Browse press releases
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- OpenAIOpenAI supportersCompanies: OpenAIWe’re excited to welcome new donors to OpenAI.
- OpenAIInterpretable machine learning through teachingCompanies: OpenAIWe’ve designed a method that encourages AIs to teach each other with examples that also make sense to humans. Our approach automatically selects the most informative examples to teach a concept—for instance, the best images to describe the concept of dogs—and experimentally we found our approach to be effective at teaching both AIs
- OpenAIDiscovering types for entity disambiguationCompanies: OpenAIWe’ve built a system for automatically figuring out which object is meant by a word by having a neural network decide if the word belongs to each of about 100 automatically-discovered “types” (non-exclusive categories).
- OpenAIRequests for Research 2.0Companies: OpenAIWe’re releasing a new batch of seven unsolved problems which have come up in the course of our research at OpenAI.
- OpenAIScaling Kubernetes to 2,500 nodesCompanies: OpenAIOur Dota(opens in a new window) project started out on Kubernetes, and as it scaled, we noticed that fresh Kubernetes nodes often have pods sitting in Pending(opens in a new window) for a long time. The game image is around 17GB, and would often take 30 minutes to pull on a fresh cluster node, so we understood why the Dota container would be Pending for a while — but this was true for other containers as well. Digg
- OpenAIBlock-sparse GPU kernelsCompanies: OpenAIWe’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art results in text sentiment analysis and generative modeling of text and images.
- OpenAILearning sparse neural networks through L₀ regularizationCompanies: OpenAIAbstract We propose a practical method for L₀ norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. AIC and BIC, well-known model selection criteria, are special cases of L₀ regularization. However, since the L₀ norm o
- OpenAIInterpretable and pedagogical examplesCompanies: OpenAIAbstract Teachers intentionally pick the most informative examples to show their students. However, if the teacher and student are neural networks, the examples that the teacher network learns to give, although effective at teaching the student, are typically uninterpretable. We show that training the student and teacher iteratively, rather than jointly, can produce interpretable teaching strategies. We evaluate inte
- OpenAILearning a hierarchyCompanies: OpenAIWe’ve developed a hierarchical reinforcement learning algorithm that learns high-level actions useful for solving a range of tasks, allowing fast solving of tasks requiring thousands of timesteps. Our algorithm, when applied to a set of navigation problems, discovers a set of high-level actions for walking and crawling in different directions, which enables the agent to master new navigation tasks quickly.
- OpenAIGeneralizing from simulationCompanies: OpenAIOur latest robotics techniques allow robot controllers, trained entirely in simulation and deployed on physical robots, to react to unplanned changes in the environment as they solve simple tasks. That is, we’ve used these techniques to build closed-loop systems rather than open-loop ones as before.
- OpenAISim-to-real transfer of robotic control with dynamics randomizationCompanies: OpenAIAbstract Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this p
- OpenAIAsymmetric actor critic for image-based robot learningCompanies: OpenAIAbstract Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies tra