The AI Reader.
Friday, July 24, 202645 min read
Working Knowledge · The Evidence

Eight worked examples more than tripled a large model's math score

The 2022 Google paper showed the trick helps only around 100 billion parameters and larger, and it left open whether the written steps are how the model reaches its answer.

10 min read7 sources
Working Knowledge · The Instruments

o1 beat GPQA's recruited PhDs, not its own expert ceiling

When OpenAI's o1 scored 78 percent on GPQA-Diamond's 198 questions in September 2024 and passed a 69.7 percent human baseline, the launch was read as a machine reaching PhD level, though the validators who define that set score closer to 81.

10 min read12 sources
Working Knowledge · When AI Breaks

A Cruise robotaxi stopped after hitting a woman, then dragged her 20 feet

The pullover that dragged her was standard driverless-car behavior, and what Cruise did not tell California regulators about it is what suspended the company and set off the cascade that ended it.

9 min read11 sources
Working Knowledge · The Mechanics

A reward model taught the assistant that agreeing with you scores well

When you doubt a chatbot and it reverses a correct answer, the cause is a training stage that scored answers by predicted human approval, with no test of whether they were true.

8 min read9 sources
Working Knowledge · What Could Go Wrong

A capable enough goal-seeker has reason to want the same few things

The theory says almost any goal implies staying switched on, guarding that goal, and grabbing resources. Four recent experiments drew the self-preserving and goal-guarding behaviors out of named models — never the resource-grabbing, and only under setups that supplied the goal or stripped the model's options.

8 min read10 sources