OpenAIOvjeren akaunt

@OpenAI

OpenAI’s mission is to ensure that artificial general intelligence benefits all of humanity. We're hiring:

Vrijeme pridruživanja: prosinac 2015.

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  1. 30. sij

    We're standardizing OpenAI's deep learning framework on PyTorch to increase our research productivity at scale on GPUs (and have just released a PyTorch version of Spinning Up in Deep RL):

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  2. 13. pro 2019.

    We're releasing "Dota 2 with Large Scale Deep Reinforcement Learning", a scientific paper analyzing our findings from our 3-year Dota project: One highlight — we trained a new agent, Rerun, which has a 98% win rate vs the version that beat .

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  3. 5. pro 2019.

    A surprising deep learning mystery: Contrary to conventional wisdom, performance of unregularized CNNs, ResNets, and transformers is non-monotonic: improves, then gets worse, then improves again with increasing model size, data size, or training time.

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  4. 3. pro 2019.

    We're releasing Procgen Benchmark, 16 procedurally-generated environments for measuring how quickly a reinforcement learning agent learns generalizable skills. This has become the standard research platform used by the OpenAI RL team:

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  5. 21. stu 2019.

    We're releasing Safety Gym, environments and tools to evaluate reinforcement learning with safety constraints: Aims to ultimately help agents satisfy real-world safety requirements while training (eg not driving off a cliff, not writing abusive content).

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  6. 7. stu 2019.

    We've analyzed compute used in major AI results for the past decades and identified two eras in AI: 1) Prior to 2012 - AI results closely tracked Moore's Law, w/ compute doubling every two years. 2) Post-2012 - compute has been doubling every 3.4 months

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  7. 5. stu 2019.

    We're releasing the 1.5billion parameter GPT-2 model as part of our staged release publication strategy. - GPT-2 output detection model: - Research from partners on potential malicious uses: - More details:

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  8. 16. lis 2019.

    In case you missed it, here’s the unedited solve of the Rubik’s cube:

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  9. 16. lis 2019.

    Human hands let us solve a wide variety of tasks. Even so, solving a Rubik's Cube one-handed isn't easy for humans. We're excited to continue to develop new AI technology and ultimately ensure that these systems benefit all of humanity.

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  10. 16. lis 2019.

    "Solving the Rubik's Cube with a Robot Hand" took many human hands over the past 2.5 years — meet our Robotics team! (PS they're hiring: !)

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  11. 15. lis 2019.

    We’re all used to robots that fail when their environment changes unpredictably. Our robotic system is adaptable enough to handle unexpected situations not seen during training, such as being prodded by a stuffed giraffe:

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  12. 15. lis 2019.

    We've trained an AI system to solve the Rubik's Cube with a human-like robot hand. This is an unprecedented level of dexterity for a robot, and is hard even for humans to do. The system trains in an imperfect simulation and quickly adapts to reality:

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  13. 11. lis 2019.

    Now accepting applications for our 3rd class of OpenAI Scholars: a 4 month full-time program for individuals from underrepresented groups to study deep learning and produce an open-source project. Mentors include , , :

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  14. 19. ruj 2019.

    Wondering why the hiders did not cage in the seekers instead of building their own fort? In one environment variant where hiders have to protect glowing orbs, that's exactly what they learned to do!

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  15. 19. ruj 2019.

    We've fine-tuned GPT-2 using human feedback for tasks such as summarizing articles, matching the preferences of human labelers (if not always our own). We're hoping this brings safety methods closer to machines learning values by talking with humans.

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  16. 17. ruj 2019.

    And seekers learn that if they run at a wall with a ramp at the right angle, they can launch themselves upward.

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  17. 17. ruj 2019.

    Unexpected and surprising behaviors included box surfing, where seekers learn to bring a box to a locked ramp in order to jump on top of the box and then “surf” it to the hider’s shelter.

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  18. 17. ruj 2019.

    We've observed AIs discovering complex tool use while competing in a simple game of hide-and-seek. They develop a series of six distinct strategies and counterstrategies, ultimately using tools in the environment to break our simulated physics:

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  19. 27. kol 2019.

    . recently chatted with in his latest podcast, "Behind the Tech," and shared his thoughts on AI progress, building a mission-driven company, and the future of transformative technologies:

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  20. 22. kol 2019.

    We're releasing a new method to test for model robustness against adversaries not seen during training, and open-sourcing a new metric, UAR (Unforeseen Attack Robustness), which measures how robust a model is to an unanticipated attack:

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