This framework rejects practices of data collection, storage, sharing, and use in ways that are opaque or seek to manipulate people, in favor of practices that empower people to help inform, shape, and govern their own data.-Ada Lovelace Institute

“The issue here isn’t just that we might have bad labels in our training set, the issue is that it appears in the validation set. If a machine learning model can become state of the art by squeezing another 0.5% out of a validation set one has to wonder. Are we really making a better model?”-Vincent D. Warmerdam

Natural language processing models have become so powerful over the last few years that we need new benchmarks for measuring their performance.-Sebastian Ruder

Here’s Sean Taylor on why causal inference is important for working data scientists: “Typically in causal inference, researchers try to estimate some quantity of interest one time for a publication. In industry, we must build systems to reliably estimate quantities, at scale, over time, for a variety of contexts.”-Brady Neal

Efforts to implement higher ethical standards and transparency in the machine learning dataset creation process can be more effective if we understand of how datasets are used in practice in the research community.-arXiv.org

To see real improvements in program synthesis, we’ll need to go beyond just language models, to a more holistic solution that incorporates best practices around human-computer interaction, software engineering, testing, and many other disciplines.-fast.ai

This repository of popular machine learning algorithms is implemented in Python and explains the math behind each algorithm. It’s a great place to get started with machine learning.-Oleksii Trekhleb

What is a "data scientist" or "machine learning engineer," really? This analysis uses tools, libraries, and frameworks to cluster engineers into cohort groups, which might be more indicative of their day-to-day work than overloaded job titles.-Paige Bailey

“Data capitalism, like capitalism itself, reinforces dynamics of power and profits. More power creates more profits, and more profits creates more power. Inequality ensures companies get richer and more influential, while everyday people wield less and less power.”-Data for Black Lives

mltrace is designed for collaborative teams working on production machine learning pipelines. It makes it easy to follow a prediction or model’s output back to its most upstream, raw data file.-Shreya Shankar

With just its first 320 hours of Māori language data, Te Hiku, a small non-profit radio station in New Zealand, was able to build a speech-to-text engine with an initial word error rate of 14 percent. Developing language tools, themselves, is a way for Māori to decolonize the sound of their language.-Wired UK

There’s a lot machine learning startups can learn from HuggingFace about community building.-Breaking the Stagnation

Every weekday, this Oxford student reads a machine learning paper, takes notes, and shares them. And now you get to benefit from their studious habits.-Vitaly Kurin

What happens when a language is omitted from the digital landscape? And what can be gained when technology acts as a bridge instead of a barrier?-TED

This dynamic model of 100K Spotify users shows how music tastes change over time.-Spotify Research

“Suffering is a perfectly natural part of getting a neural network to work well, but it can be mitigated by being thorough, defensive, paranoid, and obsessed with visualizations of basically every possible thing. The qualities that in my experience correlate most strongly to success in deep learning are patience and attention to detail.”-Andrej Karpathy

The model is a problem, too. Recognizing that allows for new ways to reduce harm that are easier than comprehensive data collection.-Patterns

Many large pre-trained models unavoidably acquire certain toxic behavior and biases from the online data they’re trained on. Here are some ways to limit unsafe and harmful content in the finished product.-Lil’Log

When Google forced out two well-known artificial intelligence experts, a long-simmering research controversy burst into the open.-The New York Times

Folks who train algorithms, take note: this mixture of social work and data science is a shining example of how to create AI with a positive social impact.-SAGE Journals

Like site reliability engineers (SREs), machine learning reliability engineers (MLREs) are responsible for reducing toil, keeping costs within budget, and ensuring smooth releases, but with more data savvy.-TestDriven.io

“Advances in algorithmic fairness have largely omitted sexual orientation and gender identity. We explore queer concerns in privacy, censorship, language, online safety, health, and employment to study the positive and negative effects of artificial intelligence on queer communities.”-arXiv.org

This repository of problems caused by AI is intended to help researchers and developers prevent similar harms from happening again.-AI Incident Database

“As I watched a mob batter down the windows of the Capitol, inspired by online echo chambers in tweets and newsfeeds, I thought of my work in machine learning, thought of that drone strike, and wondered ‘did I unwittingly help create this?’”-Jaan Altosaar

7000+ languages are spoken around the world but NLP research has mostly focused on English. It’s time to change that.-Sebastian Ruder

A ML-powered tool for understanding the science behind what differentiates a cake from a bread or a cookie.-Sara Robinson

“Companies have long seen annual reports and other corporate disclosures as opportunities to portray their business health in a positive light. Increasingly, the audience for these disclosures is not just humans, but also machine readers that process the information as an input to investment recommendations."-National Bureau of Economic Research

An analysis of the 68 most highly-cited, influential ML papers of the last few years finds that the majority don’t mention societal need or negative consequences. Instead, these papers assess themselves according to a set of values internal to the field.-Resistance AI Workshop

What does real-time machine learning even mean? How is it done? What are the use cases?-Chip Huyen

“In this paper, we survey the many concerns raised about the way we collect and use data in machine learning and advocate that a more cautious and thorough understanding of data is necessary to address several of the practical and ethical issues of the field.”-arXiv.org

In machine learning research, it’s easy to fall into the trap of only working with benchmark data sets. You can guard against this by working on concrete problems. You don’t need a grand goal, either, just a concrete one.-Brandon Rohrer

“The machine learning community currently has no standardized process for documenting datasets, which can lead to severe consequences in high-stakes domains... [W]e propose that every dataset be accompanied with a datasheet that documents its motivation, composition, collection process, recommended uses, and so on.”-arXiv.org

