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Examining the design of interactive articles by synthesizing theory from disciplines such as education, journalism, and visualization.

A collection of articles and comments with the goal of understanding how to design robust and general purpose self-organizing systems

Training an end-to-end differentiable, self-organising cellular automata for classifying MNIST digits.

Part one of a three part deep dive into the curve neuron family.

How to tune hyperparameters for your machine learning model using Bayesian optimization.

An overview of all the neurons in the first five layers of InceptionV1, organized into a taxonomy of 'neuron groups.'

By focusing on linear dimensionality reduction, we show how to visualize many dynamic phenomena in neural networks.

By studying the connections between neurons, we can find meaningful algorithms in the weights of neural networks.

What can we learn if we invest heavily in reverse engineering a single neural network?

Training an end-to-end differentiable, self-organising cellular automata model of morphogenesis, able to both grow and regenerate specific patterns.

Exploring the baseline input hyperparameter, and how it impacts interpretations of neural network behavior.

The Current State of Machine Learning EducationHaving taken the main slate of the seminal machine learning courses at one of the top universities for AI, I have found a general guideline most classes follow.

If the course instead focuses on more general machine learning principles, it introduces other avenues such as unsupervised and reinforcement learning.

The GapHaving analyzed both the current state of machine learning education as well as the skills needed to create important applied machine learning systems, we now comment on the gap between the two sides.

CitationFor attribution in academic contexts or books, please cite this work asJupinder Parmar, "The Gap: Where Machine Learning Ed…

While protestors marched through the city demanding justice for George Floyd and an end to police brutality, Minneapolis police trained surveillance tools to identify them.

In a new twist, these surveillance systems are starting to seep out of metropolitan police departments and into the suburbs.

Less easily spotted, amid the pandemonium, were the automated systems law enforcement rely on to coordinate their response to the protests, spy on them, identify them, and later locate them for arrest.

But police could be confident in the dragnet surveillance systems being built across the country to help them spy on and target protestors and rioters.

CitationFor attribution in academic contexts or…

To me, AI democratization means making it possible for everyone to create artificial intelligence systems.

For the purposes of this piece, I focus primarily on the "having access to powerful AI models" part of democratization since GPT-3 is such a pre-built AI model.

GPT-3 and other very very large models created at Microsoft and Google are very concerning in how they affect “democratization” of AI.

CitationFor attribution in academic contexts or books, please cite this work asMark Riedl, "AI Democratization in the Era of GPT-3", The Gradient, 2020.

BibTeX citation:@article{rield2020democratizationgpt3,author = {Riedl, Mark},title = {AI Democratization in the Era of GPT-3},journal = {The Gr…

Through this post, I want to establish a link between Graph Neural Networks (GNNs) and Transformers.

Graph Neural Networks (GNNs) or Graph Convolutional Networks (GCNs) build representations of nodes and edges in graph data.

Are Transformers learning neural syntax?

Transformers are a special case of Graph Neural Networks.

CitationFor attribution in academic contexts or books, please cite this work asChaitanya K. Joshi, "Transformers are Graph Neural Networks", The Gradient, 2020.

The result described above is true, with one little twist: instead of using state-of-the-art artificial deep neural networks, researchers trained “natural” neural networks - more precisely, a flock of four pigeons - to diagnose breast cancer.

In the end, neural networks perhaps aren’t that different from (lazy) humans after all ...

Shortcut Learning in Deep Neural Networks.

Shortcut Learning in Deep Neural Networks.

Jörn-Henrik Jacobsen et al., "Shortcuts: Neural Networks Love to Cheat", The Gradient, 2020.

The real problem is that language does productivity in a very particular way, and it remains unclear how.

It may also provide fresh ideas, or simply the relief of knowing that compositionality does not have to be tackled entirely in one go.

The compositionality principle says that the meaning of the sentence Dogs sleep is made of the meaning of dogs and the meaning of sleep.

Adhering to purely bottom-up compositionality does make for a somewhat cumbersome semantics, though.

CitationFor attribution in academic contexts or books, please cite this work asAurelie Herbelot, "How to Stop Worrying About Compositionality", The Gradient, 2020.

Given these apparent similarities, many questions arise: How similar are human and machine vision really?

Geirhos et al.

The following figure shows two examples of the Synthetic Visual Reasoning Test (SVRT) (Fleuret et al., 2011).

A large recognition gap was identifiable for our DNN when testing machine-selected stimuli - unlike for the machine algorithms tested by Ullman et al.

Human and machine illustration taken from https://www.flickr.com/photos/gleonhard/33661762360 under the license https://creativecommons.org/licenses/by-sa/2.0/CitationFor attribution in academic contexts or books, please cite this work asJudy Borowski and Christina Funke, "Challenges of Comparing Human and Machine P…

— 🔥Kareem Carr🔥 (@kareem_carr) June 23, 2020Further discussion on the subject also occurred on reddit in the thread "[Discussion] about data bias vs inductive bias in machine learning sparked by the PULSE paper/demo".

— Yann LeCun (@ylecun) June 26, 2020The PULSE model and this exchange were later covered in VentureBeat with the article "A deep learning pioneer’s teachable moment on AI bias".

Regardless of which stance you agree with, it makes sense to at least understand the criticisms directed at Dr. LeCun.

— Yann LeCun (@ylecun) June 21, 2020Which again led to questions regarding the validity of the initial claim:Yes.

CitationFor attribution in academic contexts or books, please cite thi…

This is a follow-up article to our article on building speech-to-text (STT) models, Towards an ImageNet Moment for Speech-to-Text.

Сriticisms of the IndustryIn general, the majority of STT papers we have read were written by researchers from the industry (e.g.

Most criticisms of STT papers and solutions can be attributed to either the"academic" part or the "industry" part of the researchers’ background.

The majority of modern STT papers usually just heavily overfit on the LibriSpeech ASR corpus (LibriSpeech) with increasingly more extravagant methods.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov, "A Speech-To-Text Practitioner’s Criticisms …

Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade.

IntroductionFollowing the success and the democratization (the so-called "ImageNet moment", i.e.

This piece will describe our pursuit of an ImageNet moment for STT, which has so far not been found, and particularly in the context of Russian language.

(i) is easy to estimate just by looking at the model's performance during the first 20-25% of its epochs.

CitationFor attribution in academic contexts or books, please cite this work asAlexander Veysov, "Toward's an ImageNet Moment for Speech-to-Text", The Gradient, 2020.

My investigation in reproducible ML has also relied on personal notes and records hosted on Mendeley and Github.

What Makes a ML Paper Reproducible?

The biggest factors are that we cannot take all of our assumptions about so-called reproducible ML at face value.

At the same time, our process and systems must result in reproducible work that does not lead us astray.

AcknowledgmentsFeature image source: https://xkcd.com/242/CitationFor attribution in academic contexts or books, please cite this work asEdward Raff, "Quantifying Independently Reproducible Machine Learning", The Gradient, 2020.

--The AI system GPT-2, in a December 2019 interview with The Economist, "An artificial intelligence predicts the future"Innateness, empiricism, and recent developments in deep learningConsider two classic hypotheses about the development of language and cognition.

Consider GPT-2, an AI system that was recently featured in The New Yorker and interviewed by The Economist.

The popular blog StatStarCodex featured it, too, in a podcast entitled "GPT-2 as a step towards General Intelligence".

Compared to any previous system for generating natural language, GPT-2 has a number of remarkable strengths.

I speak fluent EnglishIf you run your experiments talktotransformer.com, you will quickly learn th…

Every day we hear claims that Artificial Intelligence (AI) systems are about to transform the economy, creating mass unemployment and vast monopolies.

In September 2017, a group of distinguished economists gathered in Toronto to set out a research agenda for the Economics of Artificial Intelligence (AI).

Previous editions of the Economics of AI conference included papers about the impact of AI in sectors such as media or health-care.

Lack of diversity in the AI research workforce, and the increasing influence of the private sector in setting AI research (and ethical) agendas as part of the industrialization of AI research suggest that this could be a problem, but the evidence base is lackin…

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Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations.

Widely used Linear Programming (LP) relaxations only work well when networks are trained to facilitate verification...

This precludes applications that involve verification-agnostic networks, i.e., networks not specially trained for verification.

On the other hand, semidefinite programming (SDP) relaxations have successfully be applied to verification-agnostic networks, but do not currently scale beyond small networks due to poor time and space asymptotics.

For two verification-agnostic networks on MNIST and CIFAR-10, we significantly im…

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: High-Resolution Neural Face Swapping for Visual Effects (Disney Research Studios, ETH Zurich, 2020)

Beyond Accuracy: Behavioral Testing of NLP Models with CheckList (USA, 2020)

Thieves on Sesame Street! Model Extraction of BERT-based APIs (UMass & Google Research, ICLR, 2019)

Time-Aware User Embeddings as a Service (Yahoo! Research, Temple University, 2020)

Are Labels Necessary for Neural Architecture Search? (Johns Hopkins University, Facebook AI Research, 2020)

GShard: Scaling Giant Mo…

Data Fest пройдет в этом году в онлайн формате 19 и 20 сентября 2020. Фестиваль организован сообществом Open Data Science и как обычно соберет исследователей, инженеров и разработчиков в области анализа данных, искусственного интеллекта и машинного обучения. Регистрация. Ну а дальше к деталям. Читать дальше →

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Статьи на сегодня: PointRend: Image Segmentation as Rendering (Facebook AI Research, 2020)

Natural- To Formal-Language Generation Using Tensor Product Representations (USA, 2019)

Linformer: Self-Attention with Linear Complexity (Facebook AI, 2020)

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution (Johns Hopkins University, Google, 2020)

Training Generative Adversarial Networks with Limited Data (NVIDIA, 2020)

Multi-Modal Dense Video Captioning (Tampere University…

Недавно завершился «Deep Learning in Natural Language Processing», открытый образовательный курс по обработке естественного языка. По традиции кураторы курса — сотрудники проекта DeepPavlov, открытой библиотеки для разговорного искусственного интеллекта, которую разрабатывают в лаборатории нейронных систем и глубокого обучения МФТИ. Курс проводился при информационной поддержке сообщества Open Data Science. Если нужно больше деталей по формату курса, то вам сюда. Один из ключевых элементов «DL in NLP» — это возможность почувствовать себя исследователем и реализовать собственный проект. Периодически мы рассказываем на Medium о проектах, которые участники создают в рамках наших образовательных…

Здравствуйте, товарищи! На выходных проходил хакасборкатон — гонки на самоуправляемых моделях автомобилей на базе комплекта donkeycar при содействии Х5, FLESS и сообщества энтузиастов self-driving. Задача заключалась в следующем: сначала надо было собрать машинку из запчастей, затем ее обучить проходить трассу. Победитель определялся по самому быстрому прохождению 3 кругов. За наезд на конус — дисквалификация. Хотя подобная задача для машинного обучения не нова, но сложности могут поджидать на всем пути: от невозможности заставить нормально работать вайфай до нежелания обученной модели пилотировать железо по треку. И все это в жестких временных рамках! Когда мы собирались на это соревновани…

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Статьи на сегодня: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks (China, 2020)

TAPAS: Weakly Supervised Table Parsing via Pre-training (Google, 2020)

DeepFaceLab: A simple, flexible and extensible faceswapping framework (2020)

End-to-End Object Detection with Transformers (Facebook AI, 2020)

Language Models are Few-Shot Learners (OpenAI, 2020)

TabNet: Attentive Interpretable Tabular Learning (Google Cloud AI, 2020) Читать дальше →

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество!

Статьи на сегодня: Efficient Document Re-Ranking for Transformers by Precomputing Term Representations; EARL: Speedup Transformer-based Rankers with Pre-computed Representation (2020)

MakeItTalk: Speaker-Aware Talking Head Animation (Adobe, University of Massachusetts Amherst, Huya, 2020)

Jukebox: A Generative Model for Music (OpenAI, 2020)

Recipes for building an open-domain chatbot (Facebook AI Research, 2020)

One-Shot Object Detection without Fine-Tuning (HKUST, Hong Kong, Tencent, 2020)

f-BRS: Rethinki…

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Статьи на сегодня: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization (Georgia Institute of Technology, Atlanta, USA, 2016)

X3D: Expanding Architectures for Efficient Video Recognition (Facebook AI Research, 2020)

Adaptive Attention Span in Transformers (Facebook AI Research, 2019)

ResNeSt: Split-Attention Networks (Amazon, 2020)

Weight Standardization (Johns Hopkins University, 2019)

Supervised Contrastive Learning (Google Research, MIT, 2020)

Improved Training Speed, Accurac…

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Статьи на сегодня: TResNet: High Performance GPU-Dedicated Architecture (DAMO Academy, Alibaba Group, 2020)

Controllable Person Image Synthesis with Attribute-Decomposed GAN (China, 2020)

Learning to See Through Obstructions (Taiwan, USA, 2020)

Tracking Objects as Points (UT Austin, Intel Labs, 2020)

CookGAN: Meal Image Synthesis from Ingredients (USA, UK, 2020)

Designing Network Design Spaces (FAIR, 2020)

Gradient Centralization: A New Optimization Technique for Deep Neural Networks (Hong Kong, Alibaba, 2…

TLDR: кому перестановки делают больнее — меряем свёрткой графов.

Код: RolX и ванильная трёхслойная GCN на мотифах. Выгорание на рабочем месте повстречал ещё в начале своей карьеры — и с тех пор живо интересуюсь этим вопросом. Представьте обстановку. Большой проект внедрения SAP. Высокие ставки. Амбициозные сроки. Нагрузку каждый воспринимал по-своему. Кто-то сорвался и самоустранился от выполнения обязанностей, кто-то стал токсичнее, у меня самого в какой-то момент чувство юмора пропало. Ненадолго. Управление изменениями (дисциплина, направленная на снижение напряжения во время внедрения информационных систем) многим обязана медикам. Во-первых, сам феномен эмоционального выгорания впервые з…

Привет, Хабр! Продолжаем публиковать рецензии на научные статьи от членов сообщества Open Data Science из канала #article_essense. Хотите получать их раньше всех — вступайте в сообщество! Первая часть мартовской сборки обзоров опубликована ранее.

Статьи на сегодня: NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (UC Berkeley, Google Research, UC San Diego, 2020)

Scene Text Recognition via Transformer (China, 2020)

PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization (Imperial College London, Google Research, 2019)

Lagrangian Neural Networks (Princeton, Oregon, Google, Flatiron, 2020)

Deformable Style Transfer (Chicago, USA, 2020)

Rethinking…

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Статьи на сегодня: Fast Differentiable Sorting and Ranking (Google Brain, 2020)

MaxUp: A Simple Way to Improve Generalization of Neural Network Training (UT Austin, 2020)

Deep Nearest Neighbor Anomaly Detection (Jerusalem, Israel, 2020)

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch (Google, 2020)

SpERT: Span-based Joint Entity and Relation Extraction with Transformer Pre-training (RheinMain University, Germany, 2019)

High-Resolution Daytime Translation Without Domain Labels (Samsung AI Cen…

Источник: https://mlr3book.mlr-org.com/ Привет, Хабр! В этом сообщении мы рассмотрим самый продуманный на сегодняшний день подход к машинному обучению на языке R — пакет mlr3 и экосистему вокруг него. Данный подход основан на «нормальном» ООП с использованием R6-классов и на представлении всех операций с данными и моделями в виде графа вычислений. Это позволяет создавать упорядоченные и гибкие пайплайны для задач машинного обучения, но на первых порах может показаться сложным и запутанным. Ниже постараемся внести определенную ясность и замотивировать к использованию mlr3 в ваших проектах. Содержание: Немного истории и сравнение с конкурирующими решениями

Технические детали: R6-классы и паке…

Привет от ODS. Мы откликнулись на идею tutu.ru поработать с их датасетом пассажиропотока РФ. И если в посте Milfgard огромная таблица выводов и научпоп, то мы хотим рассказать что под капотом.

Что, опять очередной пост про COVID-19? Да, но нет. Нам это было интересно именно с точки зрения математических методов и работы с интересным набором данных. Прежде, чем вы увидите под катом красивые картинки и графики, я обязан сказать несколько вещей: любое моделирование — это очень сложный процесс, внутри которого невероятное количество ЕСЛИ и ПРЕДПОЛОЖИМ. Мы о них расскажем.

те, кто работал над этой статьей — не эпидемиологи или вирусологи. Мы просто группа любителей теории графов, практикующих ме…

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Представлены обзоры 11 статей по Computer Vision, Natural Language Processing, Reinforcement learning и другим темам. Читать дальше →

I’ve entitled this talk ‘Queering Machine Learning’, which is theme I want to explore with you today.

This is a queering of machine learning, and a powerful tool of self-refelection; an approach to machine learning research that is more critical and responsible; a tool available not only to queer researchers, but to everyone.

We can already see this same type of failure beginning to manifest in machine learning as well, with examples abound.

By organising in machine learning, and by queering machine learning, we build collective community and collective strength that makes it possible for belonging and loneliness and solitude to co-exist and strengthen each other for the benefit of our fiel…

Read in 5mins (800 words)Today’s queer scientist is exceptional.

Role of the Queer ScientistFor queer people to hold a recognised role in scientific life requires an acknowledgement that to be queer has consequences.

