Latest Posts
- Attacking machine learning with adversarial examplesAdversarial 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.
- Adversarial attacks on neural network policiesTitle: Adversarial attacks on neural network policies Quick Take: • What happened: New research highlights that small, carefully crafted changes to an agent’s inputs can reliably cause neural network–based policies to take incorrect actions, severely degrading performance. • Why it matters: As reinforcement learning (RL) policies move into robotics, autonomy, and operations, these vulnerabilities translate… Read more: Adversarial attacks on neural network policies
- Team updateThe 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.
- PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modificationsTitle: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications Quick Take: • What happened: A new variant of PixelCNN, called PixelCNN++, introduces a discretized logistic mixture likelihood and several architectural tweaks to improve autoregressive image modeling. • Why it matters: The approach delivers better likelihoods, training stability, and sample quality, setting… Read more: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
- Faulty reward functions in the wildReinforcement 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.
- UniverseWe’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.
- #Exploration: A study of count-based exploration for deep reinforcement learningTitle: AI Development Update (Details Not Provided) Quick Take: • What happened – An AI-related update was referenced, but the article title and description were not provided. • Why it matters – Without specifics on the technology, timeline, or stakeholders, it’s unclear how this development could affect users, businesses, or competitors. • Key numbers /… Read more: #Exploration: A study of count-based exploration for deep reinforcement learning
- OpenAI and MicrosoftWe’re working with Microsoft to start running most of our large-scale experiments on Azure.
- On the quantitative analysis of decoder-based generative modelsI don’t have the article title or description to base the post on. Here’s the exact format ready for your content—paste in the details and I’ll finalize. Title: Quick Take: • What happened: • Why it matters: • Key numbers / launch details: • Who is involved: • Impact on users / industry: What’s Happening:… Read more: On the quantitative analysis of decoder-based generative models
- A connection between generative adversarial networks, inverse reinforcement learning, and energy-based modelsI’m missing the article title and description, so I can’t produce a factually complete news brief. Here’s a ready-to-fill version in your requested format—drop in the details and it will be publication-ready. Title: [Insert Article Title] Quick Take: • What happened: [1–2 sentences on the announcement/event] • Why it matters: [Core significance for AI, business,… Read more: A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models