“Our benchmarking results indicated that Redis was the best option, so we decided to optimize our feature storage mechanism, tripling our cost reduction. Additionally, we also saw a 38% decrease in Redis latencies, helping to improve the runtime performance of serving models.”-DoorDash Engineering

This paper is a starting place for those who want to design and create AI from an ethical position that centers Indigenous concerns. It is an attempt to capture multiple layers of a discussion that happened over 20 months, across 20 timezones, during two workshops, and between Indigenous people...-Indigenous AI

If you want to use natural language processing (NLP), you need a corpus (a collection of texts) to work with. Here’s how to build a corpus of one’s own.-Python in Plain English

“Amba Kak and others support a moratorium on any use of facial recognition, not just because the technology isn’t good enough yet, but also because there needs to be a broader discussion of how to prevent it from being misused.-Nature

“Technical know-how, whether in government or in the technology industry, cannot substitute for contextual understanding and lived experiences in determining whether it’s appropriate to apply AI systems in sensitive social domains.”-The Boston Globe

Syllable and rhyme scheme go in, parody lyrics come out.-arXiv.org

Form extraction is a hard and important problem. Healthcare professionals, climate scientists, and human rights workers all rely on data that’s often locked away in non-digitized documents.-Jonathan Stray

It is not just the data. Popular compression techniques can amplify bias in deep neural networks.-arXiv.org

What if you could draw a machine learning model? This new library makes it easier to create rule based systems that are scikit-learn compatible.-Vincent D. Warmerdam

“[T]his story in 2012 launched the idea into public consciousness that companies can create algorithms that can diagnose, solve, predict the future, and generally model the human situation better than humans can.” But for the most part, that’s just not true, even seven years later.-Colin Fraser

Machine learning allows you to build complex systems fast, but it’s dangerous to think of these quick wins as coming for free.-Google Research

What’s the difference between testing a machine learning system and a traditional software system? Between model testing and model evaluation? And how do you write a model test, period?-Jeremy Jordan

A straightforward example of deploying a sklearn model using Flask and a Docker container. You’ll need some basic knowledge of Docker to get started.-Chris Albon

How DeepMind and Google Maps teamed up to improve the accuracy of ETAs by up to 50% in some cities.-DeepMind

To support ethical AI, you need to build a culture where employees have the right mindset to create ethical products. Salesforce has a number of processes and programs for doing just that.-Salesforce

The CoNLL-2003 dataset is one of the most widely used open source datasets for building natural language processing systems. Over the past 17 years, it’s been cited more than 2,500 times in research literature. The problem? It includes five times as many male names as female names.-OneZero

How one company trained a neural network model that identifies crops and weeds, and then deployed that model to robots in the fields.-PyTorch

Across the board—in the voices of chatbots and virtual assistants, in stock images, and in film and television—AI is portrayed as white people, and that’s a problem.-Philosophy & Technology

“The continent would do well to adopt a dose of critical appraisal when regulating, deploying, and reporting AI. This requires challenging the mindset that portrays AI with God-like power and as something that exists and learns independent of those that create it. People create, control, and are responsible for any system."-SCRIPTed

How the Shopify team implemented a model to categorize all their products at Shopify, and enabled cross-platform teams to deliver personalized insights to business owners.-Shopify Engineering

“Products like Genderify are harmful. They’re built on top of biased and inaccurate data, by people who seem to have no interest in risk management or the societal impact of their product, and released for basically no cost into the open for anyone to use.”-Sarah L. Fossheim

A curated collection of blogs, articles, and papers describing how different companies have been using machine learning in production.-Eugene Yan

“The aim of this work is to help community members better identify and understand the scale and scope of anti-Black bias within our AI community and illustrate some concrete steps that members can take to help mitigate these issues and build a more just community."-Devin Guillory

If you’re using 80 Million Tiny Images to benchmark computer-vision algorithms, it’s time to find another data set.-The Register

“I discuss how working at Viaduct opened my eyes to the challenges of operationalizing machine learning, and how neither my classes nor research forced me to consider these challenges.”-Shreya Shankar

And while you’re learning about natural language processing, add this bibliography to your reading list to make sure you’re mindful about the real harm NLP can cause, no matter your intentions.-Association for Computational Linguistics

For visual learners especially, this mind map of keywords can guide you through what natural language processing topic to study next.-Tae-Hwan Jung

GitHub offers almost no features for data science, but they’re trying to change that. This series of GitHub Actions integrate parts of the data science and machine learning workflow with a software development workflow.-The Github Blog

This guide covers the process of creating an end-to-end proof-of-concept machine learning product, from start to finish.-Vicki Boykis

Deep learning models may be all the rage, but let’s shine a light on some of their less-well-known cousins: rules-based models.-R Views

“To think we can come up with a value system to guide AI without looking into other cultures’ value systems, and then to call it universal, is off.”-People + AI Research

What’s even cooler than a multi-task deep learning library? Reading the story of how two co-workers built this library together.-ShopRunner

The tidyverse's take on machine learning is finally here.-Rebecca Barter

Unlike most places on the internet, the comments section on this Reddit post have a lot of constructive, helpful advice.-Jari Safi

Africa has over 2,000 languages, but African languages account for a small portion of available resources and publications in Natural Language Processing. To fix that problem, an open-source, continent-wide research effort was formed.-Cornell University

The Stanford Question Answering Dataset (SQuAD) has become the archetypal QA dataset for NLP modeling. The emergence of non-English SQuAD replicas means that NLP can be truly democratized. And it’s a good reminder that English is neither synonymous nor representative of natural language.-Towards Data Science