Challenges Facing Queer ScientistsFor the queer scientist, every encounter involves a conscious act of deliberation, risk assessment, and effort, well before any effort of research is begun.

For queer scientists, every new encounter—with a colleague, supervisor, possible letter-writer, examiner, moderator, student, interviewer, acquaintance, or future-friend—sets up a stressful coming-out scene.

To be queer in science is to ask to belong and to be safe.

Agents with a self-attention “bottleneck” not only can solve these tasks from pixel inputs with only 4000 parameters, but they are also better at generalization.

Redirecting to attentionagent.github.io, where the article resides.

When assignment weights change in a ramp-up experiment, there are periods of constant assignment weights that we define as epochs.

In an OCE with constant assignment weights and a representative sample, this is an unbiased estimator.However, when there are changing assignment weights, then an unweighted average of data across the epochs can be a biased estimate.

Epoch: If assignment weights are changed at times $Z^*_1, ..., Z^*_J$ then the assignment weights are constant during $[Z^*_j, Z^*_{j+1})$.

An experimenter who changes assignment weights gets the same answer as the experimenter who doesn’t change assignment weights (modulo some rounding errors) so long as they use the adjusted estim…

The goal of this post is to nerd out over biochemistry and energy metabolism in the animal kingdom, and potentially inspire others on their own biohacking lite adventure.

It’s highly amusing to think that every single time you breathe out (in a fasted state) you are literally breathing out your fat carbon by carbon.

Energy deficit.

To validate the energy deficit math I spent 100 days around late 2019 very carefully tracking my daily energy input and output.

That said, focusing on fat, both approaches show me losing body fat at roughly the same rate, though they are off by an absolute offset.

Paralleling the state of affairs in optimization, we have a variety of (provably efficient) algorithms for sampling from log-concave distributions, under a variety of access models to the distribution.

Formalizing the sampling problemThe formulation of the sampling problem we will consider is as follows:Problem: Sample from a distribution $p(x) \propto e^{-f(x)}$ given black-box access to $f$ and $abla f$.

Similarly, for sampling, when $p$ is log-concave, the distribution is unimodal and a Markov Chain which is a close relative of gradient descent — Langevin Monte Carlo — is efficient.

[](http://www.andrew.cmu.edu/user/aristesk/table_opt.jpg)Before we move on to non-log-concave distribution…

Training GANs - From Theory to PracticeGANs, originally discovered in the context of unsupervised learning, have had far reaching implications to science, engineering, and society.

However, training GANs remains challenging (in part) due to the lack of convergent algorithms for nonconvex-nonconcave min-max optimization.

In this post, we present a new first-order algorithm for min-max optimization which is particularly suited to GANs.

ConclusionIn this post we have shown how to develop a practical and convergent first-order algorithm for training GANs.

Our simulations show that a version of this algorithm can lead to more stable training of GANs.

Unlike minimization, where algorithms can always be shown to converge to some local minimum, there is no notion of a local equilibrium in min-max optimization that exists for general nonconvex-nonconcave functions.

Our greedy min-max equilibriumWe use the greedy max function to define a new second-order notion of local optimality for min-max optimization, which we refer to as a greedy min-max equilibrium.

This allows us to define a notion of greedy min-max equilibrium.

Greedy min-max equilibrium: $(x^{\star}, y^{\star})$ is an $\varepsilon$-greedy min-max equilibrium if\(\|abla_y f(x^\star,y^\star)\| \leq \varepsilon, \qquadabla^2_y f(x^\star,y^\star) \preceq \sqrt{\varepsilon},\)\(\|abla_x…

Exponential Learning Rate Schedules for Deep Learning (Part 1)This blog post concerns our ICLR20 paper on a surprising discovery about learning rate (LR), the most basic hyperparameter in deep learning.

These divergent approaches suggest that LR, the most basic and intuitive hyperparameter in deep learning, has not revealed all its mysteries yet.

SOTA performance with exponential LRAs mentioned, reaching state-of-the-art accuracy requires reducing the learning rate a few times.

Suppose the training has $K$ phases, and the learning rate is divided by some constant $C_I>1$ when entering phase $I$.

ConclusionWe hope that this bit of theory and supporting experiments have changed your outlook o…

In this tutorial, you will discover the benefits of using ensemble methods for machine learning.

— Page 1, Ensemble Machine Learning, 2012.

Originally developed to reduce the variance—thereby improving the accuracy—of an automated decision-making system …— Page 1, Ensemble Machine Learning, 2012.

Let’s take a closer look at these two properties in order to better understand the benefits of using ensemble learning on a project.

Related TutorialsBooksArticlesSummaryIn this post, you discovered the benefits of using ensemble methods for machine learning.

This is referred to generally as ensemble machine learning, or simply ensemble learning.

In this post, you will discover a gentle introduction to ensemble learning.

Ensemble Machine LearningApplied machine learning often involves fitting and evaluating models on a dataset.

This is called an ensemble machine learning model, or simply an ensemble, and the process of finding a well-performing ensemble model is referred to as “ensemble learning“.

BooksArticlesSummaryIn this post, you discovered a gentle introduction to ensemble learning.

Sections and chapters on ensemble learning in the most popular and common machine learning textbooks.

Ensemble Learning Book ListThe books dedicated to the topic of ensemble learning that we will cover are as follows:There are also some books from Packt, but I won’t be reviewing them; they are:Did I miss a book on ensemble learning?

Section 16.2: Classification and regression trees (CART)Section 16.4: BoostingSection 16.6: Ensemble learningThe book “The Elements of Statistical Learning” published in 2016 covers the key ensemble learning algorithms as well as the theory for ensemble learning generally.

Chapter 8: Model Inference and AveragingChapter 10: Boosting and Additive TreesChapter 15:…

The most common use of the softmax function in applied machine learning is in its use as an activation function in a neural network model.

A linear activation function is simply the sum of the weighted input to the node, required as input for any activation function.

The sigmoid activation function can also be used as an activation function for multi-class classification problems where classes are non-mutually exclusive.

Softmax Activation FunctionThe softmax function is used as the activation function in the output layer of neural network models that predict a multinomial probability distribution.

add ( Dense ( 3 , activation = 'softmax' ) )By definition, the softmax activation will output…

In this tutorial, you will discover how to develop and evaluate LARS Regression models in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:LARS Regression Example of LARS Regression Tuning LARS HyperparametersLARS RegressionLinear regression refers to a model that assumes a linear relationship between input variables and the target variable.

# define model model = Lars ( )We can evaluate the LARS Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Lars ( ) # fit model model .

BooksAPIsArticlesSumm…

Tutorial OverviewThis tutorial is divided into three parts; they are:Nearest Centroids Algorithm Nearest Centroids With Scikit-Learn Tuning Nearest Centroid HyperparametersNearest Centroids AlgorithmNearest Centroids is a classification machine learning algorithm.

in their 2002 paper titled “Diagnosis Of Multiple Cancer Types By Shrunken Centroids Of Gene Expression.”Nearest Centroids With Scikit-LearnThe Nearest Shrunken Centroids is available in the scikit-learn Python machine learning library via the NearestCentroid class.

# create the nearest centroid model model = NearestCentroid ( metric = 'euclidean' , shrink_threshold = 0.5 )We can demonstrate the Nearest Shrunken Centroids with a w…

In this tutorial, you will discover how to develop and evaluate Lasso Regression models in Python.

Example of Lasso RegressionIn this section, we will demonstrate how to use the Lasso Regression algorithm.

# define model model = Lasso ( alpha = 1.0 )We can evaluate the Lasso Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Lasso ( alpha = 1.0 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial, you discovered how to develop and evaluate Lasso Regression models in Python.

In this tutorial, you will discover how to develop and evaluate Ridge Regression models in Python.

Example of Ridge RegressionIn this section, we will demonstrate how to use the Ridge Regression algorithm.

# define model model = Ridge ( alpha = 1.0 )We can evaluate the Ridge Regression model on the housing dataset using repeated 10-fold cross-validation and report the average mean absolute error (MAE) on the dataset.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = Ridge ( alpha = 1.0 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial, you discovered how to develop and evaluate Ridge Regression models in Python.

In this tutorial, you will discover how to develop Elastic Net regularized regression in Python.

Tutorial OverviewThis tutorial is divided into three parts; they are:Elastic Net Regression Example of Elastic Net Regression Tuning Elastic Net HyperparametersElastic Net RegressionLinear regression refers to a model that assumes a linear relationship between input variables and the target variable.

Example of Elastic Net RegressionIn this section, we will demonstrate how to use the Elastic Net regression algorithm.

values X , y = data [ : , : - 1 ] , data [ : , - 1 ] # define model model = ElasticNet ( alpha = 1.0 , l1_ratio = 0.5 ) # fit model model .

BooksAPIsArticlesSummaryIn this tutorial,…

Tutorial OverviewThis tutorial is divided into four parts; they are:Regression With Outliers Regression Dataset With Outliers Robust Regression Algorithms Compare Robust Regression AlgorithmsRegression With OutliersRegression predictive modeling involves predicting a numeric variable given some input, often numerical input.

Machine learning algorithms used for regression predictive modeling tasks are also referred to as “regression” or “regression algorithms.” The most common method is linear regression.

Regression Dataset With OutliersWe can define a synthetic regression dataset using the make_regression() function.

std ( ) return X , y # load dataset X , y = get_dataset ( ) # summarize sh…

Tutorial OverviewThis tutorial is divided into three parts; they are:Gaussian Processes for Classification Gaussian Processes With Scikit-Learn Tune Gaussian Processes HyperparametersGaussian Processes for ClassificationGaussian Processes, or GP for short, are a generalization of the Gaussian probability distribution (e.g.

Gaussian probability distribution functions summarize the distribution of random variables, whereas Gaussian processes summarize the properties of the functions, e.g.

Gaussian processes and Gaussian processes for classification is a complex topic.

# create the model model = GaussianProcessClassifier ( )The complete example of evaluating the Gaussian Processes Classifier m…

After completing this tutorial, you will know:The Nearest Radius Neighbors Classifier is a simple extension of the k-nearest neighbors classification algorithm.

Tutorial OverviewThis tutorial is divided into three parts; they are:Radius Neighbors Classifier Radius Neighbors Classifier With Scikit-Learn Tune Radius Neighbors Classifier HyperparametersRadius Neighbors ClassifierRadius Neighbors is a classification machine learning algorithm.

Radius Neighbors Classifier With Scikit-LearnThe Radius Neighbors Classifier is available in the scikit-learn Python machine learning library via the RadiusNeighborsClassifier class.

Tune Radius Neighbors Classifier HyperparametersThe hyperparameters for …

In this tutorial, you will discover the Linear Discriminant Analysis classification machine learning algorithm in Python.

After completing this tutorial, you will know:The Linear Discriminant Analysis is a simple linear machine learning algorithm for classification.

Tutorial OverviewThis tutorial is divided into three parts; they are:Linear Discriminant Analysis Linear Discriminant Analysis With scikit-learn Tune LDA HyperparametersLinear Discriminant AnalysisLinear Discriminant Analysis, or LDA for short, is a classification machine learning algorithm.

Linear Discriminant Analysis With scikit-learnThe Linear Discriminant Analysis is available in the scikit-learn Python machine learning lib…

In this tutorial, you will discover how to hill climb the test set for machine learning.

We implicitly hill climb the test set when we overuse the test set to evaluate our modeling pipelines.

Tutorial OverviewThis tutorial is divided into five parts; they are:Hill Climb the Test Set Hill Climbing Algorithm How to Implement Hill Climbing Hill Climb Diabetes Classification Dataset Hill Climb Housing Regression DatasetHill Climb the Test SetMachine learning competitions, like those on Kaggle, provide a complete training dataset as well as just the input for the test set.

PapersArticlesSummaryIn this tutorial, you discovered how to hill climb the test set for machine learning.

We implicitly hil…

Neural Architecture Search (NAS) automates network architecture engineering.

By dissecting the methods for NAS into three components: search space, search algorithm and child model evolution strategy, this post reviews many interesting ideas for better, faster and more cost-efficient automatic neural architecture search.

Search space: The NAS search space defines a set of operations (e.g.

So far we have seen many interesting new ideas on automating the network architecture engineering through neural architecture search and many have achieved very impressive performance.

Liu et al (2020) delve into the question “Can we find high-quality neural architecture without human-annotated labels?” an…

2007) sketched an idea of using a forward dynamics prediction model to estimate learning progress and assigned intrinsic exploration reward accordingly.

And by definition we have \(p(s_f, \Omega \vert s_0) = p^J(s_f \vert s_0, \Omega) p^C(\Omega \vert s_0)\).

Combining them, we get mutual information \(I(\Omega; s_f \vert s_0)\) to maximize:\[\begin{aligned} I(\Omega; s_f \vert s_0) &= H(s_f \vert s_0) - H(s_f \vert s_0, \Omega) \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p(s_f, \Omega \vert s_0) \log \frac{p(s_f, \Omega \vert s_0)}{p^C(\Omega \vert s_0)} \\ &= - \sum_{s_f} p(s_f \vert s_0) \log p(s_f \vert s_0) + \sum_{s_f, \Omega} p^J(s_f \vert s_0, \Ome…

(2018) added a set of auxiliary losses to enable training a deep Transformer model on character-level language modeling which outperformed LSTMs.

Longer Attention Span (Transformer-XL)The vanilla Transformer has a fixed and limited attention span.

Image Transformer (Parmer, et al 2018) embraces a formulation of image generation similar to sequence modeling within the Transformer framework.

The top row illustrates the attention connectivity patterns in (a) Transformer, (b) Sparse Transformer with strided attention, and (c) Sparse Transformer with fixed attention.

2019)Cited as:@article{weng2020transformer, title = "The Transformer Family", author = "Weng, Lilian", journal = "lilianweng.githu…

Next, we will look into several categories of curriculum learning, as illustrated in Fig.

This framework of proposing curriculum automatically through another RL agent was formalized as Teacher-Student Curriculum Learning (TSCL; Matiisen, et al.

(Image source: Jabri, et al 2019)Learning a latent skill space can be done in different ways, such as in Hausman, et al.

(Image source: Czarnecki, et al., 2018)Cited as:@article{weng2020curriculum, title = "Curriculum for Reinforcement Learning", author = "Weng, Lilian", journal = "lilianweng.github.io/lil-log", year = "2020", url = "https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html" }References[1] Jeffrey L.…

In this post I'd like share some of that agenda, in what I call the "no bullshit" approach to AI.

And since we don't see these things, we don't label datasets with them and hence these "symbols" never make it to AI, neither from the symbolic approach, nor machine learning approach.

Notably the stuff deep learning is mostly successfully used for these days is not mission critical.

The science wayThe scientific approach is really what this blog was all about, before it veered into making cynical posts about the general AI stupidity out there.

Failure of deep learning on delivering of many promises will likely lead to a similar winter.

Full loyalty to the charter is expected, to the point of even varying the compensation by the level of "faith" .

It is often better to invest resources in getting slightly better data, add one more sensor, than train some ridiculously huge deep learning model and expect miracles.

With honesty and integrity rarely found in Silicon Valley, he went in and said what many were whispering for a while - AI is not really "AI".

Deep learning in clinical applicationsThere was some buzz about deep learning replacing radiologists, nonsense initiated by Hinton and then promptly repeated by Andrew Ng.

The realization that deep learning is not going to cut it with respect to self driving cars and many oth…

As usual, these number are not really measuring reliably the safety of AV's and there are plenty ways to game them, or overreport.

Please refer to my last years post for a deeper discussion (and 2017 post here, 2018 post here) on why these numbers are essentially flawed.

Nevertheless these are the only official numbers we get, the only glimpse of transparency into this giant corporate endeavor called the "self driving car".

Nevertheless even Waymo and Cruise disengagements are still approximately an order of magnitude from the upper bound of human crash rate.

They finally have recorded some autonomous testing miles with the DMV, all 12.2 of them.

Representing the state of a quantum system is far more challenging.

This is exactly where we thought deep neural networks could help.

In the last several years, there have been huge advances in representing complex, high-dimensional probability distributions with neural networks.

We wanted to use deep neural networks to tackle more realistic problems in chemistry and condensed matter physics, and that meant including electrons in our calculations.

In most quantum chemistry methods, antisymmetry is introduced using a function called the determinant.

GPE and GPI in contextThe work on GPE and GPI is at the intersection of two separate branches of research related to these operations individually.

The first, related to GPE, is the work on the successor representation, initiated with Dayan’s seminal paper from 1993.

The second branch of research at the origins of GPE and GPI, related to the latter, is concerned with composing behaviours to create new behaviours.

Both the composition of behaviours and hierarchical RL are today dynamic areas of research (see further reading: "GPI, hierarchical RL, and related approaches").

The fast adaptation provided by GPE and GPI is promising for building faster learning RL agents.

Graph Neural Networks extend the learning bias imposed by Convolutional Neural Networks and Recurrent Neural Networks by generalising the concept of “proximity”, allowing us to have arbitrarily complex connections to handle not only traffic ahead or behind us, but also along adjacent and intersecting roads.

These mechanisms allow Graph Neural Networks to capitalise on the connectivity structure of the road network more effectively.

This ability of Graph Neural Networks to generalise over combinatorial spaces is what grants our modeling technique its power.

