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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.
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.
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.
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.
#Exploration: A study of count-based exploration for deep reinforcement learning
OpenAI and Microsoft
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
On the quantitative analysis of decoder-based generative models
A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models
RL²: Fast reinforcement learning via slow reinforcement learning
Variational lossy autoencoder
Extensions and limitations of the neural GPU
Semi-supervised knowledge transfer for deep learning from private training data
Report from the self-organizing conference
Last week we hosted over a hundred and fifty AI practitioners in our offices for our first self-organizing conference on machine learning.
Transfer from simulation to real world through learning deep inverse dynamics model
Infrastructure for deep learning
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.
Machine Learning Unconference
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
Team update
We’ve hired more great people to help us achieve our goals. Welcome, everyone!