AI product developers tend to focus on ML models, while often overlooking quality data.

Lastly, no environment currently exists that addresses all stages of the ML value chain and supports the entire AI product lifespan.

📝 EditorialNo, I am not repeating last week’s editorial but is just that the momentum around generative AI makes it hard to write about something else.

If we thought the previous week was impressive in terms of generative AI releases nothing compares with the last few days.

Together with the PaLM API, Google also released MakerSuite, a tool that allow developers to quickly prototype generative AI apps.

The pace of innovation in generative AI is like nothing we have seen before.

Generative AI VMsMicrosoft previews a new generation of Azure VMs optimized for generative AI workloads —> Read more.

Join us at Tecton’s webinar!

Tecton recently rolled out version 0.6, which includes new capabilities to simplify and accelerate feature engineering at scale.

The update introduces notebook-driven development, allowing users to write, run, and revise a feature within one notebook before applying it to a Tecton cluster.

Want to learn more about these new capabilities, including how you and your team can shift notebook development into production?

Join us for our product webinar on Tuesday, March 28 at 9:30 a.m. PT / 12:30 p.m.

Created Using MidjourneyIn the middle of the ChatGPT frenzy, DeepMind’s CEO Demis Hassabis gave an interview to Time Magazine in which he mentioned their intentions to launch a similar model this year.

The foundation of the rumored ChatGPT competitor is based on Sparrow, a model outlined research paper DeepMind published in late 2022.

The original goal of Sparrow’s research was to enable safer conversational agents but now seems to be positioned as the core component of DeepMind’s ChatGPT alternatives.

🛠 ML WorkMadrona Ventures has been one of the most active VC firms evangelizing the potential of generative AI and foundation models.

Could you elaborate on your investment thesis and vision of the generative AI landscape?

We also are big believers in Generative AI being immensely helpful in enterprise use cases as well as consumer use cases.

There is a lot of research in new generative AI areas such as chain-of-thought reasoning, regular learning or action taking .

Controversial question, which top 10 tech incumbent in dangerously falling behind in the generative AI race?

Created Using MidjourneyIn this issue:The principles of horizontal federated learning.

💡 ML Concept of the Day: Understanding Horizontal Federated LearningIn the previous edition of this series, we introduced a taxonomy to understand the different types of federated learning architectures.

For instance, imagine a federated learning model applied to a group of social networks or blogging platforms.

Not surprisingly HFL is also known as sample-based federated learning or homogeneous federated learning.

🔎 ML Research You Should Know About: Personalized Federated LearningIn “Federated Reconstruction: Partially Local Federated Learning”, researchers from Google Brain proposes partially local fed…

We had a great chat about generative AI.

📝 EditorialWe are accustomed to be inundated by news about generative AI but this week was remarkable even by those high standards.

The number of announcements by large companies incorporating generative AI initiatives has been remarkable.

🤖 Cool AI Tech ReleasesEinstein GPTSalesforce.com unveiled a version of its Einstein platform powered by generative AI capabilities —> Read more.

Discord Generative AIDiscord announced a new series of tools powered by AI including a new version of its AI chatbot —> Read more.

Given just a few examples of how an API can be used, Toolformer annotates a large language modeling dataset with potential API calls.

Through a self-supervised loss, the model determines which API calls are useful in predicting future tokens and fine-tunes itself accordingly.

Inside the Toolformer ArchitectureThe core idea behind Toolformer is to enhance a language model (M) with the ability to use different tools via API calls.

The dataset is the first step to convert the dataset of plain texts into an augmented dataset by inserting API calls.

This is done in three steps: sampling potential API calls, executing the API calls and filtering the API calls based on their usefulness in predicti…

Created Using MidjourneyIn this issue:A taxonomy for classifying federated learning methods.

Meta AI research for building highly scalable and asynchronous federated learning pipelinesMicrosoft Research’s FLUTE framework for federated learning architectures.

💡 ML Concept of the Day: A Taxonomy to Understand Federated LearningOne of the most common mistakes around federated learning, is to think about it as a single type of architecture.

While there is no consistent taxonomy to study federated learning, there are some categorization schemes that are proven to be quite useful.

The following taxonomy might be relevant to understand the different variations of federated learning architectures:

This week, the AI powerhouse unveiled the first version of the ChatGPT and Whisper API which enable more robust experiencing for integrating conversational and audio based intelligent experiences in applications.

The ChatGPT API has to be one of the most anticipated releases by the developer community.

OpenAI also unveiled the first API of Whisper, its famous speech recognition model that was released in 2022.

In addition to the Whisper and ChatGPT API releases, OpenAI announced the availability of dedicated instances for its API via the Azure cloud.

🤖 Cool AI Tech ReleasesChatGPT and Whisper APIsOpenAI released the first API version of its ChatGPT and Whisper API as well as dedicated insta…

At TheSequence, we’re exploring what MLOps culture looks like across the industry at the start of 2023.

A huge variety of tools are available for ML development, but the culture and practices still have some catching up to do.

How do you see MLOps evolving this year?

TAKE A SURVEYShare your thoughts in our 5-minute survey.

We’ll follow up to share the research results and pick a random winner for a $100 Amazon certificate!

The CAI approach improves upon previous methods, such as reinforcement learning from human feedback by not requiring any human feedback labels for harmfulness.

4) to reduce iteration time by eliminating the need to collect new human feedback labels when altering the objective.

ChatGPT:Image Credit: Scale AIClaude:Image Credit: Scale AIHowever, in some examples, ChatGPT can reason through the semantics, while Claude thinks is silly.

ChatGPT:Image Credit: Scale AIImage Credit: Scale AIImage Credit: Scale AIClaude:Image Credit: Scale AIIn other areas, such as analysis of fictional work, Claude seems to be more impressive.

ChatGPT:Image Credit: Scale AIClaude:Image Credit: Scale AIClaude is sti…

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При этом мало кто понимает – а как вообще нейросети вроде ChatGPT работают внутри?

Вы начинаете печатать в ответ: «Да не, у меня уже дела(( я иду в...», и вот тут подключается Т9.

Языковые модели без всякого труда генерируют длинные тексты, но делают они это по принципу «слово за словом».

Большинство людей легко понимают из контекста, что в одном случае «она» – это приманка, а в другом – рыба.

Краткое резюме: GPT-2 вышла в 2019 году, и она превосходила свою предшественницу и по объему тренировочных текстовых данных, и по размеру самой модели (числу параметров) в 10 раз.

Более детальное описание каждой ступени можно найти в моем докладе тут и в этой статье.

Определение географии пилота и выбор объектов для тестирования (пилотная группа, внедряем MVP) и сравнения (контрольная группа, ничего не внедряем).

О том, почему подобное ручное сравнение это плохо и что должно улучшить АБ (и как объяснить это бизнесу!

А если планируем эффект на пару категорий продаж, то проверять стоит на них, а не на тотал продажах.

Внедряем как получилось, а потом бахаем causal impact и вуаля, у нас есть оценка и пилота, и ролл-аута.

В этой статье, я на простом примере расскажу о том, как решать эту проблему с помощью diffusers и dstack.

Чтобы запустить сценарий через dstack , сценарий должен быть определен как workflow через YAML-файл в .dstack/workflows .

Настройка AWS в качестве remoteПо умолчанию workflows в dstack запускаются локально.

ЗаключениеЕсли эта статья показалась вам интересной, вы можете углубиться в эту тему, изучив документацию по diffusers и dstack.

В одной из следующих статей мы углубимся не только в генерацию изображений, но и в файнтьюнинг Stable Diffusion.

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…

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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.

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 …

In reality A.I.

And here we come back to the psychosis we're in right now and particularly how I think most of it is really unjustified and wrong.

Later on numerous flaws were pointed out in the study and as it turns out A.I.

The problem with GPT though, as with many other AI contraptions, is that obviously it knows extremely little about the world.

We will just sugar rush ourselves with AI Elon Musk type hyper-promises, scare ourselves to the point of anxiety by the nonexistent AI daemons until we all slowly but surely go totally insane.

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.

We give an in-depth overview of modularity in our survey on Modular Deep Learning.

Green components illustrate different routing functions, shade-of-purple components illustrate different modular computation functions.

It subsumes standard multi-task learning methods, modules that adapt a pre-trained model (known as 'adapters'), and rescaling methods.

In multi-task learning settings, modular task-specific components are trained jointly to mitigate catastrophic interference, with fixed or learned routing.

CitationFor attribution in academic contexts or books, please cite our survey as:Jonas Pfeiffer and Sebastian Ruder and Ivan Vulić and Edoardo M. Ponti, "Modular Deep Learning".

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…

Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images.

In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections.

To achieve this, we first propose a novel neural renderer with decomposed rendering components to learn the interaction between surface and environment lighting.

We then propose an SDF-based neural surface model that leverages this learned neural renderer to represent general scenes.

Our model additionally synthesizes indirect illuminations caused by inter-reflections from shiny surfaces by marching surface-reflected…

Aiming to link natural language descriptions to specific regions in a 3D scene represented as 3D point clouds, 3D visual grounding is a very fundamental task for human-robot interaction.

Specifically, a new task termed Embodied Reference Understanding (ERU) is first designed for this concern.

Different from existing datasets, our ScanERU is the first to cover semi-synthetic scene integration with textual, real-world visual, and synthetic gestural information.

Additionally, this paper formulates a heuristic framework based on attention mechanisms and human body movements to enlighten the research of ERU.

Experimental results demonstrate the superiority of the proposed method, especially in t…

Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained.

Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects.

To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching.

Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier.

CORA$^+$ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark.

Domain generalization (DG) tends to alleviate the poor generalization capability of deep neural networks by learning model with multiple source domains.

A classical solution to DG is domain augmentation, the common belief of which is that diversifying source domains will be conducive to the out-of-distribution generalization.

Our explorations empirically reveal that the correlation between model generalization and the diversity of domains may be not strictly positive, which limits the effectiveness of domain augmentation.

To this end, we propose a new perspective on DG that recasts it as a convex game between domains.

We first encourage each diversified domain to enhance model generalizatio…

DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception.

This work begins by applying this appealing paradigm to LiDAR-based point cloud segmentation and obtains a simple yet effective baseline.

Although the naive adaptation obtains fair results, the instance segmentation performance is noticeably inferior to previous works.

It is embedded into backbone features and later guides the mask prediction and query update processes iteratively, leading to Position-Aware Segmentation (PA-Seg) and Masked Focal Attention (MFA).

The method, named Position-guided Point cloud Panoptic segmentation transFormer (P3Former), outperforms previous stat…

Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability.

How can we remove such copyrighted concepts or images without retraining the model from scratch?

To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i.e., preventing the generation of a target concept.

This prevents the model from generating target concepts given its text condition.

Extensive experiments show that our method can successfully prevent the generation of the ablated concept while preserving closely related concepts in the model.