We discovered that Graph Neural Networks are particularly sensitive to changes in the training curriculum - the primary cause of this in…

What can I expect in the interview process?

Feryal: The interview process at DeepMind can vary depending on the particular role you’re applying for.

Phase two - technical interviewsThis part of the process involves several sessions - including one with a technical quiz that covers a large breadth of topics in computer science, statistics, mathematics and machine learning.

~30min] interviews with researchers and leads about your specific research background and interests.

Phase four - culture interviewTowards the end of the interview process, you will once again connect with the recruitment team to discuss DeepMind’s culture and mission.

The ‘dry’ form is relatively common among people over 65, and usually causes only mild sight loss.

Our contribution highlights the potential of using AI in preventative studies for diseases such as exAMD.

The Moorfields Eye Hospital AMD datasetWe used a dataset of anonymised retinal scans from Moorfields patients with exAMD in one eye, and at high-risk of developing exAMD in their other eye.

To address this, we worked with retinal experts to review all scans for each eye and specify the scan when exAMD was first evident.

In our previous work, now continuing in collaboration with Google Health, we developed a model capable of segmenting these eye scans into thirteen anatomical categories.

Specification gaming is a behaviour that satisfies the literal specification of an objective without achieving the intended outcome.

We have all had experiences with specification gaming, even if not by this name.

In this post, we review possible causes for specification gaming, share examples of where this happens in practice, and argue for further work on principled approaches to overcoming specification problems.

In a Lego stacking task, the desired outcome was for a red block to end up on top of a blue block.

The agent was rewarded for the height of the bottom face of the red block when it is not touching the block.

The practical implications of modelling glassThe glass transition is a ubiquitous phenomenon which manifests in more than window (silica) glasses.

Understanding the glass transition may result in other applications of disordered materials, in fields as diverse as biorenewable polymers and food processing.

Our new work, published in Nature Physics, could help us gain an understanding of the structural changes that may occur near the glass transition.

Leveraging graph neural networks to model glassy dynamicsGlasses can be modelled as particles interacting via a short-range repulsive potential which essentially prevents particles from getting too close to each other.

We then trained a neural n…

Combining off-policy learning with memory is challenging because you need to know what you might remember when executing a different behaviour.

Within that strand, we distinguish two types of rewards: firstly, long-term novelty rewards encourage visiting many states throughout training, across many episodes.

Secondly, short-term novelty rewards encourage visiting many states over a short span of time (e.g., within a single episode of a game).

However, learning density models of high dimensional spaces is fraught with problems due to the curse of dimensionality.

For example, in Montezuma’s Revenge, unlike undirected exploration strategies, long-term novelty rewards allow the agent to surpass…

Modelling natural languageFinding machine learning tasks which both drive the development of better memory architectures and push us further towards artificial general intelligence is challenging.

Transferring knowledgeSuch samples would likely astound Shannon, 70 years on from his early language model experiments.

Google’s prominent natural language model, BERT, achieves state-of-the-art performance on a wide array of NLP benchmarks, and is now a part of Google Search.

Benchmarking language modelsA popular long-range language model benchmark is WikiText-103, which is comprised of English-language Wikipedia articles, and was developed by researchers at Salesforce AI.

As such, we’ve compiled…

Meanwhile, in close contact with this study of reward learning in animals, computer scientists have developed algorithms for reinforcement learning in artificial systems.

A chain of prediction: temporal difference learningReinforcement learning is one of the oldest and most powerful ideas linking neuroscience and AI.

An important breakthrough in solving the problem of reward prediction was the temporal difference learning (TD) algorithm.

Around the same time, in the late 80s and early 90s, neuroscientists were struggling to understand the behaviour of dopamine neurons.

Distributional reinforcement learning

In our study published today in Nature, we demonstrate how artificial intelligence research can drive and accelerate new scientific discoveries.

Our system, AlphaFold – described in peer-reviewed papers now published in Nature and PROTEINS – is the culmination of several years of work, and builds on decades of prior research using large genomic datasets to predict protein structure.

What is the protein folding problem?

What any given protein can do depends on its unique 3D structure.

Why is protein folding important?

Such sparsity-based architectures usually require additional layers to implicitly produce a full attention mechanism.

Towards FAVOR: Fast Attention via Matrix AssociativityThe decomposition described above allows one to store the implicit attention matrix with linear, rather than quadratic, memory complexity.

While the original attention mechanism multiplies the stored attention matrix with the value input to obtain the final result, after decomposing the attention matrix, one can rearrange matrix multiplications to approximate the result of the regular attention mechanism, without explicitly constructing the quadratic-sized attention matrix.

Left: Standard unidirectional attention requires…

At which point a scramble ensues to find the contract, read through it, and discover what exactly was agreed to.

These documents are legally validated by all the parties involved, which means that the data they contain is intrinsically accurate.

This was the best result we’d ever achieved over three years of on-and-off experiments — and, incredibly, Google needed a relatively tiny data set to achieve it.

The data set was small and the model failed on both entity name and signer name.

So as a next step we changed up our labeling and expanded the data set.

At Google, it is our ongoing goal to support faculty who are conducting innovative research that will have positive societal impact.

As part of that goal, earlier this year we launched the Award for Inclusion Research program, a global program that supports academic research in computing and technology addressing the needs of underrepresented populations.

The Award for Inclusion Research program allows faculty and Google researchers an opportunity to partner on their research initiatives and build new and constructive long-term relationships.

We received 100+ applications from over 100 universities, globally, and today we are excited to announce the 16 proposals chosen for funding, focused …

A key part of our strategy at Google Cloud is the creation of industry-specific solutions that address vertical needs.

Procurement DocAI is one of our newest solutions, and it’s deployed by many customers and partners including Unifiedpost.

Google Cloud’s Procurement DocAI delivered two key benefits to address Unifiedpost’s business needs:Lower TCO of procure-to-pay processing .

Unifiedpost will be able to lower their TCO of procure-to-pay processing costs by up to 60% with Procurement DocAI.

The collaboration between Google Cloud and Unifiedpost is just one of the latest examples of how we’re providing AI-powered functional solutions to solve business problems by leveraging our Deployed AI…

Artificial intelligence (AI) continues to transform industries across the globe, and business decision makers of all kinds are taking notice. One example is the mortgage industry; lending institutions like banks and mortgage brokers process hundreds of pages of borrower paperwork for every loan - a heavily manual process that adds thousands of dollars to the cost of issuing a loan. In this industry, borrowers and lenders have high expectations; they want a mortgage document processing solution catered to improving operational efficiency, while ensuring speed and data accuracy. They also want a document automation process that helps enhance their current security and compliance posture.At Go…

Machine learning models are now being used to accomplish many challenging tasks. With their vast potential, ML models also raise questions about their usage, construction, and limitations. Documenting the answers to these questions helps to bring clarity and shared understanding. To help advance these goals, Google has introduced model cards.Model cards aim to provide a concise, holistic picture of a machine learning model. To start, a model card explains what a model does, its intended audience, and who maintains it. A model card also provides insight into the construction of the model, including its architecture and the training data used. Not only does a model card include raw performanc…

Today, during our Search On event, we shared an update on how Duplex and Google Assistant are helping people in their everyday lives.

From providing more accurate business information in products like Google Maps, to booking appointments and reservations on your behalf, to waiting on hold for you, we’re continuing to bring Duplex to new places to make life a little easier.

Keeping local businesses information freshThis pandemic has shown us how critical up-to-date local information is, both for people trying to find services nearby and for businesses looking for ways to serve their customers.

We began using Duplex to automatically update business information and add it to Search and Maps at…

The next app, Editor, allows users to load the georectified historical maps as the background and then trace their geographic features (e.g., building footprints, roads, etc.).

Finally, our map renderer, Kartta, visualizes the spatiotemporal vector tiles allowing the users to navigate space and time on historical maps.

Warper and Editor work together to let users upload a map, anchor it to a base map using control points, and trace geographic features like building footprints and roads.

High-level overview of rǝ’s 3D reconstruction pipeline, which takes annotated images and maps and prepares them for 3D rendering.

Key ResultsStreet level view of 3D-reconstructed Chelsea, ManhattanConclusion…

Muratcan Cicek, a PhD candidate at UC Santa Cruz, worked as a summer intern on Google’s Project Euphonia, which aims to improve computers’ abilities to understand impaired speech.

This work was especially relevant and important for Muratcan, who was born with cerebral palsy and has a severe speech impairment.

Before his internship, Muratcan recorded 2,000 phrases for Project Euphonia.

The prototype allowed Muratcan to share the transcription in a video call so others could better understand him.

Muratcan says, “Euphonia transformed my communication skills in a way that I can leverage in my career as an engineer without feeling insecure about my condition.”

In “Measuring and Reducing Gendered Correlations in Pre-trained Models” we perform a case study on BERT and its low-memory counterpart ALBERT, looking at correlations related to gender, and formulate a series of best practices for using pre-trained language models.

We will soon release a series of checkpoints, Zari1, which reduce gendered correlations while maintaining state-of-the-art accuracy on standard NLP task metrics.

At least some of this is due to models preferentially using gendered correlations in reasoning.

While configuration choices often seem innocuous, we find they can cause significant changes for gendered correlations, both for better and for worse.

While configuration choi…

More and more companies want to adopt the latest cloud-based artificial intelligence (AI) and machine learning (ML) technologies, but they are subject to an increasing array of data privacy regulations.

This is an important concern for customers, who are interested in using AI and ML systems to drive better business outcomes while complying with new data privacy laws.

At Google Cloud, we are committed to giving you increased control and visibility over your data.

Helping you address global privacy and data protection requirements enables you to apply machine learning to accelerate your business with confidence.

To learn more about our three pillars of sovereignty in Google Cloud, see this b…

Google created the PhD Fellowship Program in 2009 to recognize and support outstanding graduate students who seek to influence the future of technology by pursuing exceptional research in computer science and related fields.

Now in its twelfth year, these Fellowships have helped support approximately 500 graduate students globally in North America and Europe, Africa, Australia, East Asia, and India.

It is our ongoing goal to continue to support the academic community as a whole, and these Fellows as they make their mark on the world.

We congratulate all of this year’s awardees!

Algorithms, Optimizations and MarketsJan van den Brand, KTH Royal Institute of TechnologyMahsa Derakhshan, Univers…

When Fernanda Viégas was in college, it took three years with three different majors before she decided she wanted to study graphic design and art history.

Today Fernanda, who grew up in Rio de Janeiro, Brazil, is a senior researcher at Google.

She and her colleagues make sure people at Google think about fairness and values–and putting Google’s AI Principles into practice–when they work on artificial intelligence.

Her team recently launched a series of “AI Explorables," a collection of interactive articles to better explain machine learning to everyone.

I recently sat down with Fernanda via Google Meet to talk about her role and the importance of putting people first when it comes to AI.

OpenAI released its first commercial product back in June: an API for developers to access advanced technologies for building new applications and services.

The API features a powerful general purpose language model, GPT-3, and has received tens of thousands of applications to date.

In addition to offering GPT-3 and future models via the OpenAI API, and as part of a multiyear partnership announced last year, OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.

GPT-3 is the most powerful model behind the API today, with 175 billion parameters.

Today, the API remains in a limited beta as OpenAI and academic partners test and assess the capabilities and limitati…

We've applied reinforcement learning from human feedback to train language models that are better at summarization.

Our approach follows directly from our previous work on learning from human feedback.

In particular, our 1.3 billion parameter (1.3B) model trained with human feedback outperforms our 12B model trained only with supervised learning.

Note that our human feedback models generate summaries that are significantly shorter than summaries from models trained on CNN/DM.

This suggests that our human feedback models have learned something more general about how to summarize text, and are not specific to Reddit posts.

Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.

The OpenAI Scholars program provides stipends and mentorship to individuals from underrepresented groups to study deep learning and open-source a project.

Demo Day introductions by Sam Altman and Greg BrockmanLearn more about our Scholars program.

I joined the Scholars program in order to learn from the brilliant folks at OpenAI and to immerse myself in AI research.

The OpenAI Scholars program was this magical opportunity to get started by learning from the very best minds in the field.

However, the same broad class of models has not been successful in producing strong features for image classification.

From language GPT to image GPTIn language, unsupervised learning algorithms that rely on word prediction (like GPT-2 and BERT) have been extremely successful, achieving top performance on a wide array of language tasks.

Because masked language models like BERT have outperformed generative models on most language tasks, we also evaluate the performance of BERT on our image models.

LimitationsWhile we have shown that iGPT is capable of learning powerful image features, there are still significant limitations to our approach.

Notably, we achieved our results by directly applyi…

We’re releasing an API for accessing new AI models developed by OpenAI.

Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task.

Your browser does not support videoGiven any text prompt, the API will return a text completion, attempting to match the pattern you gave it.

We've designed the API to be both simple for anyone to use but also flexible enough to make machine learning teams more productive.

Today the API runs models with weights from the GPT-3 family with many speed and throughput improvements.

We’re excited to announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.

Procgen CompetitionSign up for ProcgenThe Procgen Competition focuses on improving sample efficiency and generalization in reinforcement learning.

Since all content is procedurally generated, each Procgen environment intrinsically requires agents to generalize to never-before-seen situations.

Moreover, we designed Procgen environments to be fast and simple to use.

One well-known way to reduce the environment sample complexity is to leverage human priors and demonstrations of the desired behavior.

Other measures of AI progressIn addition to efficiency, many other measures shed light on overall algorithmic progress in AI.

Shufflenet achieved AlexNet-level performance with an 18x inference efficiency increase in 5 years (15-month doubling time), which suggests that training efficiency and inference efficiency might improve at similar rates.

This efficiency analysis suggests that policymakers could develop accurate intuitions about the cost of deploying AI capabilities—and how these costs are going to alter over time—by more closely assessing the rate of improvements in efficiency for AI systems.

Our results suggest that for AI tasks with high levels of investment (researcher time and/o…

Curated samples Provided with genre, artist, and lyrics as input, Jukebox outputs a new music sample produced from scratch.

We can then train a model to generate audio in this compressed space, and upsample back to the raw audio space.

Now in raw audio, our models must learn to tackle high diversity as well as very long range structure, and the raw audio domain is particularly unforgiving of errors in short, medium, or long term timing.

To better understand future implications for the music community, we shared Jukebox with an initial set of 10 musicians from various genres to discuss their feedback on this work.

While Jukebox is an interesting research result, these musicians did not find …

Can I (as an academic) conduct impartial research on the risks associated with large-scale AI systems when I lack the computing resources of industry?

Can I (as an AI developer) verify that my competitors in a given area of AI development will follow best practices rather than cut corners to gain an advantage?

AI developers should pilot bias and safety bounties for AI systems to strengthen incentives and processes for broad-based scrutiny of AI systems.

Standard setting bodies should work with academia and industry to develop audit trail requirements for safety-critical applications of AI systems.

Organizations developing AI and funding bodies should support research into the interpretabili…

We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability.

Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.

This is the goal of the OpenAI Microscope.

Microscope systematically visualizes every neuron in several commonly studied vision models, and makes all of those neurons linkable.

Our initial release includes nine frequently studied vision models, along with several visualization techniques we’ve found particularly u…

We are standardizing OpenAI’s deep learning framework on PyTorch.

The main reason we've chosen PyTorch is to increase our research productivity at scale on GPUs.

It is very easy to try and execute new research ideas in PyTorch; for example, switching to PyTorch decreased our iteration time on research ideas in generative modeling from weeks to days.

Going forward we'll primarily use PyTorch as our deep learning framework but sometimes use other ones when there's a specific technical reason to do so.

Many of our teams have already made the switch, and we look forward to contributing to the PyTorch community in upcoming months.

Lobe automatically selects the right machine learning architecture and starts training without any setup or configuration.

It fills them with confidence that they can actually use machine learning.

And when you have confidence you become more creative and start looking around and asking ‘What other stuff can I do with this?’”Lobe, which is available for download on Windows or Mac computers, uses open-source machine learning architectures and transfer learning to train custom machine learning models on the user’s own machine.

“We really want to empower more people to leverage machine learning and try it for the first time,” said Jake Cohen, Lobe senior program manager.

Cachor said he’d thoug…

To further accelerate time to insight in Azure Synapse Analytics, we are introducing the Knowledge center to simplify access to pre-loaded sample data and to streamline the getting started process for data professionals.

At Microsoft, we have been on a quest to advance AI beyond existing techniques, by taking a more holistic, human-centric approach to learning and understanding.

I believe the joint XYZ-code is a foundational component of this aspiration, if grounded with external knowledge sources in the downstream AI tasks.

We can derive more powerful representations by intersecting X, Y, and Z.X-code: Text representation from big dataThe quest to achieve universal representation of monolingual text is our X-code.

Similarly, our work with XYZ-code breaks down AI capabilities into smaller building blocks that can be combined in unique ways to make integrative AI more effective.

Just as Gutenberg’s printing …

Now, with Haptic PIVOT, we bring the physics of forces to VR controllers.

This week, we’re presenting Haptic PIVOT at the 2020 ACM Symposium on User Interface Software and Technology (UIST).

Haptic PIVOT serves on-demand control and haptic rendering of virtual objects as the hand reaches for them.

From the physical to the virtual—on demandAt the core of PIVOT’s design is its hinge mechanism and haptic handle.

