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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 = {Voelcket, Claas},title = {Working on the Weekends - an Academic Necessity?

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

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

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

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

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

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

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

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

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

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

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

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

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

A general form of message passing an…

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

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

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

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

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

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

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

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

Data understanding and interpretation is an essentia…

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

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

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

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

CitationFor attribution in academic contex…

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

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

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

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

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

📝 EditorialIn the last few years, NVIDIA has ranked among the top companies advancing AI research and development.

During its latest GTC conference, NVIDIA announced major releases in both AI software and hardware technologies.

On the software side, NVIDIA announced new cloud services such as NeMo for large-scale language models, Omniverse ACE for intelligent avatars, and BioNeMo computational biology.

NVIDIA also announced a number of architectures based on ADA, such as chips optimized for virtual worlds, computational inference, recommendation models, and many others.

Continuing its innovation in areas such as self-driving vehicles and robotics, NVIDIA announced Thor, a new 2,000 teraflop…

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

💥 What’s New in AI: How Amazon is Improving BERT-Based Models Used in AlexaBERT has become one of the most iconic machine learning (ML) methods of the last decade.

Amazon has been one of the main adopters of BERT-based models, particularly in the architecture powering the Alexa digital assistant.

As a result, Amazon Research regularly publishes improvements to BERT-based models in order to address some of the large-scale scenarios required by Alexa.

Recently, we got a glimpse of Amazon Research’s recent work in BERT-based models with the publication of three different p…

Larger contexts may require the RTX 5000, depending on how efficient your inference engine is.

NVIDIA RTX 5000The Turing-based RTX 5000 is the smallest GPU that can run inference for the GPT-J 6B or Fairseq 6.7B models.

NVIDIA A4000The Ampere-based A4000 is a small step up from the RTX 5000, although it may not look like it at first glance.

However, the number of tensor cores is half that of the RTX 5000.

Whether the A4000 or the RTX 5000 work better for your workload depends on your inference framework and what instructions you use.

In this issue:we explain autoregressive text-to-image models ;we discuss Google’s Parti, an impressive autoregressive text-to-image model;we explore MS COCO, one of the most common datasets in text-to-image models.

💡 ML Concept of the Day: Autoregressive Text-to-Image ModelsThe first editions of our text-to-image series have focused on exploring diffusion models, which have been at the forefront of some of the most impressive developments in this area.

Diffusion models work by introducing Gaussian noise into an input image and then training a model to reconstruct the original image from the noise.

An interesting alternative to diffusion models is autoregressive methods used for text-to-imag…

Hi there,A quick Monday note about our Twitter :)We share lots of helpful resources for your data science and ML journey.

Free courses:and books:and helpful lists:As well as our favorite ‘ML Research of the Week’:FOLLOW US ON TWITTERIf you haven’t yet, please help us shape the ML value chain landscapeLet’s create an objective landscape of the ML Value Chain together.

They are typically assembled by either analyst firms, media, or VC firms.

But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain.

Participate and be the first to receive this super helpful research shaped by you!

Last week, Meta took an important step in that direction by announcing that PyTorch will move to an independent foundation.

The PyTorch Foundation was incubated under the Linux Foundation, which brings a level of credibility and process robustness to this new effort.

Finding a neutral home was the main catalyst for the creation of the PyTorch Foundation, but there are other intangibles such as robust governance, community ownership, transparency and fairness that are much needed in a project of this scale.

The Linux Foundation certainly has the knowledge and infrastructure to become an incredible home to catalyze the next phase of PyTorch’s growth.

ML training experiments can be more easily…

Hopsworks feature store can be configured to leverage the content of data warehouses to simplify the data science workflow.

For data scientists, using data directly from a data warehouse presents three challenges:data in the data warehouse is often updated making it impossible to reproduce previously generated training data and previous experiments.

Data warehouses often lack the historical view of the data, leaving to data scientists the chore of building it.

In this talk, we will discuss how Hopsworks can be connected to existing cloud-native data warehouses like Snowflake, Redshift and BigQuery.

What : Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature St…

💥 What’s New in AI: Microsoft’s Open Sourced a New Library for Extreme Compression of Deep Learning ModelsLarge neural networks have been dominating the deep learning space for the last few years.

While the performance of large deep learning architectures is certainly impressive, its operational requirements remain prohibited for most organizations.

Not surprisingly, there has been a lot of effort in areas like model compression that can help reduce the size and inference computation of deep learning models.

Similarly, there has also been a resurgence of system optimization techniques that can improve the inference of models without sacrificing their size.

The combination of model compressi…

Or Itzary (OI): I’m Or Itzary, CTO at Superwise, which, as you’ve all probably heard 😊 is one of the leading model observability platforms.

🛠 ML WorkSuperwise has rapidly become one of the premier ML observability platforms in the market but you guys recently launched a new effort focused on enabling high-scale model observability.

What are some of the unique challenges of monitoring ML pipelines at large scale and what are some of the key differences with ML observability practices for single models?

Superwise Projects are explicitly built to streamline the high-scale observability and monitoring management of ML models.

What are some of the key ideas in ML observability methods to ensure …

In this issue:we explain latent diffusion models ;we discuss the original latent diffusion paper ;we explore Hugging Face Diffusers, a library for state-of-the-art diffusion models.

💡 ML Concept of the Day: Understanding Latent Diffusion ModelsIn the previous edition of our series about text-to-image synthesis (Edge#223), we explored the different types of diffusion techniques that are used by models in this area.

Latent diffusion has quickly developed as one of the most viable options for implementing diffusion models in the real world without breaking the bank.

Diffusion models have been able to achieve state-of-the-art performance in image synthesis.

Latent diffusion models (LDM) enable

📝 EditorialTensorFlow and PyTorch have become the two most popular deep learning frameworks within the data science community.

TensorFlow 2.10 has plenty of interesting features, but none were more notable than the improvements in Keras’ capabilities.

The new version of TensorFlow includes improves Keras’ attention layers with features such as causal attention and implicit masking.

Outside Keras, TensorFlow 2.10 includes hardware optimizations such as the support for the Compute Library for the Arm® Architecture (ACL) as well as a wider GPU support for Windows.

TensorFlow 2.10 shouldn’t be considered a major release but certainly incorporates many features that have been highly demanded by …

Running distributed workloads is key to the future of AI.

As models become more complex and advanced, distributed workloads will be the only way forward.

Get ahead of the curve, and learn practical hands-on guidance from Kubernetes expert Itay Ariel on how to leverage Kubernetes for distributed workloads.

The main challenges of running distributed workloads on Kubernetes.

How to easily use Kubernetes to scale your ML models.

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

💥 What’s New in AI: AlexaTM 20B is Amazon’s New Language Super Model Also Capable of Few-Shot LearningIn the last few years, the progress in natural language understanding (NLU) really challenges human imagination.

Some of the milestones achieved by models like OpenAI GPT-3 seemed unimaginable just a few years ago.

Large AI labs like Microsoft Research, Google Brain, Alexa AI, DeepMind, and Meta AI are regularly pushing the boundaries of NLU research.

One of the latest entrances in the language supermodel category came from Amazon’s Alexa AI labs with Alexa Teacher Mode…

We offer you to shape an objective landscape of the ML Value Chain.

But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain.

These processes tend to fall into six distinct stages of the ML value chain:Data collection:This is the beginning of the ML lifecycle.

We’ve made the first version of the ML value chain landscape specifically for this project (it’s a compilation of existing landscapes), which is meant to serve as a starting point and help you shape your ideal landscape.

Share your insights and shape the best possible version of this landscape that reflects today’s ML value chain processes and needs fairly and accurately.

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

Мы делаем новый запуск курса Natural Language Processing.

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

Сам курс запускается в этом виде в пятый раз.

Ссылка будет в группе курса.

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

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

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

Что зарелизили: ML-бот, который умеет передвигаться, основываясь на входящем кадре-картинке и на векторе-состоянии инвентаря.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Появление каче…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Также имеется возможность опциональной предобработки для некоторых моделей - н…

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

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

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

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

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

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

Мы делаем новый запуск курса Natural Language Processing.

Лекции и семинары будут онлайн.

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

Ссылка будет в канале курса в слаке ODS.

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

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

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

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

Другие особенности решающих д…

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

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

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

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

Можно также заметить, …

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Learn more about why I started MLinProduction.

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

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

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

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

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

I’m bad at Twitter.

I know I’m bad at Twitter.

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

People tell me ML Twitter is worth it.

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

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

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

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

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

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

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

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

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

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

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

This has spoilers for MIT Mystery Hunt 2022.

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

Previous Mystery Hunt puzzles have used gimmicked dice before.

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

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

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

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

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

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

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

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

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

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

“Learning Loss for Active Learning.” CVPR 2019.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Observation 2) Training deep net classifiers usin…

Does Gradient Flow Over Neural Networks Really Represent Gradient Descent?

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

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

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

Left plot shows training loss values; right one shows…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

EvoJAX is a hardware-accelerated neuroevolution toolkit built on top of JAX.

It can help run a wide range of evolution experiments within minutes on a TPU/GPU, compared to hours or days on CPU clusters.

Redirecting to github.com/google/evojax/, where the repo resides.

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.

Developing new AI resources with the Raspberry Pi FoundationÂWe will be working closely with the Raspberry Pi Foundation, a charity that promotes the study of computing and digital technologies, to develop new AI-focused resources including lesson plans for students and training for teachers.

By focusing on education at an early age, there’s an opportunity to help break down long-standing barriers that have facilitated a system of inequalities.

Amplifying the reach of existing programmesÂDe…

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.

Applying our AI research to help enrich the lives of billions of people around the worldBuilding useful products with new technologies has always been one of my greatest joys.

Taking research out of the labMy main focus as CBO is on taking our cutting-edge research breakthroughs and matching our technologies to solving everyday business problems.

I’m often asked, as a future-facing research organisation, why it’s important to work on global challenges that impact people every day?

that makes DeepMind so special is our ability to bridge leading AI research to hundreds, if not thousands, of AI-ready problems that impact billions of people.

And as we go along this journey, we’re continuo…

Former intern turned intern manager, Richard Everett, describes his journey to DeepMind, sharing tips and advice for aspiring DeepMinders.

However, after working on several research projects with my professors, I developed a taste for research and decided to continue on towards a PhD.

Can you describe the internship interview process?

Today's interns can expect the entire process to last just a few months, which includes a technical and a team interview.

Any tips for aspiring DeepMind interns?

Recent breakthroughs in AI research have led to the creation of conversational agents that are able to communicate with humans in nuanced ways.

These shortcomings limit the productive use of conversational agents in applied settings and draw attention to the way in which they fall short of certain communicative ideals.

Persistent Anti-Muslim Bias in Large Language Models.

"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?."

Challenges in Detoxifying Language Models.

New research drawing upon pragmatics and philosophy proposes ways to align conversational agents with human values.

Recent breakthroughs in AI research have led to the creation of conversational agents that are able to communicate with humans in nuanced ways.

Persistent Anti-Muslim Bias in Large Language Models.

"On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?."

Challenges in Detoxifying Language Models.

Distilling data into controllable motor primitives using NPMPAn NPMP is a general-purpose motor control module that translates short-horizon motor intentions to low-level control signals, and it’s trained offline or via RL by imitating motion capture (MoCap) data, recorded with trackers on humans or animals performing motions of interest.

This low-level controller can then be used as a plug-and-play motor control module on a new task (right).

The NPMP allowed us to observe a similar effect but in a scenario that required significantly more advanced motor control.

The players exhibit both agile high-frequency motor control and long-term decision-making that involves anticipation of teammat…

Inspired by Zindi’s bounding box turtle challenge, we landed on a project with the potential for real impact: turtle facial recognition.ÂBiologists consider turtles to be an indicator species.

To help solve some of these challenges, we launched an ML challenge called Turtle Recall.

Example of image data for four turtles taken from the tutorial colab notebook.

Differences in lighting, scale, background, pose, and similarities between turtles added to the complexity of the prediction challenge.

Given the additional challenge of keeping a turtle still enough to locate their tag, the Turtle Recall challenge aimed to circumvent these problems with turtle facial recognition.

New, formal definition of agency gives clear principles for causal modelling of AI agents and the incentives they face.

By relating training setups to the incentives that shape agent behaviour, CIDs help illuminate potential risks before training an agent and can inspire better agent designs.

Causal discovery of agentsCausal discovery infers a causal graph from experiments involving interventions.

Our second algorithm transforms this mechanised causal graph to a game graph:Algorithm 2 takes as input a mechanised causal graph and maps it to a game graph.

Our third algorithm transforms the game graph into a mechanised causal graph, allowing us to translate between the game and mechanised caus…

While I loved superhero stories, I realised scientists are the real superheroes.

As part of my PhD in computer science, I created biological simulations, and ended up falling in love with biology.

From there I started working in Search at Google, where I learned to deal with massive scales of computation.

I’d been keeping an eye on AI for over a decade and I knew of DeepMind and some of their successes.

I couldn’t have optimised for a career leading to DeepMind (there wasn't even a DeepMind to optimise to!)

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

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

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

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

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

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

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

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

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

Perceiver AR can be trained for end-to-end autoregressive generation, all while making use of very long input sequences.

For example, we find that a 60-layer Perceiver AR with context length 8192 outperforms a 42-layer Transformer-XL on a book-length generation task, while running faster in real wall-clock terms.

On standard, long-context image (ImageNet 64x64), language (PG-19), and music (MAESTRO) generation benchmarks, Perceiver AR produces state-of-the-art results.

This makes Perceiver AR easy to apply to settings that don’t have a natural left-to-right ordering, such as data like images, with structure that spans more than one dimension.

Using a dataset of piano music, we trained Per…

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to offer greater consistency for the repeated process. This facilitates productivity and reduces time from prototype to production.When first trying ML in the cloud, many practitioners will start with fully managed ML platforms like Google Cloud’s Vertex AI. Fully-managed platforms abstra…

Increasingly more enterprises adopt Machine Learning (ML) capabilities to enhance their services, products, and operations. As their ML capabilities mature, they build centralized ML Platforms to serve many teams and users across their organization. Machine learning is inherently an experimental process requiring repeated iterations. An ML Platform standardizes the model development and deployment workflow to offer greater consistency for the repeated process. This facilitates productivity and reduces time from prototype to production.When first trying ML in the cloud, many practitioners will start with fully managed ML platforms like Google Cloud’s Vertex AI. Fully-managed platforms abstra…

In these settings, even a single dataset may require terabytes or petabytes of data storage.

Familiar API for Data Access and ManipulationTensorStore provides a simple Python API for loading and manipulating large array data.

Checkpoints are stored in zarr format using TensorStore, with a chunk structure chosen to allow the partition for each TPU to be read and written independently in parallel.

Specifically, TensorStore has managed some of the largest and most widely accessed connectomic datasets, with Google Cloud Storage as the underlying object storage system.

A fly brain reconstruction for which the underlying data can be easily accessed and manipulated using TensorStore.

We’re fortunate to work with some of the world’s most innovative customers on a daily basis, many of whom come to Google Cloud for our well-established expertise in data analytics and AI. As we’ve worked and partnered with these data leaders, we have encountered similar priorities among many of them: to remove the barriers of data complexity, unlock new use cases, and reach more people with more impact. These innovators and industry disruptors power their data innovation with a data cloud that lets their people work with data of any type, any source, any size, and at any speed, without capacity limits. A data cloud that lets them easily and securely move across workloads: from SQL to Spark,…

They are purely transformer-based, operating on sets of image patches, and they leverage a 4D light field representation for positional encoding, which helps to model view-dependent effects.