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions.

Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters.

Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization.

Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a…

Existing Optimal Transport (OT) methods mainly derive the optimal transport plan/matching under the criterion of transport cost/distance minimization, which may cause incorrect matching in some cases.

In this paper, we propose a novel KeyPoint-Guided model by ReLation preservation (KPG-RL) that searches for the optimal matching (i.e., transport plan) guided by the keypoints in OT.

The proposed KPG-RL model can be solved by Sinkhorn's algorithm and is applicable even when distributions are supported in different spaces.

Based on the learned transport plan from dual KPG-RL, we propose a novel manifold barycentric projection to transport source data to the target domain.

As applications, we ap…

Recent trends in semi-supervised learning have significantly boosted the performance of 3D semi-supervised medical image segmentation.

Compared with 2D images, 3D medical volumes involve information from different directions, e.g., transverse, sagittal, and coronal planes, so as to naturally provide complementary views.

These complementary views and the intrinsic similarity among adjacent 3D slices inspire us to develop a novel annotation way and its corresponding semi-supervised model for effective segmentation.

Specifically, we firstly propose the orthogonal annotation by only labeling two orthogonal slices in a labeled volume, which significantly relieves the burden of annotation.

Experi…

How to generate the ground-truth (GT) image is a critical issue for training realistic image super-resolution (Real-ISR) models.

Existing methods mostly take a set of high-resolution (HR) images as GTs and apply various degradations to simulate their low-resolution (LR) counterparts.

With the above considerations, we propose a human guided GT generation scheme.

A human guided GT image dataset with both positive and negative samples is then constructed, and a loss function is proposed to train the Real-ISR models.

Experiments show that the Real-ISR models trained on our dataset can produce perceptually more realistic results with less artifacts.

Instrument playing technique (IPT) is a key element of musical presentation.

In this paper, we formulate it as a frame-level multi-label classification problem and apply it to Guzheng, a Chinese plucked string instrument.

We create a new dataset, Guzheng\_Tech99, containing Guzheng recordings and onset, offset, pitch, IPT annotations of each note.

Because different IPTs vary a lot in their lengths, we propose a new method to solve this problem using multi-scale network and self-attention.

The multi-scale network extracts features from different scales, and the self-attention mechanism applied to the feature maps at the coarsest scale further enhances the long-range feature extraction.

In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed.

First, we propose Deterministic Neural Color Mapping (DNCM) to consistently operate on each pixel via an image-adaptive color mapping matrix, avoiding artifacts and supporting high-resolution inputs with a small memory footprint.

Second, we develop a two-stage pipeline by dividing the task into color normalization and stylization, which allows efficient style switching by extracting color styles as presets and reusing them on normalized input images.

Due to the unavailability of pairwise …

This paper introduces the Masked Voxel Jigsaw and Reconstruction (MV-JAR) method for LiDAR-based self-supervised pre-training and a carefully designed data-efficient 3D object detection benchmark on the Waymo dataset.

Inspired by the scene-voxel-point hierarchy in downstream 3D object detectors, we design masking and reconstruction strategies accounting for voxel distributions in the scene and local point distributions within the voxel.

We employ a Reversed-Furthest-Voxel-Sampling strategy to address the uneven distribution of LiDAR points and propose MV-JAR, which combines two techniques for modeling the aforementioned distributions, resulting in superior performance.

Our experiments revea…

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map.

In this task, the features of the foreground objects and the backgrounds are quite different.

However, all previous methods handle backgrounds and objects as a whole using a monolithic model.

First, SIEDOB disassembles the edited input into background regions and instance-level objects.

Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB.

In media industry, the demand of SDR-to-HDRTV up-conversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range).

The research community has started tackling this low-level vision task by learning-based approaches.

When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience.

Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models.

Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dar…

Recent text-to-video generation approaches rely on computationally heavy training and require large-scale video datasets.

In this paper, we introduce a new task of zero-shot text-to-video generation and propose a low-cost approach (without any training or optimization) by leveraging the power of existing text-to-image synthesis methods (e.g., Stable Diffusion), making them suitable for the video domain.

Experiments show that this leads to low overhead, yet high-quality and remarkably consistent video generation.

Moreover, our approach is not limited to text-to-video synthesis but is also applicable to other tasks such as conditional and content-specialized video generation, and Video Instru…

Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective.

In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly exists in first-person recordings, even within a short duration; 2) The out-of-view sound components can be created while wearers shift their attention.

To address the first problem, we propose a geometry-aware temporal aggregation module to handle the egomotion explicitly.

During training, we take advantage of the naturally available audio-visual temporal synchronization as the ``free'' self-supervision to avoid costly labeling.

Ext…

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning that solely learns from the sequences of nodes' historical first-hop interactions.

We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research.

We hope our work can provide new insights and facilitate the development of the dynamic graph learning field.

All the resources including datasets, data loaders, algorithms, and executing scripts are publicly available at https://github.com/yule-BUAA/DyGLib.

PDFAbstract

Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.

However, prior research has shown that in-context learning can suffer from high instability due to variations in training examples, example order, and prompt formats.

In this paper, we revisit this problem from the view of predictive bias.

Specifically, we introduce a metric to evaluate the predictive bias of a fixed prompt against labels or a given attributes.

Our results indicate that our method can enhance the model's in-context learning performance i…

Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to guide the student, while self-KD does not need a real teacher to require the soft labels.

We decompose the KD loss and find the non-target loss from it forces the student's non-target logits to match the teacher's, but the sum of the two non-target logits is different, preventing them from being identical.

It can be generally used for KD and self-KD to better use the soft labels for distillation loss.

USKD generates customized soft labels for both target and non-target classes without a teacher.

It smooths the target logit of the student as the soft target label and uses the rank of the intermediate feature t…

Recently, there have been surging needs to generate customized images by inverting diffusion models from exemplar images.

In this work, we propose ReVersion for the Relation Inversion task, which aims to learn a specific relation (represented as "relation prompt") from exemplar images.

The learned relation prompt can then be applied to generate relation-specific images with new objects, backgrounds, and styles.

Specifically, we propose a novel relation-steering contrastive learning scheme to impose two critical properties of the relation prompt: 1) The relation prompt should capture the interaction between objects, enforced by the preposition prior.

To comprehensively evaluate this new task…

The goal of building a benchmark (suite of datasets) is to provide a unified protocol for fair evaluation and thus facilitate the evolution of a specific area.

Nonetheless, we point out that existing protocols of action recognition could yield partial evaluations due to several limitations.

To comprehensively probe the effectiveness of spatiotemporal representation learning, we introduce BEAR, a new BEnchmark on video Action Recognition.

BEAR is a collection of 18 video datasets grouped into 5 categories (anomaly, gesture, daily, sports, and instructional), which covers a diverse set of real-world applications.

With BEAR, we thoroughly evaluate 6 common spatiotemporal models pre-trained by …

Reducing this noise is a critical task called image denoising.

Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks.

For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions.

To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training.

Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios.

This paper introduces a general model called CIPNN - Continuous Indeterminate Probability Neural Network, and this model is based on IPNN, which is used for discrete latent random variables.

Currently, posterior of continuous latent variables is regarded as intractable, with the new theory proposed by IPNN this problem can be solved.

First, we derive the analytical solution of the posterior calculation of continuous latent random variables and propose a general classification model (CIPNN).

Second, we propose a general auto-encoder called CIPAE - Continuous Indeterminate Probability Auto-Encoder, the decoder part is not a neural network and uses a fully probabilistic inference model for the…

Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching.

Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity.

However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback.

In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations.

Besides, it …

Since DNNs usually adopt some external training data from an untrusted third party, a robust backdoor defense strategy during the training stage is of importance.

We argue that the core of training-time defense is to select poisoned samples and to handle them properly.

In this work, we summarize the training-time defenses from a unified framework as splitting the poisoned dataset into two data pools.

Under our framework, we propose an adaptively splitting dataset-based defense (ASD).

With the split clean data pool and polluted data pool, ASD successfully defends against backdoor attacks during training.

Generalization is the ability of quantum machine learning models to make accurate predictions on new data by learning from training data.

Here, we introduce the data quantum Fisher information metric (DQFIM) to determine when a model can generalize.

For variational learning of unitaries, the DQFIM quantifies the amount of circuit parameters and training data needed to successfully train and generalize.

Further, we can improve generalization by removing symmetries from training data.

Our work opens up new approaches to improve generalization in quantum machine learning.

We investigate the optimization of multilayer perceptrons on symmetric data.

We compare the strategy of constraining the architecture to be equivariant to that of using augmentation.

We show that, under natural assumptions on the loss and non-linearities, the sets of equivariant stationary points are identical for the two strategies, and that the set of equivariant layers is invariant under the gradient flow for augmented models.

Finally, we show that stationary points may be unstable for augmented training although they are stable for the equivariant modelsPDFAbstract

The various types of elements, deposited in the training history, are a large amount of wealth for improving learning deep models.

In this survey, we comprehensively review and summarize the topic--``Historical Learning: Learning Models with Learning History'', which learns better neural models with the help of their learning history during its optimization, from three detailed aspects: Historical Type (what), Functional Part (where) and Storage Form (how).

To our best knowledge, it is the first survey that systematically studies the methodologies which make use of various historical statistics when training deep neural networks.

We also expose future challenges of this topic and encourage …

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks.

Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information.

In this paper, we revisit the segmentation granularity of Chinese PLMs.

We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words.

We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT.

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.

To explore this, we collaborated with researchers at the University of Freiburg and Nuremberg to develop Visual Language Maps (VLMaps), a map representation that directly fuses pre-trained visual-language embeddings into a 3D reconstruction of the environment.

At runtime, a robot can query the VLMap to locate visual landmarks given natural language descriptions, or to build open-vocabulary obstacle maps for path planning.

Open-vocabulary obstacle mapsA single VLMap of the same environment can also be used to build open-vocabulary obstacle maps for path planning.

Right: Obstacle maps generated for different embodiments with corresponding navigation paths.

ConclusionVLMaps takes an initial st…

Good [and bad] search experiences have immediate impacts for retailersGoogle Cloud first studied search abandonment and explored its impacts on shoppers and retailers in 2021.

When U.S. consumers have what they feel is a successful search on a retail website, the vast majority (92%) purchase the item they were searching for.

In contrast, after an unsuccessful search using the search function or search box on a retail website, more than half (53%) of U.S. consumers say they typically abandon their carts and go elsewhere if there’s at least one item they can’t find on a website.

The search bar is a retailer’s most important online assetU.S. shoppers depend on the search function or search box…

G2 is the industry’s first cloud VM powered by the newly announced NVIDIA L4 Tensor Core GPU, and is purpose-built for large inference AI workloads like generative AI.