300 (and counting!) datasets for training your natural language processing models.-Quantum Stat

Before Grubhub had a suite of tools to help with machine learning model deployments, the difficulty of getting scheduled jobs in production resulted in multiple bespoke solutions, duplicated code, and lots of model maintenance overhead.-Grubhub Bytes

These notes with code, examples, and drawings are great for beginners who want to understand enough linear algebra to be comfortable with machine learning and deep learning.-Hadrien Jean

If you want to know how data science projects at companies actually work, there’s no better talk to watch than this one.-Jacqueline Nolis

“Flow fields are something that many programmers reach for early on when they first get into creating algorithmic artwork, but few take the time to polish their use and explore the crazy variety of ways they can be used."-Tyler Hobbs

How and why the default sorting algorithm for Python became widely-used, yet relatively unrecognizable.-Dev

Who was the most prolific author of machine learning and natural language processing papers in 2019? The most prolific company? The most prolific country?-Marek Rei

Curriculums help humans progressively go from understanding simple concepts to solving hard problems. They might do they same for reinforcement learning models.-Lil’Log

Is this deep learning model as smart as a first grader? You might just while away an hour or two feeding it stories and asking it questions.-Albert Learns to Read

Sometimes the best way to unpack a complicated concept is to play with it, visually.-Machine Learning Tokyo

“In the Real World, the distribution of input more likely changes than not, ‘curve balls’ from long-tails come out of nowhere, and you don’t always have an answer.”-Delip Rao

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning exacerbates this problem because any model trained with said data may have memorized it, putting users at risk of a successful privacy attack exposing their information. Yet, having models unlearn is notoriously difficult.-Cornell University

This free course covers exploratory data analysis, preparing data so it’s ready for predictive modeling, training supervised machine learning models, and evaluating those models—all using real-world data.-Julia Silge

Recommending the most relevant content out of billions of options in real time at scale introduces a ton of machine learning engineering problems. Here’s a detailed look at the system Instagram built to provide its users with personalized content.-Instagram Engineering

Changing algorithms is easier than changing people: software on computers can be updated; the “wetware” in our brains has so far proven much less pliable.-The New York Times

This completely AI generated text adventure will respond logically to just about any command you enter, such as “Eat the moon,” “Summon a giraffe,” or “Join the Great British Bakeoff.”-Perception, Control, Cognition

No matter how many deep learning models you’ve successfully trained, deploying those models in production is a whole different ballgame.-Alireza Dirafzoon

“As the field is still young, many machine-learning developers lack experience in building enterprise applications, and many business stakeholders have insufficient knowledge of machine learning to know what questions to ask as they scope and manage projects."-Harvard Business Review

Case studies, resources, and 27 exercises for learning how to deploy a machine learning system in the real world.-Chip Huyen

This book is offered as free to read on the “read first, buy later” principle. The first three chapters are available now, and you can sign up for the mailing list to get updated when more are added.-Andriy Burkov

“[A] formula designed to reduce prison populations in Virginia led some judges to impose harsher sentences for young or black defendants, and more lenient ones for rapists.”-The Washington Post

This article will be a treat for those interested in video games, deep reinforcement learning, AND physics.-Ubisoft Montreal

A Swedish media company discovered many correlations between churn and reader activity, including reader gender, number of images viewed, and number of push notifications sent.-MittMedia

Brandon Rohrer offers high praise for this long read: “If you like to think deeply about building and measuring intelligence, I highly recommend this read. It's a cogent review of intelligence measurement, its challenges, past approaches, and their limitations. [And it] takes the brave step of proposing a path forward.”-François Chollet

Earlier this year, thieves used AI to impersonate a CEO’s voice and successfully demand a fraudulent transfer of nearly $250,000. To defend against similar cyber crimes, one company built a detector system to discern between real and fake audio examples—and you can too.-Dessa News

“I am not opposed to metrics; I am alarmed about the harms caused when metrics are overemphasized, a phenomenon that we see frequently with AI, and which is having a negative, real-world impact.”-fast.ai

The war for machine learning frameworks has two remaining big contenders: PyTorch and TensorFlow. Researchers prefer one, while industry practitioners prefer the other.-The Gradient

You can launch this open-source, visual similarity engine just by running two Python scripts.-Yusuke Matsui

“Every single piece of decision-making in a high-tech neural network initially rests on a human being manually putting something together and making a choice.”-Normcore Tech

“Open any magazine, click randomly on any article on Medium, visit any public event at a think tank; chances are, concerns raised by the age of AI is the topic. Some of it will be bunk, some of it very thoughtful, but the topic is not exactly under-discussed. What is under-discussed is how unevenly this change will happen, because we misunderstand and overestimate the preconditions for AI outside the private sector."-Code for America Blog

This post shows examples of successfully shipping large natural language processing transformer models.-Towards Data Science

What’s a good learning rate? How many hidden layers should your network have? Is dropout actually useful? Why are your gradients vanishing?-Towards Data Science

With a neural net as your Dungeon Master, you can conjure anything simply by referring to it and teleport anywhere by just saying the word.-AI Weirdness

This resource is made for non-experts, and the illustrations are really helpful for wrapping your mind around new concepts.-Kings College London

One of the research scientists (https://twitter.com/iamtrask/status/1163737631395651584) at DeepMind gives this tutorial gets a ringing endorsement: “This is one of the first truly introductory tutorials of RL & Deep Learning I've seen. My kind of tutorial... lots of toy code examples and simple analogies!!!”-Analytics Vidhya

