Autonomous Systems @ UK AI Safety Institute (AISI)
DPhil AI Safety @ Oxford (Hertford college, CS dept, AIMS CDT)
Former senior data scientist and software engineer + SERI MATS
I’m particularly interested in sustainable collaboration and the long-term future of value. I’d love to contribute to a safer and more prosperous future with AI! Always interested in discussions about axiology, x-risks, s-risks.
I enjoy meeting new perspectives and growing my understanding of the world and the people in it. I also love to read—let me know your suggestions! In no particular order, here are some I’ve enjoyed recently
Ord—The Precipice
Pearl—The Book of Why
Bostrom—Superintelligence
McCall Smith—The No. 1 Ladies’ Detective Agency (and series)
Melville—Moby-Dick
Abelson & Sussman—Structure and Interpretation of Computer Programs
Stross—Accelerando
Graeme—The Rosie Project (and trilogy)
Cooperative gaming is a relatively recent but fruitful interest for me. Here are some of my favourites
Hanabi (can’t recommend enough; try it out!)
Pandemic (ironic at time of writing...)
Dungeons and Dragons (I DM a bit and it keeps me on my creative toes)
Overcooked (my partner and I enjoy the foody themes and frantic realtime coordination playing this)
People who’ve got to know me only recently are sometimes surprised to learn that I’m a pretty handy trumpeter and hornist.
My rough model of what’s going on (not published) is that ‘relevant effective evidence’ is what’s needed to succeed at a given subtask (of the cognitive kind that AI agents are being tested on here).
Relevant effective evidence is accumulated in pretraining (data is information is evidence!) as well as through in-context evidence-gathering activities [1] .
Generic webdata and the earlier corpora had some applicable data for these sorts of tasks. More recently, a greater fraction of the curated pretraining and posttraining data are dedicated to software engineering.
(It’s complicated by changes to posttraining, but certainly ‘effective training data’ were increasing roughly exponentially with training date for some time, which maps to the exponential ‘time horizon’ via subtask completion chance.)
This is all kind of annoying retrodiction but it adds up, at least in hindsight.
In-context evidence-gathering (i.e. exploration) competence comes from particularly generalisable learned heuristics, namely how best to experiment or try things out and interpret findings, but is still largely domain-specific.