By switching from NVIDIA A10G GPUs to G2 instances with L4 GPUs, organizations can lower their production infrastructure costs up to 40%.

We also found that customers switching from NVIDIA T4 GPUs to L4 GPUs can achieve 2x-4x better performance.

With optimized Vertex AI support for G2 VMs, AI users can tap the latest generative AI models and technologies.

Here are what some of them have to say about the benefits that G2 with NVIDIA L4 GPUs bring:

At Google Cloud, we’re committed to solving these challenges with continued investment in our state-of-the-art machine learning product for document processing and insights: Document AI Workbench, which helps users quickly build models with world-class accuracy trained for their specific use cases.

Today, we’re announcing the newest model type to help users automate document processing, Custom Document Classifier (CDC).

Benefits of classification models with Document AI WorkbenchOur customers use Document AI Workbench to ultimately save time and money, building models with state of the art accuracy in a fraction of the time that traditional development methods require.

By combining Document…

Every mortgage company must disclose fees including the county recording fee, in every closing statement for every mortgage.

To improve the customer experience and efficiency, we needed to streamline the mortgage process, from pre-approval to closing and post-closing to servicing.

Our agents provide a human touch with sympathy and empathy to help customers overcome challenges in the mortgage process.

Since there’s no standard formula to calculate the recording fee, customers are sometimes undercharged or overcharged.

Every day, our loan servicing system generates a list of loans along with the loan information, property information, and county information.

Over 4,000 attendees will join in person to learn and network with peers at the 2023 Gartner® Data & Analytics Summit.

We expect that many of you will want to talk about data governance, analytics, AI, BI, data management, data products, data fabrics and everything in between!

Our presence at this event is focused on creating meaningful connections for you with the many customers and partners who make the Google Cloud Data community so great.

We’ll kick off with a session featuring Equifax’s Chief Product & Data Analytics Officer, Bryson Koehler and Google Cloud’s Ritika Gunnar.

If there is anything we can do to help, stop by the Google Data Cloud booth (#434).

This differs from single image captioning and standard video captioning, which consists of describing short videos with a single sentence.

In this post, we introduce “Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning”, to appear at CVPR 2023.

Vid2Seq also generalizes well to the few-shot dense video captioning setting, the video paragraph captioning task, and the standard video captioning task.

Comparison to state-of-the-art methods for dense video captioning (left) and for video clip captioning (right), on the CIDEr metric (higher is better).

Vid2Seq can be effectively pretrained on unlabeled narrated videos at scale, and achieves state-of-the-art resul…

Starting today, you can now register at the Early Bird rate of $899 USD* for Google Cloud Next ‘23, taking place in person, August 29-31, 2023.

The emergence of generative AI is a transformational opportunity that some say may be as meaningful as the cloud itself.

Beyond generative AI, there are breakthroughs in cybersecurity, better and smarter ways to gather and gain insights from data, advances in application development, and so much more.

It’s clear that there has never been a better time to work in the cloud industry.

*The $899 USD early bird price is valid through 11:59 PM PT on Wednesday, May 31, or until it’s sold out.

The Impact Lab team, part of Google’s Responsible AI Team, employs a range of interdisciplinary methodologies to ensure critical and rich analysis of the potential implications of technology development.

We examine systemic social issues and generate useful artifacts for responsible AI development.

Grounding in civil and human rights valuesIn partnership with our Civil and Human Rights Program, our research and analysis process is grounded in internationally recognized human rights frameworks and standards including the Universal Declaration of Human Rights and the UN Guiding Principles on Business and Human Rights.

Responsible data collection and analysis requires an additional level of ca…

We encourage our engineers and data scientists to earn Google Cloud certifications, such as the Professional Cloud Architect and Professional Data Engineer.

In addition, Google Cloud offers on-demand training to understand, implement, and scale data science and ML tools, like these role-based learning paths for Data Engineers and ML Engineers.

We also use Cloud Hero, a gamified Google Cloud training experience that uses hands-on labs to teach skills in an interactive learning environment.

With the right tools, techniques, and mindset, data scientists and ML engineers can develop the skills necessary to excel and progress in our field.

Learn more about the Google Cloud Advanced Solutions Lab…

Although Coop has a rich history spanning nearly 160 years, the machine learning (ML) team supporting its modern operations is quite young.

Setting up new grounds for innovationOver a two-day workshop with the Google Cloud team, Coop kicked things off by ingesting data from its vast data pipelines and SAP systems to BigQuery.

At the same time, Coop’s ML team implemented physical accumulation cues of incoming new information and sorted out what kind of information this was.

Next, the Coop team turned to Vertex AI Workbench to further develop its data science workflow, finding it surprisingly fast to get started.

The goal was to train forecasting models to support Coop’s distribution centers …

As deep learning models become increasingly complex and datasets larger, distributed training is all but a necessity.

But distributed training comes with its own set of challenges.

In this post, we’ll show you how to speed up training of a PyTorch + Hugging Face model using Reduction Server, a Vertex AI feature that optimizes bandwidth and latency of multi-node distributed training on NVIDIA GPUs for synchronous data parallel algorithms.

Overview of Distributed Data ParallelBefore diving into the details of Reduction Server and how to submit jobs on the Vertex AI training service, it’s useful to understand the basics of distributed data parallelism.

Data parallelism is just one way of perfo…

Developing machine learning (ML) tools in pathology to assist with the microscopic review represents a compelling research area with many potential applications.

To our knowledge, this is the first demonstration that medical experts can learn new prognostic features from machine learning, a promising start for the future of this “learning from deep learning” paradigm.

To our knowledge, this is the first demonstration of pathologists learning to identify and score a specific pathology feature originally identified by an ML-based approach.

Putting things in context: learning from deep learning as a paradigmOur work is an example of people “learning from deep learning”.

Coupling deep learning …

Generative AI is poised to usher in a new wave of interactive, multimodal experiences that transform how we interact with information, brands, and one another.

Harnessing the power of decades of Google’s research, innovation, and investment in AI, Google Cloud is bringing businesses and governments the ability to generate text, images, code, videos, audio, and more from simple natural language prompts.

To address these needs, Google Cloud will launch a range of products that infuse generative AI into our offerings, empowering developers to responsibly build with enterprise-level safety, security, and privacy.

“Google Cloud is bringing decades of AI research, innovation, and investment to th…

In line with our iterative deployment philosophy, we are gradually rolling out plugins in ChatGPT so we can study their real-world use, impact, and safety and alignment challenges—all of which we’ll have to get right in order to achieve our mission.

Users have been asking for plugins since we launched ChatGPT (and many developers are experimenting with similar ideas) because they unlock a vast range of possible use cases.

We’re starting with a small set of users and are planning to gradually roll out larger-scale access as we learn more (for plugin developers, ChatGPT users, and after an alpha period, API users who would like to integrate plugins into their products).

Plugin developer…

We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market.

Using a new rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating both human expertise and classifications from GPT-4.

The influence spans all wage levels, with higher-income jobs potentially facing greater exposure.

Notably, the impact is not limited to industries with higher recent productivity growth.

We conclude that Generative Pre-trained Transformers exhibit characteristics of general-purpose technologies (GPTs), suggesting that as these models could have notable economic, social, and policy implicati…

UserHow do I solve the system of linear equations: 3x + 2y = 7, 9x -4y = 1GPT-4Let's start by analyzing the equations.

Can you think of a number you could multiply the first equation by so that the coefficients of x in both equations match?

If you subtract equation 2 from equation 1, what will the resulting equation look like?

Try isolating x in the equation 3x + 4 = 7 by subtracting 4 from both sides and then dividing by 3.

So, the solution to the system of linear equations is x = 1 and y = 2.

Model: The ChatGPT model family we are releasing today, gpt-3.5-turbo , is the same model used in the ChatGPT product.

It is priced at $0.002 per 1k tokens, which is 10x cheaper than our existing GPT-3.5 models.

It’s also our best model for many non-chat use cases—we’ve seen early testers migrate from text-davinci-003 to gpt-3.5-turbo with only a small amount of adjustment needed to their prompts.

API: Traditionally, GPT models consume unstructured text, which is represented to the model as a sequence of “tokens.” ChatGPT models instead consume a sequence of messages together with metadata.

We’ve created a new endpoint to interact with our ChatGPT models:

The short termThere are several things we think are important to do now to prepare for AGI.

We believe this is the best way to carefully steward AGI into existence—a gradual transition to a world with AGI is better than a sudden one.

As our systems get closer to AGI, we are becoming increasingly cautious with the creation and deployment of our models.

As our systems get closer to AGI, we are becoming increasingly cautious with the creation and deployment of our models.

We hope to contribute to the world an AGI aligned with such flourishing.

Our shift from models like the first version of GPT-3 to InstructGPT and ChatGPT is an early example of this.

The institutions of the world will need to be strengthened with additional capabilities and experience to be prepared for complex decisions about AGI.

Importantly, we think we often have to make progress on AI safety and capabilities together.

Our best safety work has come from working with our most capable models.

We have a clause in our Charter about assisting other organizations to advance safety instead of racing with them in late-stage AGI development.

We therefore think a lot about the behavior of AI systems we build in the run-up to AGI, and the way in which that behavior is determined.

Improving our methods for aligning AI systems with human values is a top priority for our company, particularly as AI systems become more capable.

Addressing biasesMany are rightly worried about biases in the design and impact of AI systems.

We believe there are at least three building blocks required in order to achieve these goals in the context of AI system behavior.

We want as many users as possible to find our AI systems useful to them “out of the box” and to feel that our technology understands and respects their values.

We believe there are at least three building blocks required in order to achieve these goals in the context of AI system behavior.

We believe that AI should be a useful tool for individual people, and thus customizable by each user up to limits defined by society.

There will therefore always be some bounds on system behavior.

We also recently began soliciting public input on AI in education (one particularly important context in which our technology is being deployed).

We are in the early stages of piloting efforts to solicit public input on topics like system behavior, disclosure mechanisms (such as watermarking), and our deployment policies more broadly.

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…

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 around the world.

[^footnote-expansion-update]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 support free access availability to as many people as possible.

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

While it is impossible to reliably detect all AI-written text, we believe good classifiers can inform mitigations for false claims that AI-generated text was written by a human: for example, running automated misinformation campaigns, using AI tools for academic dishonesty, and positioning an AI chatbot as a human.

Our classifier is not fully reliable. 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…

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 — …

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 shares this vision and our values, and our partnership is instrumental to our progress.

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 theÂ…

[05:38] And perhaps one of the most exciting emerging capabilities of language models recently is their ability to in-context learn, which has been introducing a new paradigm for using these models.

The “pre-train and then fine-tune” paradigm have been established paradigm for years, since maybe the inception of BERT and similar pre-trained language models.

Language models that have been optimized for dialogue have amazing language capabilities; they do really good at understanding language, at following instructions.