“Speed is not a word that is regularly associated with machine learning teams. When we talk and write about accomplishments in machine learning, there is often a focus on the problem, the algorithmic approach, and the results—but no mention of the time that it took to get there.”-Neal Lathia

Alphabet’s DeepMind lost $572 million last year. Does this mean AI is falling apart? No. But considering DeepMind’s strategy does raise some interesting questions about how much it can offer society beyond mastering the game of Go.-Wired

Escape Python dependency hell! Instead of configuring a unique Docker container for each project, how about a unique Python environment?-Weights & Biases

“I wasn’t quite prepared for just how much I would feel like a beginner. You need to give yourself the space and time to fail. If you learn from enough failures, you’ll succeed.”-Greg Brockman

This reference guide gets a ringing endorsement from Senior Research Scientist Andrew: “Machine learning in a company is 10% data science and 90% other challenges. It's VERY hard. Everything in this guide is ON POINT, and it's stuff you won't learn in an ML book.”-Google

This repository contains a curated list of open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.-The Institute for Ethical Machine Learning

Learn topic modeling, classification, language modeling, and translation, completely free.-fast.ai

Input a card name, card type, and card mana cost... get a custom card image and card text back!-Max Woolf

This paper shows how to train a deep learning-based model to fill in missing or lost information in a piece of music.-Georgia Tech

Children use the mutual exclusivity bias to learn new words. Standard neural nets show the opposite bias, making it harder for them to learn in common scenarios.-Cornell University

Get ready to chuckle at gems like “yeti – snow + economics = homo economicus” and “American Idol – singers = Project Runway.”-graceavery

“For a while, we resisted the A.I. label, understanding that our platform wasn’t going to make Watson sweat anytime soon. But eventually, we gave up and just decided to kind of go along with the hype. The market wanted us to be an A.I. company so we chuckled and decided to call ourselves one.”-Gen

The machine learning community is obsessed with deep learning on big dense datasets, but problems like cyber insurance with small sparse data require Bayesian methods.-Tower Street

In 2010, the ratio of AI scientific papers to patents filed was 8:1. In 2016, it was 3:1. We’re in the implementation phase now.-O’Reilly

It feels like GANs pop up everywhere these days. If you’re looking for a fundamental understanding of what GANs can do, this is a great overview.-Machine Learning Mastery

There’s real damage being done by deepfake techniques, but it’s happening in pornography, not politics.-The Verge

Start off your Monday morning with a good chuckle.-Janelle Shane

GAN's impact on fashion goes way deeper than virtual fitting rooms and creating avatars to customer's measurements.-Forbes

Product management for ML projects can be difficult because engineering changes from a deterministic process to a probabilistic one. It requires “an approach that involves learning from data instead of programmatically following a set of human rules.”-Domino Data Lab

Stripe trains hundreds of new models each day, each powered by billions of data points. Running infrastructure at this scale poses a very practical data science and ML problem: how do you give every team the tools they need to train their models without requiring them to operate their own infrastructure?-Stripe

Bias in AI doesn’t mean just bias against people. Sometimes it just picking up the wrong signal, period. Case in point: a system built to detect skin cancer was detecting rulers instead.-Benedict Evans

Not only could a simple color printout hide someone from an AI video surveillance system for nefarious purposes, it could offer protection to people who don't want to be tracked in everyday life.-arXiv

Dadabots was developed by two music technologists who wanted to prove that a neural network was capable of capturing the subtle stylistic differences between Death Metal, Math Rock, and other lesser-known genres.-Motherboard

This post discusses the obsession with finding the best model and emphasizes what should be done instead: Take a step back and see the bigger picture in which the machine learning model is embedded.-bentoML

“The field of research on bias and fairness needs to go beyond technical debiasing to include a wider social analysis of how AI is used in context. This necessitates including a wider range of disciplinary expertise.”-AI Now Institute

Practical improvements to image synthesis models are happening almost too quickly to keep up with, but there are still several open research problems left to tackled.-Distill

“Our online tests validate that the COTA v2 deep learning system performs significantly better than the COTA v1 system in terms of key metrics, including model performance, ticket handling time, and customer satisfaction.”-Uber Engineering

One PhD graduate shares their experience trying to find an AI research position in Europe, from the application process through to salary negotiations.-Generalized Error

Supervised machine learning, while powerful, needs labeled data to be effective. This visualization shows how active learning data labeling strategies can improve your models.-Fast Forward Labs

The concept of biased data is often too broad to be useful. This framework includes 5 ways of categorizing bias: historical, representation, measurement, evaluation, and aggregation.-Cornell University

Discover learning curves and how they can be used to diagnose the learning and generalization behavior of machine learning models.-Machine Learning Mastery

An insider explains how algorithms are rewiring finance.-Logic

“I personally think adversarial examples are highly worth studying, and should inspire serious concern. However, most of the justifications for why exactly they’re worrisome strike me as overly literal. I think much of the confusion comes from conflating an unsolved research problem with a real-world threat model.”-Catherine Olson

Six companies own most U.S. media. Given that each of these companies owns thousands of these types of assets, how do you determine which ones are the most important?-Parse.ly Engineering

This article digs into the hows and whys of the Python package Fair Classifier, which quantifies the fairness of a model and uses an adversarial network to help ensure equity in machine learning models.-Insight

Last week, Google celebrated J.S. Bach’s 334th birthday with “the first AI-powered Google Doodle.” Here's how their team built a model that takes a user-created melody and harmonizes it in Bach’s style.-Magenta

That's one way to save money in the Big Apple! Here's how a data engineer created a 100% automated Instagram account to earn free meals at restaurants looking for promotion.-Chris Buetti