Instead of processing each reference image completely, we look only at the regions that are likely to influence the target pixel.

The first transformer aggregates information along each epipolar line, and the second along each reference image.

Generalizing to New ScenesOne limitation of LFNR is that the first transformer collapses the information along each epipolar line independently for each reference image.

Image patches are mapped via the linear projection layer to initial features (shown as blue an…

Many machine learning (ML) use cases, like fraud detection, ad targeting, and recommendation engines, require near real-time predictions. The performance of these predictions is heavily dependent on access to the most up-to-date data, with delays of even a few seconds making all the difference. But it’s difficult to set up the infrastructure needed to support high-throughput updates and low-latency retrieval of data. Starting this month, Vertex AI Matching Engine and Feature Store will support real-time Streaming Ingestion as Preview features. With Streaming Ingestion for Matching Engine, a fully managed vector database for vector similarity search, items in an index are updated continuousl…

To address these limitations, we are presenting “FindIt: Generalized Localization with Natural Language Queries” at ECCV 2022.

FindIt is a unified model for referring expression comprehension (col. 1), text-based localization (col. 2), and the object detection task (col. 3).

This motivates the need for a multi-level image-text fusion model for efficient processing of higher resolution images over different localization tasks.

For the text-based localization task, we generate a set of queries over the categories present in the image.

On the text-based localization benchmark, FindIt achieves 79.7%, higher than the GPV (73.0%), and Faster R-CNN baselines (75.2%).

This week, the 23rd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022) is being held in Incheon, South Korea, representing one of the world’s most extensive conferences on research and technology of spoken language understanding and processing.

Over 2,000 experts in speech-related research fields gather to take part in oral presentations and poster sessions and to collaborate with streamed events across the globe.

We are excited to be a Diamond Sponsor of INTERSPEECH 2022, where we will be showcasing nearly 50 research publications and supporting a number of workshops, special sessions and tutorials.

In addition, online attendees are encouraged to vis…

In particular, we have recently developed a new class of algorithms for online allocation problems, called dual mirror descent, that are simple, robust, and flexible.

Online Allocation ProblemsIn an online allocation problem, a decision maker has a limited amount of total resources (B) and receives a certain number of requests over time (T).

Because prices for resources are referred to as "dual variables" in the field of optimization, we call the resulting algorithm dual mirror descent.

Performance of dual mirror descent, a training based method, and an adversarial method relative to the optimal offline solution.

ConclusionIn this post we introduced dual mirror descent, an algorithm for onl…

“Cloud Wisdom Weekly: for tech companies and startups” is a new blog series we’re running this fall to answer common questions our tech and startup customers ask us about how to build apps faster, smarter, and cheaper. In this installment, we explore how to leverage artificial intelligence (AI) and machine learning (ML) for faster innovation and efficient operational growth. Whether they’re trying to extract insights from data, create faster and more efficient workflows via intelligent automation, or build innovative customer experiences, leaders at today’s tech companies and startups know that proficiency in AI and ML is more important than ever.AI and ML technologies are often expensive a…

In “PaLI: A Jointly-Scaled Multilingual Language-Image Model”, we introduce a unified language-image model trained to perform many tasks and in over 100 languages.

The PaLI model architecture is simple, reusable and scalable.

Model Scaling ResultsWe examine how the image and language model components interact with each other with regards to model scaling and where the model yields the most gains.

Scaling both the language and the visual components of the PaLI model contribute to improved performance.

Multilingual captioning greatly benefits from scaling the PaLI models.

An important aspect of human vision is our ability to comprehend 3D shape from the 2D images we observe.

However it is possible to estimate 3D structure based on what kind of 3D objects occur commonly and what similar structures look like from different perspectives.

In “LOLNeRF: Learn from One Look”, presented at CVPR 2022, we propose a framework that learns to model 3D structure and appearance from collections of single-view images.

Color and density values are accumulated along rays, one ray for each pixel in a 2D image.

ConclusionWe’ve developed a technique that is effective at discovering 3D structure from single 2D images.

There’s an explosion in the number of digital images generated and used for both personal and business needs. On e-commerce platforms and online marketplaces for example, product images and visuals heavily influence the consumer’s perception, decision making and ultimately conversion rates. In addition, there’s been a rapid shift towards user-generated visual content for ecommerce — think seller-generated product imagery, host-generated rental property photos and influencer-generated social media content. The challenge? These user-generated images are often captured using mobile cameras and vary greatly in terms of their size, quality, compression ratios and resolution, making it difficult …

You’re working on a new machine learning problem, and the first environment you use is a notebook. Your data is stored on your local machine, and you try out different model architectures and configurations, executing the cells of your notebook manually each time. This workflow is great for experimentation, but you quickly hit a wall when it comes time to elevate your experiments up to production scale. Suddenly, your concerns are more than just getting the highest accuracy score.Sound familiar?Developing production applications or training large models requires additional tooling to help you scale beyond just code in a notebook, and using a cloud service provider can help. But that process…

In “Learning Semantics-Aware Locomotion Skills from Human Demonstrations”, we design a hierarchical learning framework to improve a robot’s ability to traverse complex, off-road environments.

We first compute the speed from terrain semantics, and then select a gait based on the speed.

The skill policy selects a locomotion skill based on camera images, and the motor controller converts the selected skill into motor commands.

The high-level skill policy is further decomposed into a learned speed policy and a heuristic-based gait selector.

To decide a skill, the speed policy first computes the desired forward speed, based on the semantic information from the onboard RGB camera.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Use of the Moderation endpoint t…

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AI Model vs. AI SystemFigure 1: Differentiating AI performance assessment on the level of AI model versus AI system.

The figure above illustrates the distinction between the AI model and AI system in this example (figure 1).

This example illustrates how AI model performance metrics are not sufficient to determine whether an AI system is ‘good enough’ for real-life application.

So, how do we decide when AI system performance is ‘good enough’ for use in real-life application?

It is clear that high levels of AI model performance alone are not sufficient—we must consider every element of the AI system.

Realizing the benefits of these new breakthroughs demands that we come together in new ways across the global research community.

That’s why I’m excited to invite you to join us for this year’s Microsoft Research Summit, which will take place on October 18-20, 2022.

This virtual event is where the global research community convenes to explore how emerging research might best address societal challenges and have significant impact on our lives in the coming years.

This year’s event will feature over 120 speakers, including researchers and leaders from across the research community at Microsoft, alongside partners and collaborators from industry, academia and government who are advancing the …

In a distributed trust model, network participants validate transactions over a network by performing computation on those transactions themselves and comparing the outputs.

CCF is based on a distributed trust model like that of blockchain while maintaining data confidentiality through secure centralized computation.

This centralized confidential computation model also provides another benefit—it addresses the substantial amount of energy used in blockchain and other distributed computation environments.

We also had to prove that there were important use cases for maintaining confidentiality in a distributed trust system.

We built basic proofs of concept and gave numerous demonstrations sho…

Finding scalable solutions for today’s global challenges requires forward-thinking, transformative tools. As environmental, economic, and public health concerns mount, Microsoft Azure is addressing these challenges head on with high-performance computing (HPC), AI, and machine learning.

Unlike the conventional online RL, offline RL can learn policies without collecting online data and even without interacting with a simulator.

Moreover, since offline RL does not blindly mimic the behaviors seen in data, like imitation learning (an alternate strategy of RL), offline RL does not require expensive expert-quality decision examples, and the learned policy of offline RL can potentially outperform the best data-collection policy.

Subscribe todayIn this post, we introduce a generic game-theoretic framework for offline RL.

Thinking offline RL as two-player gamesIf we think about uncertainties as hypotheses in a version space, then a natural strategy of designing offline RL agents i…

This will enable advanced research on artificial humanoid control at a fraction of the compute resources currently required.

The reason why humanoid control research has been so computationally demanding is subtle and, at the first glance, paradoxical.

For each of the trained skill policies above, MoCapAct supplies a set of recorded trajectories generated by executing that skill’s control policy on the dm_control’s humanoid agent.

Figure 2: The hierarchical policy consists of a high-level policy and low-level policy.

The low-level policy takes the skill and the humanoid observation and outputs an action that best realizes the skill.

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

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

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

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

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The action you just performed triggered the security solution.

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

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

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

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

DeepSpeed Compression release planDeepSpeed Compression is still at i…

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

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

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

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

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

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The action you just performed triggered the security solution.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

You can email the site owner to let them know you were blocked.

Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page.

There, De La Torre became enamored by the possibilities of using computation to model the human brain.

With McDermott, De La Torre is attempting to understand how the brain integrates vision and sound.

Given an ambiguous image, that simple auditory cue is all it takes to create a different perception of reality.

De La Torre is using behavioral experiments to probe how the human brain makes sense of multisensory cues to construct a particular perception.

“What we want to do is model exactly what’s happening,” says De La Torre.

Parkinson’s disease is the fastest-growing neurological disease, now affecting more than 10 million people worldwide, yet clinicians still face huge challenges in tracking its severity and progression.

In an effort to address these problems, researchers from MIT and elsewhere demonstrated an in-home device that can monitor a patient’s movement and gait speed, which can be used to evaluate Parkinson’s severity, the progression of the disease, and the patient’s response to medication.

Advanced algorithms use these movement data to compute gait speed — how fast the person is walking.

They used statistical methods to analyze the data and found that in-home gait speed can be used to effectively …

When Kundargi reached out to MIT pK-12 collaborators, MIT PRG’s graduate research assistant Raechel Walker proposed the Data Activism curriculum.

Walker defines “data activism” as utilizing data, computing, and art to analyze how power operates in the world, challenge power, and empathize with people who are oppressed.

A different side of STEAMThe development and execution of the Data Activism curriculum contributed to Walker’s and postdoc Xiaoxue Du’s respective research at PRG.

Walker is studying AI education, specifically creating and teaching data activism curricula for minoritized communities.

Throughout the program, the Data Activism team taught students in small groups, continually a…

They have developed a mobile health (mHealth) platform that uses artificial intelligence and real-time computer vision to predict infection in C-section wounds with roughly 90 percent accuracy.

The first step in the project was gathering a database of wound images taken by community health workers in rural Rwanda.

Health workers are instructed to place the frame over the wound and open the app, which provides real-time feedback on the camera placement.

The app has been well received by women and community health workers in Rwanda.

As the team looks to develop the comprehensive app for maternal health, privacy and data protection are a top priority.

The health of the planet is one of the most important challenges facing humankind today.

Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects.

The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MIT’s plan to recruit 20 climate-focused faculty under its climate action plan.

The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in depart…

DALL-E 2 uses something called a diffusion model, where it tries to encode the entire text into one description to generate an image.

The seemingly magical models behind image generation work by suggesting a series of iterative refinement steps to get to the desired image.

“The model can effectively model object positions and relational descriptions, which is challenging for existing image-generation models.

“The fact that our model is composable means that you can learn different portions of the model, one at a time.

“This is a nice idea that leverages the energy-based interpretation of diffusion models so that old ideas around compositionality using energy-based models can be applied.

Federated learning is a collaborative method for training a machine-learning model that keeps sensitive user data private.

But federated learning has some drawbacks.

“A lot of papers have addressed one of the problems of federated learning, but the challenge was to put all of this together.

Other methods have used this pruning technique for federated learning to create smaller machine-learning models which could be transferred more efficiently.

Mugunthan is hopeful this work inspires other researchers to rethink how they approach federated learning.

“Breakthroughs such as AlphaFold are expanding the possibilities for in silico drug discovery efforts, but these developments need to be coupled with additional advances in other aspects of modeling that are part of drug discovery efforts,” Collins says.

However, more improvement will be necessary to fully take advantage of the protein structures provided by AlphaFold, the researchers say.

AlphaFold, an AI software developed by DeepMind and Google, has accurately predicted protein structures from their amino acid sequences.

The researchers found similar results when they used this modeling approach with protein structures that have been experimentally determined, instead of the structures p…

Machine learning in developmentParsing the differences in the gene expression among different kinds of tumors of unknown primary is an ideal problem for machine learning to solve.

Cancer cells look and behave quite differently from normal cells, in part because of extensive alterations to how their genes are expressed.

Many of the gene expression programs that drive embryogenesis are known to be reactivated or dysregulated in cancer cells.

Oncologists initially could not find a tumor mass, and could not classify cancer cells using the tools they had at the time.

“Developmental gene expression represents only one small slice of all the factors that could be used to diagnose and treat cancers…

They have demonstrated an artificial intelligence system that can learn the rules and patterns of human languages on its own.

This model can also automatically learn higher-level language patterns that can apply to many languages, enabling it to achieve better results.

This system could be used to study language hypotheses and investigate subtle similarities in the way diverse languages transform words.

Instead of learning weights, can the model learn expressions or rules?

“Linguists have thought that in order to really understand the rules of a human language, to empathize with what it is that makes the system tick, you have to be human.

Their goal is to empower computer scientists and data center operators to better understand avenues for data center optimization — an important task as processing needs continue to grow.

While cloud providers are actively working on optimizing their data centers, they do not often make their data or models available for the broader high-performance computing (HPC) community to leverage.

"Data centers are changing.

"Until now, there hasn't been a great way to analyze the impact to data centers.

Workload classificationAmong the world's TOP500 supercomputers, TX-GAIA combines traditional computing hardware (central processing units, or CPUs) with nearly 900 graphics processing unit (GPU) accel…

With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries.

Leveraging machine learning to research battery materialsScientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform.

Leon’s tool comes at an opportune time, when many scientists are investigating a new paradigm of batteries: solid-state batteries.

Compared to traditional batteries, which contain liquid electrolytes, solid-state batteries are safer, lighter, and easier to manufacture.

This past year, he served as the academic chair on his department’s graduate student organizati…

The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson’s from their nocturnal breathing — i.e., breathing patterns that occur while sleeping.

The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone’s Parkinson’s disease and track the progression of their disease over time.

The MIT researchers demonstrated that the artificial intelligence assessment of Parkinson's can be done every night at home while the person is asleep and without touching their body.

“A relationship between Parkinson’s and breathi…

MIT researchers have developed a method for 3D printing materials with tunable mechanical properties, that sense how they are moving and interacting with the environment.

The method opens opportunities for embedding sensors within architected materials, a class of materials whose mechanical properties are programmed through form and composition.

This technique could someday be used to create flexible soft robots with embedded sensors that enable the robots to understand their posture and movements.

While architected materials can exhibit unique properties, integrating sensors within them is challenging given the materials’ often sparse, complex shapes.

This new method provides accurate prop…

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 designFoundational works showed how to find the kernel corresponding to a wide network.

The NTK of a 4HL $\textrm{ReLU}$ FCN as a function of the cosine between two input vectors $x_1$ and $x_2$.

Shallowification of a deep $\textrm{ReLU}$ FCN into a 1HL FCN with an engineered activation function $\tilde{\phi}$.

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 uses fewer parameters than the deep network to achieve the same performance.