They are also conversational in nature and do store knowledge from the training data that they were trained on.

Let’s talk about, for example, connecting language models to se…

And I saw that this is really going to change the world dramatically.

Now suddenly we can start to attack: what is intelligence, really?

Sébastien Bubeck: So, of course, I want to say maybe it’s a good point to bring up the limitations of the system also.

It’s able to use a search engine and make searches when it needs to, which is really, really incredible.

Because it’s still lacking some of the fundamental aspects, two of them, which are really, really important.

At Microsoft, we’re committed to democratizing AI and giving you access to advanced generative AI models. As part of that commitment, in 2019, we started a long-term partnership with OpenAI. In January 2023, we announced the third phase of this partnership, including the general availability of Azure OpenAI Service.

Today, we are excited to announce that GPT-4 is available in preview in Azure OpenAI Service. Customers and partners already using Azure OpenAI Service can join the waitlist to access GPT-4 and start building with OpenAI’s most advanced model yet. With this milestone, we are proud to bring the world’s most advanced AI models—including GPT-3.5, ChatGPT, and DALL•E 2—to Azure customers, backed by Azure AI-optimized infrastructure, enterprise-readiness, compliance, data security, and privacy controls, along with many integrations with other Azure services.

Join Microsoft at NVIDIA GTC, a free online global technology conference (GTC), March 20–23 to learn how organizations of any size can power AI innovation with purpose-built cloud infrastructure from Microsoft.

In the competitive race for AI innovation, pushing the envelope requires both experience and leadership-class supercomputing scalability. Powerful and massively scalable infrastructure is paramount as the complexity and size of AI models accelerate.

Today, we are thrilled to announce that ChatGPT is available in preview in Azure OpenAI Service. With Azure OpenAI Service, customers and partners can apply the most advanced AI models—including Dall-E 2, GPT-3.5, Codex, and other large language models backed by the unique supercomputing and enterprise capabilities of Azure—to innovate in new ways.

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

Explore sessionsPODCASTMicrosoft Research’s Philipp Witte on improving carbon sequestration with AIReducing carbon dioxide in the atmosphere could play an important role in minimizing climate change.

Philipp Witte, a researcher with Microsoft Research for Industry, recently chatted with Fixing the Future from IEEE Spectrum about how AI can help improve carbon sequestration.

OPPORTUNITYMicrosoft Research Data Science Summer School – Apply nowMicrosoft Research New York City’s Data Science Summer School (…

Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. At Azure Open Source Day, we highlighted Microsoft’s commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI.

Today, we are pleased to announce the public preview of Microsoft’s Florence foundation model, trained with billions of text-image pairs and integrated as cost-effective, production-ready computer vision services in Azure Cognitive Service for Vision.

VIDEO Responsible AI Toolbox demo Learn how this suite of tools can help assess machine learning models through a lens of responsible AI.

For Microsoft Research, that is often Azure Machine Learning, the Microsoft platform for end-to-end ML model development.

Before long, Azure Machine Learning approached Microsoft Research with another signal: customers wanted to use the tools together, in one interface.

Enter the Responsible AI Mitigations Library and the Responsible AI Tracker, which were developed by Microsoft Research in collaboration with Aether.

Practices for responsible AI will need to continue to evolve with AI advancements to support these efforts.

Bringing together purpose-built AI infrastructure and managed data and AI services into one environment streamlines management and automation, often reducing the complexity of building, training, and bringing AI models into production.

At Microsoft, our aim is to be the most trusted co-innovation partner through every stage of the digital evolution, committed to working with communications service providers (CSPs), enterprises, developers, and ISVs alike on the future of a ubiquitous cloud that unlocks the true potential of modern connected apps.

The Microsoft Azure Cognitive Speech Services platform is a comprehensive collection of technologies and services aimed at accelerating the incorporation of speech into applications and amplifying differentiation to the market as a result. Among the services available are Speech to Text, Text to Speech, custom neural voice (CNV) conversation transcription service, speaker recognition, Speech translation, Speech SDK, and Speech Device Development Kit (DDK).

High-performance computing and orbital data deliver unprecedented insights into weather patterns, improving planning, forecasting, and decision-making, in an ever-evolving agriculture supply chain.

Using machine learning, their method learns to “grow” a larger model from a smaller model in a way that encodes knowledge the smaller model has already gained.

Plus, the models trained using the MIT method performed as well as, or better than, models trained with other techniques that also use smaller models to enable faster training of larger models.

Transformer architectures are unique because, as these types of neural network models get bigger, they achieve much better results.

“This has led to an arms race of companies trying to train larger and larger transformers on larger and larger datasets.

Learning to growKim and his collaborators use machine learning to learn a linear mapping of …

The project is leveraging a new generation of remote sensing devices to provide high-resolution plant water stress at regional to global scales.

Drought monitoring can offer fundamental information on drought location, frequency, and severity, but assessing the impact of drought on vegetation is extremely challenging.

The map has zero resolution and is more of a drought recap or summary, unable to predict future drought scenarios.

Terrer and Jiao plan to generate metrics for plant water stress at an unprecedented resolution of 10-30 meters.

“According to the current soil moisture and lagged response time, we hope to predict plant water stress in the future,” says Jiao.

A tool for building trustKulik and her group focus on transition metal complexes, molecules comprised of metals found in the middle of the periodic table that are surrounded by organic ligands.

“Characterizing these complexes and discovering new materials currently happens slowly, often driven by a researcher’s intuition,” says Kulik.

He built a machine learning platform to determine how accurate density functional models were in predicting structure and behavior of transition metal molecules.

Optimizing for multiple propertiesIn a related research thrust, which they showcased in a recent publication in JACS Au, Kulik’s group demonstrated an approach for quickly homing in on transition meta…

However, because traditional hash functions generate codes randomly, sometimes two pieces of data can be hashed with the same value.

Certain types of hash functions, known as perfect hash functions, are designed to sort data in a way that prevents collisions.

But they must be specially constructed for each dataset and take more time to compute than traditional hash functions.

They found that, in certain situations, using learned models instead of traditional hash functions could result in half as many collisions.

Their research, which will be presented at the International Conference on Very Large Databases, demonstrates how a hash function can be designed to significantly speed up searches…

If the government doesn’t start asking questions, then “I am extremely worried” about the future of AI, Mądry said in response to a question from Rep. Gerald Connolly.

In his prepared remarks, Mądry raised three overarching points.

Finally, he said too little attention has been paid to problems that will result from the nature of the AI “supply chain” — the way AI systems are built on top of each other.

Layered on top of such systems are many AI systems designed to handle a particular task, like figuring out whom a company should hire.

“We are at an inflection point in terms of what future AI will bring.

Matthew Kearney was drawn to MIT by the culture of its cross-country team.

He arrived at MIT expecting to major in electrical engineering and computer science but fell in love with philosophy after taking 24.02 (Moral Problems and the Good Life).

He’s majoring in both while also completing a master’s degree in computer science and engineering.

Following graduation, he will pursue a DPhil in computer science at Oxford University as a Rhodes Scholar.

As he wraps up his time at MIT, Kearney is also looking forward to closing out his final track seasons strong, following the success of the cross-country team.

The premise of standard Covid-19 vaccines, such as those produced by Moderna and Pfizer, is to activate the part of the immune system that releases neutralizing antibodies.

A vaccine of this sort will not keep people from getting Covid-19, but it could keep them from getting very sick or dying.

"A lot of people wonder about what approaches will be used to make Covid-19 vaccines in the future,” Offit says.

Should T-cell vaccines be used instead of, or in combination with, standard spike protein vaccines?

The mechanism behind current flu vaccines, like current Covid-19 vaccines, is to induce neutralizing antibodies, but those vaccines don’t always work for different influenza strains.

In the study, the authors focused on two types of deep classifiers: fully connected deep networks and convolutional neural networks (CNNs).

A previous study examined the structural properties that develop in large neural networks at the final stages of training.

Co-author and MIT McGovern Institute postdoc Akshay Rangamani states, “Our analysis shows that neural collapse emerges from the minimization of the square loss with highly expressive deep neural networks.

Thus far, the fact that CNNs and not dense networks represent the success story of deep networks has been almost completely ignored by machine learning theory.

Instead, the theory presented here suggests that this is an important i…

At this point, the omnipresent nature of language models is well-known: Applications in natural language processing, speech recognition, conversational AI, and generative tasks abound.

Running these large language models is also very expensive because of the amount of parameters and the computational resources they need.

The team evaluated, for example, popular BERT pretrained language models with their “textual entailment” ones on stereotype, profession, and emotion bias tests.

While we may still be far away from a neutral language model utopia, this research is ongoing in that pursuit.

A language model without explicit logic learning makes plenty of biased reasoning, but adding logic lear…

The unique competition pushes teams to spend hours coding and refining their armies in a quest for the perfectly crafted game plan.

Open to student teams around the world, Battlecode tasks participants with writing the code to program entire armies — not just individual bots — before they duke it out.

A crowd of students in the audience gasps and cheers as the battle’s outcome hangs in the balance.

In an upper corner of the screen, the people who have programmed the robot armies’ strategies narrate the action in real time.

Of the 16 finalist teams, three were made up entirely of MIT students, while another included three MIT students and one Yale University student.

Meanwhile, new generative design systems can take great advantage of this flexibility to create innovative designs for parts of a new building, car, or virtually any other device.

They used an automated design system but stopped the process periodically to allow human engineers to evaluate the work in progress and make tweaks or adjustments before letting the computer resume its design process.

Already, automated design systems have found many applications.

“If we can make things in a better way, if we can make whatever we want, why not make it better?” she asks.

The computer system then revised the design accordingly, removing the highlighted strut and strengthening some other struts to co…

Now, the program is headed into its fourth year, supporting 10 teams in its second round of projects.

Projects selected for the program span the entirety of the biopharmaceutical industry, from drug development to commercial and manufacturing.

“The research projects in the second round of funding have the potential to lead to transformative breakthroughs in health care,” says Anantha Chandrakasan, dean of the School of Engineering and co-chair of the MIT-Takeda Program.

In Round 1 of the program, one project led by scientists and engineers at MIT and Takeda researched speech-related biomarkers for frontotemporal dementia.

Meanwhile, MIT researchers helped Takeda by providing the expertise t…

Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles.

But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions.

“Uncertainty quantification is essential for both developers and users of machine-learning models.

They design the metamodel to produce the uncertainty quantification output using a technique that includes both types of uncertainty: data uncertainty and model uncertainty.

This technique could help researchers enable more machine-learning models to effectively perform unc…

It’s similar with AI models.

For her PhD at MIT, Vartak decided to build tools to help data scientists develop, test, and iterate on machine-learning models.

“At Verta, we help manage models, help run models, and make sure they’re working as expected, which we call model monitoring,” Vartak explains.