The reaction time of catching a flying virtual object is significantly shorter than grabbing a stationary virtual object (we can simulate the catch of a 55.9-mph throw through visuo-motor illusions!).

Today, we are happy to announce that Turing multilingual language model (T-ULRv2) is the state of the art at the top of the Google XTREME public leaderboard.

Created by the Microsoft Turing team in collaboration with Microsoft Research, the model beat the previous best from Alibaba (VECO) by 3.5 points in average score.

Universal Language RepresentationPUBLICATION Towards Language Agnostic Universal RepresentationsThe Microsoft Turing team has long believed that language representation should be universal.

The Turing Universal Language Representation (T-ULRv2) model is our latest cross-lingual innovation, which incorporates our recent innovation of InfoXLM, to create a universal model that …

Refining vision and language pretraining for novel object captioningNovel object captioning (NOC) aims to generate image captions capable of describing novel objects that are not present in the caption training data.

Recently, researchers have developed the novel object captioning challenge (nocaps) to evaluate NOC.

In this challenge, existing computer vision techniques can be leveraged to recognize novel objects.

The combined skill achieves the compositionality generalization, allowing for zero-shot captioning on novel objects.

Looking forward: High potential for performance improvementsWe have demonstrated the power of learning visual vocabulary for novel object captioning.

The breakthrough in a benchmark challenge is a milestone in Microsoft’s push to make its products and services inclusive and accessible to all users.

The new model is now available to customers via the Azure Cognitive Services Computer Vision offering, which is part of Azure AI, enabling developers to use this capability to improve accessibility in their own services.

Automatic image captioning helps all users access the important content in any image, from a photo returned as a search result to an image included in a presentation.

The app uses image captioning to describe photos, including those from social media apps.

“So, there are several apps that use image captioning as way to fill in…

Earlier this year we announced a free ‘introduction to Machine Learning’ course with Udacity, empowering 10,000 scholars from all over the world to learn the basics of machine learning. Today, we announce the new Machine Learning Engineer for Microsoft Azure Nanodegree Program on Udacity—students can now sign up and start taking this new Nanodegree.

“We are in a data desert,” said Mary Bellard, principal innovation architect lead at Microsoft who also oversees the AI for Accessibility program.

We don’t have enough data to power these ideas.”To begin to shrink that data desert, Microsoft researchers have been working for the past year and a half to investigate and suggest ways to make AI systems more inclusive of people with disabilities.

Moreover, computer vision algorithms typically learn from large image datasets of pictures downloaded from the internet.

Working with Microsoft funding and researchers, Gurari’s team has developed a new public dataset to train, validate and test image captioning algorithms.

Earlier this year, Microsoft…

Microsoft’s Cecily Morrison awarded MBE for services to inclusive designCecily Morrison, a principal researcher at Microsoft’s Research Lab in Cambridge, has been awarded an MBE in the Queen’s Birthday Honours List.

Tell us about the MBEThe MBE is for services to inclusive design, which is where we think about how we can design technology for each and every one of us.

What’s exciting for me is the recognition of how important inclusive design is for the world.

When we think about inclusive design and disability more generally, we don’t see someone’s physical body or cognitive abilities as being different.

While I was there, I realised that it was the way technology is built that informs the…

Data is growing exponentially, and over 80 percent of data is unstructured, creating a challenge for organizations to find and surface the right information to their customers. What organizations need is a solution that enables them to uncover latent insights from all their content by quickly identifying relevant information and meaningful patterns.

Organizations today are striving to build agility and resilience to the fast-changing environment we live in. AI and machine learning innovation can help tackle these emerging challenges and enable cost efficiencies.

The goal of neural architecture search (NAS) is to have computers automatically search for the best-performing neural networks.

SOURCE CODE GitHub: ArchaiWe’ve sought to address many of these concerns with a goal of making state-of-the-art NAS research more widely usable.

Currently, Differentiable Architecture Search (DARTS), Petridish, Differentiable ArchiTecture Approximation (DATA), and eXperts Neural Architecture Search (XNAS) are implemented.

Search-space abstractions: A significant amount of current NAS research focuses on rather small search spaces made popular by a few early efforts.

A significant amount of current NAS research focuses on rather small search spaces made popular by a…

Recent years have seen a surge of applying of statistical models, including neural nets, to code intelligence tasks.

To address this, researchers from Microsoft Research Asia (Natural Language Computing Group) working together with Developer Division and Bing introduce CodeXGLUE, a benchmark dataset and open challenge for code intelligence.

CodeXGLUE includes six existing code intelligence datasets — BigCloneBench, POJ-104, Defects4J, Bugs2Fix, CONCODE, and CodeSearchNet — but also newly introduced datasets that are highlighted in the table above.

One is for binary classification between code, and the other is for retrieving semantically similar code given code as the query.

We encourage re…

In our recent paper, “Geometric Dataset Distances via Optimal Transport,” we propose the Optimal Transport Dataset Distance, or the OTDD for short, an approach to defining and computing similarities, or distances, between classification datasets.

Optimal transport: Comparing by ‘transporting’Optimal transport traces its roots back to 18th-century France, where the mathematician Gaspard Monge was concerned with finding optimal ways to transport dirt and rubble from one location to another.

Optimal transport was born as a method to find least-cost schemes to transport dirt and rubble from one place to another.

And here, too, optimal transport comes to our rescue—we can use it to compute these…

The U.K.’s Royal Academy of Engineering has elected Facebook Research Scientist Mark Harman as a Fellow for his achievements in academia and industry, including his work on search-based software engineering (SBSE), intelligent software testing tools, and web-enabled simulation (WES) approaches.

SBSE is an approach that uses search-based optimization algorithms to find solutions to highly complex software engineering problems.

Using the technique allows for smoother testing, design, and project management in software engineering.

For the next 25 years, he worked solely in academia, where he wrote, edited, and reviewed hundreds of papers, and authored books about software testing and programm…

What the research is:We’ve built a data classification system that uses multiple data signals, a scalable system architecture, and machine learning to detect semantic types within Facebook at scale.

This is important in situations where it’s necessary to detect where an organization’s data is stored in many different formats across various data stores.

In these cases, a classification system enables organizations to automatically enforce privacy- and security-related policies, such as access control policies.

Why it matters:Organizations generally have a well-defined set of privacy policies aimed at ensuring that people’s privacy is respected.

Read the full paper:Secure and scalable data cl…

Pietrucha is among a team of laboratory researchers that aims to develop AI systems that can sense when a person's cognitive fatigue is interfering with their performance.

The system would then suggest interventions, or even take action in dire scenarios, to help the individual recover or to prevent harm.

This vision has its roots in decades-long research at the laboratory in using technology to "read" a person's cognitive or emotional state.

On the other end of the spectrum, the machine might take actions necessary to ensure the survival of the human team member when the human is incapable of doing so.

An AI teammate may know just how to lift their partner up.

Together, they have written a new book, “What to Expect When You’re Expecting Robots: The Future of Human-Robot Collaboration,” published this month by Basic Books.

What we can expect, they write, is that robots of the future will no longer work for us, but with us.

As such, Shah and Major say that robots and humans will have to establish a mutual understanding.

“We were working in parallel universes, me in industry, and Julie in academia, each trying to galvanize understanding for the need to accommodate machines and robots,” Major recalls.

“There could also be transponders for people that broadcast to robots,” Shah says.

Now the startup OpenSpace is bringing some of the benefits of digital work to the real world with a solution that uses 360-degree cameras and computer vision to create comprehensive, time-stamped digital replicas of construction sites.

All customers need to do is walk their job site with a small 360-degree camera on their hard hat.

People have long used photographs to document construction projects, and many times contracts for large construction projects require photos of progress to be taken.

Once a few tours of the job site have been uploaded to OpenSpace’s platform, it can map pictures onto site plans within 15 minutes.

But even in states that have resumed construction, Kalanithi says c…

Lost languages are more than a mere academic curiosity; without them, we miss an entire body of knowledge about the people who spoke them.

The team’s ultimate goal is for the system to be able to decipher lost languages that have eluded linguists for decades, using just a few thousand words.

The resulting model can segment words in an ancient language and map them to counterparts in a related language.

The proposed algorithm can assess the proximity between two languages; in fact, when tested on known languages, it can even accurately identify language families.

While Basque and Latin were closer to Iberian than other languages, they were still too different to be considered related.

MIT researchers have identified a brain pathway critical in enabling primates to effortlessly identify objects in their field of vision.

The findings enrich existing models of the neural circuitry involved in visual perception and help to further unravel the computational code for solving object recognition in the primate brain.

Monkey versus machineIn 2019, Kar, DiCarlo, and colleagues identified that primates must use some recurrent circuits during rapid object recognition.

“These results provide evidence that this recurrently connected network is critical for rapid object recognition, the behavior we're studying.

“This study demonstrates the importance of prefrontal cortical circuits in …

Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards.

The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity.

Video data from each camera combined on the fly in chronological order can be exported easily.

RIOThe Reconnaissance of Influence Operations (RIO) software system automates the detection of disinformation narratives, networks, and influential actors.

Since 2010, Lincoln Laboratory has had 66 technologies recognized with R&D 100 Awards.

Today, artificial intelligence — and the computing systems that underlie it — are more than just matters of technology; they are matters of state and society, of governance and the public interest.

Building on those broader principles, the AI Policy Forum, a global effort convened by the MIT Stephen A. Schwarzman College of Computing, will provide an overarching policy framework and tools for governments and companies to implement in concrete ways.

“Our goal is to help policymakers in making practical decisions about AI policy,” says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing.

We need for this to be a global collaboration and engage scientists, technologists, polic…

Enter synthetic data: artificial information developers and engineers can use as a stand-in for real data.

They call it the Synthetic Data Vault.

When data scientists were asked to solve problems using this synthetic data, their solutions were as effective as those made with real data 70 percent of the time.

GANs are more often used in artificial image generation, but they work well for synthetic data, too: CTGAN outperformed classic synthetic data creation techniques in 85 percent of the cases tested in Xu's study.

Or companies might also want to use synthetic data to plan for scenarios they haven't yet experienced, like a huge bump in user traffic.

Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs.

MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.

“This technique is part of a known subfield of machine learning, but people have not brought it to biology,” Berger says.

In recent years, biologists have begun using machine learning to scour huge databases of potential drug compounds to find molecules that interact with particular targets.

To overcome that, the researchers used a technique called Gaussian process to assign uncertainty values to the data th…

Powered by the MIT Innovation Initiative (MITii) and launched in October 2019, the MIT Proto Ventures program takes an entirely new approach to venture formation from within MIT.

Under the leadership of MITii Venture Builder Luis Ruben Soenksen PhD '19, the program announced last week that two new MIT startups will launch as part of Proto Ventures.

“The Proto Ventures program has nurtured everything I know is relevant in order to develop high-impact scientific ventures in today’s world,” says Soenksen.

MIT Innovation Initiative actively seeks new Venture Builders to develop additional channels within the MIT Proto Ventures program.

“The role of MIT Venture Builder is one of the most excitin…

SMART CAMP was formed in 2019 to focus on ways to produce living cells as medicine delivered to humans to treat a range of illnesses and medical conditions, including tissue degenerative diseases, cancer, and autoimmune disorders.

“We look forward to leveraging the ideas fostered in SMART CAMP to build data analytics and optical imaging capabilities for this pressing medical challenge of glaucoma prediction,” says Barbastathis.

Chew says, “Our earlier SMART and NTU scientific collaborations on progenitor cells in the central nervous system are now being extended to cell therapy translation.

Our research will seek to plug current gaps and deliver valuable impact to cell therapy research and …

Every year, roughly one out of eight U.S. deaths is caused at least in part by heart failure.

The system determined the right level more than half of the time, and correctly diagnosed level 3 cases 90 percent of the time.

Liao’s hope is that these consensus labels can serve as a universal standard to benchmark future machine learning development.

“Our model can turn both images and text into compact numerical abstractions from which an interpretation can be derived,” says Chauhan.

“These correlations will be valuable for improving search through a large database of X-ray images and reports, to make retrospective analysis even more effective,” Chauhan says.

Milo Phillips-Brown, a postdoc in MIT Philosophy, was recently named the inaugural recipient of the MAC3 Society and Ethics in Computing Research Award, which provides support to promising PhD candidates or postdocs conducting interdisciplinary research on the societal and ethical dimensions of computing.

“I’m thrilled to be the inaugural recipient of the MAC3 Society and Ethics in Computing Research Award.

Phillips-Brown PhD ’19 received his doctorate in philosophy from MIT and his bachelor’s in philosophy from Reed College.

He is a research fellow in digital ethics and governance at the Jain Family Institute and a member of the Society for Philosophy and Disability.

The MAC3 Society and E…

The Standard Model of particle physics describes all the known elementary particles and three of the four fundamental forces governing the universe; everything except gravity.

This month’s paper is one in a series aimed at enabling studies in theoretical physics that are currently computationally intractable.

“Our aim is to develop new algorithms for a key component of numerical calculations in theoretical physics,” says Kanwar.

“These calculations inform us about the inner workings of the Standard Model of particle physics, our most fundamental theory of matter.

This paves the way for significantly accelerated research into the fundamental forces of nature using physics-informed machine le…

Reinforcement learning is supervised learning on optimized dataThe two most common perspectives on Reinforcement learning (RL) are optimization and dynamic programming.

In contrast deep supervised learning has been extremely successful and we may hence ask: Can we use supervised learning to perform RL?

Seen from this supervised learning perspective, many RL algorithms can be viewed as alternating between finding good data and doing supervised learning on that data.

The table below compares the supervised learning perspective to the optimization and dynamic programming perspectives:Â Optimization Perspective Dynamic Programming Perspective Supervised Learning Perspective What are we optimizi…

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningTo operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$.

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Future directionsPlan2Explore demonstrate…

Plan2Explore: Active Model-Building for Self-Supervised Visual Reinforcement LearningTo operate successfully in unstructured open-world environments, autonomous intelligent agents need to solve many different tasks and learn new tasks quickly.

The world model captures general knowledge, allowing Plan2Explore to quickly solve new tasks through planning in its own imagination.

Learning the world modelPlan2Explore learns a world model that predicts future outcomes given past observations $o_{1:t}$ and actions $a_{1:t}$ (see figure below).

Most prior work on self-supervised exploration used model-free methods that reinforce past behavior that resulted in novel experience.

Solving tasks with the…

AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsOur method learns complex behaviors by training offline from prior datasets (expert demonstrations, data from previous experiments, or random exploration data) and then fine-tuning quickly with online interaction.

Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems.

Figure 2: On-policy methods are slow to learn compared to off-policy methods, due to the ability of off-policy methods to “stitch" good trajectories together, illustrated on the left.

We aim to study tasks representative of the difficulties of real-world robot learning, where …

Who Will Change AI?

Midway Through the ProgramEarly on Day 3 of the 4 day AI4ALL program, I began to really understand the significance of AI.

Through the eye-opening lecture presentations and discussions, I realized that AI really is everywhere!

AI really can be for everyone, whether you’re a developer or a user — it’s not limited to people with mad coding skills.

Final ThoughtsIn less than a week, the AI4ALL program has shaped my view of AI and my learning process.

Estimating the fatality rate is difficult but doable with better dataThe case fatality rate quantifies how dangerous COVID-19 is, and how risk of death varies with strata like geography, age, and race.

Current estimates of the COVID-19 case fatality rate (CFR) are biased for dozens of reasons, from under-testing of asymptomatic cases to government misreporting.

The mathematical form of the naive estimator $E_{\rm naive}$ allows us to see easily what we need to do to make it unbiased.

If we collect data properly, even the naive estimator $E_{\rm naive}$ has good performance.

I’d like to re-emphasize a point here: collecting data as above will make the naive estimator $E_{\rm naive}$ unbias…

Exploring Exploration: Comparing Children with RL Agents in Unified EnvironmentsDespite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data.

The main thing that we know about the child exploration is that children form hypotheses about how the world works, and they engage in exploration to test those hypotheses.

How do AI agents explore?

We do this using DeepMind Lab, an existing platform for training and evaluating RL agents.

Conclusion and future workIn conclusion, this work only begins to touch on a number of deep questions regarding …

Can RL From Pixels be as Efficient as RL From State?

To date, it has been commonly assumed that RL operating on coordinate state is significantly more data-efficient than pixel-based RL.

In principle, if the environment is fully observable, we should also be able to learn representations that capture the state.

Contrastive Learning in RL SettingCURL was inspired by recent advances in contrastive representation learning in computer vision (CPC, CPCv2, MoCo, SimCLR).

Contrastive Learning vs Data AugmentationIf data augmentation with RL performs so well, do we need unsupervised representation learning?

One might naturally wonder what it might take for learning systems to scale in complexity in the same way as programmed systems have.

In other words, the society of primitive agents form a super-agent that solves the MDP as a consequence of the primitive agents' optimal auction strategies.

Societal decision-making frames standard reinforcement learning from the perspective of self-organizing primitive agents.

As we discuss next, the primitive agents need not be restricted to literal actions.

In some sense these complex learning systems are grown rather than built because every component at every abstraction layer is learning.

In offline RL, we assume all experience is collected offline, fixed and no additional data can be collected.