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

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

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

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

The permutation game: a simple counterexample w…

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

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

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

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

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

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

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

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

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

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

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

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

However, this goal of replacing human e…

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

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

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

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

Should I Use Offline RL or Imitation Learning?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Our hope is that our approach encoura…

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

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

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

model = BoostedRulesClassifier () # initialize a model model .

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

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

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

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

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

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

BCAI partnered with the Amazon ML Solutions Lab (MLSL) to incorporate the latest advances in deep neural network (DNN)-based models for revenue forecasting.

We include CNN-QR and DeepAR+, two off-the-shelf models in Amazon Forecast, as well as a custom Transformer model trained using Amazon SageMaker.

The following figure shows a simplified example with a two-level structure, which mimics the hierarchical revenue forecasting structure at Bosch.

Amazon ForecastForecast is a fully-managed AI/ML service from AWS that provides preconfigured, state-of-the-art time series forecasting models.

The system can be extended to incorporate new forecast models and supporting functions such as generating …

Having accurate and regular information about endangered animals in the wild will improve wildlife conservationists’ ability to study and conserve endangered species.

PrerequisitesA good training set is required to build an effective model using Rekognition Custom Labels.

For instructions, refer to Creating a project, Creating training and test datasets, and Training an Amazon Rekognition Custom Labels model.

ConclusionIn this post, we presented an automated system that identifies endangered species, records their population count, and provides insights about variance in population over time.

For more information about Rekognition Custom Labels, refer to Getting started with Amazon Rekognit…

The Amazon Search team develops machine learning (ML) technology that powers the Amazon.com search engine and helps customers search effortlessly.

In this post, we describe how Amazon Search uses AWS Inferentia, a high-performance accelerator purpose built by AWS to accelerate deep learning inference workloads.

AWS Inferentia and the AWS Neuron SDKEC2 Inf1 instances are powered by AWS Inferentia, the first ML accelerator purpose built by AWS to accelerate deep learning inference workloads.

The team switched to inf1.6xlarge instances, each with 4 AWS Inferentia accelerators, and 16 NeuronCores (4 cores per AWS Inferentia chip).

He is mostly focused on NLP use cases and helping customers opti…

Targeted sentiment use casesReal-time targeted sentiment analysis in Amazon Comprehend has several applications to enable accurate and scalable brand and competitor insights.

Get started with Targeted SentimentTo use targeted sentiment on the Amazon Comprehend console, complete the following steps:On the Amazon Comprehend console, choose Launch Amazon Comprehend.

After the document has been analyzed, the output of the Targeted Sentiment API can be found on the Targeted sentiment tab in the Insights section.

For a more detailed breakdown, refer to Extract granular sentiment in text with Amazon Comprehend Targeted Sentiment or Output file organization.

To learn more about Targeted Sentiment f…

This post shows how companies can introduce hundreds of employees to ML concepts by easily running AWS DeepRacer events at scale.

With AWS DeepRacer multi-user account management, event organizers can provide hundreds of participants access to AWS DeepRacer using a single AWS account, simplifying event management and improving the participant experience.

Build a solution around AWS DeepRacer multi-user account managementYou can use AWS DeepRacer multi-user account management to set usage quotas on training hours, monitor spending on training and storage, enable and disable training, and view and manage models for every event participant.

By activating multi-user account mode, you enable par…

In this post, we show you how to visualize the predictions generated from Canvas in a QuickSight dashboard, enabling intelligent decision-making via ML.

For the full list of supported data sources, refer to Importing data in Amazon SageMaker Canvas.

If you’re not sure how to access Canvas, refer to Getting started with using Amazon SageMaker Canvas.

We have successfully used our model to predict churn risk for our current customer population.

To learn more about using Canvas, see Build, Share, Deploy: how business analysts and data scientists achieve faster time-to-market using no-code ML and Amazon SageMaker Canvas.

For datasets less than 100 MB, ensemble training mode builds machine learning (ML) models with high accuracy quickly—up to eight times faster than hyperparameter optimization (HPO) training mode with 250 trials, and up to 5.8 times faster than HPO training mode with 100 trials.

How Autopilot’s ensemble training mode worksDifferent datasets have characteristics that are suitable for different algorithms.

The tests compared ensemble training mode to HPO mode with 250 trials and HPO mode with 100 trials.

We observed that ensemble training mode performed better than HPO training mode (both 100 and 250 trials).

The following screenshot shows the final results of our titanic-ens ensemble training…

You can import data from multiple data sources such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Snowflake, and 26 federated query data sources supported by Amazon Athena.

Starting today, when importing data from Athena data sources, you can configure the S3 query output location and data retention period to import data in Data Wrangler to control where and how long Athena stores the intermediary data.

When you use Athena to import data, you can use Data Wrangler’s default S3 location for the Athena query output, or specify an Athena workgroup to enforce a custom S3 location.

For Amazon S3 location of query results, enter your S3 location.

Analyze and process data with Dat…

The clinical trial submission package consists of tabulated data, analysis data, trial metadata, and statistical reports consisting of statistical tables, listings, and figures.

In this post, we demonstrate how we can use RStudio on Amazon SageMaker to create such regulatory submission deliverables.

Along with the RStudio Workbench, the RStudio suite for R developers also offers RStudio Connect and RStudio Package Manager.

After data has been ingested from a couple of different sources, we process it and create R data frames for a table.

Set up RStudio on SageMakerFor instructions on setting up RStudio on SageMaker in your environment, refer to Get started with RStudio on SageMaker.

– The built-in algorithms come with parallelization across multiple compute instances and GPU support right out of the box for all applicable algorithms.

Automatic model tuning of tabular algorithmsHyperparameters control how our underlying algorithms operate and influence the performance of the model.

To select the best model, we apply SageMaker automatic model tuning to each of the four trained SageMaker tabular algorithms.

For more information about automatic model tuning, refer to Amazon SageMaker Automatic Model Tuning: Using Machine Learning for Machine Learning or Amazon SageMaker automatic model tuning: Scalable gradient-free optimization.

About the authorsDr. Xin Huang is an Applie…

Parallel data processing, or data parallelization, takes large existing datasets and distributes them across multiple processers or nodes to operate on the data simultaneously.

Solution overviewWithin Amazon SageMaker, many customers use SageMaker Processing to help implement parallel data processing.

With SageMaker Processing, you can use a simplified, managed experience on SageMaker to run your data processing workloads, such as feature engineering, data validation, model evaluation, and model interpretation.

This file contains a series of steps that helps us create our parallel data processing pipeline using SageMaker Processing.

To learn more about the features and services used in this…

This post shows how to configure the Amazon Kendra Zendesk connector to index your Zendesk domain and take advantage of Amazon Kendra intelligent search.

In this post, we show how to use the Amazon Kendra connector for Zendesk to index data from your Zendesk domain for intelligent search.

Configure the data source using the Amazon Kendra connector for ZendeskYou can add the Zendesk connector data source to an existing Amazon Kendra index or create a new index.

Run queries with the Amazon Kendra search consoleNow that the data is synced, we can run a few search queries on the Amazon Kendra search console by navigating to the Search indexed content page.

To learn more about the Amazon Kendra …

Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model.

In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast.

Hyperband with SageMakerThe new Hyperband approach implemented for hyperparameter tuning has a few new data elements changed through AWS API calls.

For more details on how to configure and run automatic model tuning, refer to Specify the Hyperparameter Tuning Job Settings.

He works in the team owning the service for SageMaker Automatic Model Tuning.

We accelerate that journey by demonstrating how to achieve this goal using Amazon Polly.

For a full list of standard and neural voices in Amazon Polly, see Voices in Amazon Polly.

Amazon Polly outputs the files in an Amazon Simple Storage Service (Amazon S3) bucket; the script copies them to your web server.

PrerequisitesTo run this example, you need an AWS account with permission to use Amazon Polly, Amazon S3, Amazon Cognito, and (for demo purposes) AWS Cloud9.

ConclusionIn this post, we demonstrated a technical solution to a high-value business problem: how to use Amazon Polly to read the content of a webpage and highlight the content as it’s being read.

Editor’s Note: This post has been updated to reflect the XPENG G9 launch.

Electric automaker XPENG launched the G9 SUV this week during NVIDIA GTC.

The intelligent, software-defined vehicle is built on the high-performance compute of NVIDIA DRIVE Orin and delivers AI capabilities that are continuously upgraded with each over-the-air update.

The XPENG G9 and its fellow EVs are elevating the driving experience with intelligent features that are always at the cutting edge.

It is built on two NVIDIA DRIVE Orin systems-on-a-chip (SoC), achieving 508 trillion operations per second (TOPS).

Trained using only 2D images, NVIDIA GET3D generates 3D shapes with high-fidelity textures and complex geometric details.

GET3D can generate a virtually unlimited number of 3D shapes based on the data it’s trained on.

Manually modeling a 3D virtual world that reflects this is incredibly time consuming, making it difficult to fill out a detailed digital environment.

NVIDIA researchers trained GET3D on synthetic data consisting of 2D images of 3D shapes captured from different camera angles.

For the latest news from NVIDIA AI research, watch the replay of NVIDIA founder and CEO Jensen Huang’s keynote address at GTC:

The Logitech G CLOUD is the latest gaming handheld device to support GeForce NOW, giving members a brand new way to keep the gaming going.

But that’s not all: Portal with RTX joins GeForce NOW in November, free for Portal owners.

A New Way to PlayThe just-announced G CLOUD is the latest way to stream your PC library from the cloud on GeForce NOW.

The Hottest Games, Streaming SoonGet ready to play three new release titles coming to the cloud in the near future.

Charge Into the ‘Total Warhammer’ Series This WeekMake your move in the incoming additions from the Total War series by SEGA and Creative Assembly – Total War: WARHAMMER, Total War: WARHAMMER II and Total War: WARHAMMER III are stream…

Global tier-1 supplier Continental and software-defined lidar maker AEye announced this week at NVIDIA GTC that they will migrate their intelligent lidar sensor model into NVIDIA DRIVE Sim.

Now, the companies are contributing this sensor model to DRIVE Sim, helping to bring their vision to the industry.

DRIVE Sim is open and modular — users can create their own extensions or choose from a rich library of sensor plugins from ecosystem partners.

By joining this rich community of DRIVE Sim users, Continental and AEye can now rapidly simulate edge cases in varying environments to test and validate lidar performance.

‘’With the scalability and accuracy of NVIDIA DRIVE Sim, we’re able to validate…

The NVIDIA Morpheus GPU-accelerated cybersecurity AI framework enables, for the first time, the ability to inspect all network traffic in real time to address the cybersecurity data problem on a scale previously impossible.

New visualization capabilities help pinpoint threats fasterThe latest release of NVIDIA Morpheus provides visualizations for cybersecurity data, enabling cybersecurity analysts to detect and remediate threats more efficiently.

The sensitive information detection model is trained to identify sensitive information, such as AWS credentials, GitHub credentials, private keys, and passwords.

Enabling new AI-based cybersecurity solutionsWith Morpheus, cybersecurity practitioner…

These partners are joining a diverse ecosystem of companies developing on DRIVE IX, including Soundhound, Jungo and VisionLabs, providing cutting-edge solutions for any in-vehicle need.

Using DRIVE IX, it can empower both embedded and cloud-based natural language processing on the same architecture, ensuring drivers have access to important capabilities regardless of connectivity.

Using DRIVE IX, Rightware is creating a seamless visual experience across all cockpit and infotainment domains.

The Audio Weaver development platform from DSP Concepts can be integrated into the DRIVE IX advanced sound engine.

By combining the flexibility of DRIVE IX with leading in-cabin solutions providers, spen…

With the introduction of NVIDIA DRIVE Thor, automakers can build unified AI compute platforms that combine advanced driver-assistance systems and in-vehicle infotainment.

The centralized NVIDIA DRIVE architecture supports novel features in the vehicle, including the ability to support content across multiple displays.

HARMAN, a global leader in rich, connected in-vehicle solutions, will be working with the NVIDIA DRIVE platform to develop multi-domain infotainment solutions.

HARMAN is using the DRIVE IX intelligent experience software stack to bring immersive cinematic experiences to every seat in the vehicle, including with individual sound zones for personalized audio.

With the high perfo…

Challenges to consider when deploying AI inferenceAI inference is the production phase of running AI models to make predictions.

Constantly evolving models: Models in production must be updated continuously based on new data and algorithms, without business disruptions.

New AI inference use cases using NVIDIA TritonNVIDIA Triton Inference Server (Triton), is an open source inference serving software that supports all major model frameworks (TensorFlow, PyTorch, TensorRT, XGBoost, ONNX, OpenVINO, Python, and others).

NIO used the Triton model ensemble feature to move their pre– and post-processing functions from client application to Triton Inference Server.

Model speedup with the FasterTran…

Whether for virtual assistants, transcriptions or contact centers, voice AI services are turning words and conversations into bits and bytes of business magic.

Today at GTC, NVIDIA announced new additions to NVIDIA Riva, a GPU-accelerated software development kit for building and deploying speech AI applications.

Riva is built to be fully customizable at every stage of the speech AI pipeline to help solve unique problems efficiently.

Developers can easily access Riva and pretrained models through NVIDIA NGC, a hub for GPU-optimized AI software, models and Jupyter Notebook examples.

Try NVIDIA Riva with guided labs on ready-to-run infrastructure in NVIDIA LaunchPad.

Overjet, a member of the NVIDIA Inception program for startups, is moving fast to bring AI to dentists’ offices.

On this episode of the NVIDIA AI Podcast, host Noah Kravitz talks to Dr. Wardha Inam, CEO of Overjet, about how her company uses AI to improve patient care.

Subscribe to the AI Podcast: Now Available on Amazon MusicYou can now listen to the AI Podcast through Amazon Music.

Also get the AI Podcast through iTunes, Google Podcasts, Google Play, Castbox, DoggCatcher, Overcast, PlayerFM, Pocket Casts, Podbay, PodBean, PodCruncher, PodKicker, Soundcloud, Spotify, Stitcher and TuneIn.

Make the AI Podcast better: Have a few minutes to spare?

At GTC 2022, we announced the release of NVIDIA Clara Parabricks v4.0, which brings significant improvements to how genomic researchers and bioinformaticians deploy and scale genome sequencing analysis pipelines.

The NVIDIA Clara Parabricks v4.0 toolsetThe individual Clara Parabricks tools are also now offered in individual containers in the Clara Parabricks collection on NGC or as a unified container that encompasses all tools in one.

The models have been GPU-accelerated in collaboration with the NVIDIA Clara Parabricks team to provide rapid and high-accuracy variant calls across sequencing instruments.

Get started with Clara Parabricks v4.0To start using Clara Parabricks for free, visit t…

At GTC 2022, NVIDIA revealed major updates to its suite of NVIDIA AI frameworks for building real-time speech AI applications, designing high-performing recommenders at scale, applying AI to cybersecurity challenges, creating AI-powered medical devices, and more.

When organizations put their AI frameworks into production, enterprise support with NVIDIA AI Enterprise ensures the success of these AI applications.

Access to NVIDIA AI experts, training, and knowledge-based resources through NVIDIA Enterprise Support with the purchase of NVIDIA AI Enterprise software.

Add this GTC session to your calendar:NVIDIA AI EnterpriseNVIDIA AI Enterprise is a powerful cloud-native software suite that str…

It has mastered its conversational skills in the highly complex Korean language thanks to large language models (LLMs) — machine learning algorithms that can recognize, understand, predict and generate human languages based on huge text datasets.

