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Robust adversarial inputs
We’ve created images that reliably fool neural network classifiers when viewed from varied scales and perspectives. This challenges a claim from last week that self-driving cars would be hard to trick maliciously since they capture images from multiple scales, angles, perspectives, and the like.
Khaleej Times - Dubai News, UAE News, Gulf, News, Latest news, Arab news, Gulf News, Dubai Labour News - Khaleej Times
Khaleej Times - Dubai News, UAE News, Gulf, News, Latest news, Arab news, Gulf News, Dubai Labour News Khaleej Times
Prime Members Enjoyed Biggest Global Shopping Event in Amazon History - About Amazon
Prime Members Enjoyed Biggest Global Shopping Event in Amazon History About Amazon
Investing in Menlo Park and the Community - meta.com
Investing in Menlo Park and the Community meta.com
Amazon’s efforts to address homelessness expand to Washington, D.C. - About Amazon
Amazon’s efforts to address homelessness expand to Washington, D.C. About Amazon
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? - meta.com
Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? meta.com
Our First Communities Summit and New Tools For Group Admins - meta.com
Our First Communities Summit and New Tools For Group Admins meta.com
Giving People More Control Over Their Facebook Profile Picture - meta.com
Giving People More Control Over Their Facebook Profile Picture meta.com
Introducing Hard Questions - meta.com
Introducing Hard Questions meta.com
The world’s most luxurious dog manors - Emirates 24|7
The world’s most luxurious dog manors Emirates 24|7
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
Learning from human preferences
One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind’s safety team, we’ve developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better.
Learning to cooperate, compete, and communicate
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of your competitors (and if you’re competing against clones of yourself, the environment exactly matches your skill level). Second, a multiagent environment has no stable equilibrium: no matter how smart an agent is, there’s always pressure to get sma...
Learning to cooperate, compete, and communicate
Multiagent environments where agents compete for resources are stepping stones on the path to AGI. Multiagent environments have two useful properties: first, there is a natural curriculum—the difficulty of the environment is determined by the skill of your competitors (and if you’re competing against clones of yourself, the environment exactly matches your skill level). Second, a multiagent environment has no stable equilibrium: no matter how smart an agent is, there’s always pressure to get sma...
Using Data to Help Communities Recover and Rebuild - meta.com
Using Data to Help Communities Recover and Rebuild meta.com
Making Facebook Live More Accessible With Closed Captions - meta.com
Making Facebook Live More Accessible With Closed Captions meta.com
OpenAI Baselines: DQN
We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants.
OpenAI Baselines: DQN
We’re open-sourcing OpenAI Baselines, our internal effort to reproduce reinforcement learning algorithms with performance on par with published results. We’ll release the algorithms over upcoming months; today’s release includes DQN and three of its variants.
Excess Wear and Use Guide - Tesla
Excess Wear and Use Guide Tesla
Messenger Secret Conversations Technical Whitepaper - meta.com
Messenger Secret Conversations Technical Whitepaper meta.com
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