Oliver—or call me Oly: I don’t mind which!
I’m particularly interested in sustainable collaboration and the long-term future of value. Currently based in London, I’m in my early-ish career working as a senior software engineer/data scientist, and doing occasional AI alignment work with SERI.
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! Recently I’ve enjoyed
Ord—The Precipice
Pearl—The Book of Why
Bostrom—Superintelligence
McCall Smith—The No. 1 Ladies’ Detective Agency
Abelson & Sussman—Structure and Interpretation of Computer Programs
Stross—Accelerando
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.
This is a fantastic point well articulated, reminiscent of some conversations we had a few months ago at Lightcone.
I think we probably agree on what things there actually are, but I think this particular definition of ‘general purpose search’ is slightly too general to be a most useful pointer/carving.
This because it seems to include things like matrix inversion for least-squares solutions (unless ‘from a fairly broad range of possible problems/goals’ is taken to preclude this meaningfully?) which I deem importantly different. I’d class matrix inversion least-squares as a (powerful) heuristic[1] (a ‘proposal’ in my deliberation terminology), but not as (proper) search itself.
I think it remains useful to distinguish algorithms which evaluate/promote or otherwise weigh proposals[2]. This is what I’ve started calling ‘proper deliberation’ and it’s generally what I mean when I talk about search.
In the case of applying matrix inversion to ordinary least squares, for me, the ‘general deliberation’ consists of something like
noticing the relevant features of the problem (this is ‘abstraction/pattern-matching magic’)
cognitively retrieving the OLS abstraction and matrix-inversion as a cached heuristic (this is ‘propose’)
thinking ‘yes, this will work’ (this is ‘promote’)
applying matrix inversion to solve
A clever/practised enough deliberator does steps 1, 2 and 3 ‘right’ and doesn’t need to iterate for this particular problem (my point here is that if your heuristics are good enough you can deliberate with only one proposal and say ‘yep, good enough, let’s go’). But counterfactually step 2 might make various alternative proposals, or step 3 might think ‘actually there are too many dimensions in this case for inversion to be tractable’ or something, and thus there’s an evaluation and an internal update.
Peter Barnett and Ian McKenzie coined ‘God-level heuristic’ for really solid mathematically-justified heuristics like this, which I quite like
I don’t require this to be a ‘full consequentialist model-based valuation’, but that would be one example. See my deliberation simple examples for less sophisticated versions which are quite pervasive and nevertheless embody the ‘propose;promote’ breakdown