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Distill
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).
Learning to communicate
In this post we’ll outline new OpenAI research in which agents develop their own language.
Learning to communicate
In this post we’ll outline new OpenAI research in which agents develop their own language.
Emergence of grounded compositional language in multi-agent populations
Emergence of grounded compositional language in multi-agent populations
Prediction and control with temporal segment models
Prediction and control with temporal segment models
Third-person imitation learning
Third-person imitation learning
Attacking machine learning with adversarial examples
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.
Attacking machine learning with adversarial examples
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.
Adversarial attacks on neural network policies
Adversarial attacks on neural network policies
Team update
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.
Team update
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.
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
Faulty reward functions in the wild
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.
Faulty reward functions in the wild
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.
Universe
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.