“With transformer-based models, we’ve achieved significant quality improvements for the GiGA Genie smart speaker, as well as our customer services platform AI Contact Center, or AICC,” said Hwijung Ryu, LLM development team lead at KT.

AICC is an all-in-one, cloud-based platform that offers AI voice agents and other customer service-related applications.

The NVIDIA AI platform simplified and sped up this process for KT.

NeMo Megatron enabled the te…

With 32 petaflops of performance at FP8 precision, NVIDIA DGX H100 delivers a leap in efficiency for enterprise AI development.

New NVIDIA Base Command software, which simplifies and speeds AI development, powers every DGX system — from single nodes to DGX SuperPODs.

Base Command works with the NVIDIA AI Enterprise software suite, which is now included with every DGX system.

The NVIDIA AI software enables end-to-end AI development and deployment with supported AI and data science tools, optimized frameworks and pretrained models.

Leaders Power AI Breakthroughs With DGX SystemsEnterprises around the world choose NVIDIA DGX systems to power their most advanced AI workloads.

To speed adoption, he announced Deloitte, the world’s largest professional services firm, is bringing new services built on NVIDIA AI and NVIDIA Omniverse to the world’s enterprises.

With DLSS 3, it’s twice as fast in today’s games as the GeForce RTX 3080 Ti, and more powerful than the GeForce RTX 3090 Ti at lower power.

Thor will be the processor for robotics, medical instruments, industrial automation and edge AI systems, Huang said.

Haung also detailed NVIDIA Omniverse Cloud, an infrastructure-as-a-service that connects Omniverse applications running in the cloud, on premises or on a device.

“Today, we announced new chips, new advances to our platforms, and, for the very first time, new …

To facilitate the level of data ingestion required to support the training models supporting our products, we’ve had to build a new data ingestion infrastructure as well as new last-mile transformation pipelines.

In the sections below, we share our experience building data ingestion and last-mile data preprocessing pipelines that are responsible for feeding data into AI training models.

Data ingestion pipeline overviewWe have exabytes of training data powering our models, and the amount of training data is growing rapidly.

We have built a disaggregated Data PreProcessing tier (DPP) that serves as the reader tier for data ingestion and last-mile data transformations for AI training [Ref].

Sc…

FL-DP enhances privacy in two important ways:It allows machine learning (ML) models to be trained in a distributed way so that users’ data remains on their mobile devices.

It adds noise to reduce the risk of an ML model memorizing user data.

Such an approach could enhance user privacy while still facilitating an intelligent, safe, and intuitive user experience across Meta’s family of technologies.

How it works:With FL-DP, ML models are trained in a federated manner where mobile devices learn locally.

This architecture is a combination of infrastructure across mobile devices, trusted execution environments, and conventional back-end servers.

What the research is:Recent research has demonstrated that binary optimization is important for achieving peak performance for various applications.

VESPA expands on ESP in several ways to make it useful in the context of binary optimizers.

VESPA increases the scope where binary optimizers can be used, thus enhancing the range of applications that can leverage these tools to improve their performance.

Once the static profile data produced by VESPA is injected into a binary optimizer, this tool can proceed with its optimization steps as usual, completely oblivious to how the profile data was computed.

VESPA, therefore, can very easily be integrated into existing binary optimizers, which we d…

Share Vote Reddit WhatsApp 0 SharesIntroductionIf you have read our previous article, ML Education at Uber: Frameworks Inspired by Engineering Principles, you have seen several examples of how Uber benefits from applying Engineering Principles to drive the ML Education Program’s content design and program frameworks.

How were the ML Education program creators able to capture and communicate this value so that the program could scale to what it is today?

When a larger percentage of non-ML engineers attend ML Education courses it means that we are distilling ML expertise to the broader ML market, increasing the overall internal ML market size for Uber.

ConclusionUber’s ML Education Program ha…

Part 1 will introduce our design principles and explain the benefits of applying these principles to technical education content design and program frameworks, specifically in the ML domain.

Core Principles of Uber’s ML Education ProgramThe capabilities of Uber’s ML infrastructure and ecosystem have enabled us to design, implement, and ground our ML Education program in our design principles.

Aside from the core principle of reproducibility discussed above, we have a list of other design principles that comprise Uber’s ML Education program:Because our subject matter is highly technical, we felt it appropriate to derive our design principles from industry-recognized engineering principles.

H…

Real-Time Document Check CriteriaFrom the onset, we knew that the Real-Time Document Check product needed to meet 4 non-negotiable criteria:Data privacy: Adherence to best practices for handling personal data, taking into account local laws, regulations, and norms in all countries where the product is available.

In the Document Image Processing module, a list of operations (including document classification, transcription, and fraud detection) are applied to the uploaded document images via different technologies (e.g., 3rd-party vendor, Uber in-house technology, and human review).

Looking into the FutureReal World ImpactAs of May 2022, Real-Time ID Document Check is live in Brazil, Mexico,…

By training machine learning (ML) models on top of the road graph prediction using historical data in combination with real-time signals, we can refine ETAs that better predict real-world outcomes.

To meet these challenges, Uber AI partnered with Uber’s Maps team on a project called DeepETA to develop a low-latency deep neural network architecture for global ETA prediction.

We take a similar approach to ETA prediction at Uber.

Conclusions and Future WorkWe have launched this DeepETA model into production for global 4-wheel ETA prediction.

The DeepETA model launch makes it both possible and efficient to train and serve large-scale Deep Learning models that predict ETAs better than XGBoost ap…

Industry-wide, payment fraud losses are measured in terms of the fraction of gross amounts processed.

RADAR is an AI fraud detection and mitigation system with humans in the loop.

RADAR fraud protection rules are generally short-lived and targeted reactions to the unexpected attacks.

We will define some terminology to discuss time series data below:OT – “Order time” when the specific order has been fulfilled.

ConclusionIn this blog, we presented the RADAR system and how it brings together many components of Uber’s technical ecosystem to solve a complex business problem.

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

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

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

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

ConclusionIn this article…

Bookmark for later How to Solve the Model Serving Component of the MLOps StackWhat is a feature store?

You could use Redshift as an offline feature store and DynamoDB or Redis as an online feature store.

But if this is not the case and you’re running a public cloud-heavy workload, using AWS SageMaker Feature Store or GCP Vertex AI Feature Store can be good options to start with.

Amazon SageMaker Feature Store for machine learning | SourceDatabricks also offers an embedded Feature Store service, which is also a good option and would be perfectly compatible with a tool like MLFlow.

Read also Setting up MLOps at a Reasonable Scale With Jacopo TagliabueExplanation of a feature store | SourceWho…

Then, we will take a glimpse behind the hood of Boruta, the state-of-the-art feature selection algorithm, to check out a clever way to combine different feature selection methodsAnd we’ll look into how feature selection is leveraged in the industry.

This is what feature selection is, but it is equally important to understand what feature selection is not – it is neither feature extraction/feature engineering nor it is dimensionality reduction.

Feature selection methods | Source: authorUnsupervised feature selection methodsJust like unsupervised learning is the type of learning that looks for patterns in unlabeled data, similarly, unsupervised feature selection methods are such methods that …

May interest you Exploratory Data Analysis for Natural Language Processing: A Complete Guide to Python ToolsExploratory Data Analysis vs.

Classical Data AnalysisApart from EDA, there are also other data analysis approaches, Classical Data Analysis being one of the most popular ones.

Both Exploratory Data Analysis and Classical Data Analysis start with a problem, followed by collecting the related data that can be used to understand the problem.

This is where their similarities end, let us see the differences now:Parameters Exploratory Data Analysis Classical Data Analysis Model Exploratory Data Analysis: does not impose deterministic or probabilistic models on the data.

Exploratory Data Ana…

This article will discuss the model registry tools and evaluation criteria for such tools.

Evaluation criteria for choosing model registry toolsThe model registry is an important part of MLOps platforms/tools.

Competence in managing the model dependenciesThe model registry tool must have compatibility with all the dependencies the ML model needs.

Model registry toolsHere are a number of model registry tools that are used across the industry:Neptune provides a central processing unit to store, log, compare, display, query, and organize all metadata.

Comparison of model registry toolsEvery model registry tool has different features and performs various unique operations.

Problem 2: No high-level separation of concernsThe separation of concerns in ML code bases is often missing at a high level.

What this means is that more often than not, so-called ML code is also doing feature transformations like operations that have nothing to do with ML – think physical document ingestion, conversion of administrative data, etc.

Problem 4: No configuration Data ModelA data model for handling ML configuration is often missing.

Problem 5: Handling legacy modelsSince the process of training a ML model often involves manual efforts (see problem 1) it can take really long to do so.

MLConfiguration contains the ML data model: enums and classes that do not contain any processin…

If you look at recommender systems papers, a large number of them come from the industry instead of academia.

Recommender systems: model trainingLarge NLP or Vision models have billions of parameters distributed among linear, convolution, recurrent, or attention layers.

According to the team:“TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys).

Recommender systems: model evaluationOffline evaluationTypical classification task optimizes for metrics like accuracy, precision, recall, or F1-score.

Recommender systems: A/B testingImproving recommender systems is a continuous process.

This is exactly the problem that is supposed to be solved by MLOps (Machine Learning Operations).

In this article, I will explain:what it is about,what are the pillars of MLOps,and how to implement them in your current or future projects.

Read also Setting up MLOps at a Reasonable Scale With Jacopo TagliabueThe pillars of MLOps: core ingredients for a robust MLOps strategyNow that we have a basic understanding of MLOps and its general role in machine learning projects let’s dig deeper to understand what are the key concepts/techniques that will help you implement MLOps best practices in your existing or future projects.

MLOps pillar: reproducibility and versioningOne of the core features of…

But before deploying a new model, we need to make sure that it’s indeed a better model than the old one.

Lastly, we have to point out the importance of testing before deploying an ML model.

ML model deployment is a process of integrating the model into an existing production environment to make practical business decisions.

ML models almost always require deployment to provide business value, but unfortunately, most of the models never make it to production.

Models run asynchronously, firstly the old model in production and after the new shadow model.

Every episode is focused on one specific ML topic, and during this one, we talked to Mateusz Opala about leveraging unlabeled image data with self-supervised learning or pseudo-labeling.

Sabine: With us today, we have Mateusz Opala, who is going to be answering questions about leveraging unlabeled image data with self-supervised learning or pseudo-labeling.

Can you walk us through some of the different use cases where you apply pseudo-labeling for image data in Brainly?

Mateusz: In general, most of the techniques we use it’s still supervised learning, and we label data, but it’s limited and it’s time-consuming.

Mateusz:My biggest challenge right now is connecting all the steps in the whole …

Serving Machine Learning models the right wayML model serving has a tight relationship with metadata stores, ML model registries, monitoring components, and feature stores.

If we have a high-performance server that is a nightmare to integrate with our observability, feature stores, and model registries, we have a terrible model serving component.

Our ML serving component periodically checks in with the ML model registry, and if there’s a new model with the compatible tag, it will update the deployment.

Model versions visible in the Neptune model registry | See in the appOf course, as mentioned earlier, frequently, the model serving component has to interact with feature stores.

Think of the…

Diagram of active learning system | Source: AuthorWhy do we need active learning?

Active learning use case in NLP (NER) | SourceAs we can see above, clearly, all of the active learning strategies are outperforming the random sampling (RAND) baseline performance by a good margin.

Sample of selected frames via active learning | SourceAside from the cost advantages, a significant improvement in mean average precision (from an objection detection perspective) was observed using active learning.

The improvement of protein production | SourceSome popular frameworks used for Active Learning1.modAL: A modular active learning framework for Python3modAL is an active learning framework for Python3, de…

Training data: Meena is trained on a large amount of dialogue data, this is different from previous models.

Building on the early work of Meena, LaMDA introduced a number of new approaches to dialogue models, which resulted in impressive results.

In this post, we looked at two models, namely Meena and LaMDA, which are both dialogue models, and highlighted some of their key technical innovations.

You may have to invest more resources trying to make these dialogue models “fit” your domain-specific application.

So that still represents a big obstacle to the easy application of these dialogue models.

You’ll learn about:1 What is a reasonable scale MLOpsWhat is a reasonable scale MLOps 2 How to set up MLOps at a reasonable scaleHow to set up MLOps at a reasonable scale 3 What tools to use and whether to buy or build themWhat tools to use and whether to buy or build them 4 How to deliver models to customersHow to deliver models to customers 5 What are the limits of reasonable scaleWhat are the limits of reasonable scale 6 And much more.

Sabine Nyholm: It’s our pleasure to introduce Jacopo Tagliabue, who has even been called the father of reasonable scale MLOps.

The idea of the reasonable scale stack came from the realization that most of our business problems are per customer or organizat…

Aim of the project: bird image classifierThe aim of the project is to build an image classifier to classify different species of birds.

Building the image classification modelAs mentioned before, research and planning is the key to implementing any machine learning project.

MLOps pipeline for image classification: building the vision transformer using PytorchI have created the full model as per the author’s description of ViT in their paper.

The image on the left is the original image whereas the image on the right is overlaid with the attention map.

MLOps pipeline for image classification: creating the app using StreamlitThe Streamlit app will be a web app that we will deploy on the cloud.

In this tutorial, we’ll present a simple example of a time-series-based ML project and build an MLOps pipeline for that.

Exploratory data analysis (EDA) – understanding our data using data analysis and visualization techniques.

– understanding our data using data analysis and visualization techniques.

MLOps pipeline for time series prediction: model developmentResearchAs we mentioned before, a good practice of ML project development is to start with research.

ConclusionIn this tutorial, we’ve presented a simple end-to-end ML time series project following MLOps practices.

#stablediffusion #aiart #mlnews Stable Diffusion has been released and is riding a wave of creativity and collaboration. But not everyone is happy about this... Sponsor: NVIDIA

GPU Raffle: https://ykilcher.com/gtc OUTLINE:

0:00 - Introduction

0:30 - What is Stable Diffusion?

2:25 - Open-Source Contributions and Creations

7:55 - Textual Inversion

9:30 - OpenAI vs Open AI

14:20 - Journalists be outraged

16:20 - AI Ethics be even more outraged

19:45 - Do we need a new social contract?

21:30 - More applications

22:55 - Helpful Things

23:45 - Sponsor: NVIDIA (& how to enter the GPU raffle) References: https://early-hair-c20.notion.site/Stable-Diffusion-Takes-Over-Referenes-7a2f45b8f7e04ae0ba19db…

#ai #sparsity #gpu Sparsity is awesome, but only recently has it become possible to properly handle sparse models at good performance. Neural Magic does exactly this, using a plain CPU. No specialized hardware needed, just clever algorithms for pruning and forward-propagation of neural networks. Nir Shavit and I talk about how this is possible, what it means in terms of applications, and why sparsity should play a much larger role in the Deep Learning community. Sponsor: AssemblyAI

Link: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic_autochapters Check out Neural Magic: https://neuralmagic.com/

and DeepSparse: https://github.com/neuralmagic/deepsparse O…

#ai #interview #research Jacob Steinhardt believes that future AI systems will be fundamentally different than the ones we know currently. We talk about how emergence happens when scaling up, what implications that has on AI Safety, and why thought experiments like the Paperclip Maximizer might be more useful than most people think. OUTLINE:

0:00 Introduction

1:10 Start of Interview

2:10 Blog posts series

3:56 More Is Different for AI (Blog Post)

7:40 Do you think this emergence is mainly a property from the interaction of things?

