Talking Machines

Gods and Robots

Responsibility, Risk, and Publishing

ICML 2021: Test of Time(ly) Award

Learning with Less, Invisible Labor and Combating Anti-Blackness

Let's Reflect

Predicting Floods and Really Doing Good

ICLR: accessible, inclusive, virtual

Humans in the Loop and Outside of the Classroom

The Evolution of ML and Furry Little Animals

Talking Machines Live and Understanding Modeling Viruses

Prioritizing Problems and 100 episodes

The Great AI Fallacy

If a Machine Could Predict Your Death, Should it?

Predicting the Decade and Distributing Conferences

Debating Project Debater and Hello NeurIPS

De-Enchanting AI with the Law

How to Ask an Actionable Question

Children are the Future and Ada Lovelace Day

News from Neil and Updates from DALI

A Cooperative Path to Artificial Intelligence

What Does Red Sound Like

Not What But Why

Idea Pandemics and Workshop Walkthrough and Deep Quaggles

The View from Addis Ababa

DSA Addis Ababa and ICML Los Angeles

Data Trusts and Citation Trends

Reproducibly and Revisiting History

Insights from AISTATS

The Deep End of Deep Learning

Exploring MARS and Getting back to Bayesics

The Sweetness of a Bitter Lesson and Bringing ML and Healthcare Closer

Slowed Down Conferences and Even More Summer Schools

Jupyter Notebooks and Modern Model Distribution

Real World Real Time and Five Papers for Mike Tipping

The Bezos Paradox and Machine Learning Languages

Being Global Bit by Bit

The Possibility Of Explanation and The End of Season Four

Neural Information Processing Systems and Distributed Internal Intelligence Systems

Data Driven Ideas and Actionable Privacy

AI for Good and The Real World

Systems Design and Tools for Transparency

How to Research in Hype and CIFAR's Strategy

Troubling Trends and Climbing Mountains

Gaussian Processes, Grad School, and Richard Zemel

Long Term Fairness

Simulated Learning and Real World Ethics

ICML 2018 with Jennifer Dy

Aspirational Asimov and How to Survive a Conference

Explanations and Reviews

Statements on Statements

The Futility of Artificial Carpenters and Further Reading

Economies, Work and AI

Explainability and the Inexplicable

Good Data Practice Rules

Can an AI Practitioner Fix a Radio?

Natural vs Artificial Intelligence and Doing Unexpected Work

Scientific Rigor and Turning Information into Action

Code Review for Community Change

The Pace of Change and The Public View of ML

The Long View and Learning in Person

Machine Learning in the Field and Bayesian Baked Goods

Data Science Africa with Dina Machuve

The Church of Bayes and Collecting Data

Getting a Start in ML and Applied AI at Facebook

Bias Variance Dilemma for Humans and the Arm Farm

Overfitting and Asking Ecological Questions with ML

Graphons and "Inferencing"

Hosts of Talking Machines: Neil Lawrence and Ryan Adams

ANGLICAN and Probabilistic Programming

Eric Lander and Restricted Boltzmann Machines

Generative Art and Hamiltonian Monte Carlo

Perturb-and-MAP and Machine Learning in the Flint Water Crisis

Automatic Translation and t-SNE

Fantasizing Cats and Data Numbers

Spark and ICML

Computational Learning Theory and Machine Learning for Understanding Cells

Sparse Coding and MADBITS

Remembering David MacKay

Machine Learning and Society

Software and Statistics for Machine Learning

Machine Learning in Healthcare and The AlphaGo Matches

AI Safety and The Legacy of Bletchley Park

Robotics and Machine Learning Music Videos

OpenAI and Gaussian Processes

Real Human Actions and Women in Machine Learning

Open Source Releases and The End of Season One

Probabilistic Programming and Digital Humanities

Workshops at NIPS and Crowdsourcing in Machine Learning

Machine Learning Mastery and Cancer Clusters

Data from Video Games and The Master Algorithm

Strong AI and Autoencoders

Active Learning and Machine Learning in Neuroscience

Machine Learning in Biology and Getting into Grad School

Machine Learning for Sports and Real Time Predictions

Really Really Big Data and Machine Learning in Business

Solving Intelligence and Machine Learning Fundamentals

Working With Data and Machine Learning in Advertising

The Economic Impact of Machine Learning and Using The Kernel Trick on Big Data

How We Think About Privacy and Finding Features in Black Boxes

Interdisciplinary Data and Helping Humans Be Creative

Starting Simple and Machine Learning in Meds

Spinning Programming Plates and Creative Algorithms

The Automatic Statistician and Electrified Meat

The Future of Machine Learning from the Inside Out

The History of Machine Learning from the Inside Out

Using Models in the Wild and Women in Machine Learning

Common Sense Problems and Learning about Machine Learning

Machine Learning and Magical Thinking

Hello World!