In order to develop effective algorithms for offline RL, we need widely available benchmarks that are easy to use and can accurately measure progress on this problem.

Narrow and biased data distributions are a common property in real-world datasets that can create problems for offline RL algorithms.

The Flow project proposes to use autonomous vehicles for reducing traffic congestion, which we believe is a compelling use case for offline RL.

Future DirectionsIn the near future, we would be excited to see offline RL applications move from simulated domains to real-world domains where s…

Therefore, we start rethinking machine learning and domain adaptation systems, and try to introduce a continuous learning protocol under domain adaptation scenario.

Open Compound Domain Adaptation (OCDA)The goal of domain adaptation is to adapt the model learned on the training data to the test data of a different distribution.

We propose to study Open Compound Domain Adaptation (OCDA), a continuous and more realistic setting for domain adaptation (Figure 2).

The newly proposed Open Compound Domain Adaptation (OCDA) serves as a more comprehensive and more realistic touchstone for evaluating domain adaptation and transfer learning systems.

Figure 3: The differences between single-target doma…

OmniTact: A Multi-Directional High-Resolution Touch SensorHuman thumb next to our OmniTact sensor, and a US penny for scale.

Recently, the GelSight sensor has caught significant interest for learning-based robotics due to its low cost and rich signal.

Comparison of GelSight-style sensor (left side) to our OmniTact sensor (right side).

The OmniTact SensorOur OmniTact sensor design aims to address these limitations.

We additionally compared performance with another multi-directional tactile sensor, the OptoForce sensor, which only had a success rate of 17%.

Four Novel Approaches to Manipulating Fabric using Model-Free and Model-Based Deep Learning in SimulationHumans manipulate 2D deformable structures such as fabric on a daily basis, from putting on clothes to making beds.

Model-Free MethodsModel-Free Learning without DemonstrationsIn this paper we present a model-free deep reinforcement learning approach for smoothing cloth.

An example of real robot cloth smoothing experiments with varying starting states and cloth colors.

Since this policy is easy to define, we code an algorithmic supervisor in simulation and perform imitation learning using Dataset Aggregation (DAgger).

Several episodes of both manipulating rope and cloth using our method,…

The following sections explain the dataset and preprocessing steps, the model training, and model deployment procedures.

After exploring different algorithms, the built-in XGBoost in Amazon SageMaker was used due to its better prediction performance and inference speed.

Amazon SageMaker extracts the metric from Amazon CloudWatch Logs with a regular expression.

If you’d like to compare models for A/B testing, Amazon SageMaker supports hosting representational state transfer (REST) endpoints for multiple models.

For instructions on creating a RESTful API, see Call an Amazon SageMaker model endpoint using Amazon API Gateway and AWS Lambda.

In the post Building a visual search application with Amazon SageMaker and Amazon ES , we shared how to build a visual search application using Amazon SageMaker and the Amazon ES KNN’s Euclidean distance metric.

In this post, you build a very simple search application that demonstrates the potential of using KNN with Amazon ES compared to the traditional Amazon ES ranking method, including a web application for testing the KNN-based search queries in your browser.

The application also compares the search results with Elasticsearch match queries to demonstrate the difference between KNN search and full-text search.

The function passes the search query embedding vector as the search value for…

This post discusses their challenges and why they chose Amazon Rekognition as their solution.

Since Arcanum started using Amazon Rekognition, the number of hits has doubled.

“Amazon Rekognition made Hungarian culture, history, and heritage more accessible to the world,” says Előd Biszak, Arcanum CEO.

You can test Amazon Rekognition features such as facial analysis, face comparison, or celebrity recognition on images specific to your use case on the Amazon Rekognition console.

For more information about Amazon Rekognition, see Amazon Rekognition Documentation.

With the new ability to launch Amazon SageMaker Studio in your Amazon Virtual Private Cloud (Amazon VPC), you can control the data flow from your Amazon SageMaker Studio notebooks.

In this post, we explore how the Amazon SageMaker Studio VPC connectivity works, implement a sample architecture, and demonstrate some security controls in action.

We need an Amazon SageMaker API endpoint to launch Studio notebooks, training jobs, processing jobs, and deploy endpoints, and an Amazon SageMaker RunTime endpoint for services to call the Amazon SageMaker inference endpoint.

Creating an Amazon SageMaker Studio domain inside a VPCWith the infrastructure in place, you’re ready to create an Amazon SageMa…

Amazon SageMaker multi-model endpoints (MMEs) are a cost-effective solution to deploy a large number of ML models or per-user models.

For more information about MME, see Save on inference costs by using Amazon SageMaker multi-model endpoints.

Create an Amazon SageMaker model with multi-model support.

Create an Amazon SageMaker inference pipeline with an Sklearn model and multi-model enabled linear learner model.

Creating an Amazon SageMaker model with multi-model supportWhen the training jobs are complete, you’re ready to create an MME.

In this post, we describe how you can combine Amazon SageMaker with Amazon Forecast to include unstructured text data into your time series use cases.

This related data may include time-varying data (such as price, events, and weather) and categorical data (such as color, genre, or region).

Creating and querying the forecastWhen you’re satisfied with the accuracy metrics from your trained Forecast model, it’s time to generate a forecast.

You also learned how to train a topic model and use the generated topic vectors as related time series for Forecast.

To read more about how you can build an end-to-end operational workflow with Amazon Forecast and AWS StepFunctions, see here.

Create a Lambda function that reads in a CSV file from Amazon S3, calls the Amazon Fraud Detector get_event_prediction function for each record in the CSV file, and writes a CSV file to Amazon S3.

Creating and publishing a detectorYou can create and publish a detector version using the Amazon Fraud Detector console or via the APIs.

AmazonFraudDetectorFullAccessPolicy – Provides permissions to create resources and generate fraud predictions in Amazon Fraud Detector.

– Provides permissions to create resources and generate fraud predictions in Amazon Fraud Detector.

For more information about Amazon Fraud Detector, including links to additional blog posts, sample notebooks, user guide, and API…

In this post, we show you how you can use Amazon Rekognition PPE detection to improve safety processes by automatically detecting if persons in images are wearing PPE.

With Amazon Rekognition PPE detection, businesses can augment manual checks with automated PPE detection.

Amazon Rekognition PPE detection also predicts a confidence score for whether the protective equipment is covering the corresponding body part of the person.

We have included a demo web application that implements this reference architecture in the Amazon Rekognition PPE detection GitHub repo.

To test PPE detection with your own images, sign in to the Amazon Rekognition console and upload your images in the Amazon Rekogni…

We trained a custom model that detects playful behaviors of cats in a video using Amazon Rekognition Custom Labels.

About Amazon Rekognition Custom LabelsAmazon Rekognition Custom Labels is an automated ML feature that enables you to quickly train your own custom models for detecting business-specific objects and scenes from images—no ML experience required.

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

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

When the inferred result file is uploaded to …

Amazon Rekognition Custom Labels provides a UI for viewing and labeling a dataset on the Amazon Rekognition console, suitable for small datasets.

Use the Region Table to choose a Region that supports Amazon Rekognition Custom Labels and Ground Truth.

It’s finally time to train the custom computer vision model using Amazon Rekognition Custom Labels.

For instructions on completing these steps on the console, see Getting Started with Amazon Recognition Custom Labels and Training a custom single class object detection model with Amazon Rekognition Custom Labels.

Amazon Rekognition Custom Labels saves the detailed results for each test image in a JSON file stored in Amazon S3.

This post shares our experience using Amazon CodeGuru Profiler to help one of our customers optimize their application under tight deadlines.

We had previously used other application profiling tools and realized how invaluable they can be when diagnosing these types of issues.

CodeGuru workflowAfter we decided on CodeGuru, it was easy to get CodeGuru Profiler installed and start capturing metrics.

CodeGuru Profiler monitors the environment and generates a flame graph and a recommendation report.

To learn more about how to get started with CodeGuru Profiler and CodeGuru Reviewer, check the documentation found here.

Customer obsession, one of the key Amazon Leadership principles that guides everything we do at Amazon, has helped Amazon Translate be recognized as an industry leading neural machine translation provider.

This year, Intento ranked Amazon Translate #1 on the list of top-performing machine translation providers in its The State of Machine Translation 2020 report.

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation.

Neural machine translation is a form of machine translation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translat…

Many encouraged government executives and federal agencies to act with a greater sense of urgency.

This is a really important conversation.”Just Get StartedThese and other speakers cited a common theme: agencies need to get started now.

CNAS recently released a report calling for $25 billion per year in federal AI investment by 2025.

The RAND Corp. released a congressionally mandated assessment of the DoD’s AI posture last year that recommended defense agencies need to create mechanisms for connecting AI researchers, technology developers and operators.

He urged government executives to take an active role in guiding those changes, encouraging them to be “brave, bold and benevolent.”

After his AI-enhanced vintage video went viral, Denis Shiryaev launched a startup to bottle the magic.

He ran them through an expanding workflow of AI models, including DeOldify for adding color and other open-source algorithms for removing visual noise.

He got requests from a media company in the Netherlands to enhance an old film of Amsterdam.

Displays in the Moscow subway played a vintage video he enhanced of the Russian capital.

The RTX card also trains the team’s custom AI models in eight hours, a job that used to take a week.

What Is Computer Vision?

Computer vision is a broad term for the work done with deep neural networks to develop human-like vision capabilities for applications, most often run on NVIDIA GPUs.

Yet computer vision can do much more than just make sports calls.

Developed with convolutional neural networks, computer vision can perform segmentation, classification and detection for a myriad of applications.

With industry changes from computer vision spanning sports, automotive, agriculture, retail, banking, construction, insurance and beyond, much is at stake.

And when it comes to in-vehicle inference, NVIDIA Xavier has been proven the best — and the only — platform capable of real-world AI processing, yet again.

NVIDIA GPUs smashed performance records across AI inference in data center and edge computing systems in the latest round of MLPerf benchmarks, the only consortium-based and peer-reviewed inference performance tests.

NVIDIA Xavier extended its performance leadership demonstrated in the first AI inference tests, held last year, while supporting all new use cases added for energy-efficient, edge compute SoC.

The new NVIDIA A100 GPU, based on the NVIDIA Ampere architecture, also rose above the competition, outperforming CPUs by up to 237x i…

Three trends continue to drive the AI inference market for both training and inference: growing data sets, increasingly complex and diverse networks, and real-time AI services.

MLPerf Inference 0.7 workloads.

MLPerf Inference 0.7 platform categories and scenarios.

Triton delivers nearly equal performance to a highly customized inference serving implementation in MLPerf Inference 0.7 results.

Accelerate inference applications todayGiven the continuing trends driving AI inference, the NVIDIA inference platform and full-stack approach deliver the best performance, highest versatility, and best programmability, as evidenced by the MLPerf Inference 0.7 test performance.

The A100, introduced in May, outperformed CPUs by up to 237x in data center inference, according to the MLPerf Inference 0.7 benchmarks.

Adoption of NVIDIA AI Inference Passes Tipping PointAI inference passed a major milestone this year.

Total cloud AI Inference compute capacity on NVIDIA GPUs has been growing roughly tenfold every two years.

Why AI Inference Is HardUse cases for AI are clearly expanding, but AI inference is hard for many reasons.

Our frameworks include NVIDIA Merlin for recommendation systems, NVIDIA Jarvis for conversational AI, NVIDIA Maxine for video conferencing, NVIDIA Clara for healthcare, and many others available today.

Now, those benefits are extending to Adobe Photoshop users with the introduction of GPU-accelerated neural filters.

These AI-powered tools, leveraging NVIDIA RTX GPUs with the Adobe creative applications, are being showcased at Adobe MAX, which is bringing together creators from around the world virtually through Oct. 22.

Adobe and NVIDIA are closely collaborating on AI technology to improve creative tools in Creative Cloud and Photoshop.

October Studio Driver Ready For DownloadAlongside these updates to Adobe Photoshop, Adobe Premiere Pro and Adobe Premiere Elements, there are new releases of Adobe After Effects, Adobe Substance Alchemist, Notch and Daz 3D — all supported in the new Octobe…

When you use Vulkan-hpp, some of those runtime errors are turned into compile-time errors.

Finally, each value of an enum class skips the prefix VK_ENUM_NAME , as that in turn would be redundant with the namespace and the enum class name.

An enum class value is not allowed to begin with a digit, so the ‘e’ prefix prevents that.

You also can’t compare two enum class values from different enum classes.

The helper class vk::Flags provides functionality that allows you to apply bitwise operators on enum class values from the same enum class, but only those values.

Amid a pandemic that’s put much of the world’s work, learning, even family reunions online, two of the leaders who have made today’s virtual world possible met Thursday on, where else — Zoom — to talk about what’s next.

NVIDIA CEO Jensen Huang and Zoom CEO Eric Yuan spoke Thursday at the online video conference company’s Zoomtopia user event in a casual, wide-ranging conversation.

“We built a time machine,” Huang said, touching on NVIDIA’s work in drug discovery as an example.

“In the future we will say we’re ‘going to the office,’” Huang said.

NVIDIA, for example, has created a platform called NVIDIA Omniverse that lets colleagues working in different places and with different tools collab…

They include one system dubbed Leonardo, unveiled today at Italy’s CINECA research center, using NVIDIA technologies to deliver the world’s most powerful AI system.

Joining Leonardo are a wave of new AI supercomputers planned for the Czech Republic, Luxembourg and Slovenia that will act as national centers of competence, expanding skills and creating jobs.

NVIDIA GPUs, InfiniBand Power Latest SystemsAll four supercomputers announced use NVIDIA Ampere architecture GPUs and NVIDIA Mellanox HDR InfiniBand networks to tap an ecosystem of hundreds of HPC and AI applications.

Atos, an NVIDIA systems partner headquartered in France, will build three of the four systems; Hewlett Packard Enterprise …

Using AI and a supercomputer simulation, Ken Dill’s team drew the equivalent of wanted posters for a gang of proteins that make up COVID-19.

The center has a history of applying its knowledge to viral proteins, helping others identify drugs to disable them.

AI provides MELD key information to predict a protein’s 3D structure from its sequence of amino acids.

“So, both these worlds — AI inference and physics simulations — are playing big roles in helping drug discovery,” said Dill.

Playing a Waiting GameThe COVID-19 challenge gave Laufer researchers with a passion for chemistry a driving focus.

In the battle against COVID-19, Greater Paris University Hospitals – Public Assistance Hospital of Paris (AP-HP is the French acronym) isn’t just on the medical front lines — it’s on the data front lines as well.

With a network of 39 hospitals treating 8.3 million patients each year, AP-HP is a major actor in the fight against COVID-19.

Along with its COVID-19 cases comes an awful lot of data, including now geodata that can potentially help lessen the impact of the pandemic.

The expected volume of COVID-19 data and geodata would probably have tested AP-HP’s data crunching capacity.

RAPIDS accelerates analytics and data science pipelines on NVIDIA GPUs by taking advantage of GPU parallelism …

Not everyone needs to be a developer, but everyone will need to be an AI decision maker.

It was one of several GTC events advancing the conversation on diversity, equity and ethics in AI.

This year, we strengthened our support for women and underrepresented developers and scientists at GTC by providing conference passes to members of professional organizations supporting women, Black and Latino developers.

A Forbes report last year named GTC as one of the U.S.’s top conferences for women to attend to further their careers in AI.

Dinner with Strangers: Developer Diversity in AIIn a virtual edition of the popular Dinner with Strangers networking events at GTC, experts from NVIDIA and NSBE par…

That’s where Lilt, an AI-powered enterprise language translation company, comes in.

Lilt CEO Spence Green spoke with AI Podcast host Noah Kravitz about how the company is using a human-in-the-loop process to achieve fast, accurate and affordable translation.

Lilt does so with a predictive typing software, in which professional translators receive AI-based suggestions of how to translate content.

Lilt was founded in 2015, and evolved from a solely software company into a software and services business.

Make the AI Podcast BetterHave a few minutes to spare?

Today, we are excited to release Ludwig version 0.3, featuring several updates that take our framework to the next level.

Finding the parameters that yield the best performance on a data set is a time-consuming job that can be automated by hyper-parameter optimization techniques.

The hyper-parameter optimization architecture is easy to expand and we plan to integrate with additional samplers and executors in the near future, like RayTune.

Nonetheless, Ludwig version 0.3 ships with a revamped, more modular, and easier-to-extend backend based on TensorFlow 2, lending to greater flexibility all around.

Moving forwardWith the addition of hyper-parameter optimization, Ludwig version 0.3 has soli…

Instead of programming only a single desktop or laptop, users can leverage this system to program the whole computer cluster.

Fiber allows users to write programs that run on a computer cluster without needing to dive into the details of the computer cluster.

This overall architecture is summarized in Figure 2, below:Job-backed processesFiber introduces a new concept called job-backed processes (also called a Fiber processes).

When starting a new Fiber process, Fiber creates a new job with the proper Fiber back end on the current computer cluster.

Our hypothesis was that Fiber should perform similarly to multiprocessing because neither Fiber nor multiprocessing rely on complex scheduling me…

Unfortunately, adding support for a new deep learning framework across an entire machine learning stack is resource and time-intensive.

Using multiple deep learning frameworksDeep learning (DL) is advancing very quickly and different DL frameworks are effective at different tasks.

Over the last year, we have deployed hundreds of Neuropod models across Uber ATG, Uber AI, and the core Uber business.

Deep learning with NeuropodLet’s take a look at the overall deep learning process when using Neuropod to see how it helps make experimentation, deployment, and iteration easier.

Next stepsNeuropod has allowed Uber to quickly build and deploy new deep learning models, but that’s just the start.

The “spikes” at intersections result from the SDV crossing the same intersection multiple times as part of a “grid-coverage” driving pattern.

Data mining the scenario “pedestrian crossing the street”While the SDV perception system is designed to detect pedestrians, only a subset of pedestrians actually cross the street.

Analyzing the “pedestrian crossing the street” scenarioThe scenario of a pedestrian crossing the street has many relevant measurements, including the pedestrian crossing speed, road width, distance walked, crossing duration, distance walked on crosswalk, and traffic light state(s) at the time of crossing.

Let’s start by analyzing just one measurement: the pedestrian crossing…

For instance, in a social network we may use link prediction to power a friendship recommendation system, or in the case of biological network data, we might use link prediction to infer possible relationships between drugs, proteins, and diseases.

In principle, it can be combined with a wide variety of link prediction approaches based on GNNs, but we adopted a specific GNN, variational graph autoencoders (VGAEs), as our base link prediction framework9.

Experiment setupTo test how Meta-Graph might work in a real-world setting, we designed three novel benchmarks for few-shot link prediction.

In this few-shot link prediction setting, there are train/val/test splits at both the edge level and …

We’ll treat working memory capacity as the length of the longest list of random digits that the participant can memorize.

InferenceWe use Bayesian inference to incorporate our new observation into an estimate of the participant’s working memory capacity.

It models the probability of correctly remembering the list of digits of different lengths for people with different working memory capacities, as shown in Figure 1, below:We also need a sense of what working memory capacities are plausible.

Computing the optimal designOur score for experimental designs, EIG, is notoriously difficult to estimate.

In our paper, we showed that this method can be remarkably accurate on a range of different exp…

Last year we introduced the Paired Open-Ended Trailblazer (POET) to explore the idea of open-ended algorithms.

ANNECS: A new way to measure progress in open-ended systemsQuantifying the performance of open-ended algorithms has remained elusive for the field.

Compare those from Original POET in Figure 4a to those produced by Enhanced POET in Figure 4b, below.

If this piques your interest, be sure to check out videos of example Enhanced POET agents on the Uber AI YouTube channel.

Towards that end, we are not only releasing a paper with full technical details, but also have open sourced the code for Enhanced POET.

A trained model, which requires as input the data set artifact, the model training code, and configuration files governing model training.

Example sequence of events: registering a new data setUpon user-registration of a new data set, the VerCD Data set Service stores the dependency metadata in our database.

Data set service APIThe data set service is responsible for tracking the dependencies for building a given data set.

The REST API supports the functions of creating a new data set, reading the metadata for a data set, updating the metadata of a data set, deleting a data set, and getting the artifact locations of the data set (such as in S3 or HDFS).

For instance, the VerCD data set serv…

To better assess the performance of our models, we built a backtesting service for measuring forecast model error rates.

The backtesting service runs in a distributed system, allowing multiple models (>10), many backtesting windows (>20), and models for different cities (>200) to run simultaneously.

Backtesting at scaleOur data science teams regularly create forecast models and statistics to better understand budget spending and project financial performance.

For the purposes of our backtesting service, we chose to leverage two primary backtesting data split mechanisms, backtesting with an expanding window and backtesting with a sliding window:Above, we showcase three windows for each metho…

In this list, you’ll find:reinforcement learning tutorials,examples of where to apply reinforcement learning,interesting reinforcement learning projects,courses to master reinforcement learning.

Machine Learning for Humans: Reinforcement Learning – This tutorial is part of an ebook titled ‘Machine Learning for Humans’.

It starts with an overview of reinforcement learning with its processes and tasks, explores different approaches to reinforcement learning, and ends with a fundamental introduction of deep reinforcement learning.

Applications of Reinforcement Learning in Real World – Explore how reinforcement learning frameworks are undervalued when it comes to devising decision-making models…

Luckily, you can solve it before it occurs (with gradient clipping) – let’s first look at the problem in-depth.

See the article:How to fix exploding gradients: gradient clippingThere are a couple of techniques that focus on Exploding Gradient problems.

Effect of gradient clipping in a recurrent network with two parameters w and b. Gradient clipping can make gradient descent perform more reasonably in the vicinity of extremely steep cliffs.

Gradient clipping in deep learning frameworksNow we know why Exploding Gradients occur and how Gradient Clipping can resolve it.

You’ve successfully understood the Gradient Clipping Methods, what problem it solves, and the Exploding Gradient Problem.

Standard GAN loss function (min-max GAN loss)The standard GAN loss function, also known as the min-max loss, was first described in a 2014 paper by Ian Goodfellow et al., titled “Generative Adversarial Networks“.

The Standard GAN loss function can further be categorized into two parts: Discriminator loss and Generator loss.

Alternate GAN loss functionsSeveral different variations to the original GAN loss have been proposed since its inception.

The following modified loss function plays the same min-max game as in the Standard GAN Loss function.

SummaryIn this blog, we discussed:The original Generative Adversarial Networks loss functions along with the modified ones.

Well, this shows perfectly how your plans can be destroyed with a not well-calibrated model (also known as an ill-calibrated model, or a model with a very high Brier score).

But others, like the Brier score in the weather forecasting model above, are often neglected.

The mathematical formulation of the Brier Score depends on the type of predicted variable.

In case the event doesn’t occur in reality, the Brier Score will be:As you may have noticed, the Brier score is a distance in the probability domain.

I hope that, after reading this article you have a good understanding of what Brier score and model calibration are and you’ll be able to use that in your ML projects.

That is why throughout this article we will mostly talk about performing Data Augmentation with various DL frameworks.

Data Augmentation techniquesWe can apply various changes to the initial data.

Data Augmentation in Deep LearningAs mentioned above in Deep Learning, Data Augmentation is a common practice.

Data Augmentation in TensorFlow and KerasTo augment images when using TensorFlow or Keras as our DL framework we can:Write our own augmentation pipelines or layers using tf.image .

Keras preprocessingAs mentioned above, Keras has a variety of preprocessing layers that may be used for Data Augmentation.

This time you’ll see the just created Kubeflow pipeline, and next to it there’s the OPEN PIPELINES DASHBOARD command.

SetupIn the first cell of your notebook, you’ll install TFX, Kubeflow (kfp) and a package called skaffold:# Install tfx and kfp Python packages.

This file defines the TFX pipeline and various components in the pipeline.

!gsutil cp data /advertising.csv gs://{ GOOGLE_CLOUD_PROJECT }-kubeflowpipelines-default/advert-pred/ data / data .csvYou can navigate to the Cloud Storage browser to confirm that the file has been uploaded.

!tfx pipeline create \ - -pipeline-path=kubeflow_dag_runner.py \ - -endpoint={ENDPOINT} \ - -build-target-image={CUSTOM_TFX_IMAGE}Running the command abo…

In this article, we’ll talk about popular loss functions in PyTorch, and about building custom loss functions.

How to add PyTorch loss functionsWhich loss functions are available in PyTorch?

How to create a custom loss function in PyTorchWhat are the loss functions?

How to add PyTorch loss functionsPyTorch’s torch.nn module has multiple standard loss functions that you can use in your project.

Hopefully this article will serve as your quick start guide to using PyTorch loss functions in your machine learning tasks.

Image processing is a method to perform operations on an image to extract information from it or enhance it.

Digital image processing has a broad range of applications such as image restoration, medical imaging, remote sensing, image segmentation, etc.

In this article, we will be covering the top 6 image processing techniques for machine learning.

Image Restoration Linear Filtering Independent Component Analysis Pixelation Template Matching Image Generation Technique (GAN)See also: Best Image Processing Tools Used in Machine Learning1.

An example of image restoration using image inpainting with OpenCVImage impainting also known as “Compensation of paint loss ”.

Machine Learning has application in so many different fields, that sometimes it may be hard to keep track of all the new things happening every day.

» Machine learning speeds up quantum chemistry calculations by Emily Velasco, California Institute of Technology on Phys.org | October 7Quantum chemistry has its limits.

Fortunately, nothing harmful 😉 But it’s still interesting (and funny) so go check yourself 🙂» How LinkedIn Uses Machine Learning in its Recruiter Recommendation Systems by Jesus Rodriguez on KDnuggets | October 8LinkedIn uses some very innovative machine learning techniques to optimize candidate recommendations.

The author discusses whether the current state of Machine Learning…

So to help you stay on top of the latest trends, we’ve gathered the best resources.

He started the community to help other students, developers, and researchers become better at computer vision.

Tombone’s Computer Vision Blog has everything about Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence.

It should be on your list of the best resources on Computer Vision and Machine Learning.

The author (who has a PhD in Computer Vision and a Master’s in Theology and a Master’s in Philosophy!)

Essential PIL image conceptsWe will start by understanding a couple of PIL image concepts that are critical.

Note: We import the Image class from PIL not Pillow because Pillow is a PIL fork.

from PIL import Image im = Image.open( "peacock.png" )With the image loaded, Let’s start talking about those image concepts.

Understanding the Image classAs we have earlier we had to import the Image class from PIL before reading in our image.

Finally, we save and display the image using PIL show image function.

Key points covered:Creating a good project directory structureDealing with changing data: Data VersioningKeeping track of ML experimentsProper evaluation and managing metrics and KPIsModel Deployment: how to get it rightLet’s jump in.

│ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks.

A data scientist should ask questions like : Any biases in the data – Biases in the data can be of all types.

Evaluating an unsupervised NLP modelAs a special case, let’s discuss how you would evaluate an unsupervised NLP model.

If you enjoyed this post, a great next st…

This video explains a new approach to Visually supervise Language models that achieves performance gains on Language-Only tasks like the GLUE benchmark and SQuAD question answering. This is done by constructing a token-image matching (vokens) and classifying corresponding tokens with a a weakly supervised loss function.

Thanks for watching! Please Subscribe! Paper Links:

Vokenization: https://arxiv.org/pdf/2010.06775.pdf

ImageBERT: https://arxiv.org/pdf/2001.07966.pdf

VilBERT: https://arxiv.org/pdf/1908.02265.pdf

LXMERT: https://arxiv.org/pdf/1908.07490.pdf

UNITER: https://arxiv.org/pdf/1909.11740.pdf

Visual Genome: https://visualgenome.org/

12-in-1: Multi-task Vision and Language Represent…

This video explains the latest work in Pattern-Exploiting Training. This paper shows that this distillation scheme from knowledge captured in pre-trained language models to discriminative classifiers can also work in the Few-shot setting. This is compared directly with GPT-3's performance using 32 labeled examples for different tasks like BoolQ or Winograde Schema. This is very interesting, but not a fair, apples-to-apples, comparison with GPT-3. Thanks for watching! Please Subscribe! Paper Links:

Paper Link: https://arxiv.org/abs/2009.07118

First PET Paper: https://arxiv.org/pdf/2001.07676.pdf

Next Word Prediction Demo: https://github.com/renatoviolin/next_word_prediction

Hacker News React…

This video explains the Retrieval-Augmented Generation (RAG) model! This approach combines Dense Passage Retrieval with a Seq2Seq BART generator. This is tested out on knowledge intensive tasks like open-domain QA, jeopardy question generation, and FEVER fact verification. This looks like a really interesting paradigm for building language models that produce factually accurate generations! Thanks for watching! Please Subscribe! Paper Links:

Original Paper: https://arxiv.org/pdf/2005.11401.pdf

FB Blog Post (Animation used in Intro): https://ai.facebook.com/blog/retrieval-augmented-generation-streamlining-the-creation-of-intelligent-natural-language-processing-models

HuggingFace RAG descript…

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Start with part 1: https://youtu.be/X8jsijhllIA

Ben Eater implementing Hamming codes on breadboards: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by Vincent Rubinetti. Download the music on Bandcamp: https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown Stream the music on Spotify: https://open.spotify.com…

A discovery-oriented introduction to error correction codes.

Part 2: https://youtu.be/b3NxrZOu_CE

Ben Eater:'s take: https://youtu.be/h0jloehRKas

Brought to you by you: https://3b1b.co/thanks You can read Hamming's own perspective on his discovery of these codes in chapter 12 of "The Art of Doing Science and Engineering".

https://amzn.to/3lwcnmh ------------------ These animations are largely made using manim, a scrappy open-source python library: https://github.com/3b1b/manim If you want to check it out, I feel compelled to warn you that it's not the most well-documented tool, and it has many other quirks you might expect in a library someone wrote with only their own use in mind. Music by…

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A fun puzzle stemming from repeated exponentiation.

Full playlist: https://www.youtube.com/playlist?list=PLZHQObOWTQDP5CVelJJ1bNDouqrAhVPev

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https://twitter.com/ThuyNganVu/status/1261014161464516608?s=20 Play along on Desmos:

https://www.desmos.com/calculator/nul32eaaa9 Related videos.

Calculus series:

https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr In particular look at:

https://youtu.be/CfW845LNObM Numberphile on Grahm's constant:

https://youtu.be/XTeJ64KD5cg ------------------

Video timeline (thanks to user "noonesperfect")

0:36 Question 1

1:13 Answer 1

1:29 …

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❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://app.wandb.ai/safijari/dqn-tutorial/reports/Deep-Q-Networks-with-the-Cartpole-Environment--Vmlldzo4MDc2MQ 📝 The paper "IQ-MPM: An Interface Quadrature Material Point Method for Non-sticky Strongly Two-Way Coupled Nonlinear Solids and Fluids" is available here:

https://yzhu.io/publication/mpmcoupling2020siggraph/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Hadd…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their report for this paper is available here: https://wandb.ai/wandb/in-domain-gan/reports/In-Domain-GAN-Inversion--VmlldzoyODE5Mzk 📝 The paper "In-Domain GAN Inversion for Real Image Editing" is available here:

https://genforce.github.io/idinvert/ Check out the research group's other works, there is lots of cool stuff there:

https://genforce.github.io/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, …

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned instrumentation is available here: https://app.wandb.ai/stacey/sfmlearner/reports/See-3D-from-Video%3A-Depth-Perception-for-Self-Driving-Cars--Vmlldzo2Nzg2Nw 📝 The paper "A Massively Parallel and Scalable Multi-GPU Material Point Method " is available here:

https://sites.google.com/view/siggraph2020-multigpu 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon C…

❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Flow-edge Guided Video Completion" is available here:

http://chengao.vision/FGVC/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric Martel, Gordon Child, Haris Husic, Javier Bustamante, Joshua Goller, Lorin Atzberger, Lukas Biewald, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Robin Graham, Steef, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned post is available here: https://www.wandb.com/articles/code-comparer 📝 The paper "Neural Supersampling for Real-time Rendering" is available here:

https://research.fb.com/blog/2020/07/introducing-neural-supersampling-for-real-time-rendering/

https://research.fb.com/publications/neural-supersampling-for-real-time-rendering/ 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Lau, Eric M…

❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers ❤️ Their mentioned instrumentation is available here: https://app.wandb.ai/lavanyashukla/cnndetection/reports/Detecting-CNN-Generated-Images--Vmlldzo2MTU1Mw 📝 The paper "Learning Body Shape Variation in Physics-based Characters" is available here:

http://mrl.snu.ac.kr/publications/ProjectMorphCon/MorphCon.html 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:

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❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Specular Manifold Sampling for Rendering High-Frequency Caustics and Glints" is available here:

http://rgl.epfl.ch/publications/Zeltner2020Specular My rendering course is available here, and is free for everyone: https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ The PostDoc call is available here - https://www.cg.tuwien.ac.at/news/2020-10-02-Lighting-Simulation-Architectural-Design-%E2%80%93-Post-Doc-Position Mitsuba Renderer: https://www.mitsuba-renderer.org/

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Math Optimization Track https://ods.ai/tracks/optimization-df2020 Polynomial optimization (PO) is a powerful tool for translating real-life situations into mathematical language. The goal of PO is to find a global maximum or minimum of a problem where we optimize a multivariate polynomial objective over a set described by polynomial constraints. PO algorithms can provide better results than the algorithms searching for local optima, but these results might come at a high computational cost. Register and get access to the tracks: https://ods.ai/events/datafest2020

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Dreamer — современный model-based метод обучения с подкреплением, позволяющий значительно сократить взаимодействие со средой при обучении. Важной его составляющей является модель, сжимающая наблюдение получаемое в виде изображения в небольшое латентное представление. Такая модель обычно реализуется в виде вариационного автокодировщика. Однако подход основаный на автокодировщике часто приводит к исчезновению маленьких, но важных при обучении объектов. В недавней статье был предложен альтернативный способ получения латентного представления, основанный на constrastive learning. На семинаре мы разберем предложенное решение и обсудим полученные результаты. Докладчик: Константин Махнев. Слайды: h…

