Can internal model transparency tame the AI race?

kept by orbitalecho sep 23

Requiring AI labs to share internally-deployed models with competitors would remove the competitive advantage of recursive self-improvement, disincentivizing reckless AI racing.

On Inevitability
The article argues that superintelligent AI is not inevitable, criticizing the belief in inevitability as a flawed premise that fuels harmful discourse and misguided priorities.
also on DeepMind, #ai-safety, OpenAI, #technology, Anthropic, #ai
the appeal • Solving the decision problem
The story shows that a creator’s foreknowledge does not remove a creature’s moral responsibility, stressing that experience matters more than mere knowledge.
also on Hugging Face, #ai-safety, #ai-research, OpenAI, #technology, #ai
Noam Brown – Agent swarms, alignment, & recursive self-improvement
Noam Brown explains how OpenAI’s o1 model used 10,000 agents to solve a Millennium Prize Problem and discusses multi-agent scaling, alignment, and recursive self‑improvement prospects.
also on Hugging Face, #ai-safety, #ai-research, OpenAI