Faster physics in Python
【RSS 原文 · 未经 AI 筛选或翻译】We’re open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of robotics research.
【RSS 原文 · 未经 AI 筛选或翻译】We’re open-sourcing a high-performance Python library for robotic simulation using the MuJoCo engine, developed over our past year of robotics research.
【RSS 原文 · 未经 AI 筛选或翻译】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.
【RSS 原文 · 未经 AI 筛选或翻译】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 smarter. These environments have a very different feel from traditional environments, and it’ll take a lot more research before we become good at them.
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【RSS 原文 · 未经 AI 筛选或翻译】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.
【RSS 原文 · 未经 AI 筛选或翻译】We’ve created a robotics system, trained entirely in simulation and deployed on a physical robot, which can learn a new task after seeing it done once.
【RSS 原文 · 未经 AI 筛选或翻译】We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym.
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【RSS 原文 · 未经 AI 筛选或翻译】We’ve developed an unsupervised system which learns an excellent representation of sentiment, despite being trained only to predict the next character in the text of Amazon reviews.
【RSS 原文 · 未经 AI 筛选或翻译】We’ve created the world’s first Spam-detecting AI trained entirely in simulation and deployed on a physical robot.
【RSS 原文 · 未经 AI 筛选或翻译】We’ve discovered that evolution strategies (ES), an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern RL benchmarks (e.g. Atari/MuJoCo), while overcoming many of RL’s inconveniences.
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【RSS 原文 · 未经 AI 筛选或翻译】We’re excited to support today’s launch of Distill, a new kind of journal aimed at excellent communication of machine learning results (novel or existing).
【RSS 原文 · 未经 AI 筛选或翻译】In this post we’ll outline new OpenAI research in which agents develop their own language.
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【RSS 原文 · 未经 AI 筛选或翻译】Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult.
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【RSS 原文 · 未经 AI 筛选或翻译】The OpenAI team is now 45 people. Together, we’re pushing the frontier of AI capabilities—whether by validating novel ideas, creating new software systems, or deploying machine learning on robots.
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【RSS 原文 · 未经 AI 筛选或翻译】Reinforcement learning algorithms can break in surprising, counterintuitive ways. In this post we’ll explore one failure mode, which is where you misspecify your reward function.
【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.
【RSS 原文 · 未经 AI 筛选或翻译】We’re working with Microsoft to start running most of our large-scale experiments on Azure.
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【RSS 原文 · 未经 AI 筛选或翻译】Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
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【RSS 原文 · 未经 AI 筛选或翻译】Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
【RSS 原文 · 未经 AI 筛选或翻译】The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
【RSS 原文 · 未经 AI 筛选或翻译】We’ve hired more great people to help us achieve our goals. Welcome, everyone!
【RSS 原文 · 未经 AI 筛选或翻译】Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
【RSS 原文 · 未经 AI 筛选或翻译】We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.
【RSS 原文 · 未经 AI 筛选或翻译】OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible.