AI for Science

The anecdote that opens the panel does the work: a robot asked to pick up the extinct animal selected a dinosaur toy, with nothing in its training data connecting the phrase to the object. That transfer from language models into machines with hands is the premise of the current wave. What the practitioners then describe is where it stops. Physical intelligence is about exerting force and using a body to do it, which is knowledge about consequences — the one thing a corpus of internet images contains almost nothing about. The humanoid question gets an honest treatment: not that human shape is optimal, but that the world is already built for it, plus a development-loop argument about collecting data and deploying on the same hardware. The most useful passage is scepticism about the field's favourite shortcut: generated video looks realistic and does not hold up for dexterous manipulation, because looking right and being physically consistent are different properties.

Google I/O

Four months after Davos, Hassabis put a sharper number on the same forecast: AGI around 2030, give or take a year, arriving gradually rather than as a single moment. His test for it is concrete — a model with a 1901 knowledge cutoff that could produce Einstein's 1905 insights — and by that standard current systems plainly fail. The interview is more useful than the Davos panel on two fronts. First, competitive position: he argues Google's advantage is being the only organisation holding the full stack from chips to billion-user products, citing 900 million monthly users on the Gemini app. Second, method: the AlphaFold story of choosing to fold every known protein at once rather than run a request service is his working example of what acceleration should look like. He closes on a warning aimed at the Bay Area — that direction matters more than velocity, and that the current frenetic pace is not conducive to the deep work the next advances require.

Google I/O

The claim most likely to matter here is about data rather than computation. The measurements behind modern structural biology began accumulating in the 1970s and took roughly fifty years of painstaking work to become the database that made the protein-structure breakthrough possible — and a quantum computer could produce valuable training sets where collecting them experimentally is impractical. That inverts the usual framing, because a dataset does not need a fully error-corrected machine: it needs to be produced once, correctly, and then has permanent value. The technical status report is specific, with coherence times improved roughly tenfold and the remaining obstacles described as system-level engineering rather than physics. The most actionable statement concerns cryptography, where an algorithmic result rather than hardware progress moved the timeline inward.

Google I/O

The number that matters here is not a benchmark but a shape: scaling up to 2.2 million virtual cores and back down to zero across four hours. Peak capacity has never been the hard part of scientific computing, because institutions have built large clusters for decades — the difficulty was that the cluster was sized for the peak and idle the rest of the year. Returning to zero removes that calculation, which suits genomics precisely, since analysis is bursty by nature. The acceleration claim is a change in the kind of activity rather than a productivity gain: eight hours means return tomorrow, thirty-five minutes means adjust and run again. The hardware explanation is unusually clear that cores per chip rather than total core count drives the improvement, and two operational findings — provisioning cost and a storage benchmark that showed no difference — are more portable than the hardware itself.

AWS re:Invent