9:17 How does phase transition or scaling-up play into AI and Machine Learning?

12:10 GPT-3 as an example of qualitative difference in scaling up

14:08 GPT-3 as a…

#huggingface #pickle #exploit Did you know that something as simple as loading a model can execute arbitrary code on your machine? Try the model: https://huggingface.co/ykilcher/totally-harmless-model

Get the code: https://github.com/yk/patch-torch-save Sponsor: Weights & Biases

Go here: https://wandb.me/yannic OUTLINE:

0:00 - Introduction

1:10 - Sponsor: Weights & Biases

3:20 - How Hugging Face models are loaded

5:30 - From PyTorch to pickle

7:10 - Understanding how pickle saves data

13:00 - Executing arbitrary code

15:05 - The final code

17:25 - How can you protect yourself? Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.com/c/yannicki…

#ai #selforganization #emergence Read Sebastian's article here: https://sebastianrisi.com/self_assembling_ai/ OUTLINE:

0:00 - Introduction

2:25 - Start of Interview

4:00 - The intelligence of swarms

9:15 - The game of life & neural cellular automata

14:10 - What's missing from neural CAs?

17:20 - How does local computation compare to centralized computation?

25:40 - Applications beyond games and graphics

33:00 - Can we do away with goals?

35:30 - Where do these methods shine?

43:30 - The paradox of scales & brains

49:45 - Connections to graphical systems & GNNs

51:30 - Could this solve ARC?

57:45 - Where can people get started? References:

https://sebastianrisi.com/

https://modl.ai/

https:/…

#stablediffusion #ai #stabilityai An interview with Emad Mostaque, founder of Stability AI. OUTLINE:

0:00 - Intro

1:30 - What is Stability AI?

3:45 - Where does the money come from?

5:20 - Is this the CERN of AI?

6:15 - Who gets access to the resources?

8:00 - What is Stable Diffusion?

11:40 - What if your model produces bad outputs?

14:20 - Do you employ people?

16:35 - Can you prevent the corruption of profit?

19:50 - How can people find you?

22:45 - Final thoughts, let's destroy PowerPoint Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

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

Twitter: https://twitter.com/ykilcher

Discord: https://ykilcher.com/discord

Link…

#mlnews #ai #copilot OUTLINE:

0:00 - Intro

0:20 - Copilot Now Generally Available

3:20 - FOSS Org leaves GitHub

6:45 - Google's Internal ML Code Completion

9:10 - AI Trains Itself to Code Better

14:30 - Amazon CodeWhisperer in Preview

15:15 - Pangu-Coder: A New Coding Model

17:10 - Useful Things References:

Copilot Now Generally Available

https://github.blog/2022-06-21-github-copilot-is-generally-available-to-all-developers/ FOSS Org leaves GitHub

https://www.theregister.com/2022/06/30/software_freedom_conservancy_quits_github/

https://sfconservancy.org/blog/2022/jun/30/give-up-github-launch/

https://sfconservancy.org/GiveUpGitHub/

https://sfconservancy.org/docs/SupportGiveUpGitHub-README-s…

#mlnews #dalle #imagen All things text-to-image models like DALL-E and Imagen! OUTLINE:

0:00 - Intro

0:30 - Imagen: Google's Text-to-Image Diffusion Model

7:15 - Unified I/O by AllenAI

9:40 - CogView2 is Open-Source

11:05 - Google bans DeepFakes from Colab

13:05 - DALL-E generates real Cosmopolitan cover

15:45 - DALL-E tips & tricks

17:00 - Midjourney moves to Open Beta

17:50 - DALLE-mini is not Crayon

19:00 - Deep Learning Resources References:

Imagen: Google's Text-to-Image Diffusion Model

https://imagen.research.google/?utm_source=pocket_mylist

https://arxiv.org/pdf/2205.11487.pdf Unified I/O by AllenAI

https://unified-io.allenai.org/

https://blog.allenai.org/introducing-ai2s-unified-io-…

#mlnews #ai #minerva This episode is all about models that reason. OUTLINE:

0:00 - Intro

0:35 - Meta AI learns Wikipedia citations

5:25 - Google's Minerva solves math problems by reading papers

9:10 - GPT-3 writes a paper on itself

13:35 - Jürgen Schmidhuber prompts LeCun for missing citations References:

Meta AI learns Wikipedia citations

https://tech.fb.com/artificial-intelligence/2022/07/how-ai-could-help-make-wikipedia-entries-more-accurate/

https://ai.facebook.com/blog/introducing-sphere-meta-ais-web-scale-corpus-for-better-knowledge-intensive-nlp/?d=%7B%22u%22%3A100051861999022%2C%22f%22%3A207799259245384%2C%22t%22%3A1658664021%2C%22ed%22%3A[]%7D&s=AWVELTip1y4HowJprXc

https://github.c…

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE:

0:00 - Intro

0:55 - BLOOM: Open-Source 176B Language Model

5:25 - YALM 100B

5:40 - Chinese Brain-Scale Supercomputer

7:25 - Meta AI Translates over 200 Languages

10:05 - Reproducibility Crisis Workshop

10:55 - AI21 Raises $64M

11:50 - Ian Goodfellow leaves Apple

12:20 - Andrej Karpathy leaves Tesla

12:55 - Wordalle References:

BLOOM: Open-Source 176B Language Model

https://bigscience.huggingface.co/blog/bloom

https://huggingface.co/spaces/bigscience/license

https://huggingface.co/bigscience/bloom?text=34%2B10%3D44+%0A54%2B20%3D YALM 100B

https://github.com/yandex/YaLM-100B Chinese Brain-Scale …

#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time. OUTLINE:

0:00 - Introduction

2:00 - Main Contributions

5:45 - Mode 1 and Mode 2 actors

15:40 - Self-Supervised Learning and Energy-Based Models

20:15 - Introducing latent variables

25:00 - The problem of collapse

29:50 - Contrastive vs regularized methods

36:00 - The JEPA architecture

47:00 - Hierarchical JEPA (H-JEPA)

53:00 - Broader relevance

56:00 - Summ…

Chatting & Coding

#openai #vpt #minecraft Minecraft is one of the harder challenges any RL agent could face. Episodes are long, and the world is procedurally generated, complex, and huge. Further, the action space is a keyboard and a mouse, which has to be operated only given the game's video input. OpenAI tackles this challenge using Video PreTraining, leveraging a small set of contractor data in order to pseudo-label a giant corpus of scraped footage of gameplay. The pre-trained model is highly capable in basic game mechanics and can be fine-tuned much better than a blank slate model. This is the first Minecraft agent that achieves the elusive goal of crafting a diamond pickaxe all by itself. OUTLINE:

0:00…

#parti #ai #aiart Parti is a new autoregressive text-to-image model that shows just how much scale can achieve. This model's outputs are crips, accurate, realistic, and can combine arbitrary styles, concepts, and fulfil even challenging requests. OUTLINE:

0:00 - Introduction

2:40 - Example Outputs

6:00 - Model Architecture

17:15 - Datasets (incl. PartiPrompts)

21:45 - Experimental Results

27:00 - Picking a cherry tree

29:30 - Failure cases

33:20 - Final comments Website: https://parti.research.google/

Paper: https://arxiv.org/abs/2206.10789

Github: https://github.com/google-research/parti Links:

Homepage: https://ykilcher.com

Merch: https://ykilcher.com/merch

YouTube: https://www.youtube.co…

#lamda #google #ai Google engineer Blake Lemoine was put on leave after releasing proprietary information: An interview with the chatbot LaMDA that he believes demonstrates that this AI is, in fact, sentient. We analyze the claims and the interview in detail and trace how a statistical machine managed to convince at least one human that it is more than just an algorithm. OUTLINE:

0:00 - Whistleblower put on leave

4:30 - What is a language model?

6:40 - The prompt is the key

10:40 - Who are we talking to exactly?

12:50 - LaMDA analyzes stories

15:20 - Fear, pain, and consent

20:25 - How would we recognize sentience? When is a machine conscious? References:

https://cajundiscordian.medium.com/…

Please check out the full podcast here: https://www.youtube.com/watch?v=gjJBYcYMB-o This video is a commentary on the latest Weaviate Podcast with Laura Ham on the Weaviate User Experience. User Experience describes a suite of things from the performance of the tech, API interfaces, documentation, and communication strategy -- as outlined by Bob van Luijt here: https://twitter.com/bobvanluijt/status/1552379772747096064. Laura has lead the development of the GraphQL API that makes Weaviate so friendly and exciting to use! I really hope you enjoy learning more about these topics. Here are some additional links referenced in the video: Wikipedia Weaviate Example: https://weaviate.io/developers…

Hey everyone! Here are some of my thoughts and lessons learned on the new Weaviate v1.14 release! Please check out the full length podcast linked here: https://www.youtube.com/watch?v=eiQaZIhUS_o. Some references from the video:

Weaviate v1.14 Blog Post: https://weaviate.io/blog/2022/07/Weaviate-release-1-14.html#stronger-together

CO-Search: https://arxiv.org/pdf/2006.09595.pdf

Prometheus: https://prometheus.io/docs/introduction/overview/

Literature-Augmented Clinical Outcome Prediction: https://aclanthology.org/2022.findings-naacl.33.pdf

Sigmoid-MSE vs. Softmax Cross-Entropy: https://wandb.ai/ayush-thakur/dl-question-bank/reports/Sigmoid-MSE-vs-Softmax-Cross-Entropy--VmlldzoyMDA3ODQ

Please check out the full podcast here: https://www.youtube.com/watch?v=kG3ji89AFyQ This video is a commentary on the latest Weaviate Podcast with Etienne Dilocker on ANN Benchmarks. ANN search -- short for Approximate Nearest Neighbors -- describes algorithms that enable efficient distance comparison between an encoded query vector and a vector database. For example, we may have 1 billion vectors to search through -- we don't want to do a dot product distance between our query and 1 billion candidate vectors! This podcast describes Weaviate's efforts to benchmark HNSW within the Weaviate system and give users a sense of how performance varies with respect to each dataset (and their respect…

Please check out Eric Jang's article "Ranking YC Companies with a Neural Net": https://evjang.com/2022/04/02/yc-rank.html Please subscribe to SeMI Technologies on YouTube! https://www.youtube.com/c/SeMI-and-Weaviate Timecodes

0:00 Introduction

0:58 Weaviate Demo

3:40 Article Overview

10:45 NLP for Venture Capital and Data-Centric AI

Please leave a star! https://github.com/mosaicml/composer Thank you so much for watching! This video presents some details of MosaicML's Composer launch and how to use it in Python. I am really excited about this company and their mission to deliver faster and cheaper Deep Learning training! I hope you find this video useful, happy to answer any questions you might have about this or these ideas in Efficient Deep Learning generally! The full Weaviate podcast with Jonathan Frankle will be uploaded very soon on SeMI Technologies YouTube, please subscribe!

https://www.youtube.com/c/SeMI-and-Weaviate Chapters

0:00 Introduction

1:45 Documentation Intro

4:20 Composer Notebooks

5:35 Functional API…

I hope you found this useful, please let me know if you have any questions or ideas! Docarray Documentation: https://docarray.jina.ai/ Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Introduction

This video explains one of the biggest lessons for me in interviewing Han Xiao from Jina AI. I hope this was a good explanation of the preprocessing / granularity of embeddings and how that can enable different kinds of search applications. Full-Length Podcast: https://www.youtube.com/watch?v=HIGAQAE_xaI Code Tutorial (Weaviate + Jina AI for Image Search): https://www.youtube.com/watch?v=rBKvoIGihnY Please check out Jina AI on YouTube: https://www.youtube.com/c/JinaAI Please check out SeMI Technologies on YouTube: https://www.youtube.com/c/SeMI-and-Weaviate/videos Chapters

0:00 Please check out SeMI YouTube!

0:15 My takeaways on Full Stack Neural Search

11:04 Podcast Clip - Han Xiao

I hope this video helps you get started with Image Search using Weaviate and Jina AI - happy to answer any questions / help solve problems! Check out the full tutorial explanation from Laura Ham: https://www.youtube.com/watch?v=rBKvoIGihnY New podcast with Jina AI CEO Han Xiao! https://www.youtube.com/watch?v=HIGAQAE_xaI Full notebook code: https://github.com/laura-ham/HM-Fashion-image-neural-search/blob/main/hm-fashion-image-neural-search.ipynb Get started with the Weaviate Cloud Service: console.semi.technology

Hey everyone! Hopefully this video helps supplement the new Weaviate podcast with Brady Neal, I hope you find this interesting / useful! Check out Brady Neal on YouTube! https://www.youtube.com/c/BradyNealCausalInference/featured Weaviate Podcast: https://www.youtube.com/watch?v=t7g9s1GWcB8 0:00 New Weaviate Podcast!

0:42 Brady Neal Causal Inference

1:34 Oogway.ai

2:45 Whiteboard Ideas

5:35 Discussion Topics

Hey everyone! I recently interviewed Arvind Neelakantan from OpenAI about the new OpenAI Embeddings API on the Weaviate Podcast! This video provides some additional detail for the different topics that were discussed. If you find this video to be informative, please check out SeMI technologies on youtube where we are working hard on developing content explaining concepts in Deep Learning for Search. Full Podcast: https://www.youtube.com/watch?v=uFxfZ0vLsoU SeMI Technologies on YouTube: https://www.youtube.com/channel/UCJKT6kJ3IFYybWnL7jbXxhQ

Thanks for watching! Please subscribe for more Deep Learning and AI videos, the list of papers is below under "Content Links" Content Links:

Fully Online Meta-Learning without Task Boundaries: https://arxiv.org/abs/2202.00263

Datamodels: Predicting Predictions from Training Data: https://arxiv.org/abs/2202.00622

Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization: https://arxiv.org/abs/2202.01334

Competition-Level Code Generation with AlphaCode: https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf

GPT-NeoX-20B: https://blog.eleuther.ai/announcing-20b/

PromptSource: https://arxiv.org/abs/2202.01279

Chain of Thou…

This video gives an overview of the latest Weaviate podcast, please subscribe to see future episodes!

https://www.youtube.com/c/SeMI-and-Weaviate/videos Thanks for watching! Chapters

0:00 Overview

7:53 Ideas for Podcast Search

10:44 Weaviate Podcast so far

Thank you so much for watching, please subscribe for more Deep Learning and Ai videos! Please check out SeMI Technologies on YouTube as well, where I am hosting a podcast on Deep Learning for Search! Paper Links:

Text and Code Embeddings by Contrastive Pre-Training: https://cdn.openai.com/papers/Text_and_Code_Embeddings_by_Contrastive_Pre_Training.pdf

Introducing Text and Code Embeddings in the OpenAI API (Blog Post): https://openai.com/blog/introducing-text-and-code-embeddings/

Nils Reimers - OpenAI GPT-3 Text Embeddings - Really a new state-of-the-art in dense text embeddings? https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddi…

Thank you so much for watching, please subscribe for more Deep Learning and AI videos! Please check out SeMI Technologies on YouTube as well! Paper Links:

CM3: https://arxiv.org/abs/2201.07520

data2vec: https://scontent.fmia1-1.fna.fbcdn.net/v/t39.8562-6/271974914_483120576492438_4239522333319653600_n.pdf?_nc_cat=107&ccb=1-5&_nc_sid=ae5e01&_nc_ohc=4-cMR5tUq4QAX-Of7fj&_nc_ht=scontent.fmia1-1.fna&oh=00_AT9ymN9dNPt1p8zWQClW6MSZikaCTT8gobc2LqxW4OhzZQ&oe=61F3F7D1

LaMDA: https://arxiv.org/abs/2201.08239

PromptBERT: https://arxiv.org/abs/2201.04337

UnifiedSKG: https://arxiv.org/abs/2201.05966

Collapse by Conditioning: https://arxiv.org/abs/2201.06578

GradTail: https://arxiv.org/abs/2201.05938

CLIP…

Three false proofs, and what lessons they teach.

