We’ve found that self-play allows simulated AIs to discover physical skills like tackling, ducking, faking, kicking, catching, and diving for the ball, without explicitly designing an environment with these skills in mind. Self-play ensures that the environment is always the right difficulty for an AI to improve.
Recommended
Building self-improving tax agents with CodexOpenai Operation "Doppelganger": Russian influence activity targeting UkraineOpenai Everyday work with ChatGPT WorkOpenai Romance scams: AI-enabled romance scam workflowsOpenai Will Hurd joins OpenAI’s board of directorsOpenai Statement from the OpenAI Board of Directors on the Nonprofit Commission ReportOpenai
How we grew Mintlify by doing things that don't scaleMintlify
The Language of LGBTQ Inclusion and Allyship - Deepgram Blog ⚡️Deepgram
Let’s Play Master and Servant: Real Time Synchronization Tool for Redis MigrationRedis
Transform and Future-Proof Your Architecture with MACHThenewstack
How to Use Voice to Control Music with Python and Deepgram - Deepgram Blog ⚡️Deepgram
Dynamic Data Capture Sets the Stage for Extreme PersonalizationThenewstack
Introducing the Go Playground - The Go Programming LanguageGo
nference Accelerates Self-Supervised Language Model Training with Cerebras CS-2 System - CerebrasCerebras
Static export - MintlifyMintlify
Competitive prompt engineeringModal
Building an answering machineMotherduck
More Control, Less Hassle: Self-Serve Recovery with Point-in-Time RestoreMotherduck
GitOps policy-as-code: Securing Kubernetes with Argo CD and KyvernoCncf
Self-hosted GitLab OAuth - MintlifyMintlify
