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Ingredients for robotics research
We’re releasing eight simulated robotics environments and a Baselines implementation of Hindsight Experience Replay, all developed for our research over the past year. We’ve used these environments to train models which work on physical robots. We’re also releasing a set of requests for robotics research.
We Just Made It Easier to Add More Friends and Family to Your Messenger Audio and Video Chats - meta.com
We Just Made It Easier to Add More Friends and Family to Your Messenger Audio and Video Chats meta.com
Preparing for malicious uses of AI
We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.
Preparing for malicious uses of AI
We’ve co-authored a paper that forecasts how malicious actors could misuse AI technology, and potential ways we can prevent and mitigate these threats. This paper is the outcome of almost a year of sustained work with our colleagues at the Future of Humanity Institute, the Centre for the Study of Existential Risk, the Center for a New American Security, the Electronic Frontier Foundation, and others.
Interpretable machine learning through teaching
We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept—for instance, the best images to describe the concept of dogs—and experimentally we found our approach to be effective at teaching both AIs
Interpretable machine learning through teaching
We’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept—for instance, the best images to describe the concept of dogs—and experimentally we found our approach to be effective at teaching both AIs
Energy Products Support - Tesla
Energy Products Support Tesla
E-Verify - Tesla
E-Verify Tesla
Discovering types for entity disambiguation
We’ve built a system for automatically figuring out which object is meant by a word by having a neural network decide if the word belongs to each of about 100 automatically-discovered “types” (non-exclusive categories).
Announcing the teams competing for the $3.5 million Alexa Prize - About Amazon
Announcing the teams competing for the $3.5 million Alexa Prize About Amazon
Tesla Announces New Long-Term Performance Award for Elon Musk - Tesla Investor Relations
Tesla Announces New Long-Term Performance Award for Elon Musk Tesla Investor Relations
Hard Questions: Social Media and Democracy - meta.com
Hard Questions: Social Media and Democracy meta.com
Bringing People Closer Together - meta.com
Bringing People Closer Together meta.com
Alexa and her backup singers are ready to ring in the new year - About Amazon
Alexa and her backup singers are ready to ring in the new year About Amazon
Replacing Disputed Flags With Related Articles - meta.com
Replacing Disputed Flags With Related Articles meta.com
New Tools to Prevent Harassment - meta.com
New Tools to Prevent Harassment meta.com
Hard Questions: Is Spending Time on Social Media Bad for Us? - meta.com
Hard Questions: Is Spending Time on Social Media Bad for Us? meta.com
Block-sparse GPU kernels
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art results in text sentiment analysis and generative modeling of text and images.
Block-sparse GPU kernels
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art results in text sentiment analysis and generative modeling of text and images.
Facebook’s 2017 Year In Review - meta.com
Facebook’s 2017 Year In Review meta.com
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