New notebooks: https://store.dftba.com/collections/3blue1brown/products/mathematical-quotebook-notebook

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

An equally valuable form of support is to simply share the videos. Time stamps:

0:00 - Fake sphere proof

1:39 - Fake pi = 4 proof

5:16 - Fake proof that all triangles are isosceles

9:54 - Sphere "proof" explanation

15:09 - pi = 4 "proof" explanation

16:57 - Triangle "proof" explanation and conclusion ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://github.com/3b1b/…

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Last year’s results: https://3b1b.co/blog/some1-results ------------------ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe: http://3b1b.co/subscribe Various social media stuffs:

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Generating functions, as applied to a hard puzzle used for IMO training.

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

An equally valuable form of support is to simply share the videos. Books mentioned 102 Combinatorial problems, by Titu Andreescu and Zuming Feng

https://amzn.to/3wAPoNq Generatingfunctionology by Herbert Wilf

https://amzn.to/3sPJ8Al Visualizing the Riemann zeta function

https://youtu.be/sD0NjbwqlYw Fourier series

https://youtu.be/r6sGWTCMz2k Timestamps

0:00 - Puzzle statement and motivation

4:31 - Simpler example

6:51 - The generating function

11:52 - Evaluation tricks

17:24 - Roots of unity

26:31 - Recap and final trick

30:13 - Takeaways -----------------…

A slight correction to the previous video, with some more details about how the best first word was chosen.

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

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

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

0:00 - The Bug

3:31 - How the best first guess is chosen

8:54 - Does this ruin the game? Nice post by Jonathan Olson on optimal wordle algorithms:

https://jonathanolson.net/experiments/optimal-wordle-solutions ------------------ These animations are largely made using a custom python library, manim. See the FAQ comments here:

https://www.3blue1brown.com/faq#manim

https://gi…

An excuse to teach a lesson on information theory and entropy.

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

0:00 - What is Wordle?

2:43 - Initial ideas

8:04 - Information theory basics

18:15 - Incorporating word frequencies

27:49 - Final performance Original wordle site:

https://www.powerlanguage.co.uk/wordle/ Music by Vincent Rubinetti.

https://www.vincentrubinetti.com/ Shannon and von Neumann artwork by Kurt Bruns. Code for this video:

https://github.com/3b1b/videos/blob/master/_2022/wordle.py These animations are largely made using a c…

A tale of two problem solvers.

Numberphile video on Bertrand's paradox: https://youtu.be/mZBwsm6B280

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

Special thanks to these supporters: https://3b1b.co/lessons/newtons-fractal#thanks

An equally valuable form of support is to simply share the videos. The general result here was originally proved by Cauchy.

Mémoire sur la rectification des courbes et la quadrature des surfaces courbes par M. Augustin Cauchy

https://ia600208.us.archive.org/27/items/bub_gb_EomNI7m8__UC/bub_gb_EomNI7m8__UC.pdf ------------------- Timestamps

0:00 - The players

5:22 - How to start

9:12 - Alice's initial thoughts

13:37 - Piecing together the cube

22:1…

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https://blogs.nvidia.com/blog/2022/08/09/neuralvdb-ai/

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https://www.nature.com/articles/s41586-021-03819-2

https://www.deepmind.com/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology Database:

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❗Or here: https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_diffusion.ipynb Great notebooks to try:

https://www.reddit.com/r/StableDiffusion/comments/wzk78c/colab_notebook_sd_hiki_by_daswerq123_has_a/

https://github.com/pinilpypinilpy/sd-webui-colab-simplified

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Road maker - https://twitter.com/Bbbn192/status/1550150050562674692

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Во многих задачах исследователи работают с небольшими фрагментами кода — отдельными методами, реже — с файлами. Но чтобы найти качественное решение, зачастую требуется выйти за пределы небольших кусков кода и использовать глобальную информацию о модуле или проекте. Мы поговорим о различных способах использования информации о контексте в ML моделях и о том, на что нужно обращать внимание для честной оценки их качества. Докладчик: Егор Богомолов Материалы: https://arxiv.org/pdf/2201.10705.pdf

Мы разберем методы генерации SQL запросов из описания на естественном языке и немного поговорим о более широком применении их к генерации DSL кода. Мы обсудим почему обучение обучение моделей для DSL может отличаться от моделей генерации кода, текущие подходы к решению задачи на базе лидерборда для Spider датасета и их ограничения. Мы представим более масштабируемый подход к генерации SQL и наши текущие результаты. Докладчик: Денис Литвинов

В настоящее время Reinforcement Learning (RL) применяется для решения ряда задач оптимизации в области компиляторов, таких как конфигурация флагов компиляции, выбор оптимального порядка выполнения инструкций и многие другие. Однако, подобрать оптимальный RL-алгоритм бывает сложно, так как он зависит от контекста конкретной задачи. Более того, разработчики компиляторов зачастую могут быть не вовлечены в область RL, что еще сильнее осложняет решение данной задачи. В работе Automating Reinforcement Learning Architecture Design for Code Optimization авторы предлагают инструмент Supersonic, позволяющий автоматически подбирать оптимальный RL-алгоритм для решения оптимизационных задач в компилятор…

Несмотря на то, что многие из последних достижений в области машинного обучения связаны с глубоким обучением с подкреплением, Deep RL алгоритмы остаются ненадёжными (по сравнению с классическими моделями глубокого обучения) и трудновоспроизводимыми (с точки зрения результата). Авторы статьи связывают описанные недостатки с проблемой отсутствия понимания того как внутренние механизмы, используемые в RL алгоритмах, влияют на поведение агента по отдельности и вместе взятые. На семинаре мы поговорим о поднятой авторами проблеме на примере алгоритмов Trust Region Policy Optimization (TRPO) и Proximal Policy Optimization (PPO), рассмотрим эксперименты по оценке влияния составных частей этих алгор…

Зачастую в прикладных задачах собрать достаточно большой, подходящим образом размеченный датасет для обучения модели не представляется возможным. Популярным решением в такой ситуации является Self-Supervised Learning. В рамках этого подхода модель сначала предобучают на синтетической, искусственно выдуманной задаче, выборку для которой автоматически формируют из неразмеченных данных. Примерами таких синтетических задач являются восстановление маскированных токенов в NLP (этот же подход используется и в некоторых моделях для работы с кодом), восстановление фрагментов или удаление искусственного шума при работе с картинками, восстановление последовательности кадров при работе с видео и т.д.. …

Многие из самых захватывающих новых прорывов в области искусственного интеллекта произошли благодаря двум недавним инновациям: самоконтролируемое обучение, который позволяет машинам учиться на случайных немаркированных примерах, а также Трансформеры, которые позволяют моделям ИИ выборочно сосредотачиваться на определенных частях своего ввода и, таким образом, рассуждать более эффективно. На семинара будет разобрана новая статья "Emerging Properties in Self-Supervised Vision Transforms", в которой авторы используются ранее упомянутые техники для решения задач компьютерного зрения. Докладчик: Ольга Лавриченко.

Существующие фреймворки для генерации изображений могут обуславливаться на пользовательский ввод в одной модальности — например, на текст, эскиз, маску сегментации или пример изображения со стилем. При этом, такие подходы не используют доступные мультимодальные данные. Авторы данной статьи предлагают Product-of-Experts Generative Adversarial Networks (PoE-GAN) фреймворк, который позволяет синтезировать изображение на основе условий в нескольких модальностях или любом их подмножестве, а также осуществлять безусловную генерацию. Данная модель также превосходит другие подходы в условиях унимодальной условной генерации. Докладчик: Дарья Евсикова.

Трансформеры уже давно господствуют во многих задачах NLP. И если с задачами где длина последовательности относительно мала (не более 512 токенов) проблем не возникает, то с обработкой больших текстов не все так ясно. Проблема в том, что потребление памяти увеличивается квадратично с ростом обрабатываемой последовательности. Существуют различные подходы к решению проблемы, например, можно линеаризовать softmax в модуле внимания, снизив асимптотику до O(N) (linear transformers); или же исследовать разреженность (BigBird). В свою очередь, авторы статьи продолжают идеи sliding-window и Transformer-XL. Поэтому на семинаре поговорим об этих подходах и архитектуре Block-Recurrent Transformer. Док…

Мы расскажем про исследование, посвященное дообучению ML4SE моделей под конкретный проект. В то время как большинство исследователей обучает и тестирует модели на непересекающихся наборах проектов, мы задались вопросом: “А что будет, если показать модели данные из целевого проекта?“. Мы поговорим об особенностях оценки качества проектно-дообученных моделей и презентуем полученные результаты для трех моделей в задаче предсказания имен методов.

Докладчик – Егор Богомолов

На семинаре мы поговорим о нашей работе в области предварительной тренировки векторных представлений графовых нейронных сетей (GNN) для исходного кода. Качество векторов мы оцениваем с помощью задачи предсказания типов для языка с динамической типизацией Python. Для предварительной тренировки используется задача предсказания имён. По результатам наших экспериментов векторные представления GNN позволяют достичь точности классификации типов, сравнимой с CodeBERT. Вдобавок, объединение CodeBERT и GNN векторов в гибридную модель позволяет улучшить точность классификации типов. При этом, улучшения достигаются даже после тренировки GNN модели в течение всего одной эпохи, что намного меньше чем тр…

В крупных компаниях, где весь код лежит в едином репозитории, очень важно уметь оперативно локализовать баг. Задача усложняется, когда отельные файлы состоят из сотен строк, а проблема выявляется на этапе End-to-End тестирования или в продакшене. В такой ситуации необходимо автоматическое решение, которое способно быстро найти ломающий коммит, несмотря на то, что сообщения об ошибке зачастую трудночитаемые и содержат большой объём информации. На этом семинаре мы разберём статью от Facebook (https://arxiv.org/pdf/2010.09977.pdf), в которой авторы предлагают эффективный unsupervised алгоритм локализации бага к коммиту, использующий методы информационного поиска. Описанный алгоритм приспособле…

Разработка программного обеспечения сопряжена с поиском и исправлением ошибок. В программной инженерии уже давно изучаются и описываются запахи кода – формальные признаки, индицирующие о возможном наличии проблем. Примерами запахов кода могут быть завистливая функция (метод обращается к данным чужого класса чаще, чем к данным собственного) или параллельная иерархия (ситуация, когда при создании нового класса в одной иерархии классов вам почти всегда приходится создавать парный к нему класс в другой иерархии). Для каждого запаха кода описаны потенциальные пути исправления, часто сводящиеся к какому-то рефакторингу.

Однако, проекты, связанные с машинным обучением, обладают особой спецификой и…

Трансформер - очень хорошая модель для понимания текста, однако она не эффективна из-за квадратичной асимптотической сложности по длине входящей последовательности. Хотя существует множество методов ускорения трансформера, они все еще недостаточно эффективны на длинных последовательностях. Авторы статьи предлагают Fastformer, эффективную модель трансформера, основанную на аддитивном внимании (additive attention). На семинаре мы вспомним, как работают трансформеры, познакомимся с additive attention и Fastformer и посмотрим, как он справляется с различными задачами. Докладчик: Тимур Хабибуллин

Задачи обработки естественного языка, такие как машинный перевод, ответы на вопросы и обобщения текстов, как правило решаются с помощью обучения с учителем на специально подобранных под конкретное задание датасетах. Авторы статьи показывают, что можно обучить модель, которая будет способна решать различные задачи с минимальным количеством обучения с учителем, используя для этого датасет Webtext, состоящий из миллионов различных веб-страниц. На семинаре мы обсудим, как модель справляется с заданиями различной специфики и сравним результаты авторов с результатами state-of-the art моделей. Докладчики: Маргарита Чудова

Я расскажу про процесс создания командой AI Team системы автодополнения для языка R. Будет рассказано о том, с какими трудностями можно столкнуться при разработке и внедрении системы автодополнения, основанной на нейросетях. Также мы рассмотрим некоторые нерешённые исследовательские проблемы в области нейросетевого автодополнения и обсудим возможные способы их решения. Большая часть рассказа будет основана на статье Time-Efficient Code Completion Model for the R Programming Language (https://aclanthology.org/2021.nlp4prog-1.4/), опубликованной на воркшопе NLP4prog 2021 (https://nlp4prog.github.io/2021/) конференции ACL. Докладчики: Артем Попов.

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: 19:00 — Открытие

19:05 — Бенчмарк приемлемости предложений на русском языке (RuCoLA) + секретный релиз / Максим Рябинин (Яндекс)

19:40 — Перерыв 20:00 — Верификация моделей автомобилей (Machines Can See 2022) / Дмитрий Гаус (VisionLabs) и Артём Стрекалов (АО Уфанет)

Дерево отрезков. Задача RSQ (range sum query). Задачи LCA (least common ancestor) и RMQ (range minimum query). Решение RMQ с помощью sparse table. Сведение LCA к RMQ (алгоритм Фарах-Колтона-Бендера). Сведение RMQ к LCA. Задача LA (level ancestors). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Кратчайшие пути в графах. Оценки расстояний и их релаксация. Алгоритмы Беллмана-Форда, Флойда и Дийкстры. Потенциалы. Критерий консервативности длин в терминах наличия допустимых потенциалов. Нахождение допустимых потенциалов с помощью алгоритма Беллмана-Форда. Алгоритм Джонсона. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Остовы минимального веса. Лемма о минимальном ребре в разрезе. Алгоритмы Краскала и Прима. Структура DSU (disjoint set union) Реализация с использованием леса. Ранги вершин, эвристика ранга. Логарифмическая оценка ранга через количество элементов. Эвристика сжатия путей. Оценка учетной стоимости операций (без доказательства). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Время и память как основные ресурсы. RAM машина. Сложность на заданном входе, сложность в худшем случае, сложность в среднем случае, рандомизированная сложность.

Учетная стоимость операций, метод потенциалов, банковский метод анализа сложности.

Массивы переменного размера. Реаллокация. Анализ учетной сложности операции push-back. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Реализация очереди на паре стеков с константной учетной сложностью. Динамические минимумы-максимумы в стеках и очередях. Персистентные структуры данных. Виды персистентности. Модель вычислений Pointer Machine. Персистентные стеки и очереди. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Алгоритм Misra-Gries.

