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3月21日周四
  1. OpenAI News

    Implicit generation and generalization methods for energy-based models

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve made progress towards stable and scalable training of energy-based models (EBMs) resulting in better sample quality and generalization ability than existing models. Generation in EBMs spends more compute to continually refine its answers and doing so can generate samples competitive with GANs at low temperatures, while also having mode coverage guarantees of likelihood-based models. We hope these findings stimulate further research into this promising class of models.

3月13日周三
  1. OpenAI News

    OpenAI Scholars 2019: Meet our Scholars

    【RSS 原文 · 未经 AI 筛选或翻译】Our class of eight scholars (out of 550 applicants) brings together collective expertise in literature, philosophy, cell biology, statistics, economics, quantum physics, and business innovation.

3月11日周一
  1. OpenAI News

    OpenAI LP

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve created OpenAI LP, a new “capped-profit” company that allows us to rapidly increase our investments in compute and talent while including checks and balances to actualize our mission.

3月6日周三
  1. OpenAI News

    Introducing Activation Atlases

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent. As AI systems are deployed in increasingly sensitive contexts, having a better understanding of their internal decision-making processes will let us identify weaknesses and investigate failures.

3月4日周一
  1. OpenAI News

    Neural MMO: A massively multiagent game environment

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence.

2月26日周二
2月19日周二
  1. OpenAI News

    AI safety needs social scientists

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI systems with human values requires resolving many uncertainties related to the psychology of human rationality, emotion, and biases. The aim of this paper is to spark further collaboration between machine learning and social science researchers, and we plan to hire social scientists to work on this full time at OpenAI.

2月14日周四
  1. OpenAI News

    Better language models and their implications

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve trained a large-scale unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.

2月4日周一
12月19日周三
12月14日周五
  1. OpenAI News

    How AI training scales

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve discovered that the gradient noise scale, a simple statistical metric, predicts the parallelizability of neural network training on a wide range of tasks. Since complex tasks tend to have noisier gradients, increasingly large batch sizes are likely to become useful in the future, removing one potential limit to further growth of AI systems. More broadly, these results show that neural network training need not be considered a mysterious art, but can be rigorized and systematized.

12月6日周四
  1. OpenAI News

    Quantifying generalization in reinforcement learning

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing CoinRun, a training environment which provides a metric for an agent’s ability to transfer its experience to novel situations and has already helped clarify a longstanding puzzle in reinforcement learning. CoinRun strikes a desirable balance in complexity: the environment is simpler than traditional platformer games like Sonic the Hedgehog but still poses a worthy generalization challenge for state of the art algorithms.

11月8日周四
  1. OpenAI News

    Spinning Up in Deep RL

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing Spinning Up in Deep RL, an educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning. Spinning Up consists of crystal-clear examples of RL code, educational exercises, documentation, and tutorials.

11月7日周三
  1. OpenAI News

    Learning concepts with energy functions

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot.

11月5日周一
10月31日周三
  1. OpenAI News

    Reinforcement learning with prediction-based rewards

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge.

10月22日周一
  1. OpenAI News

    Learning complex goals with iterated amplification

    【RSS 原文 · 未经 AI 筛选或翻译】We’re proposing an AI safety technique called iterated amplification that lets us specify complicated behaviors and goals that are beyond human scale, by demonstrating how to decompose a task into simpler sub-tasks, rather than by providing labeled data or a reward function. Although this idea is in its very early stages and we have only completed experiments on simple toy algorithmic domains, we’ve decided to present it in its preliminary state because we think it could prove to be a scalable approach to AI safety.

10月11日周四
  1. OpenAI News

    OpenAI Scholars 2019: Applications open

    【RSS 原文 · 未经 AI 筛选或翻译】We are now accepting applications for our second cohort of OpenAI Scholars, a program where we provide 6–10 stipends and mentorship to individuals from underrepresented groups to study deep learning full-time for 3 months and open-source a project.

10月9日周二
10月2日周二
9月10日周一
8月23日周四
  1. OpenAI News

    The International 2018: Results

    【RSS 原文 · 未经 AI 筛选或翻译】OpenAI Five lost two games against top Dota 2 players at The International in Vancouver this week, maintaining a good chance of winning for the first 20–35 minutes of both games.

8月13日周一
8月6日周一
  1. OpenAI News

    OpenAI Five Benchmark: Results

    【RSS 原文 · 未经 AI 筛选或翻译】Yesterday, OpenAI Five won a best-of-three against a team of 99.95th percentile Dota players: Blitz, Cap, Fogged, Merlini, and MoonMeander—four of whom have played Dota professionally—in front of a live audience and 100,000 concurrent livestream viewers.

7月30日周一
7月26日周四
7月25日周三
7月18日周三
7月9日周一
  1. OpenAI News

    Glow: Better reversible generative models

    【RSS 原文 · 未经 AI 筛选或翻译】We introduce Glow, a reversible generative model which uses invertible 1x1 convolutions. It extends previous work on reversible generative models and simplifies the architecture. Our model can generate realistic high resolution images, supports efficient sampling, and discovers features that can be used to manipulate attributes of data. We’re releasing code for the model and an online visualization tool so people can explore and build on these results.

7月4日周三
  1. OpenAI News

    Learning Montezuma’s Revenge from a single demonstration

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm that underpins OpenAI Five.

6月25日周一
  1. OpenAI News

    OpenAI Five

    【RSS 原文 · 未经 AI 筛选或翻译】Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2.

6月22日周五
  1. OpenAI News

    Retro Contest: Results

    【RSS 原文 · 未经 AI 筛选或翻译】The first run of our Retro Contest—exploring the development of algorithms that can generalize from previous experience—is now complete.

6月17日周日
6月11日周一
  1. OpenAI News

    Improving language understanding with unsupervised learning

    【RSS 原文 · 未经 AI 筛选或翻译】We’ve obtained state-of-the-art results on a suite of diverse language tasks with a scalable, task-agnostic system, which we’re also releasing. Our approach is a combination of two existing ideas: transformers and unsupervised pre-training. These results provide a convincing example that pairing supervised learning methods with unsupervised pre-training works very well; this is an idea that many have explored in the past, and we hope our result motivates further research into applying this idea on larger and more diverse datasets.

6月2日周六
5月30日周三
  1. OpenAI News

    OpenAI Fellows Fall 2018

    【RSS 原文 · 未经 AI 筛选或翻译】We’re now accepting applications for the next cohort of OpenAI Fellows, a program which offers a compensated 6-month apprenticeship in AI research at OpenAI.

5月25日周五
  1. OpenAI News

    Gym Retro

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games to over 1,000 games across a variety of backing emulators. We’re also releasing the tool we use to add new games to the platform.

5月16日周三
  1. OpenAI News

    AI and compute

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore’s Law had a 2-year doubling period)[^footnote-correction]. Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it’s worth preparing for the implications of systems far outside today’s capabilities.

5月3日周四
  1. OpenAI News

    AI safety via debate

    【RSS 原文 · 未经 AI 筛选或翻译】We’re proposing an AI safety technique which trains agents to debate topics with one another, using a human to judge who wins.

4月18日周三
  1. OpenAI News

    Evolved Policy Gradients

    【RSS 原文 · 未经 AI 筛选或翻译】We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training.