Решение задач является неотъемлемой частью процесса обучения программирования, и, как показывают исследования, именно с ней у школьников и студентов возникают наибольшие трудности. Однако иногда ресурсов преподавателя не хватает на оперативную помощь, или, в случае онлайн-курсов, преподаватели вообще отсутствуют и подсказок ждать неоткуда. Для таких случаев важна автоматическая генерация подсказок, способных заменить помощь эксперта. На следующем семинаре мы расскажем о нашем проекте по автоматической генерации подсказок для языка Python, существующих подходах в этой области, процессе сбора необходимых данных и их анализе. Докладчики Алёна Люлина и Анастасия Бирилло. Слайды: https://drive.g…

Липофильность - свойство молекулы, влияющее на ее способность пересекать клеточные мембраны внутри организма. Поэтому предсказание липофильности важно в процессе разработки лекарств. Липофильность может быть представлена в виде двух числовых величин - logP и logD. LogP описывает липофильность неионизируемых и нейтральных форм ионизированных молекул. Для заряженных молекул липофильность представляется в виде LogD. Поскольку оба эти дескриптора относятся к одному химическому свойству молекулы, можно предсказывать их параллельно. На данный момент наиболее эффективными подходами для предсказания молекулярных свойств методами глубокого обучения являются графовые нейросети. Несмотря на общий прог…

Model-based reinforcement learning (MBRL) набирает всё большую популярность благодаря возможности использовать меньше данных для обучения и использованию off-policy данных. Однако, проектирование стабильных и эффективных MBRL алгоритмов всё ещё остаётся тяжёлой задачей. Авторы статьи предлагают фреймворк, который позволяет рассматривать MBRL алгоритм как игру между: (1) игроком политики, максимизирующим награду при текущей модели; (2) игроком модели, который старается обучиться на данных о среде, собранных игроком политики. Для проектирования алгоритма, авторы предлагают использовать модель Штакельберга между двумя игроками и показывают, что её можно решить с помощью двухуровневой оптимизац…

В докладе мы поговорим об адаптивных градиентных методах и об их стохастических расширениях. Мы обсудим самые популярные теоретические и эвристические приемы для ускорения градиентного спуска. Отдельное внимание мы уделим противоречиям между теорией и практикой, особенно ярко возникающим при обучении нейросетей. Доклад будет базироваться на ряде статей последних лет, включая несколько появившихся в 2020 году. Базируясь на них, мы сформулируем несколько открытых проблемы в оптимизиации и обсудим, как можно пытаться их решить. Перед докладом рекомендуется ознакомиться с базовыми определениями такими, как выпуклость функции и ее градиент. Докладчик: Константин Мищенко. Слайды: https://drive.go…

Code review является важным этапом разработки ПО. Краткое описание совершенных изменений может сделать этот процесс значительно проще. Например, может быть полезна информация о том, какие рефакторинги были совершены. На семинаре будет рассказано о разработанном в лаборатории плагине для IntelliJ IDEA, который визуализирует совершенные в коммитах рефакторинги. Для обнаружения рефакторингов мы используем инструмент RefactoringMiner, который анализирует изменения в коде на Java и в качестве результата выдает список обнаруженных рефакторингов. Также мы поговорим о расширении RefactoringMiner, позволяющем обнаруживать рефакторинги в изменениях кода на Kotlin, над которым сейчас активно ведётся р…

Предсказание трехмерной структуры участка антитела — петли CDR-H3 — является важной нерешенной задачей в области биоинформатики. Различных антител очень много, но большая часть участков антител остается неизменной, меняются лишь небольшие петли, ответственные за прикрипление к антигену. Существующие алгоритмы могут хорошо предсказать общую форму антитела, но сильно ошибаются в этих местах. Всего таких участков 6, и основные проблемы вызывает петля CDR-H3, а остальные петли уже достаточно хорошо моделируются. На семинаре будут представлен обзор существующих методов машинного обучения для предсказания структуры CDR-H3, а также будут затронуты некоторые проблемы, возникающие при решении этой з…

В машинном обучении есть различные разделы, изучающие способы анализа различных данных. В CV изучают анализ изображений, в NLP работают с текстом, в ASR со звуком. Но бывают и задачи на стыке этих областей. Где может быть полезно или даже необходимо одновременно работать с информацией разного типа. К примеру: задача классификации изображений с подписями, распознавание речи по видео с аудио, поиск изображений по текстовому запросу и т.д.. На семинаре мы подробнее поговорим о том, что такое мультимодальные задачи, какими они бывают, как можно подходить к их решению и рассмотрим несколько конкретных моделей. Докладчик: Григорий Бартош. Слайды: https://drive.google.com/file/d/1_j72I246B_kkcuymx…

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Data Fest Online 2020

SysML track Как мы готовим переобучение моделей под Spark и не только. Рецепты и особенности. Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

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https://t.me/datafest

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Data Fest Online 2020

SysML track Рассказ о том, как силами DS завозим продакшн решения на GCP на примере созданной системы мониторинга метрик.

Полностью самостоятельно написанное решение с применением ансамбля моделек LSTM и бустинга для контроля нормального поведения метрик в множестве разрезов.

Процесс завезения решений ускорили с таким подходом. Работаем и с локальными серверами и с облаком, есть понимание плюсов и минусов.

Развитие систем с ML гораздо быстрее работает непосредственно, когда компетенции прода в команде DS - какие компетенции нужны, чтобы делать хорошо прод в облачке

Полностью выложу секреты-лайфхаки реализации системы мониторинга аномалий во временных рядах с дообучение…

Data Fest Online 2020

SysML track Общепринятым и проверенным временем подходом к построению DWH является схема «Звезда» или «Снежинка». Такой подход каноничен, фундаментален, вотрефоллен и совсем не отвечает той гибкости, к которой призывает Agile. Для того, чтобы сделать структуру DWH гибкой, существуют современные подходы к проектированию: Data Vault и Anchor modeling – похожие и разные одновременно.

Задавшись вопросом, который вынесен в заголовок доклада, мы пришли к неожиданному ответу: выбирать надо не между подходами, выбирать надо лучшее из двух подходов.

В своем докладе я расскажу:

- DV и AM – в чем разница и где точки соприкосновения;

- наш «гибридный» подход к построению хранилища…

Data Fest Online 2020

SysML track В open source мире существует множество инструментов для запуска, шедулинга и управления ETL процессами: Airflow, Luigi, Metaflow, ... Все они сосредоточены на описании зависимостей между тасками и графами, но не на том, что непосредственно происходит с данными внутри этих тасков и графов. Мы пошли от обратного и прежде всего выделили основные сущности любого ETL процесса: это таблицы и таски, которые читают данные, их преобразуют и прогружают в таблицы. Мы разработали свой DSL на python для единообразного описания таблиц в YT (in-house аналог Hadoop, Greenplum и ClickHouse) и тасков для MapReduce, Spark, разных SQL-диалектов (3 штуки) и голого python. В до…

Data Fest Online 2020 SysML Track Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

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Data Fest Online 2020

SysML track Расскажу про все этапы работы умной ленты ВК - всего 10 этапов. Затронем темы BigData, обучения моделей, работы под высокой нагрузкой, АБТ, поиск таргетов, мониторинги и остальное. Тема лучше всего подходит под SysML, так как я расскажу именно про полный цикл реальной продакшен системы. Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

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Data Fest Online 2020

SysML track Поговорим о важности мониторинга и профита, который он приносит.

Рассмотрим подходы к мониторингу Hadoop кластера, джоб, сбору метрик и научимся их интерпретировать.

Сравним возможные способы визуализации. Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-df2020 Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-df2020 Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-df2020 Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

Yandex track https://ods.ai/tracks/yandex-data-labeling-df2020 Посмотреть эфир и список треков и организаторов https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам https://ods.ai/events/datafest2020

Вступить в сообщество https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

A/B Testing Track https://ods.ai/tracks/ab-testing-df2020 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

Data Fest Online 2020

A/B Testing Track https://ods.ai/tracks/ab-testing-df2020 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Data Fest Online 2020

Computer Vision in Industry track https://ods.ai/tracks/cv-in-industry-df2020 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

https://vk.com/datafest

Data Fest Online 2020

Computer Vision in Industry track https://ods.ai/tracks/cv-in-industry-df2020 Посмотреть эфир и список треков и организаторов: https://datafest.ru/2020/

Зарегистрироваться на фест и получить доступ к трекам: https://ods.ai/events/datafest2020

Вступить в сообщество: https://ods.ai/ Соцсети Data Fest:

https://t.me/datafest

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Manolis Kellis is a computational biologist at MIT.

Please support this podcast by checking out our sponsors:– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– Pessimists Archive: https://pessimists.co/– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– BetterHelp: https://betterhelp.com/lex to get 10% offEPISODE LINKS:Manolis Website: http://web.mit.edu/manoli/Manolis Twitter: https://twitter.com/manoliskellisManolis YouTube: https://www.youtube.com/channel/UCkKlJ5LHrE3C7fgbnPA5DGAManolis Wikipedia: https://en.wikipedia.org/wiki/Manolis_KellisPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2l…

George Hotz (geohot) is a programmer, hacker, and the founder of Comma.ai.

On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction07:02 – Will human civilization destroy itself?

09:49 – Where are the aliens?

14:36 – Tic Tac UFO and Bob Lazar17:04 – Conspiracy theories19:07 – The programming language of life23:28 – The games that humans play31:58 – Memory leaks in the simulation34:29 – Theories of everything36:14 – Ethereum startup story44:02 – Cryptocurrency53:28 – Self-help advice57:08 – Comma.ai59:02 – Comma two1:07:50 – Tesla vs Comma.ai1:16:53 – Driver monitoring1:30:34 – Communicating uncertainty1:32:22 – Tesla Dojo1:38:50 – Tesla Au…

Chris Lattner is a world-class software & hardware engineer, leading projects at Apple, Tesla, Google, and SiFive.

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00:00 – Introduction07:12 – Working with Elon Musk, Steve Jobs, Jeff Dean12:42 – Why do programming languages matter?

18:42 – Python vs Swift29:35 – Design decisions34:53 – Types38:41 – Programming languages are a bicycle for the mind41:13 – Picking what language to learn47:12 – Most beautiful feature of a programming language56:36 – Walrus operator1:06:03 – LLVM1:11:15 – MLIR compiler framework1:15:21 – SiFive semiconductor design1:27:56 – Moore’s Law1:31:09 – Parallelization1:35:37 – Swift…

Scott Aaronson is a quantum computer scientist.

Please support this podcast by checking out our sponsors:– SimpliSafe: https://simplisafe.com/lex and use code LEX to get a free security camera– Eight Sleep: https://www.eightsleep.com/lex and use code LEX to get $200 off– ExpressVPN: https://expressvpn.com/lexpod and use code LexPod to get 3 months free– BetterHelp: https://betterhelp.com/lex and use code LEX to get 10% offEPISODE LINKS:Scott’s Blog: https://www.scottaaronson.com/blog/Our previous episode: https://www.youtube.com/watch?v=uX5t8EivCaMPODCAST INFO:Podcast website: https://lexfridman.com/podcastApple Podcasts: https://apple.co/2lwqZIrSpotify: https://spoti.fi/2nEwCF8RSS: https:/…

Lisa Feldman Barrett is a neuroscientist, psychologist, and author.

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08:03 – Life on Earth12:55 – Collective intelligence of human brains21:43 – Triune brain27:52 – The predicting brain35:48 – How the brain evolved41:47 – Free will50:40 – Is anything real?

1:03:13 – Dreams1:09:00 – Emotions are human-constructed concepts1:34:29 – Are women more emotional than men?

1:43:05 – Empathy2:14:46 – Love2:18:40 – Mortality2:20:16 – Meaning of life

Michael Malice is a political thinker, podcaster, and author.

Please support this podcast by checking out our sponsors:– SEMrush: https://www.semrush.com/partner/lex/ to get a free month of Guru– DoorDash: https://doordash.com/ and use code LEX to get $5 off– MasterClass: https://masterclass.com/lex to get 15% off annual subEPISODE LINKS:Michael’s Twitter: https://twitter.com/michaelmaliceMichael’s YouTube: https://www.youtube.com/channel/UC5tj5QCpJKIl-KIa4Gib5XwMichael’s Website: http://michaelmalice.com/about/Your Welcome podcast: https://bit.ly/30q8oz1The New Right (book): https://amzn.to/34gxLo3Dear Reader (book): https://amzn.to/2HPPlHSPODCAST INFO:Podcast website: https://lexfridman.c…

Joe Rogan is a comedian, UFC commentator, and the host of the Joe Rogan Experience.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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OUTLINE:0:00 – Introduction5:17 – Fear of mortality6:48 – Chaos of 2020 and beyond10:32 – Are we going to be okay?

19:30 – Violence, competition, and Sober October26:45 – Mike Tyson27:49 – Managing obsession30:13 – Jiu jitsu game35:55 – Best martial art for self defense39:30 – Guns43:59 – Memorable JRE moments49:31 – Ideas breed in brains of humans56:08 – Advice for Lex1:06:09 – Long-form conversation1:12:28 – …

James Gosling is the founder and lead designer of the Java programming language.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

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OUTLINE:0:00 – Introduction4:45 – Irrational numbers8:04 – Math and programming10:36 – Coding style14:41 – First computer23:54 – Lisp27:22 – Write an Emacs implementation in C35:15 – Early days of the Internet45:57 – Elon Musk, Steve Jobs, Jeff Bezos56:13 – Work hard and smart58:48 – Open source1:10:25 – Java1:28:31 – Java virtual machine1:44:05 – Android1:47:04 – Advice

Ryan Hall is a jiu jitsu black belt, UFC fighter, and a philosopher of the martial arts.

Please check out our sponsors to get a discount and to support this podcast:– PowerDot, use code LEX: https://powerdot.com/lex– Babbel: https://babbel.com and use code LEX– Cash App: download app & use code “LexPodcast”If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

On …

Stephen Wolfram is a computer scientist, mathematician, and theoretical physicist.

This is our second conversation on the podcast.

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3:52:55 – Sabine Hossenfelder and how beauty leads physics astray4:01:07 – Eric Weinstein and Geometric Unity4:06:17 – Travel faster than speed of light4:16:59 – Why does the universe exist at all

Manolis Kellis is a professor at MIT and head of the MIT Computational Biology Group.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode.

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OUTLINE:00:00 – Introduction06:20 – Epigenome10:28 – Evolution15:26 – Neanderthals27:15 – Origin of life on Earth43:44 – Life is a fight against physics49:56 – Life as a set of transformations51:35 – Time scales1:00:31 – Transformations of ideas in human civilization1:05:19 – Life is more than a rat race1:13:18 – Life sucks sometimes and that’s okay1:30:16 – Getting older1:3…

David Fravor is a navy pilot of 18 years and a primary witness in one of the most credible UFO sightings in history, video of which has been released by the Pentagon and reported on by the NY Times.

OUTLINE:00:00 – Introduction07:13 – Top Gun12:06 – Navy pilot career24:14 – AI is the third brain of a jet fighter40:37 – Sully47:34 – Landing a jet fighter on a carrier53:18 – What’s it like to fly a jet fighter?

1:05:22 – Greatest plane ever made1:11:04 – The Tic Tac UFO story1:49:16 – Intelligent extraterrestrial life1:53:30 – Why aren’t UFOs investigated more seriously1:59:52 – Tic Tac UFO details2:07:55 – What do you think the Tic Tac was?

2:16:23 – SpaceX2:30:01 – Response to Mick West Deb…

My attempt to find the words to honor my grandmother, an amazing woman who is responsible for much of who I am, who taught me how to be a man, taught me about strength, about wisdom, about compassion, about love.

If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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OUTLINE:00:00 – In memory of my grandmother02…

Eugenia Kuyda co-founder of Replika, an AI companion.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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2:27:45 – Does an AI companion need a body?

2:30:20 – Her2:37:24 – GPT-3 for conversation2:43:48 – We should be nice to AI2:46:52 – Book recommendations2:53:45 – Russian language2:58:41 – Meaning of life

My thoughts on 8 possible long-term futures of Neuralink after attending the August 2020 progress update.

This is a solo episode #2 of the podcast.

If you would like to get more information about this podcast go to https://lexfridman.com/podcast or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

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Dynamic random-access memory – or DRAM – is the most popular form of volatile computer memory in the world but it’s particularly susceptible to Rowhammer, an adversarial attack that can cause data loss and security exploits in everything from smart phones to the cloud.

Today, Dr. Stefan Saroiu, a Senior Principal Researcher in MSR’s Mobility and Networking group, explains why DRAM remains vulnerable to Rowhammer attacks today, even after several years of mitigation efforts, and then tells us how a new approach involving bespoke extensibility mechanisms for DRAM might finally hammer Rowhammer in the fight to keep data safe and secure.

Many computer science researchers set their sights on building general AI technologies that could impact hundreds of millions – or even billions – of people.

But Dr. Danielle Bragg, a senior researcher at MSR’s New England lab, has a slightly smaller and more specific population in mind: the some seventy million people worldwide who use sign languages as their primary means of communication.

Today, Dr. Bragg gives us an insightful overview of the field and talks about the unique challenges and opportunities of building systems that expand access to information in line with the needs and desires of the deaf and signing community.

https://www.microsoft.com/research

MSR’s New York City lab is home to some of the best reinforcement learning research on the planet but if you ask any of the researchers, they’ll tell you they’re very interested in getting it out of the lab and into the real world.