Whip this out the next time you tell someone you're a data scientist, and they ask “Does that mean you work on artificial intelligence?”-Variance Explained

When churn models just weren't cutting it for Uber, they created their own language in Python to properly model the time from a user's first ride to their second.-Uber Engineering

Saying a word in one’s mind, even if not spoken aloud, can result in the firing of the nerves controlling the muscles involved in speech. With some readily available equipment, you can train a model to classify these sub-vocalized words in less than a day.-Justin Alvey

With a deep learning-based system for detection and analysis of rodent vocalizations, researchers can better understand their test subjects. And it's adorably named “DeepSqueak.”-Psychology Today

Thanks to a difference algorithm, you now have the perfect guide for figuring out your minimum-viable at-home bar setup.-Tom MacWright

Make room for this on your reading list.-Christoph Molnar

This gives a whole new meaning to “Photoshopped.”-Youngjoo Jo

“Now it is difficult to publish anything that is not neural network related. This is not a good development. We suspect that the field would progress faster if researchers pursued a diversity of approaches and techniques instead of chasing the current vogue.”-The Gradient

The degrees of freedom in versioning machine learning systems poses a unique challenge. Each broad approach to tackle this problem has pros and cons to keep in mind.-Emily Gorcenski

This study of 25 years of artificial-intelligence research suggests that deep learning may soon be on its way out.-MIT Technology Review

Researchers can now detect AI-generated fake video with a 95% success rate. Because few images are available online showing people with their eyes closed, there's less training data available for deepfakes to get natural blinking right.-Fast Company

“I’ve watched lots of companies attempt to deploy machine learning—some succeed wildly and some fail spectacularly. One constant is that machine learning teams have a hard time setting goals and setting expectations. Why is this?”-Lukas Biewald

It’s tricky to know what neural networks are actually learning as they're trained. This post does a good job breaking down what's going on inside.-Data Science and Robots

“Sometimes… we do not just want to solve known problems, because unknown problems are also important. Consequently, we are exploring algorithms that continually invent both problems and solutions of increasing complexity and diversity.”-Uber Engineering

Was 2018 an important inflection point for the machine learning industry? Checkout this roundup of key trends and the impact they may have on ML this year.-Emmanuel Ameisen

This library might help out when you need data version control for your next machine learning project.-rstojnic

A really cool example of using machine vision to spot gender bias in Google Image search results.-Pew Research Center

Kernel density estimation (KDE) a useful statistical tool that’s way less scary than it sounds. This interactive shows how KDE lets you create a smooth curve given a set of data.-Matthew Conlen

Naming and locating several objects at once in an image with no prior information about how many objects are supposed to be detected is much harder than identifying a single object. Here’s how to do it using TensorFlow and R.-Tensorflow for R Blog

“In accepting the existing narratives about A.I., vast zones of contest and imagination are relinquished. What is achieved is resignation — the normalization of massive data capture, a one-way transfer to technology companies, and the application of automated, predictive solutions to each and every societal problem.”-Medium

Check out this collection of art, music and design using machine learning from a NeurlIPS 2018 workshop.-Neural Information Processing Systems

The latest advance in generative adversarial networks allowed researchers to generate fake images of faces with an previously unknown level of control over elements like age, race, gender—even freckles.-MIT Technology Review

“The growth rate of machine learning papers has been around 3.5% a month since July — which is around a 50% growth rate annually. This means around 2,200 machine learning papers a month and that we can expect around 30,000 new machine learning papers next year.”-Atlas ML

“58% of Americans feel that computer programs will always reflect some level of human bias – although 40% think these programs can be designed in a way that is bias-free.”-Pew Research Center

Learn how Multi-Task Learning—a general method in which a single architecture is trained towards learning several different tasks at the same time—can be applied to natural language processing.-Hugging Face

It's a bit hard to read, but if you squint hard enough this flowchart should help you discern if something's truly AI—or just hyped up and mislabeled.-MIT Technology Review

One highlight from this global survey about how AI is used at companies: those working in manufacturing and risk see AI as more valuable than those in other fields like marketing and sales or human resources.-McKinsey & Company

Vicki Boykis describes this long read best: “Great piece the real, practical concerns of deep learning applications (aka not robots killing us): fake images, text, and soundbytes that we won't know aren't human-generated.”-Current Affairs

When a reinforcement learning agent was incentivized to be curious and avoid "boredom” while playing Mario, it discovered warp levels, how to defeat bosses, and more.-OpenAI

Speaking of AI-generated imagery... it's so easy to use that anyone can make a fake video or image, no matter their motives. Luckily, technology for discerning true images from manipulated creations is catching up.-MIT Technology Review

Generating realistic images based on descriptions is much harder than describing an image—for humans and computers. But this new generative model is making that task easier.-Insight

“Over the last few years ML has lost some of its luster in my mind - the hype around deep learning and ML has added a lot of noise into the system, and for someone who cares about doing good science that's been hard for me.”-r/MachineLearning

A fast and modular implementation for Faster R-CNN and Mask R-CNN written entirely in PyTorch 1.0. It's 30% quicker than mmdetection during training.-Facebook Research

The code used to generate this portrait is mostly the work of another artist and programmer. This raises a question about attribution in the open and collaborate AI art community, which is taking its first steps into mainstream attention.-The Wall Street Journal

Changing photos used to be tedious and time-consuming. Fast-forward to now: nearly anyone can use deep learning and AI to generate incredibly realistic “fake videos”—President Obama saying something he never said, for instance.-The Wall Street Journal

