Executive summary: The post argues that business leaders and journalists fundamentally misunderstand the core issues around AI adoption and transition, focusing too much on accuracy, correctness, and rule-following rather than the deeper challenges of context alignment, shared languages, compositional reasoning, and provable computing.
Key points:
The focus on accuracy, factual correctness, and rule-following suits bureaucratic automation but misses the real productivity gains from shared contexts, languages, and reasoning.
Hallucinations show alignment requires shared frames of reference across timescales from pre-training to dialogue.
Richer language and compositional, collective reasoning make economies more productive and civilizations safer, not just better bureaucracies.
Making AI systems “accurate rule-followers” fails to really improve reasoning—instead we need languages, protocols, and provably correct computing.
Key ingredients for safe, beneficial AI: context alignment, shared languages, provable computing, governance mechanisms—not control, values, or trust.
“Trust” shouldn’t oversimplify algorithms—instead educate on the difference between context alignment (“trust in science”) and algorithmic correctness (“trust in math”).
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: The post argues that business leaders and journalists fundamentally misunderstand the core issues around AI adoption and transition, focusing too much on accuracy, correctness, and rule-following rather than the deeper challenges of context alignment, shared languages, compositional reasoning, and provable computing.
Key points:
The focus on accuracy, factual correctness, and rule-following suits bureaucratic automation but misses the real productivity gains from shared contexts, languages, and reasoning.
Hallucinations show alignment requires shared frames of reference across timescales from pre-training to dialogue.
Richer language and compositional, collective reasoning make economies more productive and civilizations safer, not just better bureaucracies.
Making AI systems “accurate rule-followers” fails to really improve reasoning—instead we need languages, protocols, and provably correct computing.
Key ingredients for safe, beneficial AI: context alignment, shared languages, provable computing, governance mechanisms—not control, values, or trust.
“Trust” shouldn’t oversimplify algorithms—instead educate on the difference between context alignment (“trust in science”) and algorithmic correctness (“trust in math”).
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.