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Briefings and ideas about Google DeepMind from AI podcast discussions and the research they cite, with links to the original sources.
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.
Google DeepMind's Pushmeet Kohli said AlphaFold reproduces structures deposited in the Protein Data Bank, not proteins' shifting shapes. He sees raw cryo-EM images as one possible route to that motion, though his own attempt needs more work.
After the 2026 medicine Nobel for optogenetics, Moonshots panelists argued the prizes lag AI-era science. Alexander Wissner-Gross said he once nudged Ed Boyden toward the field after reading Vernor Vinge.
Keerthana Gopalakrishnan of Google DeepMind said robot training will probably blend teleoperation, sensor-equipped handheld rigs and human video, each trading scale for precision. On world models, she said the jury is still out.
Keerthana Gopalakrishnan, research lead for Gemini Robotics at Google DeepMind, argues that people won't use unsafe robots. She splits robot safety into avoiding accidents, following intent within guardrails and handling sensor failures.
Google DeepMind's Keerthana Gopalakrishnan said humanoid sprint records show locomotion that trains well in simulation. In her view, contact-heavy hand work is the real frontier, where hands have improved but still need reliability and lower cost.
Keerthana Gopalakrishnan of Google DeepMind says robot models are at a GPT-2 stage. In her view, a GPT-3 moment needs few-shot teaching to work across many tasks and one brain to run reliably on a new robot body, a test language models never faced.
Keerthana Gopalakrishnan, who leads Gemini Robotics research at Google DeepMind, explained how a reasoning model open to developers directs an action model limited to testers, and where she says failures arise.
Journalist Garrison Lovely argues AI lab workers' leverage is near its peak as labs try to automate AI research, so staying to form safety unions may beat quitting. He says he is no labor-law expert and points leavers to whistleblower help.
MIT's Andrew McAfee said he would back legal limits on AI if AI hijacked San Francisco's Waymos, crashed them into people and couldn't be shut down for a week or a month. Panelists seeking a halt said by then it may be too late.
Google DeepMind's AlphaGenome Atlas precomputes predicted molecular effects of every possible single-letter change in the human genome. A Moonshots panelist cautioned that what combinations of variants mean still needs outcome data.