One of those researchers is Dr. Akshay Krishnamurthy and today, he explains how his work on feedback-driven data collection and provably efficient reinforcement learning algorithms is helping to move the RL needle in the real-world direction.

https://www.microsoft.com/research

Dr. Siddhartha Sen is a Principal Researcher in MSR’s New York City lab, and his research interests are, if not impossible, at least impossible sounding: optimal decision making, universal data structures, and verifiably safe AI.

Today, he tells us how he’s using reinforcement learning and HAIbrid algorithms to tap the best of both human and machine intelligence and develop AI that’s minimally disruptive, synergistic with human solutions, and safe.

This episode originally aired in August, 2018.

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft.

But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got…

Dr. Devon Hjelm is a senior researcher at the Microsoft Research lab in Montreal, and today, he joins me to dive deep into his research on Deep InfoMax, a novel self-supervised learning approach to training AI models – and getting good representations – without human annotation.

He also tells us how an interest in neural networks, first human and then machine, led to an inspiring career in deep learning research.

https://www.microsoft.com/research

This episode originally aired in June, 2019You may not know who Dr. Andrew Fitzgibbon is, but if you’ve watched a TV show or movie in the last two decades, you’ve probably seen some of his work.

An expert in 3D computer vision and graphics, and head of the new All Data AI group at Microsoft Research Cambridge, Dr. Fitzgibbon was instrumental in the development of Boujou, an Emmy Award-winning 3D camera tracker that lets filmmakers place virtual props, like the floating candles in Hogwarts School for Witchcraft and Wizardry, into live-action footage.

But that was just his warm-up act.

On today’s podcast, Dr. Fitzgibbon tells us what he’s been working on since the Emmys in 2002, including bod…

This episode originally aired in April, 2018Emotions are fundamental to human interaction, but in a world where humans are increasingly interacting with AI systems, Dr. Mary Czerwinski, Principal Researcher and Research Manager of the Visualization and Interaction for Business and Entertainment group at Microsoft Research, believes emotions may be fundamental to our interactions with machines as well.

And through her team’s work in affective computing, the quest to bring Artificial Emotional Intelligence – or AEI – to our computers may be closer than we think.

Today, Dr. Czerwinski tells us how a cognitive psychologist found her way into the research division of the world’s largest software…

This episode originally aired in April, 2019.

We hear a lot these days about “AI for good” and the efforts of many companies to harness the power of artificial intelligence to solve some of our biggest environmental challenges.

It’s rare, however, that you find a company willing to bring its environmental bona fides all the way to the C Suite.

Well, meet Dr. Lucas Joppa.

A former environmental and computer science researcher at MSR who was tapped in 2017 to become the company’s first Chief Environmental Scientist, Dr. Joppa is now the Chief Environmental Officer at Microsoft, another first, and is responsible for managing the company’s overall environmental sustainability efforts from opera…

This episode originally aired in December, 2017On today’s episode, neuroscientist and virtual reality researcher, Dr. Mar Gonzalez Franco, talks about her work in VR, explains how avatars can help increase our empathy and reduce our biases via role play, and addresses the misconceptions that exist between the immersive experiences of virtual reality and psychedelic drugs.

Forty years ago, database research was an “exotic” field and, because of its business data processing reputation, was not considered intellectually interesting in academic circles.

But that didn’t deter Dr. Philip Bernstein, now a Distinguished Scientist in MSR’s Data Management, Exploration and Mining group, and a pioneer in the field.

Today, Dr. Bernstein talks about his pioneering work in databases over the years and tells us all about Project Orleans, a distributed systems programming framework that makes life easier for programmers who aren’t distributed systems experts.

He also talks about the future of database systems in a cloud scale world, and reveals where he finds his research s…

Brad Smith is the President of Microsoft and leads a team of more than 1400 employees in 56 countries.

He plays a key role in spearheading the company’s work on critical issues involving the intersection of technology and society.

In his spare time, he’s also an author!

He also gave us a peek inside the life of a person the New York Times has described a “de facto ambassador for the technology industry at large” – himself!

https://www.microsoft.com/research

Rangan Majumder is the Partner Group Program Manager of Microsoft’s Search and AI, and he has a simple goal: to make the world smarter and more productive.

But nobody said simple was easy, so he and his team are working on better – and faster – ways to help you find the information you’re looking for, anywhere you’re looking for it.

Today, Rangan talks about how three big trends have changed the way Microsoft is building – and sharing – AI stacks across product groups.

He also tells us about Project Turing, an internal deep learning moonshot that aims to harness the resources of the web and bring the power of deep learning to a search box near you.

https://www.microsoft.com/research

Dr. Behnaz Arzani is a senior researcher in the Mobility and Networking group at MSR, and she feels your pain.

At least, that is, if you’re a network operator trying to troubleshoot an incident in a datacenter.

Her research is all about getting networks to manage themselves, so your life is as pain-free as possible.

On today’s podcast, Dr. Arzani tells us why it’s so hard to identify and resolve networking problems and then explains how content-aware, or domain-customized, auto ML frameworks might help.

https://www.microsoft.com/research

If you want an inside look at how a research idea goes from project to prototype to product, you should hang out with Gavin Jancke for a while.

He’s the General Manager of Engineering for MSR Redmond where he created – and runs – the Central Engineering Group.

Over the past two decades, he’s overseen more than seven hundred software and hardware engineering projects, from internal MSR innovations to Microsoft product group partnerships.

Today, Gavin takes us on a guided tour of the research engineering landscape and the engineering pipeline, recounting some of Central Engineering’s greatest hits.

He also explains how the lab determines which projects get engineering resources, and reveals h…

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The National Popular Vote Interstate CompactPatrick Rosenstiel joins us to discuss the The National Popular Vote.

Defending the p-valueYudi Pawitan joins us to discuss his paper Defending the P-value.

Ivan Oransky joins us to discuss his work documenting the scientific peer-review process at retractionwatch.com.

Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.

Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.

Consensus VotingMashbat Suzuki joins us to discuss the paper How Many Freemasons Are There?

The Consensus Voting Mechanism in Metric Spaces.

Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)

Voting MechanismsSteven Heilman joins us to discuss his paper Designing Stable Elections.

False ConcensusSami Yousif joins us to discuss the paper The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus.

This work empirically explores how individuals evaluate concensus under different experimental conditions reviewing online news articles.

More from Sami at samiyousif.org.

Fraud Detection in Real TimeIn this solo episode, Kyle overviews the field of fraud detection with eCommerce as a use case.

He discusses some of the techniques and system architectures used by companies to fight fraud.

Listener Survey ReviewIn this episode, Kyle and Linhda review the results of our recent survey.

Hear all about the demographic details and how we interpret these results.

Human Computer Interaction and Online PrivacyMoses Namara from the HATLab joins us to discuss his research into the interaction between privacy and human computer interaction.

Mark Glickman joins us to discuss the paper Data in the Life: Authorship Attribution in Lennon-McCartney Songs.

Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it’s accompanying codebase found here. Erik mentions the GANspace collab notebook which is a rapid way to try these ideas out for yourself.

David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.

All good things must come to an end, including this podcast.

This is the last episode we plan to release, and it doesn’t cover data science—it’s mostly reminiscing, thanking our wonderful audience (that’s you!

), and marveling at how this thing that started out as a side project grew into a huge part of our lives for over 5 years.

It’s been a ride, and a real pleasure and privilege to talk to you each week.

Thanks, best wishes, and good night!

The data science and artificial intelligence community has made amazing strides in the past few years to algorithmically automate portions of the healthcare process. This episode looks at two computer vision algorithms, one that diagnoses diabetic retinopathy and another that classifies liver cancer, and asks the question—are patients now getting better care, and achieving better outcomes, with these algorithms in the mix? The answer isn’t no, exactly, but it’s not a resounding yes, because these algorithms interact with a very complex system (the healthcare system) and other shortcomings of that system are proving hard to automate away. Getting a faster diagnosis from an image might not be…

A few weeks ago, we put out a call for data scientists interested in issues of race and racism, or people studying how those topics can be studied with data science methods, should get in touch to come talk to our audience about their work. This week we’re excited to bring on Todd Hendricks, Bay Area data scientist and a volunteer who reached out to tell us about his studies with the Stanford Open Policing dataset.Relevant Links:Stanford Open Policing ProjectProject ZeroTodd’s LinkedIn PageTodd’s email: hendricks.ta@gmail.com

This is a re-release of an episode that originally ran in October 2019.If you’re trying to manage a project that serves up analytics data for a few very distinct uses, you’d be wise to consider having custom solutions for each use case that are optimized for the needs and constraints of that use cases. You also wouldn’t be YouTube, which found themselves with this problem (gigantic data needs and several very different use cases of what they needed to do with that data) and went a different way: they built one analytics data system to serve them all. Procella, the system they built, is the topic of our episode today: by deconstructing the system, we dig into the four motivating uses of this…

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another.

Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis.

The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

Relevant links:

This is a re-release of an episode that first ran on January 29, 2017.This week: everybody's favorite WWII-era classifier metric! But it's not just for winning wars, it's a fantastic go-to metric for all your classifier quality needs.

This episode features Zach Drake, a working data scientist and PhD candidate in the Criminology, Law and Society program at George Mason University. Zach specializes in bringing data science methods to studies of criminal behavior, and got in touch after our last episode (about racially complicated recidivism algorithms). Our conversation covers a wide range of topics—common misconceptions around race and crime statistics, how methodologically-driven criminology scholars think about building crime prediction models, and how to think about policy changes when we don’t have a complete understanding of cause and effect in criminology. For the many of us currently re-thinking race and criminal …

As protests sweep across the United States in the wake of the killing of George Floyd by a Minneapolis police officer, we take a moment to dig into one of the ways that data science perpetuates and amplifies racism in the American criminal justice system. COMPAS is an algorithm that claims to give a prediction about the likelihood of an offender to re-offend if released, based on the attributes of the individual, and guess what: it shows disparities in the predictions for black and white offenders that would nudge judges toward giving harsher sentences to black individuals. We dig into this algorithm a little more deeply, unpacking how different metrics give different pictures into the “fai…

A message from Ben around algorithmic bias, and how our models are sometimes reflections of ourselves.

This is a re-release of an episode that originally aired on April 1, 2018If you've done image recognition or computer vision tasks with a neural network, you've probably used a convolutional neural net.

This episode is all about the architecture and implementation details of convolutional networks, and the tricks that make them so good at image tasks.

Relevant links:

This is a re-release of an episode that was originally released on February 26, 2017.

When you're estimating something about some object that's a member of a larger group of similar objects (say, the batting average of a baseball player, who belongs to a baseball team), how should you estimate it: use measurements of the individual, or get some extra information from the group?

The James-Stein estimator tells you how to combine individual and group information make predictions that, taken over the whole group, are more accurate than if you treated each individual, well, individually.

Relevant links:

The power of finely-grained, individual-level data comes with a drawback: it compromises the privacy of potentially anyone and everyone in the dataset. Even for de-identified datasets, there can be ways to re-identify the records or otherwise figure out sensitive personal information. That problem has motivated the study of differential privacy, a set of techniques and definitions for keeping personal information private when datasets are released or used for study. Differential privacy is getting a big boost this year, as it’s being implemented across the 2020 US Census as a way of protecting the privacy of census respondents while still opening up the dataset for research and policy use. …

What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning?

Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case.

This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems.

It’s not a free lunch, but for those (like us!)

who love crossover topics, causal trees are a smart approach from one field hopping the fence to another.

You may not realize it consciously, but beautiful visualizations have rules.

The rules are often implict and manifest themselves as expectations about how the data is summarized, presented, and annotated so you can quickly extract the information in the underlying data using just visual cues.

It’s a bit abstract but very profound, and these principles underlie the ggplot2 package in R that makes famously beautiful plots with minimal code.

This episode covers a paper by Hadley Wickham (author of ggplot2, among other R packages) that unpacks the layered approach to graphics taken in ggplot2, and makes clear the assumptions and structure of many familiar data visualizations.

Relevant links:

It’s pretty common to fit a function to a dataset when you’re a data scientist. But in many cases, it’s not clear what kind of function might be most appropriate—linear? quadratic? sinusoidal? some combination of these, and perhaps others? Gaussian processes introduce a nonparameteric option where you can fit over all the possible types of functions, using the data points in your datasets as constraints on the results that you get (the idea being that, no matter what the “true” underlying function is, it produced the data points you’re trying to fit). What this means is a very flexible, but depending on your parameters not-too-flexible, way to fit complex datasets.The math underlying GPs ge…

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October 18, 2020 podcastLet’s talk about federated learning.

Why is it important?

Why large organizations are not ready yet?

Come join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

October 12, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Monday.comMonday.com bring teams together so you can plan, manage and track everything your team is working on in one centralized placeThe Monday Apps Challenge is bringing developers around the world together to compete in order to build apps that can improve the way teams work together on monday.com.

September 26, 2020 podcastCome join me in our Discord channel speaking about all things data science.

Follow me on Twitch during my live coding sessions usually in Rust and PythonThis episode is supported by Women in Tech by Manning Conferences

September 16, 2020 podcastHey there!

Having the best time of my life 😉This is the first episode I record while I am live on my new Twitch channel 🙂 So much fun!

Feel free to follow me for the next live streaming.

You can also see me coding machine learning stuff in Rust :))Don’t forget to jump on the usual Discord and have a chatI’ll see you there!

September 4, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

We cover testing with deep learning (neuron coverage, threshold coverage, sign change coverage, layer coverage, etc.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in september 15th to the live@manning rust conference.

August 29, 2020 podcastIn this episode I speak with Adam Leon Smith, CTO at DragonFly and expert in testing strategies for software and machine learning.

On September 15th there will be a live@Manning Rust conference.

In one Rust-full day you will attend many talks about what’s special about rust, building high performance web services or video game, about web assembly and much more.

If you want to meet the tribe, tune in September 15th to the live@manning Rust conference.

August 12, 2020 podcastAfter deep learning, a new entry is about ready to go on stage.

The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype.

The new words, homomorphic encryption.

They are a consulting firm focused on data science, machine learning, and artificial intelligence.

ReferencesTowards a Homomorphic Machine Learning Big Data Pipeline for the Financial Services SectorIBM Fully Homomorphic Encryption Toolkit for Linux

August 3, 2020 podcastIn this episode I speak about a testing methodology for machine learning models that are supposed to be integrated in production environments.

Don’t forget to come chat with us in our Discord channelEnjoy the show!

—This episode is supported by Amethix Technologies.

Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve.

They are a consulting firm focused on data science, machine learning, and artificial intelligence.

July 26, 2020 podcastThe hype around GPT-3 is alarming and gives and provides us with the awful picture of people misunderstanding artificial intelligence.

In response to some comments that claim GPT-3 will take developers’ jobs, in this episode I express some personal opinions about the state of AI in generating source code (and in particular GPT-3).

If you have comments about this episode or just want to chat, come join us on the official Discord channel.

Amethix works to create and maximize the impact of the world’s leading corporations, startups, and nonprofits, so they can create a better future for everyone they serve.

They are a consulting firm focused on data science, machine learni…

July 22, 2020 podcastThere is definitely room for improvement in the family of algorithms of stochastic gradient descent.

In this episode I explain a relatively simple method that has shown to improve on the Adam optimizer.

But, watch out!

This approach does not generalize well.

Join our Discord channel and chat with us.

July 20, 2020 podcastIn this episode I speak about data transformation frameworks available for the data scientist who writes Python code.

The usual suspect is clearly Pandas, as the most widely used library and de-facto standard.

However when data volumes increase and distributed algorithms are in place (according to a map-reduce paradigm of computation), Pandas no longer performs as expected.

In this episode I explain the frameworks that are the best equivalent to Pandas in bigdata contexts.

Amethix is a consulting firm focused on data science, machine learning, and artificial intelligence.

July 5, 2020 podcastIn this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019.

As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe.

A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible.

Let’s stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future.

This episode is brought to you by ProtonmailClick on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.

June 29, 2020 podcastIn this episode I make a non exhaustive list of machine learning tools and frameworks, written in Rust.

Not all of them are mature enough for production environments.

I believe that community effort can change this very quickly.

To make a comparison with the Python ecosystem I will cover frameworks for linear algebra (numpy), dataframes (pandas), off-the-shelf machine learning (scikit-learn), deep learning (tensorflow) and reinforcement learning (openAI).

Rust is the language of the future.

Unsupervised is a podcast about Data Science in Israel.

At each episode we interview an industry professional or a researcher from academia and discuss different aspects and problems in data science.

We want to give a peek to what’s going on with data science across the Israeli industry and also to talk about different algorithms, tools, papers, methods and pretty much everything that’s interesting and related to Data Science and Machine Learning.

The podcast is aimed to data science professionals and researchers, as well as for those who work and collaborate with data science teams and beginners in the field.

We want to thank Samsung Next for hosting us.

Unsupervised is a podcast about Data Science in Israel.

At each episode we interview an industry professional or a researcher from academia and discuss different aspects and problems in data science.

We want to give a peek to what’s going on with data science across the Israeli industry and also to talk about different algorithms, tools, papers, methods and pretty much everything that’s interesting and related to Data Science and Machine Learning.

The podcast is aimed to data science professionals and researchers, as well as for those who work and collaborate with data science teams and beginners in the field.

We want to thank Samsung Next for hosting us.