My current research interests:
- alignment in systems which are complex and messy, composed of both humans and AIs?
- actually good mathematized theories of cooperation and coordination
- active inference
- bounded rationality
Research at Alignment of Complex Systems Research Group (acsresearch.org), Centre for Theoretical Studies, Charles University in Prague. Formerly research fellow Future of Humanity Institute, Oxford University
Previously I was a researcher in physics, studying phase transitions, network science and complex systems.
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Wiki Contributions
Central european experience, which is unfortunately becoming relevant also for the current US: for world-modelling purposes, you should have hypotheses like 'this thing is happening because of a russian intelligence operation' or 'this person is saying what they are saying because they are a russian asset' in your prior with nontrivial weights.
I expected quite different argument for empathy
1. argument from simulation: most important part of our environment are other people; people are very complex and hard to predict; fortunately, we have a hardware which is extremely good at 'simulating a human' - our individual brains. to guess what other person will do or why they are doing what they are doing, it seems clearly computationally efficient to just simulate their cognition on my brain. fortunately for empathy, simulations activate some of the same proprioceptive machinery and goal-modeling subagents, so the simulation leads to similar feelings
2. mirror neurons: it seems we have powerful dedicated system for imitation learning, which is extremely advantageous for overcoming genetic bottleneck. mirroring activation patterns leads to empathy
My personal impression is you are mistaken and the innovation have not stopped, but part of the conversation moved elsewhere. E.g. taking just ACS, we do have ideas from past 12 months which in our ideal world would fit into this type of glossary - free energy equilibria, levels of sharpness, convergent abstractions, gradual disempowerment risks. Personally I don't feel it is high priority to write them for LW, because they don't fit into the current zeitgeist of the site, which seems directing a lot of attention mostly to:
- advocacy
- topics a large crowd cares about (e.g. mech interpretability)
- or topics some prolific and good writer cares about (e.g. people will read posts by John Wentworth)
Hot take, but the community loosely associated with active inference is currently better place to think about agent foundations; workshops on topics like 'pluralistic alignment' or 'collective intelligence' have in total more interesting new ideas about what was traditionally understood as alignment; parts of AI safety went totally ML-mainstream, with the fastest conversation happening at x.
1Seems worth mentioning SOTA, which is https://futuresearch.ai/. Based on the competence & epistemics of Futuresearch team and their bot get very strong but not superhuman performance, roll to disbelieve this demo is actually way better and predicts future events at superhuman level.
Also I think it is a generally bad to not mention or compare to SOTA but just cite your own prior work. Shame.
2I'm skeptical of the 'wasting my time' argument.
Stance like 'going to poster sessions is great for young researchers, I don't do it anymore and just meet friends' is high-status, so, on priors, I would expect people to take it more than what's optimal.
Realistically, poster session is ~1.5h, maybe 2h with skimming what to look at. It is relatively common for people in AI to spend many hours per week digesting what are the news on twitter. I really doubt the per hour efficiency of following twitter is better than of poster sessions when approached intentionally. (While obviously aimlessly wandering between endless rows of posters is approximately useless.)
Corrected!
I broadly agree with this - we tried to describe somewhat similar set of predictions in Cyborg periods.
Few thoughts
- actually, these considerations mostly increase uncertainty and variance about timelines; if LLMs miss some magic sauce, it is possible smaller systems with the magic sauce could be competitive, and we can get really powerful systems sooner than Leopold's lines predict
- my take on what is one important thing which makes current LLMs different from humans is the gap described in Why Simulator AIs want to be Active Inference AIs; while that post intentionally avoids having a detailed scenario part, I think the ontology introduced is better for thinking about this than scaffolding
- not sure if this is clear to everyone, but I would expect the discussion of unhobbling being one of the places where Leopold would need to stay vague to not breach OpenAI confidentiality agreements; for example, if OpenAI was putting a lot of effort into make LLM-like systems be better at agency, I would expect he would not describe specific research and engineering bets
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Baraka: A guided meditation exploring the human experience; topics like order/chaos, modernity, green vs. other mtg colours.
More than "connected to something in sequences" it is connected to something which straw sequence-style rationality is prone to miss. Writings it has more resonance with are Meditations on Moloch, The Goddess of Everything Else, The Precipice.
There isn't much to spoil: it's 97m long nonverbal documentary. I would highly recommend to watch on as large screen in as good quality you can, watching it on small laptop screen is a waste.