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.
Title: Adversarial attacks on neural network policies Quick Take: • What happened: New research highlights that small, carefully crafted changes
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.
Title: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications Quick Take: • What happened: A new
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.
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.
Title: AI Development Update (Details Not Provided) Quick Take: • What happened – An AI-related update was referenced, but the
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
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