“So, the diversity of models is really high, there’s a large volume of them, and the level of scrutiny and compliance companies need around these models are very high.

The companies that ride AI to success, meanwhile, will need well-defined processes in place to manage their ever-growing list of models.

A: The framing of medical knowledge as something that can be encapsulated into multiple choice questions creates a cognitive framing of false certainty.

Medical knowledge is often taught as fixed model representations of health and disease.

Medical education also requires being aware of the biases in the way medical knowledge is created and validated.

Medical students need data science skills that will allow every clinician to contribute to, continually assess, and recalibrate medical knowledge.

There is tremendous opportunity to improve the way health care providers currently make clinical decisions, which we know are tainted with unconscious bias.

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…

To learn more about real-time endpoint architectural best practices, refer to Creating a machine learning-powered REST API with Amazon API Gateway mapping templates and Amazon SageMaker.

More on fully homomorphic encryptionFHE enables systems to perform computations on encrypted data.

It’s controlled by selecting appropriate FHE encryption parameters, which is a problem-specific, tuned parameter.

To see more information about natively supported frameworks and script mode, refer to Use Machine Learning Frameworks, Python, and R with Amazon SageMaker.

You can use Amazon SageMaker Inference Recommender to cost optimize your fleet depending on your business needs.

With Amazon Rekognition Custom Labels, you can have Amazon Rekognition train a custom model for object detection or image classification specific to your business needs.

Rekognition Custom Labels builds off of the existing capabilities of Amazon Rekognition, which is already trained on tens of millions of images across many categories.

You can then use your custom model via the Rekognition Custom Labels API and integrate it into your applications.

For more information, refer to Deleting an Amazon Rekognition Custom Labels model.

To learn more about Rekognition custom labels, visit Amazon Rekognition Custom Labels.

In this post, we show how to use SageMaker Canvas to build an ML model to predict student performance.

The solution includes the following components:Data ingestion – Importing the data from your local computer to SageMaker Canvas– Importing the data from your local computer to SageMaker Canvas Data preparation – Clean and transform the data (if required) within SageMaker Canvas– Clean and transform the data (if required) within SageMaker Canvas Build the ML model – Build the prediction model inside SageMaker Canvas to predict student performance– Build the prediction model inside SageMaker Canvas to predict student performance Prediction – Generate batch or single predictions– Generate bat…

In this post, we show how to configure a new OAuth-based authentication feature for using Snowflake in Amazon SageMaker Data Wrangler.

Register the Data Wrangler application within the IdPRefer to the following documentation for the IdPs that Data Wrangler supports:Use the documentation provided by your IdP to register your Data Wrangler application.

Store this in your preferred text editor for use later when you create the Data Wrangler data source.

Clean upIf your work with Data Wrangler is complete, shut down your Data Wrangler instance to avoid incurring additional fees.

To get started with Data Wrangler, see Prepare ML Data with Amazon SageMaker Data Wrangler.

Amazon SageMaker geospatial capabilities—now generally available in the AWS Oregon Region—provide a new and much simpler solution to this problem.

The tool makes it easy to access geospatial data sources, run purpose-built processing operations, apply pre-trained ML models, and use built-in visualization tools faster and at scale.

Results of these custom analyses are subsequently published and made observable in Amazon QuickSight so that procurement and sustainability teams can review supplier location vegetation data in one place.

SageMaker geospatial capabilities provide built-in visualization tooling powered by Foursquare Studio, which natively works from within a SageMaker notebook via …

Amazon SageMaker customers can view and manage their quota limits through Service Quotas.

In addition, they can view near real-time utilization metrics and create Amazon CloudWatch metrics to view and programmatically query SageMaker quotas.

Service Quotas simplifies limit management by allowing you to view and manage your quotas for SageMaker from a central location.

To request quota increases manually from the Service Quotas UI, you can choose the quota from the list and choose Request quota increase.

A message is sent to Amazon Simple Notification Service (Amazon SNS).

When using Data Wrangler custom transform steps to implement your custom functions, you need to implement best practices around developing and deploying code in Data Wrangler flows.

For more information about available transformation steps and implementation, refer to Transform Data and the Data Wrangler blog.

AutomationNow that you have created your Data Wrangler flow file, you can schedule your Data Wrangler jobs to automatically run at specific times and frequency.

This is a feature that comes out of the box with Data Wrangler and simplifies the process of scheduling Data Wrangler jobs.

Now you can operationalize and scale your Data Wrangler jobs without modifying your Data Wrangler flow…

In the following example figure, we show INT8 inference performance in C6i for a BERT-base model.

In this post, we show you how to build and deploy INT8 inference with your own processing container for PyTorch.

C6i instances provide performance advantages in FP32 and INT8 model deployments.

FP32 inference is enabled with AVX-512 improvements, and INT8 inference is enabled by AVX-512 VNNI instructions.

ConclusionNew EC2 C6i instances in an SageMaker endpoint can accelerate the inference deployment up to 2.5 times greater with INT8 quantization.

In our solution, we configure Microsoft Teams as a data source for an Amazon Kendra search index using the Amazon Kendra connector for Microsoft Teams.

Configure the data source using the Amazon Kendra connector for Microsoft TeamsTo add a data source to your Amazon Kendra index using the Microsoft Teams connector, you can use an existing Amazon Kendra index, or create a new Amazon Kendra index.

If you only added a new data source using the Amazon Kendra connector for Microsoft Teams, delete that data source.

ConclusionWith the Amazon Kendra connector for Microsoft Teams, organizations can make invaluable information trapped in their Microsoft Teams instances available to their users secure…

SageMaker Processing jobLet’s understand how a SageMaker Processing job runs.

To learn more about working with Step Functions and its integration with SageMaker, refer to Manage SageMaker with Step Functions.

Using the Step Functions integration capability with SageMaker, we run the preprocessing and postprocessing scripts using a SageMaker Processing job in script mode and run inference as a SageMaker Processing job using a custom container.

Create the Step Functions workflowFor quickly prototyping, we use the Step Functions Amazon States Language.

You can create a new Step Functions state machine on the Step Functions console by selecting Write your workflow in code.

Solution overviewSageMaker training jobs support ml.trn1 instances, powered by Trainium chips, which are purpose built for high-performance ML training applications in the cloud.

We will train a text classification model with SageMaker training and PyTorch using the Hugging Face Transformers Library.

Amazon SageMaker Training – SageMaker provides a fully managed training experience to easily train models without having to worry about infrastructure.

He focuses on building large-scale distributed training systems, optimizing training performance, and developing high-performance ml training hardwares, including SageMaker trainium.

He is the technical leader responsible for SageMaker training …

Therefore, VMware Carbon Black and AWS chose to build a custom MLOps pipeline using Amazon SageMaker for its ease of use, versatility, and fully managed infrastructure.

The ML model training, evaluation, and deployment pipelines are orchestrated using Amazon MWAA, referred to as a Directed Acyclic Graph (DAG).

ML model deployment pipelineTo start deployment, the user starts the GitLab job that triggers the Deployment DAG through the same Lambda function.

After publishing the model image to Amazon ECR, the MLOps pipeline triggers the Amazon MWAA training pipeline using Lambda.

We encourage you to evaluate various AWS services like SageMaker, Amazon MWAA, Amazon S3, and Amazon ECR to build a …

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications.

For more information, refer to Build a custom data labeling workflow with Amazon SageMaker Ground Truth.

His work involves productionizing machine learning models and developing novel software applications powered by machine learning to put the latest capabilities in the hands of customers.

Before that, he was with the perception team at Uber ATG and the machine learning platform team at Uber working on machine learning for autonomous driving, machine learning systems and strategic initiatives of AI.

He co-taught tutorials at ICML’17 and ICCV’19, and…

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio.

Data Wrangler enables you to access data from a wide variety of popular sources (Amazon S3, Amazon Athena, Amazon Redshift, Amazon EMR and Snowflake) and over 40 other third-party sources.

Data scientists and data engineers use Apache Spark, Apache Hive, and Presto running on Amazon EMR for large-scale data processing.

They can now quickly and simply connect to Amazon EMR without writing a single line of code, thanks to Amazon EMR being a data source for Amazon SageMaker Data Wrangler.

To get started with Data Wrangler, see Prepar…

Amazon SageMaker Ground Truth makes it easy to label objects in a single 3D frame or across a sequence of 3D point cloud frames for building ML training datasets.

Solution overviewIn this series, we cover how to visualize and label your data with Amazon SageMaker Ground Truth and demonstrate how to use this data in an Amazon SageMaker training job to create an object detection model, deployed to an Amazon SageMaker Endpoint.

Familiarity with Amazon SageMaker Ground Truth and AWS CloudFormationAn Amazon SageMaker workforce.

Follow the next steps to access the Amazon SageMaker Notebook environment:Under services search for Amazon SageMaker.

Conversion to Amazon SageMaker Ground TruthAfter vis…

In addition, members in and around Sofia, Bulgaria, can now experience the best of GeForce NOW Ultimate cloud gaming.

Plus, with five new games joining the cloud this week, and an upcoming marvel-ous reward, GeForce NOW members can look forward to a busy weekend of streaming goodness.

GDC presents the ideal time to spotlight GeForce NOW tools that enable developers to seamlessly bring their games to the cloud.

It allows developers to enhance their games to run more seamlessly, add cloud gaming into their stores and launchers, and let users connect their accounts and libraries to GeForce NOW.

Next, on to the five new games hitting GeForce NOW week for a happy weekend:Tchia (New release on Ep…

Game developer CD PROJEKT RED today at the Game Developers Conference in San Francisco unveiled a technology preview for Cyberpunk 2077 with path tracing, coming April 11.

Path tracing, also known as full ray tracing, accurately simulates light throughout an entire scene.

This technology preview, Cyberpunk 2077’s Ray Tracing: Overdrive Mode, is a sneak peak into the future of full ray tracing.

Cyberpunk 2077, previously an early adopter of ray tracing, becomes the latest modern blockbuster title to harness real-time path tracing.

Decades of Research UncorkedDecades in the making, real-time path tracing is indeed a big leap in gaming graphics.

Simulating the physics of light, using ray tracing as part of a neural graphics system, it’s capable of photorealism in 3D settings for more dynamic lighting and shadows.

GeForce gamers will be able to activate full ray tracing with the upcoming technology preview of Ray Tracing: Overdrive Mode on April 11.

NVIDIA Shader Execution Reordering helps GPUs execute incoherent workloads boosting performance; NVIDIA Real-Time Denoisers have been used to improve performance and image quality.

As a result, with full ray tracing, now practically all light sources cast physically correct soft shadows.

An expanding game roster, game engine support and continued improvements in performance and image qua…

Jensen Huang, founder and CEO of NVIDIA, interviewed AI pioneer Ilya Sutskever in a fireside chat at GTC.

