Edit: This is now up on Stampy’s Wiki, a Rob Miles project. Please use and update that page, it will be served to readers through the web interface when that’s ready.
GPT-3 showed that transformers are capable of a vast array of natural language tasks, codex/copilot extended this into programming. One demonstrations of GPT-3 is Simulated Elon Musk lives in a simulation. Important to note that there are several much better language models, but they are not publicly available.
DALL-E and DALL-E 2 are among the most visually spectacular (one person was thinking of going into graphic design and changed career plans after I showed them).
MuZero, which learned Go, Chess, and many Atari games without any directly coded info about those environments. The graphic there explains it, this seems crucial for being able to do RL in novel environments. We have systems which we can drop into a wide variety of games and they just learn how to play. For fun: The same algorithm was used in Tesla’s self-driving cars to do complex route finding. These things are general.
Edit: This is now up on Stampy’s Wiki, a Rob Miles project. Please use and update that page, it will be served to readers through the web interface when that’s ready.
GPT-3 showed that transformers are capable of a vast array of natural language tasks, codex/copilot extended this into programming. One demonstrations of GPT-3 is Simulated Elon Musk lives in a simulation. Important to note that there are several much better language models, but they are not publicly available.
DALL-E and DALL-E 2 are among the most visually spectacular (one person was thinking of going into graphic design and changed career plans after I showed them).
MuZero, which learned Go, Chess, and many Atari games without any directly coded info about those environments. The graphic there explains it, this seems crucial for being able to do RL in novel environments. We have systems which we can drop into a wide variety of games and they just learn how to play. For fun: The same algorithm was used in Tesla’s self-driving cars to do complex route finding. These things are general.
Generally capable agents emerge from open-ended play—Diverse procedurally generated environments provide vast amounts of training data for AIs to learn generally applicable skills. Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning shows how these kind of systems can be trained to follow instructions in natural language.
GATO shows you can distill 600+ individually trained tasks into one network, so we’re not limited by the tasks being fragmented.