How can you train your model on large batches when your GPU can’t hold more than a few samples? Let's find out.-Hugging Face

Ever notice how the preview image for the same show or movie on Netflix changes whenever you log back in? Here's a peek into the system that figures out which piece of artwork is the best for convincing a particular member why that title is “for them.”-The Netflix Tech Blog

For all the PMs out there: here are some tips for how to talk about data in your Product Requirement Document for a machine learning product.-The Lever

It's kind of crazy that neural network NLP is now old enough to have its own historical timeline. This post condenses about 15 years’ of work into eight milestones that impacted how these technologies are used today.-aylien

This new course uses modern tools and libraries, including python, pandas, scikit-learn, and pytorch. Unlike many educational materials in the field, this approach is “code first” rather than “math first.”-fast.ai

Avoid hefty cloud GPU costs by building a computer from scratch.-The Mission

Take advantage of Scikit-Learn's latest improvements for working with tabular data.-datas-frame

Reproducibility breaks down in three main places: the code, the data and the environment. This guide should help you narrow down where your reproducibility problems are, so you can focus on fixing them.-No Free Hunch

“The stack that is required to interact with an Amazon Echo goes well beyond the multi-layered ‘technical stack’ of data modeling, hardware, servers and networks. The full stack reaches much further into capital, labor and nature, and demands an enormous amount of each. The true costs of these systems – social, environmental, economic, and political – remain hidden and may stay that way for some time.”-Anatomy of an AI System

New prediction systems are fragile things. Change one thing, and the accuracy of the model can drop dramatically, leading to a long troubleshooting process to find the root cause. Skip the headache with this guide to building a robust versioning system for your ML projects.-The Lever

Just how much does a product owner need to know about machine learning? A Google PM shares his experience integrating machine learning into an existing product: Google Forms.-The Lever

Machine translation works well for sentences. For full documents? Not so much.-MIT Technology Review

This app for tailoring a book’s description for a target K-12 age is a nice example of how machine learning can aid in creative tasks.-Insight

This post injects some much-needed nuance into the biased algorithms discussion: humans vs machines is not a helpful framing and most critics of unjust bias aren’t anti-algorithm.-fast.ai

A few weeks ago, data science Twitter spun out a fascinating mega-thread on NLP meaning and semantics. Since Twitter threads can be tricky to parse after-the-fact, this summary, interactive tweet tree, and commented map provide three entry points into the discussion.-Hugging Face

This powerful, under-explored tool for neural network visualizations and art produces vibrant images that look like they came straight out of Annihilation.-Distill

This post digs into two themes of the Association for Computational Linguistics 2018 conference: gaining a better understanding what NLP models capture and to expose them to more challenging settings.-Sebastian Ruder

Find yourself dragged under by wave after wave of machine learning jargon? Part of Google's Machine Learning Crash Course, this glossary provides plain-English descriptions of the terms you've heard thrown around by ML experts, without sacrificing accuracy.-Google

“Does it really make sense to start learning a new skill based only on its reward signal, with neither prior experience nor higher-level instruction?”-The Gradient

Many real-world problems require integrating multiple sources of information. Feature-wise transformations offer a way to effectively capture and leverage the relationship of various sources, across a wide range of problem settings like image recognition, reinforcement learning, and style transfer.-Distill

“Any solution to the shortage of machine learning expertise requires answering this question: whether it’s so we know what skills to teach, what tools to build, or what processes to automate.”-fast.ai

It’s time to give Adam another go.-fast.ai

A searchable site that links machine learning papers on ArXiv with code on GitHub.-Papers with Code

This visual intro to machine learning covers how errors can arise due to assumptions that are overly simple (bias) or overly complex (variance).-R2D3

This evaluation compares how well IBM, Microsoft, and Face++ products are able to classify gender across skin types. All companies perform better on lighter subjects as a whole than on darker subjects as a whole with an 11.8% - 19.2% difference in error rates, and all companies perform worst on darker females.-Joy Buolamwini

“I’m convinced that machine learning can run on tiny, low-power chips, and that this combination will solve a massive number of problems we have no solutions for right now.”-Pete Warden

Researchers have predicted the outcome after simulating the entire soccer tournament 100,000 times. (Good news awaits if you’re pulling for Brazil, Germany, or Spain!)-MIT Technology Review

O'Reilly and Microsoft collaborated on a free e-book that walks you through the process of building intelligent cloud-based bots (with relevant code samples available on GitHub).-Microsoft Machine Learning Blog

The most interesting part of this project isn't the models used (Amazon's Rekognition API), but the practical considerations the team faced when introducing the “Who the Hill” app to the real world: poor lighting for photos in the Capitol halls, bad cell phone reception, and celebrity doppelgängers.-Times Open

When you use deep learning as part of an application, getting better training data is vastly more effective than making model adjustments.-Pete Warden

Part 2 of fast.ai’s free deep learning course is here! All you need is high school math and 1 year of coding experience.-fast.ai

The engineers behind Smart Compose—a Gmail feature that offers sentence completion suggestions as you type—dig into how they tackled the challenges of fairness and privacy, latency, and scale.-Google AI Blog

This book on predictive modeling is about 60% done and the authors are looking for feedback. The section on Engineering Numeric Predictors alone is fantastic.-Max Kuhn and Kjell Johnson

“Have you ever been embarrassed by the first iteration of one of your machine learning projects, where you didn’t include obvious and important features? In the practical hustle and bustle of trying to build models, we can often forget about the observation step in the scientific method and jump straight to hypothesis testing.”-Indeed Data Science