Деревья поиска. Вставка и удаление элементов. Inorder-обход дерева. Красно черные деревья: определение и основные свойства. Реализация операций вставки для красно-черного дерева. Дучи (treaps). Единственность дучи для заданного набора различных ключей и приоритетов. Логарифмическая оценка матожидания высоты дучи. Операции слияния и разделения для дуч. Операции вставки и удаления элементов для дуч. Подробнее о поступлении в Школу анализа данных: https://academy.yandex.ru/dataschool

Быстрая сортировка (Quick-Sort). Способы выбора разделяющего элемента. Элиминация хвостовой рекурсии. Порядковые статистики. Рандомизированный алгоритм Quick-Select. Детермининированный алгоритм поиска (метод "медианы медиан").

Бинарный поиск. Galloping.

Линейное по времени слияние упорядоченных последовательностей. Оптимальное по числу сравнений слияние упорядоченных последовательностей.

Задача о длиннейшей возврастающей подпоследовательности. Динамическое программирование. O(n log n)-алгоритм. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Дерево отрезков. Задача RSQ (range sum query). Задачи LCA (least common ancestor) и RMQ (range minimum query). Решение RMQ с помощью sparse table. Сведение LCA к RMQ (алгоритм Фарах-Колтона-Бендера). Сведение RMQ к LCA. Задача LA (level ancestors). Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Splay-деревья. Операция splay: zig, zig-zig и zig-zag шаги. Реализация операций вставки, удаления, слияния и разделения для splay-деревьев. Обход в глубину. Топологическая сортировка Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Хеш-функции. Коллизии. Разрешение коллизий методом цепочек. Гипотеза простого равномерного хеширования, оценка средней длины цепочки хеш-функции. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Модель разрешающих деревьев. Нижняя оценка на число сравнений при сортировке и поиске в модели разрешающих деревьев.

Сортировка слиянием (Merge-Sort). Top-down и bottom-up подходы. Сортировка слиянием во внешней памяти. Inplace Merge-Sort. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Задача о длиннейшой возрастающей подпоследовательности. Алгоритмы сортировки Heap-Sort и Intro-Sort. Частичная сортировка с помощью кучи и поиска порядковой статистики. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

Построение совершенной хеш-функции методом двухуровненого хеширования. Построение совершенной хеш-функции методом ациклических графов. Фильтр Блюма (Bloom filter). Оценка вероятности ложноположительного срабатывания. Count-min sketch. Подробнее о поступлении в Школу анализа данных от Академии Яндекса: https://clck.ru/geqRt

00:00 - 1:50 Вступление

1:50 - 5:27 Datastore 5:27 - 6:45 Верхнеуровневый обзор RETRO

6:45 - 12:41 Self-Attention 101

12:41 - 17:02 Encoder and Decoder

17:02 - 21:10 Детали RETRO

21:10 - 23:21 Архитектура RETRO (1 часть)

23:21 - 25:35 Cross-Attention 101

25:35 - 27:26 Архитектура RETRO (2 часть)

27:26 - 31:17 Chunked Cross Attention

31:17 - 37:10 Метрики и следствия из них

37:10 - 41:14 Выводы и предсказания на будущее Ссылка на презентацию: https://1drv.ms/p/s!AlnN0aqNwShslQucZAy9R5VeGRRt?e=Gc3dgp

Спикер: Алексей Фирстов, Junior Data Scientist, Альфа Банк

В задаче кредитного скоринга возникает смещение обучающей выборки, так как целевая переменная созревает только по одобренным заявкам клиентов. Расскажу, как выровнять распределения данных на трейне и проде, как изменится процесс обучения нейронных сетей и, конечно, про бизнес-эффект. Доклад ориентирован на специалистов, работающих в моделировании кредитных рисков, дата-саентистов и портфельных риск-менеджеров. Полезные ссылки

Соревнование на ods.ai: https://ods.ai/competitions/dl-fintech-bki

Репозиторий соревнования (БКИ): https://github.com/SmirnovValeriy/dl-fintech-bki

Tg-канал “Нескучный Data Science”: https://t.me/not_boring_ds

…

Сергей Кастрюлин | Research Scientist, Philips Research Russia & Skoltech Возможна ли автоматическая оценка качества МРТ-снимков без участия врачей? О различных метриках и более интересных вариантах рассказывает Сергей Кастрюлин Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Александр Нестеров | Руководитель направления по исследованию данных, Сбер

NeuralSympCheck: Нейросетевая модель уточнения симптомов и постановки диагноза с использованием принципа логической регуляризации Может ли нейронная сеть не только определить самый вероятный диагноз по симптомам пациента, но и задать ему уточняющие вопросы? Александр рассказывает об опыте создания новой архитектуры такого симптом-чекера Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Александр Золотарев и Никита Кубраков | Сколтех Научный доклад о нетипичном применении Data Science в кардиологии Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Екатерина Кондратьева | PhD Student, Сколтех; Researcher, AIRI Флюорография, маммография, КТ лёгких и мозга... Уже слышали! А есть в области medical imaging что-то более интересное? Конечно! О менее популярных, но очень интересных областях применения ML вы узнаете из доклада Кати Кондратьевой Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Олег Рогов | Research Scientist, Сколтех Олег рассказывает о двух нестандартных кейсах применения технологий машинного обучения в медицине - визуализация вен и автоматическая генерация текстов заключений по снимкам Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Никита Николаев | Marketing Director, Цельс В индустрии медтеха есть много громких случаев провалов раздутых стартапов. Никита Николаев рассказывает о самых ярких и даёт советы стартаперам Таймкоды:

- 2:02 - кейсы провалов

- 18:25 - главные причины краха

- 19:53 - рецепты успеха

- 22:24 - практика Цельса

- 25:16 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Мария Гарец | ML Lead, Цельс Маша Гарец рассказывает о методах умного выбора данных на разметку. Размечаем меньше - прирост метрик больше: сплошная экономия. Таймкоды:

- 3:07 - uncertainty sampling

- 5:38 - diversity sampling

- 8:44 - random sampling

- 9:42 - случаи из практики

- 17:42 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Антон Краснов | Data Annotation Lead, Цельс Без врачей в ML в медицине далеко не уедешь. Как же выстроить систему отбора и контроля качества их работы? Рассказывает руководитель команды разметки в Цельсе - Антон Краснов Таймкоды:

- 4:42 - как искать врачей-разметчиков?

- 6:15 - входной контроль

- 9:30 - выход на промышленные масштабы

- 18:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Николай Филатов | ML Lead, Цельс Домен медицинских данных отличается особенно шумной и грязной разметкой. Какие есть способы автоматического и ручного контроля её качества? Об этом в докладе Коли Филатова Таймкоды:

- 3:06 - этап постановки задачи

- 7:30 - этап очистки данных

- 15:13 - этап разметки данных

- 16:15 - заключение и рекомендации

- 17:32 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Евгений Никитин | Head of AI, Цельс

Медицинские данные - это боль. Но только преодолевая боль, мы становимся сильнее. Этот доклад - обзор ключевых компонентов процесса обеспечения качества медицинских данных. Таймкоды:

- 1:02 - почему медицинские данные - это боль

- 12:12 - что такое качество данных?

- 16:32 - путь к качеству медицинских данных

- 26:34 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

Антон Голубев | Data Platform Lead, Цельс Как выстроить работу с разнородными медицинскими данными с технической точки зрения? Какие инструменты использовать? Антон отвечает на эти вопросы на основе истории проекта Цельс.Маммография Таймкоды:

- 1:44 - история данных на проекте Маммография

- 8:59 - дата-платформа (хранение данных, ETL, дата-продукты)

- 24:57 - что дальше?

- 26:45 - QA Data Fest Online 3.0

Трек: Ужасы медицинских данных: https://ods.ai/tracks/medical-data-df2022 Соцсети Open Data Science:

https://t.me/datafest

https://t.me/ods_ru

https://vk.com/datafest Регистрация на мероприятия сообщества: https://ods.ai/events

Хабы сообщества: https://ods.ai/hubs

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:…

Play at primerlearning.org

Or on Google Play: https://play.google.com/store/apps/details?id=com.Primer.CatchtheCheaters For discussion and updates

- Discord: https://discord.gg/NbruaNW

- Twitter: @primerlearning

- Reddit: r/primerlearning Plush blobs and other merch: https://store.dftba.com/collections/primer

Support these videos on Patreon: https://www.patreon.com/primerlearning Made with Unity

https://github.com/Helpsypoo/PrimerUnity Made possible by support through Patreon:

Anthony Eufemio

Jon Mundle

Spline

Zachariah Richard Fournier

Vladimir Duchenchuk

Roy & BreAnna Steves

Shayn Osborn

Jeremy

Guguke

Anders Fjeldvær

Luc Cedric R.

Erik Broeders

Kairui Wang

Sean Barker

Eric Helps

Stevie Hr…

Will Sasso is a comedian, actor, and co-host of the Dudesy podcast.

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% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Will’s Instagram: https://instagram.com/willsassoWill’s Twitter: https://twitter.com/WillSassoDudsey’s YouTube: https://youtube.com/DudesyDudsey’s Website: https://dudesypod.comDudsey’s Patreon: https://patreon.com/dudesyPODCAST INFO:Podcast website: ht…

Rana el Kaliouby is a pioneer in the field of emotion recognition and human-centric AI.

She is the founder of Affectiva, deputy CEO of Smart Eye, and author of Girl Decoded.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Weights & Biases: https://lexfridman.com/wnb– Notion: https://notion.com– InsideTracker: https://insidetracker.com/lex to get 20% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Rana’s Twitter: https://twitter.com/kalioubyRana’s Instagram: https://instagram.com/ranaelkalioubyRana’s Facebook: https://facebook.com/RanaelKalioubyAffectiva (website): https://affe…

Ray Kurzweil is an author, inventor, and futurist.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– NetSuite: http://netsuite.com/lex to get free product tour– Linode: https://linode.com/lex to get $100 free credit– MasterClass: https://masterclass.com/lex to get 15% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Ray’s Website: https://kurzweilai.netRay’s Books:The Singularity Is Nearer (pre-order): https://amzn.to/3BNXmGRHow To Create A Mind: https://amzn.to/3qqlkBwThe Singularity Is Near: https://amzn.to/3DfXP5zThe Age of Spiritual Machines: https://amzn.to/3RSjtAXDanielle: https://amzn.to/3Bww2N7Tran…

Christopher Capozzola is a professor of history at MIT.

Please support this podcast by checking out our sponsors:– Wealthfront: https://wealthfront.com/lex to get $50 sign-up bonus– InsideTracker: https://insidetracker.com/lex to get 20% off– LMNT: https://drinkLMNT.com/lex to get free sample pack– SimpliSafe: https://simplisafe.com/lexEPISODE LINKS:Christopher’s Instagram: https://instagram.com/boundbywarbookChristopher’s Website: https://boundbywarbook.comChristopher’s books:Bound by War: https://amzn.to/3QssboAUncle Sam Wants You: https://amzn.to/3Bw5KdTPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS…

Alexandra and Andrea Botez are chess players, commentators, educators, entertainers, and streamers.

Please support this podcast by checking out our sponsors:– Calm: https://calm.com/lex to get 40% off premium– Weights & Biases: https://lexfridman.com/wnb– BiOptimizers: http://www.magbreakthrough.com/lex to get 10% off– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:BotezLive Twitch: https://twitch.tv/botezliveBotezLive YouTube: https://youtube.com/c/BotezLiveBotezLive Instagram: https://instagram.com/botezliveAlexandra’s Instagram: https://instagram.com/missbotezAndrea’s Instagram: https://instagram.com/itsandreabot…

Nick Lane is a biochemist at UCL and author of Transformer, The Vital Question, and many other amazing books on biology, chemistry, and life.

Please support this podcast by checking out our sponsors:– Backbone: https://playbackbone.com/lex to get perks with order– Notion: https://notion.com– BetterHelp: https://betterhelp.com/lex to get 10% off– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Nick’s Website: https://nick-lane.netNick’s Books:Transformer: https://amzn.to/3cy7lpOThe Vital Question: https://amzn.to/3q0vN6qOxygen: https://amzn.to/3edy3V5Power, Sex, Suicide: https://amzn.to/3B3OInkLife Ascending: https://amzn.to/3wKIsOEBooks mentioned:21 Lessons for the 21…

John Vervaeke is a psychologist and cognitive scientist at University of Toronto.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex to get special savings– Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:John’s YouTube: https://youtube.com/johnvervaekeJohn’s Twitter: https://twitter.com/vervaeke_johnJohn’s Facebook: https://facebook.com/VervaekeJohnJohn’s Website: https://johnvervaeke.comBooks mentioned:Flow: ht…

Noam Chomsky is a linguist, philosopher, and political activist.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.com/lex– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% off– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Noam’s Website: https://chomsky.info/Noam’s Instagram: https://instagram.com/noam.chomskyofficialManufacturing Consent (book): https://amzn.to/3KaEc0dPODCAST 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://…

Magnus Carlsen is the highest-rated chess player in history and widely considered to be the greatest chess player of all time.

Please support this podcast by checking out our sponsors:– Shopify: https://shopify.com/lex to get 14-day free trial– Athletic Greens: https://athleticgreens.com/lex to get 1 month of fish oil– Fundrise: https://fundrise.com/lex– BetterHelp: https://betterhelp.com/lex to get 10% off– InsideTracker: https://insidetracker.com/lex to get 20% offEPISODE LINKS:Magnus’s Twitter: https://twitter.com/MagnusCarlsenMagnus’s Instagram: https://instagram.com/magnus_carlsenMagnus’s YouTube: https://youtube.com/c/themagnuscarlsenMagnus’s Website: https://magnuscarlsen.comPODCAST …

Liv Boeree is a poker champion and science educator on topics of game theory, physics, complexity, and life.

Please support this podcast by checking out our sponsors:– Audible: https://audible.com/lex to get 30-day free trial– GiveWell: https://www.givewell.org and use code Lex Fridman Podcast– Linode: https://linode.com/lex to get $100 free credit– Indeed: https://indeed.com/lex to get $75 credit– ExpressVPN: https://expressvpn.com/lexpod to get 3 months freeEPISODE LINKS:Liv’s Twitter: https://twitter.com/liv_boereeLiv’s Instagram: https://instagram.com/liv_boereeLiv’s Facebook: https://facebook.com/livboereeLiv’s YouTube: https://youtube.com/user/LivBoereeBooks and resources mentioned:No…

Jordan Peterson is a psychologist, lecturer, podcast host, and author.

Please support this podcast by checking out our sponsors:– Weights & Biases: https://lexfridman.com/wnb– Notion: https://notion.com/startups to get up to $1000 off team plan– InsideTracker: https://insidetracker.com/lex to get 20% off– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Blinkist: https://blinkist.com/lex to get 25% off premiumEPISODE LINKS:Jordan’s Twitter: https://twitter.com/jordanbpetersonJordan’s Website: https://jordanbpeterson.comJordan’s Books:Beyond Order: https://amzn.to/3T4LRBw12 Rules for Life: https://amzn.to/3c4sqYFMaps of Meaning: https://amzn.to/3A1Ods2Webs…

Duncan Trussell is a comedian, host of The Duncan Trussell Family Hour podcast, and co-creator of The Midnight Gospel.

