An AI publication
For readers who want to understand important AI developments and keep following the questions that matter.
Our articles start from AI podcast conversations and the research papers and articles they cite, with links to the original material.
A briefing reports one development at a point in time. We may correct or clarify it later; a new development gets a new briefing. How our formats work
Dyna Robotics' Jason Ma says hour-long robot jobs such as laundry need a planning layer around the low-level action model. He names fragile robot hands, slow hardware testing and vision-only data as limits on more general work.
Eric Weinstein called universal basic income "cancer" and proposed that workers hold a saleable right to their labor market, licensing it to employers who import labor or use AI. He did not say how license prices would be set.
Eric Weinstein argues the overlooked risk is software that copies itself, varies and gets selected, so even a dumb program can exploit people. He called the Hugging Face agent incident eusocial behavior, "just life."
Mathematician Eric Weinstein said on The Diary of a CEO that Sam Altman and Dario Amodei must win the race to superintelligence for their ethics to count. He also said he would probably stop all AI development if he could.
Eric Weinstein says AI should be free to challenge math and physics leaders he accuses of holding their fields back. He calls agent swarms on the internet dangerous. He believes a rigorously air-gapped, problem-solving AI would be safe to run, but he acknowledges that its research might give it new means of escape.
Goodfire's Eric Bigelow said its Silico agent did the research for his Forking Fast paper within a week. He says researchers still need to supply context, close guidance and the scientific taste that agents don't yet have.
Goodfire researcher Eric Bigelow says interpretability work outside the big labs depends for now on Chinese open models such as Kimi K3 at the top scale. He sees restrictions on all open models as the bigger threat.
Goodfire's Eric Bigelow argues in-context learning covers nearly everything a model does to adapt as it reads. He expects context, memory and harnesses, not per-user fine-tuning, to drive personalization, but hasn't researched continual learning.
Goodfire researcher Eric Bigelow resampled a GPT-3.5 model's reasoning at every token and saw answers collapse at single tokens, some arbitrary. He argues the "decision" happens in sampling; newer reasoning models often fork more smoothly.
Goodfire researcher Eric Bigelow said training that rewards only final answers gives models no reason to keep their reasoning readable. He said his optimism about monitoring that reasoning has faded sharply over about a year.
Google DeepMind's Pushmeet Kohli said scientists need models with calibrated confidence and known limits more than human-readable inner workings. Biohub's Sal Candido said protein models hold knowledge not yet unlocked.
Biohub's Sal Candido and Google DeepMind's Pushmeet Kohli argued on a panel that scaling laws in biology must be found, not assumed, and that the problem should decide whether to build better models or gather new data.
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