The machine learning engineers at Conde Nast applied their expertise to help Vanity Fair’s writers and editors better craft stories that have a broad, meaningful impact.-Conde Nast Technology

The folks at TensorFlow are putting their tutorials directly into Google Collab notebooks (which requires zero setup to run!). If you've ever wanted to learn more about machine learning, this time is now. Especially since a recent survey suggests that most data scientists lack advanced machine learning expertise.-TensorFlow

Here's a new Twitter account for you to follow. This artist combines her hand-drawn work with generative adversarial networks (GANs) to create something completely new.-Helena Sarin

Is Docker really the best thing since sliced bread? Find out in this tutorial, which covers the basics of how to interact with Docker containers and create custom Docker images for your AI workloads.-Microsoft's Machine Learning Blog

This article really gets you inside a neural network's “head” by explaining the thought process as it decides between two labels for an image, like a bowtie and a pair of sunglasses.-Distill

This 101-page report “surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats.” Divvy it out across your commutes and moments of downtime this week.-maliciousaireport.com

Unpack some black boxes with this handy cheatsheet for understanding how complex ML models work.-Przemyslaw Biecek

Give this a read, whether you sign it or not.-DataPractices.org

“What will put us out of our job is Machine Learning Overkill. I have seen implementation of Machine Learning algorithms to very frivolous problems and worse still the companies have invested heavily into the idea. It is a ticking time bomb. The moment the companies realize that the ROI is negative, they will shun the Data Science practice altogether.”-Towards Data Science

A rabbit hole worthy of your time: various types of machine learning bias as tracked by academic papers.-Arvind Narayanan

In practice, recommenders don’t always work as well as we’d like them to. This post sets out to discover why.-FastForward Labs

Ever wish you could automate the front-end engineering process? Here’s how to teach a neural network to code a basic HTML and CSS website from a design mockup.-FloydHub

How do you diagnose bias and variance? And what actions should you take once you’ve detected these errors?-Dataquest

There’s no such thing as a free machine learning project. Avoid or refactor these risk factors and design patterns to keep technical debt from piling up.-Research at Google

“Over the past 12 months AI crossed a series of new thresholds, finally beating human players in a variety of different games, from the ancient game of Go to the dynamic and interactive card game, Texas Hold-Em Poker.”-New Atlas

The technically correct answer is: “It depends.” The ballpark answer is: “1,000 representative images for each class.” (With some caveats of course.)-Pete Warden

Carve out some time in your holiday schedule to explore 2017's most exciting developments in text, voice, and computer vision technologies.-Stats & Bots

Government policies such as the tax bill, reduced funding, and tightening of rules on immigration for international researchers threaten the U.S.’s advantage in AI.-MIT Technology Review

If you didn’t attend the conference on Neural Information Processing Systems last week, never fear! Catch up on the latest in AI with these day-by-day summaries.-Insight Data

80% of Americans prefer to spend their final days in their home, but only 20% actually do. This 18-layer deep neural network identifies hospitalized patients with a high risk of death in the next 3-12 months, so they can get access to palliative care sooner.-Standford ML Group

“The interleaving approach allows us to quickly prune down the initial set of ranking algorithms to the most promising candidates, enabling us to conduct experiments a rate much faster than traditional A/B testing to identify winning ideas.”-Netflix Technology Blog

This is interesting on two levels: “how to build a deep learning library” and “how someone who’s not me writes Python” (in this case, the answer is: incredibly fast).-Joel Grus

Awareness of the bias of algorithms is important, but here’s a way to actually do something about it. Run your dataset through this Python package and you’ll get back a measure that quantifies discrimination within that dataset.-Fairness Measures

Nvidia’s researchers trained algorithms on 30,000 images of celebrities, and it’s nearly impossible to tell the generated images from the real ones.-Quartz

What do you do when your training dataset fits in memory, but the dataset you're making predictions on doesn't? This post identifies where the usual pandas and scikit-learn for in-memory analytics workflow breaks down and offers some solutions for scaling out to larger problems.-Tom Augspurger

Dig into the data behind Stanford's controversial paper Deep Neural Networks Can Detect Sexual Orientation From Faces.-fast.ai

11 mistakes you may make while implementing a neural network—and how to fix them.-Daniel Holden

One of Zillow's data scientists addresses the challenges that don’t crop up in standard textbook problems or most ML competitions: feedback loops, dynamic datasets, and temporal consistency. A great read for Kagglers and non-Kagglers alike.-No Free Hunch

How Foursquare’s engineering team improved the accuracy and user experience of their location intelligence by switching from a search ranking algorithm to regression trees and probabilities.-Foursquare Engineering

Learn how BuzzFeed trained a random forest algorithm to spot planes flown by the FBI and DHS.-BuzzFeed

Producing a large, diverse, and accurate training set for machine learning models is a pricey endeavor. Apple provides a rare behind-the-scenes look at how they cut costs and improved their models by making simulated images look more realistic.-Apple Machine Learning Journal

What do feedback loops, correction cascades, and hobo-features have in common? They’re all machine learning anti-patterns that can slowly creep into your infrastructure and create a ticking time bomb.-Towards Data Science

When Joaquin Candela first started at Facebook, he worked on an ad-targeting algorithm with a handful of engineers. Five years later, he runs the Applied Machine Learning team, which comprises hundreds of employees running thousands of experiments a day. Here’s how he scaled up Facebook’s AI factory at breakneck speed.-Harvard Business Review

How Airbnb used internal and open-source tools (like Python!) to lower the overall development costs of customer lifetime value (LTV) modeling. Code examples abound.-Airbnb Engineering and Data Science

