For those in the niche of self-assembling structure, I hacked this simulator together https://t.co/ioCRgE8Qzw
It's a CTMC of transitions between quasi-static states in crystal growth, much like kTAM. And like kTAM it can build pretty much anything. E.g. ->
@maxhodak_ I suppose you could train a trial inclusion discriminator on early data, and treat your RCT as an evaluation of both discriminator and therapy jointly. Still clumsy, but maybe fits in the framework?
@apgox Tangentially, I am curious about how good a pseudo-replicator can get. Say a cluster of computers controlling replicating teleop robots. It’d be an awful lot safer
@francoisfleuret If we went ahead and spent 10% of the world GDP training a monolithic MLP on a million tokens of context, would it actually be bad? I’m not even sure.
@TaliaGraceSable If I want to be high effort about it, I'd say pick a number in the thousands, add up its digits, check if the ones digit of the result is less than 3.
@ZoldenGames If you don't mind saying, did you base your engine on any papers, projects, etc? I've been looking for a good starting point for a general purpose particle simulator.
@_AashishReddy I have, all the time. I've found gpt is maybe 80% reliable when I ask it for papers, but still has a tendency to make up titles. This is for proteomics / microbiome topics though, ymmv.
@ducx_du Do you have proposals you like for autoregressive models in latent space? I'd love to see anything like that, but defining the log-likelihood objective (without some horrifying VAE style latents) is intimidating.
@slimer48484 @argyros_selini It's a reasonably well known problem in RL fields, e.g. https://t.co/y0Q9UqIYEF. Technically it only applies to models that purely go through pre-training.