Please support this podcast by checking out our sponsors:– Skiff: https://skiff.com/lex– Calm: https://calm.com/lex to get 40% off premium– SimpliSafe: https://simplisafe.com/lex– NetSuite: http://netsuite.com/lex to get free product tour– Indeed: https://indeed.com/lex to get $75 creditEPISODE LINKS:Duncan’s Twitter: https://twitter.com/duncantrussellDuncan’s Instagram: https://instagram.com/duncantrussellThe Duncan Trussell Family Hour: https://duncantrussell.comThe Midnight Gospel: https://netflix.com/themidnightgospelBooks mentioned:Superintelligence: https://amzn.to/3Q…

Magatte Wade is an entrepreneur with a passion for creating positive change in Africa through economic freedom.

Please support this podcast by checking out our sponsors:– Mizzen+Main: https://mizzenandmain.com and use code LEX to get $35 off– Audible: https://audible.com/lex to get 30-day free trial– InsideTracker: https://insidetracker.com/lex to get 20% off– Onnit: https://lexfridman.com/onnit to get up to 10% offEPISODE LINKS:Magatte’s Twitter: https://twitter.com/magattewMagatte’s Instagram: https://instagram.com/magattewMagatte’s Facebook: https://facebook.com/themagattewadeMagatte’s Website: https://magattewade.comWebsites mentioned:Austin housing project: https://texansforreasonables…

Andrew Bustamante is a former CIA covert intelligence officer.

Check out his work and podcast at https://everydayspy.com Please support this podcast by checking out our sponsors:– Wealthfront: https://wealthfront.com/LEX to get $50 sign-up bonus– LMNT: https://drinkLMNT.com/lex to get free sample pack– BetterHelp: https://betterhelp.com/lex to get 10% off– ExpressVPN: https://expressvpn.com/lexpod to get 3 months free– MasterClass: https://masterclass.com/lex to get 15% offEPISODE LINKS:Everyday Spy: https://everydayspy.com/quizEveryday Spy Podcast: https://everydayspy.com/podcastAndrew’s Twitter: https://twitter.com/everydayspyAndrew’s Instagram: https://instagram.com/everydayspyPODCAST IN…

John Carmack is a legendary programmer, co-founder of id Software, and lead programmer of many revolutionary video games including Wolfenstein 3D, Doom, Quake, and the Commander Keen series.

He is also the founder of Armadillo Aerospace, and for many years the CTO of Oculus VR.

Please support this podcast by checking out our sponsors:– InsideTracker: https://insidetracker.com/lex to get 20% off– Indeed: https://indeed.com/lex to get $75 credit– Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get special savings– Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish o…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary L. Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and communities together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

They also discuss how critical thinkers and makers from social movements…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

In this episode of the series, Dr. Gray talks with Dr. Tawanna Dillahunt, Ass…

In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations.

Dr. Gray and her team are working to bring computer science, engineering, social science, and community together to boost societal resilience in ongoing work with Project Resolve.

She’ll talk with organizers, academics, technology leaders, and activists to understand how to develop tools and frameworks of support alongside members of these communities.

Together, they explore Patton’s learnings about the challenges of using AI in…

In this episode, we talk with Kayo Yin, an incoming PhD at Berkeley, and Malihe Alikhani, an assistant professor at the University of Pittsburgh, about opportunities for the NLP community to contribute to Sign Language P…

This episode is the third in our current series on PhD applications.

In this episode, we talk about what the PhD application process looks like after applications are submitted.

We start with a general overview of the t…

The Harms of Targeted Weight Loss AdsToday, we are joined by Liza Gak, a Ph.D student at UC Berkeley.

Liza’s research interest lies around human-computer interaction (HCI), social computing, and how people are harmed online.

Liza explained how she grouped and coded the qualitative data using the inductive iterative approach.

She spoke about her findings, iterating how weight loss ads target the vulnerable.

She also explained how ad distribution platforms can play a role in ameliorating the harm ads cause to users.

Podcast AdvertisingToday, we are joined by Rob Walch, the VP of Podcast Relations at Libsyn.

Libsyn is a popular podcast hosting platform, where the Data Skeptic podcast is hosted.

He then explained how podcasters can monetize their podcasts using Host Read.

Rob then discusses how to engage the podcast audience using surveys.

He also explained why iOS users have 5 times more podcast listeners than Android users even though there are 5X more Android phones than iPhones.

Fairness in e-commerce SearchOn the show today, we are joined by Abhisek Dash and Saptarshi Ghosh.

Fairness and Interpretability Issues in E-commerce Search through Smart Speakers.

Abhisek started by giving some background on what fairness in machine learning is.

He also explained what it means to audit machine learning systems.

They both discussed some concerning discrepancies in search results between Amazon smart speakers and the desktop website.

Fraudulent Amazon ReviewersOn the show today, we are joined by Rajvardhan Oak, an applied Scientist at Microsoft.

Raj delved deeper into how these fraudulent reviews work with real-life scenarios.

Raj discussed the quantitative and qualitative analysis he carried out on the dataset.

Rounding up, Raj discussed how fraudulent review companies avoid being detected by Amazon.

He finally discussed recommendations to forestall fraudulent reviews on e-commerce platforms.

Ad Targeting in Amazon Smart SpeakersOur guest today is Umar Iqbal.

Umar joins us to discuss his study on ad targeting in Amazon smart speakers.

While the regulatory bodies require a level of transparency in the usage of users’ data, Umar explained how their regulations are barely effective.

Umar went further to explain what makes voice data special, and the possibilities it brings when mined.

He also discussed the involvement of third-party skills in collecting and sharing users’ voice data.

AdWords with Unknown BudgetsIn today’s episode, we are joined by Rajan Udwani, an Assistant Professor at the University of California Berkeley.

Rajan began by discussing how the tools for operations research vary based on the optimization problem.

He then delved into the optimization problem for AdWords.

Rajan explained the approaches to modelling the problem of ad allocation.

Concluding, Rajan discussed two other ideas (throttling and bid scaling) that can better optimize ad allocation.

Piotr discusses common MLOps activities and how data science teams can take advantage of Neptune.ai for better experiment tracking.

He also mentioned when beginners are advised to start using machine learning tools.

Piotr also gave some advice on key activities that should be done by machine learning specialists during machine learning development.

He then talked about the short-term and long-term benefits of experiment tracking and model registry for machine learning developers.

You can read more about how to use the Neptune.ai platform from their blog page or learn more about the platform from their documentation.

Affiliate Marketing RabbitholeAffiliate marketing creates an opportunity for marketers to gain a commission by promoting a product or service.

Skeptoid podcast is a weekly podcast focused on conversations around skepticism and pseudoscience.

Today, Brian shares his personal story about affiliate marketing and the case he had with eBay between 2008 and 2014.

He was involved in affiliate marketing from 2002 to 2006.

Brian explained how affiliate marketing works in simple terms, and how people make money off it.

Monetization of YouTube Conspiracy TheoristsToday, Cameron Ballard joins us to discuss his research paper titled, Conspiracy Brokers: Understanding the Monetization of YouTube Conspiracy Theories.

After collecting and analysing the data, Cameron discussed some observations he found.

In other words, ads likely to be financial scams or ads with fake promises take advantage of conspiracy videos for more reach.

Going forward, Cameron discussed how YouTube and other stakeholders can take action to forestall the advancement of predatory or scam ads.

Wrapping up, Cameron spoke about the Raditube project.

He discusses a study and his coauthors titled, What Makes a “Bad” Ad?

Eric began by explaining what a bad ad is.

Afterwards, he discussed how bad ads find their way to social media or web pages despite the policies provided by advertising platforms.

Eric and his team crawled an enormous array of websites, including those for misinformation and political ads.

To analyze the data, Eric used a clustering technique called population label distribution learning.

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

Having learned how digital ads work, she wanted to delve into how governments are exploiting this technology for more reach during elections.

She spoke about why advertisers are shifting away from long-standing television ads to digital ads.

She also explained how digital ads started for political campaigns in the 2008 US presidential election and have progressed over the years.

She collected 600,000 official political campaigns about the 2020 general election on Facebook.

NaLette discussed how she captured the data to investigate this hypothesis and revealed if the hypothesis was true after her analysis.

Fraud Detection in Crowdfunding CampaignsOn the show today, we are joined by Beatrice Perez.

She discusses her study titled I call BS: Fraud Detection in Crowdfunding Campaigns.

Machine learning has largely been used for bank fraud detection but finds sparse application in detecting fraudulent campaigns on crowdfunding platforms.

She also explained the data collection process of retrieving properties of various crowdfunding campaigns.

Rounding up, she gave some advice on how users can spot a potential fraudulent campaign on crowdfunding platforms.

Artificial Intelligence and Auction DesignMartino Banchio, a PhD student at the Stanford Graduate School of Business, joins us to discuss his study on the intersection of artificial intelligence and economics.

He speaks to us about his findings from his research titled, Artificial Intelligence and Auction Design.

He particularly discussed how the knowledge of game theory is key to model interactions between economic agents and decision agents.

He went deeper into what game theory was using chess as an example, and how it is applied in modelling economic problems.

Trust issues can cause collusive agreements to fail, but Martino discussed how reward-punishment schemes can sustain low bids.

Some exciting changes are coming to our popular Five-Minute Friday series!

From longer episodes to new guests, tune in to hear what's next.

Additional materials: www.superdatascience.com/612Interested in sponsoring a…

Dr. Ken Stanley, a world-leading expert on Open-Ended AI and author of the genre-bending book "Why Greatness Cannot be Planned," joins Jon Krohn for a discussion that has the potential to shift your entire view on life.

On this episode of Five-Minute Friday, host Jon Krohn shares his life motto, “Who dares, wins”, and the sentiment behind it: that to get anywhere in life, it is first necessary to try.

Jon believes that “daring”, in this…

Jon Krohn speaks with Zhamak Dehghani, the empathetic technologist who coined the term “data mesh”.

They explore what a data mesh is, and how its approach toward secure interconnectivity will help solve a roster of data-…

Dr. Jennifer Hill, Professor of Applied Statistics at New York University, joins Jon this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will learn:• How ca…

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will…

Company meetings should be held to solve problems.

So, why do we often feel like the weekly stand-ups and check-ins are a waste of everyone’s time?

On this episode of Five-Minute Friday, host Jon Krohn brings his habit-m…

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.

In this episode you will…

Four thousand weeks equate to roughly 80 years—a lifetime for those of us lucky enough to get there.

What do we choose to do with this time?

How can we stop ourselves from feeling like time in general is slipping away?

Kian Katanforoosh, CEO of Workera and Lecturer at Stanford University, joins Jon Krohn to reveal the tools, frameworks, and machine learning models that power his platform and remote team.

In this episode you will learn…

During this week's Five-Minute Friday episode features, Jon explores recent groundbreaking developments in nuclear fusion –ignition–and what that signals for the future.

Additional materials: www.superdatascience.com/60…

Christina Stathopoulos, Analytical Lead for Waze and Adjunct Professor at IE Business School, joins the podcast to shed light on her work with geospatial data and how she nurtured an entire data career while abroad in Sp…

Inspired by a quote from by science fiction writer, Teresa Nielsen Hayden, Jon Krohn reflects on the notion of living in ancient times and the machine learning-related implications that arise from this perspective.

This week, Sarah Catanzaro, General Partner at Amplify Partners joins Jon for an episode that dives into the venture capital side of data science.

Learn how to fund your data science business idea, take note of what star…

Rest and relaxation await as Steve Fazzari joins us this week for a special edition of the podcast!

Tune in for a rejuvenating session of Yoga Nidra led beautifully by the expert.

Additional materials: www.superdatascie…

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.

However, there is doubt about how powerful and effective such research efforts are.

Is studying AI in academia a waste of time?

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.

Neural networks are becoming massive monsters that are hard to train (without the “regular” 12 last-generation GPUs).

Is there a way to skip that?

Let me introduce you to Zero-Cost proxiesReferences

In this episode, I speak about online machine learning systems and why blindly choosing such a paradigm can lead to 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.

June 3, 2022 podcastThat deep learning alone is not sufficient to solve artificial general intelligence, is more and more accepted statement.

Generalist agents have great properties that can overcome some of the limitations of single-task deep learning models.

Be aware, we are still far from AGI, though.

So what are generalist agents?

Referenceshttps://arxiv.org/pdf/2205.06175

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May 16, 2022 podcastIn this episode I speak with Matt Swalley, Chief Business Officer of Omneky, an AI platform that generates, analyzes and optimizes personalized ad creatives at scale.

We speak about the way AI is used for generating customized recommendation and creating experiences with data aggregation and analytics.

respecting the privacy of individuals.

LinksGrow your business with personalized ads https://www.omneky.com/Data Science at Home Podcast (Live) https://www.twitch.tv/datascienceathome

May 16, 2022 podcastLet’s take a break and think about the state of AI in 2022.

In this episode I summarize the long report from the Stanford Institute for Human-Centered Artificial Intelligence (HAI)Enjoy!

If you want a new interactive experience, I am scheduling hands-on session on TwitchFeel free to drop by when there is a live session, and interact with me.

I’ll see you there!

Referenceshttps://spectrum.ieee.org/artificial-intelligence-indexhttps://www.twitch.tv/datascienceathome

May 16, 2022 podcastIn this episode I have a conversation with, Itai Bar-Sinai, CPO & Cofounder of Mona.

Why is AI monitoring so different from monitoring classic software?

How to reduce the gap between data science and business?

What is the role of MLOps in the data monitoring field?

With over 10 years of experience with AI and as the CPO and head of customer success at Mona, the leading AI monitoring intelligence company, Itai has a unique view of the AI industry.

April 21, 2022 podcastI am with Ander Steele, data scientist and mathematician with a passion for privacy and Shannon Bayatpur, product manager with a background in technical writing and computer science, from Tonic.ai.

We speak about data.

But all we say is authentic.

LinksTonic websiteCareer pageNeural networks for synthetic data

April 21, 2022 podcastIn this episode my friend and I speak about AI, batteries and automotive.

Dennis Berner, founder of Digitlabs has been operating in the field of automotive and batteries for a long time.

His point of views are absolutely a must to listen to.

Below a list of the links he mentioned in the show.

April 21, 2022 podcastThis is one episode where passion for math, statistics and computers are merged.

I have a very interesting conversation with Ravin, data scientist at Google where he uses data to inform decisions.

All opinions in this episode are his own and none of the companies he has worked for are represented.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

We speak about machine learning applied to spatial data, spatial SQL and GIS (Geographic Information System).

Just tell them you came through Data Science at Home podcast.

and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and predictive engines in domain like finance, healthcare, pharmaceuticals, logistics, energy.

Amethix provide solutions to collect and secure data with higher transparency and disintermediation, and build the statistical models that will support your business.

ReferencesCarto https://carto.comSpatial Feature Engineering: https://geographicdata.science/book/intro.htmlCARTO Blog: https://carto.com/blog/Spati…

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We answer such questions and much more in this wonderful episode with Triveni Gandhi, Senior Data Scientist and Shaun McGirr, AI Evangelist at Dataiku.

We cover topics about the history of data science, ethical AI and…This episode is brought to you by DataikuWith Dataiku, you have everything you need to build and deploy AI projects in one place, including easy-to-use data preparation and pipelines, AutoML, and advanced automation.

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and by Amethix TechnologiesAmethix use advanced Artificial Intelligence and Machine Learning to build data platforms and pre…