The talk was recorded a day after the launch of GPT-4, the most powerful AI model to date from OpenAI, the research company Sutskever co-founded.

That generative AI model, though only a few months old, is already the most popular computer application in history.

In a sign of that complexity, Sutskever said OpenAI uses two levels of training.

Present at the CreationWhile he’s at the swirling center of modern AI today, Sutskever was also present at its creation.

To help enterprises get AI deployments across the finish line, more than 100 machine learning operations (MLOps) software providers are working with NVIDIA.

NVIDIA’s leading MLOps software partners are verified and certified for use with the NVIDIA AI Enterprise software suite, which provides an end-to-end platform for creating and accelerating production AI.

Paired with NVIDIA AI Enterprise, the tools from NVIDIA’s MLOps partners help businesses develop and deploy AI successfully.

Alibaba Cloud: Alibaba Cloud Machine Learning Platform for AI provides an all-in-one machine learning service featuring low user technical skills requirements, but with high performance results.

Accelerated by NV…

At NVIDIA GTC 2023, NVIDIA unveiled notable updates to its suite of NVIDIA AI software for developers to accelerate computing.

NVIDIA RAPIDS Accelerator for Apache SparkNVIDIA RAPIDS Accelerator for Apache Spark is now available in the NVIDIA AI Enterprise 3.1 software suite.

At GTC 2023, NVIDIA announced that RAPIDS RAFT, the toolkit providing accelerated, composable ML building blocks, can now power vector search.

This low-code AI toolkit accelerates vision AI model development for all skill levels, from beginners to expert data scientists.

Add these GTC sessions to your calendar:NVIDIA DeepStreamNVIDIA came out with the latest version of DeepStream, which adds a new runtime.

At NVIDIA GTC 2023, NVIDIA showed how AI workflows can be leveraged to help you accelerate the development of AI solutions to address a range of use cases.

AI workflows are cloud-native, packaged reference examples showing how NVIDIA AI frameworks can be used to efficiently build AI solutions such as intelligent virtual assistants, digital fingerprinting for cybersecurity, product recommendations, and more.

Become familiar with the audio transcription AI workflow, intelligent virtual assistant AI workflow, and NVIDIA Riva, and try them for free on LaunchPad.

NVIDIA retail store analytics AI workflow: You can create end-to-end retail vision AI applications for store analytics using custom da…

In many data analytics and machine learning algorithms, computational bottlenecks tend to come from a small subset of steps that dominate the end-to-end performance.

NVIDIA made RAPIDS RAFT to address these bottlenecks and maximize reuse when building algorithms for multidimensional data, such as what is often encountered in machine learning and data analytics.

A raft::device_resources instance is the easiest way to configure and manage GPU-specific resources for invoking RAFT APIs.

conda install -c rapidsai -c conda-forge -c nvidia libraft-headersInstall the precompiled binaries into your environment as well.

Key takeawaysRAFT is a library of highly reusable computational patterns for mach…

Using generative AI and the NVIDIA Morpheus cybersecurity AI framework, developers can build solutions that detect spear phishing attempts more effectively and with extremely short training times.

While phishing emails are more generic and designed to scam large numbers of people, spear phishing emails are customized for specific individuals.

While attackers are already harnessing AI to create more phishing emails and better targeted spear phishing attacks, much more can be done to leverage AI to defend against these attacks.

With NVIDIA Morpheus, developers can use AI to better detect spear phishing emails before they reach a user’s inbox.

Improve spear phishing detection with generative A…

With over 50 frameworks, pretrained models, and development tools, NVIDIA AI Enterprise, the software layer of the NVIDIA AI platform, is designed to accelerate enterprises to the leading edge of AI, while also simplifying AI to make it accessible to every enterprise.

New AI workflows: Next item prediction and route optimizationNVIDIA AI workflows are cloud-native, prepackaged reference examples that show how NVIDIA AI frameworks can be used to build AI solutions.

Two new AI workflows are available with NVIDIA AI Enterprise 3.1:NVIDIA next item prediction AI workflow for building a recommender pipeline to drive customer retention and upsales.

NVIDIA AI Enterprise available on cloud marketpl…

Back in 2016 he hand-delivered to OpenAI the first NVIDIA DGX AI supercomputer — the engine behind the large language model breakthrough powering ChatGPT.

“NVIDIA DGX H100 is the blueprint for customers building AI infrastructure worldwide,” Huang said, sharing that NVIDIA DGX H100 is now in full production.

DGX Cloud: Bringing AI to Every Company, InstantlyAnd to speed DGX capabilities to startups and enterprises racing to build new products and develop AI strategies, Huang announced NVIDIA DGX Cloud, through partnerships with Microsoft Azure, Google Cloud and Oracle Cloud Infrastructure to bring NVIDIA DGX AI supercomputers “to every company, from a browser.”DGX Cloud is optimized to run …

AT&T and Quantiphi are among the first to experience how Omniverse ACE can help increase employee productivity and enhance customer service experiences.

Deloitte’s latest hybrid-cloud offerings — which consist of NVIDIA AI and Omniverse services and platforms, including Omniverse ACE — will be added to the Deloitte Center for AI Computing.

Teams can also customize pre-built AI avatar workflows to suit their needs with applications like NVIDIA Tokkio.

Learn more about NVIDIA Omniverse ACE and register to join the early-access program, available now for developers.

Dive into the art of AI avatars at GTC, a global conference for the era of AI and the metaverse.

These include NVIDIA TAO Toolkit 5.0 for creating customized, production-ready AI models; expansions to the NVIDIA DeepStream software development kit for developing vision AI applications and services; and early access to Metropolis Microservices for powerful, cloud-native building blocks that accelerate vision AI.

The tools have now been downloaded over 1 million times by those looking to build vision AI applications.

NVIDIA TAO Toolkit is a low-code AI framework that supercharges vision AI model development for practically any developer, in any service, on any device.

Leading IT services company Infosys is using NVIDIA Metropolis to supercharge its vision AI application development and d…

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.

New NVIDIA RTX GPUs Power Professional CreatorsSix new professional-grade NVIDIA RTX GPUs — based on the Ada Lovelace architecture — enable creators to meet the demands of their most complex workloads using laptops and desktops.

The NVIDIA RTX 5000, RTX 4000, RTX 3500, RTX 3000 and RTX 2000 Ada Generation laptop GPUs deliver up to 2x the performance compared with the previous generation.

Next-generation mobile workstations featuring NVIDIA RTX GPUs will be available starting …

By taking the automotive product workflow into the virtual world, automakers can bypass traditional bottlenecks to save critical time and reduce cost.

Developers can take these in-cabin designs for a spin in the virtual world, collaborating and sharing designs for efficient refinement and validation.

With Omniverse, automakers can develop and operate complex, AI-enabled virtual environments for factory and warehouse design.

Every change can be quickly evaluated and validated in the virtual world, then implemented in the real world to ensure maximum efficiency and optimal ergonomics for factory workers.

And with AR and VR, they can view and virtually test drive a car from anywhere.

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.

There was a small systems error.

Please try refreshing the page and if the error is still there drop us a note and let us know.

Develop the user storiesIsaac Vidas , Shopify’s ML Platform Lead, at Ray Summit 2022 While building an ML platform, it is also important to remember who your users are and their profiles.

Other resources to learn ML platform designThis section has touched on the most important components to consider when building an ML platform.

MLOps best practices, learnings, and considerations from ML platform expertsWe have taken some of the best practices and learnings from the ML platform teams and consolidated them into the following points:Embrace iterating on your ML platform.

Embrace iterating on your ML platform Like any other software system, building your ML platform should not be a one-off thi…

An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition.

More specifically, in long-term ML projects, data, and concept drift, if allowed to persist, result in poor ML model performance and reduced effectiveness.

Data annotation and preprocessingCommon data preprocessing tasks for machine learning | Source: AuthorLong-term machine learning projects require managing large amounts of data.

ML teams involved in long-term machine learning projects have several options for incorporating Data Version Control (DVC) into their existing workflows and managing dataset versions.

We explored challenges that can arise in such…

Technology landscape of CI/CD MLOps systemThe infrastructure provided by the client mostly influences the technology landscape of ML model deployments.

Data governance: Ensure that the data used to train and test the model, as well as any new data used for prediction, is properly governed.

ML model explainability: Make sure the ML model is interpretable and understandable by the developers as well as other stakeholders and that the value addition provided can be easily quantified.

Setting up a CI/CD pipeline on AWS: CodePipeline based deployment | SourceWhy didn’t we go with AWS Sagemaker for code deployment?

Other aspects of our CI/CD pipeline developmentIn the above sections, we have disc…

Data quality: A data pipeline can help improve the quality of data by automating the process of cleaning and transforming the data.

Some of the most popular vendors providing tools/solutions for streaming data processing are:Integrate.ioStreamSetsHevo DataAirbyteTools for Batch Data Pipelines transfer data in intervals or chunks, and they are commonly viewed as a more traditional method for moving data since they don’t facilitate real-time data processing.

Integrate.io Easy data pipeline designBasic data pipeline configuration doesn’t require the expertise as that of a developer.

Pricing of other modules such as Stitch, Data Management Platform, Big Data Platform, and Data Fabric can be fou…

The ml model reproducibility problem is one of them.

This article is going to take you through an experience-based, step-by-step approach to solve the ml reproducibility challenge taken by my ML team working on a fraud detection system for the insurance domain.

Initially, when we start working on any project, we don’t think about model deployment, reproducibility, model retraining, etc.

Every ML reproducibility challenge we facedNow that you know about reproducibility and its different benefits, it is time to discuss the major reproducibility issues that my team and I faced during the development of this ML project.

Read more How to Solve Reproducibility in MLMachine learning reproducibilit…

However, distributed training is complex and error-prone, with many hidden pitfalls that can cause huge issues in the model training process.

This article will touch on ten of the most common errors in distributed model training and will suggest solutions to each of them.

Using model parallel when you need data parallel (and vice versa)The problemThe two main paradigms of distributed training are model parallel and data parallel.

See also Multi GPU Model Training: Monitoring and OptimizingConclusion and takeawaysDistributed model training is challenging and involves managing a variety of bugs.

By avoiding these common errors, you can ensure that you start off on the right track toward train…

Every episode is focused on one specific ML topic, and during this one, we talked to Michal Tadeusiak about managing computer vision projects.

You’ll learn about:1 Steps and milestones of a computer vision projectSteps and milestones of a computer vision project 2 Non-technical side of managing computer vision projectsNon-technical side of managing computer vision projects 3 Biggest failures and lessons learnedBiggest failures and lessons learned 4 Structuring the team for CV projectsStructuring the team for CV projects 5 …and more.

I’m joined by my co-host, Stephen, and with us today, we have Michal Tadeusiak, who will be answering questions about managing computer vision projects.

Michal:…

May be data generated through video streaming platforms like YouTube, but this is not the only thing which qualifies as streaming data.