For UX folks: A 7-step guide to stay focused on human needs when designing with machine learning.-Google Design

When you’re trying to understand the relationships in a really big dataset (three-million-grocery-orders big), a 2D scatterplot might not cut it. This immersive 3D visualization technique offers a way to make sense of data with multiple attributes and improve machine learning features and models.-Inside Machine Learning

The use-case may be farcical, but the deep learning and edge computing behind it are very real.-Hacker Noon

It all comes down to two things: the time of day and a catchy title.-Max Woolf

“While most of the machine learning talent works in big tech companies, massive and timely problems are lurking in every major industry outside tech.”-Bradford Cross

Get the key takeaways from this 280-page report, including essential data analysis packages, hiring tips, and which machine learning techniques to apply to which problems.-efinancialcareers

One investor’s self-described “unpopular” opinion-Sarah Guo

Don’t make this mistake: “[M]any organizations rush to hire ML experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization.”-RE•WORK

Save this hashtag for the moments when you need to jog your memory on some basic concepts.-Chris Albon

A brilliant, non-technical read for anyone who designs, supports, manages, or plans for products that use machine learning.-Hacker Noon

This new online publication is bringing academic journals into the 21st century: “A Distill article… isn’t just a paper. It’s an interactive medium that lets users – 'readers' is no longer sufficient – work directly with machine learning models.”-Y Combinator

One commenter put it best: 'Probably the best feature engineering slides I have found [on] the internet.' Need we say more?-HJ van Veen

How well can we predict whether or not someone is prepared for an earthquake?-Simply Statistics

Workout regimens need to be tailored to each individual. Directional correctness isn’t enough. Fitbit’s head of data science shares how his team builds a model for every user to increase motivation and prevent injuries.-Mixpanel

This grassroots effort is inviting teams to harness AI technologies to help human fact checkers identify hoaxes and deliberate misinformation in news stories. The top three teams get a cash prize, so grab a couple of friends and check out the training dataset.-Fake News Challenge

More of a visual learner? Here’s a repository of recorded talks at machine learning conferences, workshops, seminars, and more.-Dustin Tran

“As soon as it works, no one calls it AI anymore.”-Simply Statistics

You could be a poet, and not know it. Feed the works of your favorite author through this new Python library to generate as many lines of verse as you want.-Anthony Federico

With libraries like scikit-learn, it’s easy to run an algorithm on some data and automagically get an answer—without understanding exactly how you arrived there. Prepare to unpack the black box.-Jean-Nicholas Hould

“Th[e] proliferation of courses, resources, books and startups would hint that machine learning is becoming more and more accessible to the average programmer and that the market is on track to getting saturated quickly. Is this the current trend?”-Hacker News

Getting your hands on a robust dataset is the hardest part of machine learning. Finding interesting datasets is tougher still. From UFO sightings to beautiful Flickr photos, you’re sure to find something to train your model.-Oliver Cameron

Opening your data can lead to unpredictable benefits, but requires being open to unexpected uses of your data.-Idle Words

This essay is a godsend for those of us who have trouble understanding or explaining what exactly deep learning is.-WIRED

One developer shares how his team used natural language processing and machine learning in Python to pre-qualify sales leads so reps don’t have to spend hours doing it manually.-Xeneta

When humans and machines work together, they accomplish a lot more than either could on their own. This is known as hybrid intelligence—a pretty intimidating term for those unfamiliar with machine learning. Here’s a breakdown.-Clare Corthell

Machine learning amateurs, take heart. Proficiency with high level math may be essential for machine learning theory. But with out-of-the-box tools like R’s gmodels package or Python’s scikit-learn library, you don’t need to know linear algebra or calculus to build a successful predictive model. You do, however, need to know your way around a dataset.-Sharp Sight Labs

This article unpacks different filtering algorithms in an incredibly intuitive way. It’s a long read, but you’ll come away having learned a ton (did you know that NASA used Kalman filters to help Apollo spacecraft navigate to the moon?).-An Uncommon Lab

0r Why Interaction Design Matters, and so does QA-ing.-Caroline Sinders

Tay.ai is the consequence of poor training-Popular Science

It’s not surprising that Microsoft’s chatbot spewed racist invective, but here’s how it could have been avoided.-MIT Technology Review

This website is an incredible collection of interactive visualizations aimed at making tricky concepts like Markov chains and regression easy to understand. Schedule a few hours to explore this one—you’re gonna need them.-Explained Visually

Learn how to spot flaws in machine learning models with lift analysis (and why you should add it to your list of evaluation metrics).-Andy Goldschmidt

You’ve probably heard of predictive policing, but what about predictive policing for the police? One police department teamed up with researchers to test an algorithm that detects troublesome behavior of officers early on.-FiveThirtyEight

How can you ensure you’ve picked the “right model” for a very big and very complex dataset?-Rocket-Powered Data Science

What do you do when every company wants to build a deep-learning network, but the experts are in short supply? Launch a product, of course. Some startups have created computer chips and software libraries that can accelerate algorithm training, all without having to hire an experienced team of deep-learning experts.-MIT Technology Review

Random projection is frequently used in machine learning to make sense of big, diverse data. It turns out this method could be one of the ways that humans learn, too.-Georgia Tech

These days, it feels like every other article in our newsfeeds is touting the potential of machine intelligence. This article cuts through the hype and presents this year’s major accomplishments in two categories—“(1) the emergence of autonomous systems in both the physical and virtual world and (2) startups shifting away from building broad technology platforms to focusing on solving specific business problems.”-O'Reilly