From Sebastian Mallaby:

Even by the standard of a tech industry stacked with so-called geniuses, Demis Hassabis is a special case. Born poor in North London to immigrant parents, a chess prodigy by age five and wizard coder in his teens, he turned down a seven figure offer before turning 18 to feed his insatiable scientific curiosity at Cambridge. Later, he added a neuroscience PhD to his computer science skills to pursue the dream of artificial general intelligence, the ultimate goal being to unravel the mysteries of biology and theoretical physics and to usher in super-abundance. Alongside a small group of fellow travelers, that is the path he is still on, leading the AI research at Google, winning a Nobel Prize along the way, and imagining machines that will compound, or possibly supplant, the human understanding of the universe.

Space as a new compute layer. From Starcloud:

The round comes after the successful deployment of our first satellite, Starcould-1, a few months ago, which had the first @NVIDIA H100 on board and was the first to train an LLM in space. The funds will be used to develop our third satellite, which aims to be cost-competitive with Earth-based data centers in terms of AI inference cost.

Reading moral books doesn’t matter much if the later training regime teaches different habits.

From Tomek Korbak and team at OpenAI:

Can midtraining on docs about aligned AI bake in alignment priors for agents? We report an experiment where those priors are quickly washed away by RL and fail to generalize to agentic settings. But that cuts both ways: priors that AIs are misaligned fade too!