In this article, we will go through the basics of streaming data, what it is, and how it differs from traditional data.

In this article, our focus is on streaming data, but before we deal with it, it is important to understand how it differs from Batch data processing.

Such systems cannot keep up with the torrent of data produced today.” – RedhatBasic I/O flow in streaming data processing | SourceThe streaming processing engine does not just get the data from one place to another, but it transforms the data as it passes through.

Scalability: Streaming data …

Hybrid Approach: This approach is the combination of the above two, where you can use both rule-based and ML-based approaches for sentiment classification system.

In this article, you will see some of the things that I learned while working on a sentiment classification model.

Lessons learned from building sentiment classification with BERTOne thing that you may be confused about is, how exactly BERT embeddings can be used for sentiment classification.

Testing the performance of sentiment classification with BERT embeddingsProblemOnce you have your embeddings generated through the BERT model, the next stage you focus on is to use a classification model for sentiment classification.

Finally,…

Model monitoring tools will merge with the DevOps monitoring stack.

ML metadata store (just one)In the paper, ML metadata stores are mentioned in three contexts, and it is not clear whether we are talking about one component or more.

I think that ML metadata store is a way better name because it captures the essence of this component.

So if people are using an experiment tracker (or run/model-first ML metadata store) for the ML-related stuff, what should happen with this other pipeline/execution-first ML metadata store?

That, funny enough, most ML practitioners don’t even call an “ML metadata store” but an “experiment tracker”.

Data version control in machine learning vs conventional software engineeringData version control in machine learning and conventional software engineering have some similarities, but there are also some key differences to consider.

In both cases, the main goal of the data version control system is to track and manage changes to data over time.

More about Dolt:lakeFSLakeFS is an open source data version control and management platform for data lakes.

More about Pachyderm:Learn more Data version control in Pachyderm vs neptune.aiConclusionIn this article, we described some of the data versioning tools and how to do data versioning with them.

Pachyderm is a tool that can be used to optimize d…

Model monitoring for time series | SourceIn this article, we will explore the time series-forecasting model to understand how we can monitor it practically.

Model monitoring process: defining a time series projectTo start off this article, we will define a simple project or a case study where we can dive much in detail about the nitty-gritty of the model monitoring process.

After deployment, we will monitor the model performance with the current best model and check for data drift and model drift.

Building a time series model [in PyTorch]Now, let us build a TFT time series model using the PyTorch-Forecasting library.

Monitoring time series model performance in productionWhen the model is in…

They can help to ensure that machine learning models are developed and deployed efficiently and that they remain reliable and accurate over time.

AWS offers a three-layered machine learning stack to choose from based on your skill set and team’s requirements for implementing workloads to execute machine learning tasks.

The QA team manager has identified issues reported by the live processes that run on staging devices for monitoring the machine learning models.

Worth mentioning that I used the development twin for training tasks, nevertheless, it can also be automated as a workflow.

Launch an EC2 instance and integrate it into the MLOps environment for hosting both of them.

Every episode is focused on one specific ML topic, and during this one, we talked to Kuba Cieślik, founder and AI Engineer at tuul.ai, about building visual search engines.

3 Things to keep in mind while designing visual search enginesThings to keep in mind while designing visual search engines 4 Importance of embeddings in visual searchImportance of embeddings in visual search 5 Evaluation of visual search enginesEvaluation of visual search engines 6 Integrating visual search engines with other productsIntegrating visual search engines with other products 7 Basic tool stack for building visual search enginesBasic tool stack for building visual search engines 8 Scaling visual search systems…

#gpt4 #chatgpt #openai References:

https://openai.com/product/gpt-4

https://openai.com/research/gpt-4

https://cdn.openai.com/papers/gpt-4.pdf 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):

SubscribeStar: https://www.subscribestar.com/yannickilcher

Patreon: https://www.patreon.com/yannickilcher

Bitcoin (BTC…

#mlnews #chatgpt #llama ChatGPT goes around the world and is finally available via API. Stunning mind-reading performed using fMRI and Stable Diffusion. LLaMA weights leak and hilarity ensues. GTC23 is around the corner! OUTLINE:

0:00 - Introduction

0:20 - GTC 23 on March 20

1:55 - ChatGPT API is out!

4:50 - OpenAI becomes more business-friendly

7:15 - OpenAI plans for AGI

10:00 - ChatGPT influencers

12:15 - Open-Source Prompting Course

12:35 - Flan UL2 20B

13:30 - LLaMA weights leaked

15:50 - Mind-Reading from fMRI

20:10 - Random News / Helpful Things

25:30 - Interview with Bryan Catanzaro Participate in the GTC Raffle: https://ykilcher.com/gtc References:

GTC 23 on March 20

https://www.nv…

#ai #meta #languagemodel LLaMA is a series of large language models from 7B to 65B parameters, trained by Meta AI. They train for longer on more data and show that something like gpt-3 can be outperformed by significantly smaller models when trained like this. Meta also releases the trained models to the research community. OUTLINE:

0:00 - Introduction & Paper Overview

4:30 - Rant on Open-Sourcing

8:05 - Training Data

12:40 - Training Hyperparameters

14:50 - Architecture Modifications

17:10 - Optimizer

19:40 - Efficient Implementation

26:15 - Main Results

38:00 - Some more completions

40:00 - Conclusion Paper: https://arxiv.org/abs/2302.13971

Website: https://ai.facebook.com/blog/large-lang…

#ai #huggingface #coding Join me as I build streaming inference into the Hugging Face text generation server, going through cuda, python, rust, grpc, websockets, server-sent events, and more... Original repo is here: https://github.com/huggingface/text-generation-inference OpenAssistant repo is here: https://github.com/LAION-AI/Open-Assistant (see inference/) Check out https://www.wandb.courses/ for free MLOps courses! 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 b…

#openassistant #chatgpt #ai Help us collect data for OpenAssistant, the largest and most open alternative to ChatGPT.

https://open-assistant.io OUTLINE:

0:00 - Intro

0:30 - The Project

2:05 - Getting to Minimum Viable Prototype

5:30 - First Tasks

10:00 - Leaderboard

11:45 - Playing the Assistant

14:40 - Tricky Facts

16:25 - What if humans had wings?

17:05 - Can foxes be tamed?

23:45 - Can zebras be tamed?

26:15 - Yo (spam)

27:00 - More tasks

29:10 - Entitled Emails

34:35 - Final Words 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: ht…

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…

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…

A visual introduction to probability's most important theorem

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

An equally valuable form of support is to simply share the videos. -----------------

Timestamps

0:00 - Introduction

1:53 - A simplified Galton Board

4:14 - The general idea

6:15 - Dice simulations

8:55 - The true distributions for sums

11:41 - Mean, variance and standard deviation

15:54 - Unpacking the Gaussian formula

20:47 - The more elegant formulation

25:01 - A concrete example

27:10 - Sample means

28:10 - Underlying assumptions ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1b…

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…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "MusicLM: Generating Music From Text" is available here:

https://google-research.github.io/seanet/musiclm/examples/ 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 "Pastiche Master: Exemplar-Based High-Resolution Portrait Style Transfer" is available here:

https://www.mmlab-ntu.com/project/dualstylegan/ 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, Er…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Generating Long Videos of Dynamic Scenes" is available here:

https://www.timothybrooks.com/tech/long-videos/

Source code, datasets and pretrained models: https://github.com/NVlabs/long-video-gan 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, B…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Towards Robust Blind Face Restoration with Codebook Lookup TransFormer" is available here:

https://shangchenzhou.com/projects/CodeFormer/

Try it now! https://replicate.com/sczhou/codeformer

https://huggingface.co/spaces/sczhou/CodeFormer

https://github.com/sczhou/CodeFormer Video results: https://www.youtube.com/watch?v=d3VDpkXlueI 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 genero…

❤️ Check out Anyscale and try it for free here: https://www.anyscale.com/papers 📝 The paper "StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis" is available here:

https://sites.google.com/view/stylegan-t/ 📝 My material synthesis paper is available here:

https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ 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 Mashra…

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "Dreamix: Video Diffusion Models are General Video Editors" is available here:

https://dreamix-video-editing.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 Moralez, Gordon…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts" is available here:

https://www.cs.ubc.ca/~van/papers/2022-SIGGRAPH-juggle/index.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, Christian Ahlin, Edwa…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers Join the waitlist for the new Bing here:

https://www.bing.com/new Or try ChatGPT here:

https://openai.com/blog/chatgpt/ Our Separable Subsurface Scattering paper:

https://users.cg.tuwien.ac.at/zsolnai/gfx/separable-subsurface-scattering-with-activision-blizzard/ How to use ChatGPT in Bing: https://www.tomsguide.com/how-to/how-to-use-the-new-bing-with-chatgpt-and-what-you-can-do-with-it

Prompt injection: https://arstechnica.com/information-technology/2023/02/ai-powered-bing-chat-spills-its-secrets-via-prompt-injection-attack/

Google’s Bard: https://blog.google/technology/ai/bard-google-ai-search-updates/

…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Human-Timescale Adaptation in an Open-Ended Task Space" is available here:

https://sites.google.com/view/adaptive-agent/ 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, Geronim…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers MBA exam (paper!): https://mackinstitute.wharton.upenn.edu/wp-content/uploads/2023/01/Christian-Terwiesch-Chat-GTP-1.24.pdf Check out 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 Law school exam: https://techxplore.com/news/2023-01-chatgpt-bot-law-school-exam.html

Google code interview: https://www.pcmag.com/news/chatgpt-passes-google-coding-interview-for-level-3-engineer-with-…

❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers 📝 The paper "Phenaki - Realistic video generation from open-domain textual descriptions" is available here:

https://phenaki.research.google/ 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, …

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "VALL-E Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers" is available here:

https://valle-demo.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, G…

❤️ Train a neural network and track your experiments with Weights & Biases here: http://wandb.me/paperintro 📝 The paper "Muse: Text-To-Image Generation via Masked Generative Transformers" is available here:

https://muse-model.github.io/ Stable Diffusion interpolation: https://twitter.com/xsteenbrugge/status/1558508866463219712

Full video of interpolation: https://www.youtube.com/watch?v=Bo3VZCjDhGI 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 Tw…

❤️ 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 …

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

ML Party — регулярные встречи о применении машинного обучения в IT. 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.

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

Курс Deep Reinforcement Learning: https://ods.ai/tracks/drlcourse22

Сезон курсов: https://ods.ai/events/course_season_a... Обсуждаем решения домашних заданий 1-3 и подводим итоги курса. Наши соц.сети:

Telegram: https://t.me/datafest

Вконтакте: https://vk.com/datafest

Featured playlist

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:…

Sam Harris is an author, podcaster, and philosopher.

