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Two new papers take this approach, suggesting a path toward smarter self-driving cars, for example, or automated scientific discovery.
One idea is to automatically leverage human intuition to decide what’s interesting through large language models trained on mass quantities of human text.
Experts worry open-ended AI with relatively unconstrained exploratory powers could go off the rails.
System generates new tasks to increase AI agents’ abilities.
OMNI-EPIC uses LLMs to create a description and code for a new variation.
Each task is represented by a natural-language description along with computer code for the task.
If it’s interesting, the AI agent trains on the task through reinforcement learning.
“It’s like a crack team of machine learning scientists trying to solve a problem, and it isn’t guaranteed to focus on only the safe ideas.” But Foerster thinks that open-ended learning could actually increase safety, creating “actors of different interests, maintaining a balance of power.” In any case, we’re not at superintelligence yet. We’re still mostly at the level of inventing new video games..
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