Please support this podcast by checking out our sponsors:– Notion: https://notion.com– Indeed: https://indeed.com/lex to get $75 credit– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Sam’s Website: https://samharris.orgMaking Sense Podcast: https://www.samharris.org/podcasts/making-sense-episodesWaking Up App: https://www.wakingup.comSam’s YouTube: https://youtube.com/@samharrisorgSam’s Instagram: https://instagram.com/samharrisorgPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https://lexfridman.com/feed/podcast/YouTube Full E…

Chris Voss is a former FBI hostage and crisis negotiator and author of Never Split the Difference: Negotiating As If Your Life Depended On It.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get free trial– Indeed: https://indeed.com/lex to get $75 credit– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Chris’s Instagram: https://instagram.com/thefbinegotiatorChris’s Twitter: https://twitter.com/fbinegotiatorChris’s Website: https://blackswanltd.comChris’s Masterclass: https://masterclass.com/classes/chris-voss-teaches-the-art-of-negotiationNever Split the Difference (book): https://amzn.to/3J5scNCPODCAST INFO:Podcast w…

Craig Jones, Nicky Rod and Nicky Ryan, together with Ethan Crelinsten are founders of the B-Team, a legendary jiu jitsu team based in Austin, TX.

Please support this podcast by checking out our sponsors:– Eight Sleep: https://www.eightsleep.com/lex to get special savings– Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:B-Team’s Instagram: https://instagram.com/bteamjj/B-Team’s YouTube: https://www.youtube.com/c/bteamjiujitsuB-Team’s Website: https://bteamjj.com/Craig Jones’s Instagram: https://instagram.com/craigjonesbjj/Nicky Rod’s Instagram: https://instagram.com/nickyrod247/Nicky Ryan’s Instagr…

Ginni Rometty is a former long-time CEO, president, and chairman of IBM.

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Aaron Smith-Levin is a former Scientologist, Vice President of the Aftermath Foundation, and host of the Growing Up In Scientology YouTube channel.

A Billion Years: https://amzn.to/3EpLiMM2.

Blown for Good: https://amzn.to/3IqaZxNPODCAST 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…

Tim Urban is the author of the blog Wait But Why and a new book What’s Our Problem?

: A Self-Help Book for Societies.

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Andrew Strominger is a theoretical physicist at Harvard.

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Aella is a sex researcher, writer, and sex worker.

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Paul Conti is a psychiatrist.

Please support this podcast by checking out our sponsors:– NetSuite: http://netsuite.com/lex to get free product tour– Indeed: https://indeed.com/lex to get $75 credit– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Paul’s Website: https://drpaulconti.comTrauma (book): https://amzn.to/40vCVJaPaul’s LinkedIn: https://linkedin.com/in/dr-paul-m-conti-845074216PODCAST 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 & CONN…

Tim Dodd is host of the Everyday Astronaut YouTube channel, where he teaches about rocket engines and all things space travel.

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David Kipping is an astronomer at Columbia University, director of the Cool Worlds Lab, and host of the Cool Worlds YouTube channel.

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Jeremi Suri is a historian at UT Austin.

Civil War by Other Means: https://amzn.to/3hRa3cT2.

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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.

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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.

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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…

Affectionately called the Wikipediatrician, Susan Gerbic is the founder of Guerrilla Skepticism on Wikipedia (GSoW), Monterey County Skeptics and is a self-proclaimed skeptical junkie.

A Skeptical Inquirer contributor Gerbic is a fellow of CSI and winner of the James Randi Foundation award for 2017.

While her particular focus has been “Grief Vampires” (psychics), her activism encompasses all areas of skepticism.

While her particular focus has been “Grief Vampires” (psychics), her activism encompasses all areas of skepticism.

Information about her investigations into Grief Vampires can be found here https://www.abouttimeproject.org/about-7.

Bot Detection and Dyadic SurveysSara Bybee, a postdoctoral research scholar at the University of Utah, joins us today.

On the show, she shares her study which involved detecting social bots in surveys.

Sara’s study was aimed at understanding LGBTQ couples facing cancer.

Sara shared her strategy for validating her suspicion and authenticating the submissions.

Rounding up, she shared some insights about her study on LGBTQ couples facing cancer.

Zoltán joins us to discuss his research on replicating research findings.

Zoltán began by discussing the current problem with biomedicine and social science journals — the low replication rates of research findings.

He stated how low replicability affects the trustworthiness of published papers.

He also mentioned reasons for the low replicability.

He particularly discussed the design process for interacting with psychologist Daryl Bem to test the famous Bem experiment.

He joins us to discuss the findings of his survey that investigated success factors in data science projects.

Iñigo started by discussing the plethora of challenges data people face when implementing theoretical concepts in data science projects.

While data science and software engineering methodologies may have some intersections, Iñigo pointed out some peculiarities of data science projects.

He shared the stand-out methodology used for data science projects.

Concluding, Iñigo shared some best practices when conducting data science projects structured into three areas: project management, team management, and data management.

Opinion Dynamics ModelsWe are joined by Dino Carpentras, a post-doctoral researcher at the Computational Social Science group at ETH Zürich.

His research revolves around opinion dynamics where he attempts to understand agent-based models on people’s opinions.

On the show, Dino discusses his study on building opinion dynamics models.

Going forward, Dino discussed how to perform model validation, i.e., confirming that the predicted opinion is similar to the opinion in the test data.

Rounding up, he discussed the possibility of getting policymakers to adopt the results of opinion dynamics models.

Casual Affective TriggersToday, we are joined by Alexander Nolte, an Associate Professor at the University of Tartu and an Adjunct Associate Professor at Carnegie Mellon University.

He joins us to discuss his research work on the effect of Casual Affective Triggers (CAT) on surveys’ response rate.

Alexander then shared the methodology for his study and discussed the impact of using CATs on response and completion rates.

Alexander shared branching research areas similar to understanding hackathons.

If you’re interested in organizing a hackathon or understanding the hackathon space, visit the Hackathon Planning Kit website to get a ton of resources on how to organize a hackathon.

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.

NLP, transformer architectures, and machines beating humans at their own game: Jon Krohn talks to Alexander H. Miller about his work in building a machine that can outsmart humans in the game of Diplomacy by engineering …

Our list of the top 10 SuperDataScience podcast episodes for 2022 is here.

From Pandas to causality, AI breakthroughs and data storytelling, these were your most popular episodes of the year gone by.

Additional material…

Chip Huyen, co-founder of Claypot AI and author of O'Reilly's best-selling "Designing Machine Learning Systems" is here to share her expertise on designing production-ready machine learning applications, the importance o…

ChatGPT is well-known for its potential to disrupt the writing industry, but in what other, perhaps less explored, ways can we use the tool?

In this episode, Jon Krohn outlines five critical ways that ChatGPT can augment…

NLP practitioners: this episode is for you.

From the awareness of linguistic elements and annotation to getting the necessary people in the room, Vincent Warmerdam presents to Jon Krohn a recipe for a successful project …

What makes data products popular?

Brian T. O'Neill, Founder and Principal of Designing for Analytics, returns to the podcast to help us crack the code on building data products that people love.

Additional materials: ww…

Data engineering educator Andreas Kretz joins Jon Krohn for a 1-hour primer that covers everything you need to know about the most in-demand role in data.

From skills to tools, problem-solving processes and more, growing…

How to attract an AI recruiter’s attention: In this episode, Jon Krohn and Tribe AI CEO Jaclyn Rice Nelson break down the key ingredients needed to make a Tribe AI recruiter say “yes!” Get Jaclyn’s top tips for forward-t…

Transparent data science, profitable AI, and what’s missing from a data science education: Pandata’s Data Scientist in Residence Keith McCormick and Jon Krohn discuss how “insights” can never be the end product of a data…

14-year-old AI prodigy Mike Wimmer joins Jon Krohn to discuss his latest projects.

Whether he's using AI to help conserve the world's coral reefs or launching his new IOT-based company, Mike is an endless source of inspi…

Carlos Aguilar, the founder and CEO of Glean, a data exploration and visualization platform, knows a thing or two about starting and growing a tech startup.

After recently raising a $7 million seed round, he sits down wi…

MedTech, communications technology and computer vision: In this Five-Minute Friday, Jon Krohn investigates the technology that allows patients who have lost their ability to speak via medical ventilation to communicate c…

Data visualizations, color theories and color inclusivity: In this episode, Kate Strachnyi and host Jon Krohn discuss how color can make or break your data visuals, ways to make your charts and graphs more inclusive thro…

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…

(Source)In this episode, we dive into the ways in which AI and machine learning are disrupting traditional software engineering principles.

With the advent of automation and intelligent systems, developers are increasingly relying on algorithms to create efficient and effective code.

However, this reliance on AI can come at a cost to the tried-and-true methods of software engineering.

Join us as we explore the pros and cons of this paradigm shift and discuss what it means for the future of software development.

In this episode, we dive into the fascinating world of zero-knowledge proofs and their impact on data science.

Zero-knowledge proofs allow one party to prove to another that they know a secret without revealing the secret itself.

This powerful concept has numerous applications in data science, from ensuring data privacy and security to facilitating secure transactions and identity verification.

We explore the mechanics of zero-knowledge proofs, their real-world applications, and how it is revolutionizing how we handle sensitive information.

Join us as we uncover the secrets of zero-knowledge proofs and their impact on the future of data science.

🤔 What are differentiable physics engines?

How does Deep Learning tackle the most challenging robotics simulation problems?

🤖🦾Join the 2nd episode of the “DataForge” fireside chat series with Francesco and Nabi where we will be discussing the applications of NNs in physics engines and simulations.

🔥 🤔 What skills and qualifications are required for each job role in Data Science?

What are the most emerging roles in Data Science and ML?

Staying generalist or specialist?

If you are looking to learn more about the most recent emerging roles in Data Science, this live session is the perfect one for you to watch!

Data Science at Home Podcast’s team, “DataForge” fireside chats are monthly casual chats hosted by us where we host data science advocates and discuss trending topics in this amazing field.

SourceDeep learning methods are not as effective with tabular data.

Here is why, and what to do about it.

SponsorsIf you’re ready to take your WiFi game to the next level, head over to asus.click/ZenWiFi_XD5 or check out the show notes for this episode.

Trust me, with ASUS ZenWiFi XD5, you’ll get the best WiFi experience ever!

Image SourceIn this episode, I speak about online learning systems and why blindly choosing such a paradigm can lead to very unpredictable and expensive outcomes.

Also in this episode, I have to deal with an intruder 🙂LinksBirman, K.; Joseph, T. (1987).

“Exploiting virtual synchrony in distributed systems”.

Proceedings of the Eleventh ACM Symposium on Operating Systems Principles – SOSP ’87.

S2CID 7739589.

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.

We provide AI/ML, Fintech, Healthcare/RWE, and Predictive maintenance solutions.

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?

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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