AI Research Blog

Part of the reason that open-endedness research is so difficult is that many of the things AI agents create are weird . They aren't constrained to our ways of thinking, and so they'll find solutions that are really hard for us to understand. This makes it very difficult to diagnose where things have gone wrong, because you can hardly even tell what is going on. Maybe a system developed an entire culture and simulated an entire universe inside that uninterpretable mess of symbols. Maybe it's just utilizing noise and random combinations because that's easier than designing small things and the simulation favors that. When we can't distinguish between these two cases, we have a bad problem and our research cannot go to space today. So how do we fix this? One answer is to just try and figure out what's going on anyway. I think that's a valid approach, and some of that will be needed. See the fantastic Circuits/Reverse Engineering Neural Networks work for some e

I'm generally working in the area of trying to merge open-endedness, artificial creativity, and complex systems. I may update this post occasionally as I learn things, and will probably shift projects occasionally if things don't pan out. I'll try to have a policy of openly sharing my ideas because I don't care if I'm scooped. I just want cool stuff to be built, I don't care who does it (though don't worry, when I collaborate with others I don't use this policy because I understand others feel differently). My goal is to figure out if there's a way to make systems generate open-ended content that seems "human culture-like" in it's form. While I like Stanley's approach and will use many of the insights of POET and it's follow ups, just body locomotion seems like it'll cap out in a way that's distinct from culture. Maybe there's a way to transfer via some reduction, but I'd like to see if it's possible to push

(The scholars program is over, but I'm going to continue doing independent AI research for at least a year, as I continue to build my research chops. My posts might be less frequent, as I also spend some of my time now doing game design, see my other blog ) Most of my research direction is trying to determine if it's possible to create a language model with very little (or no) training data. Ideally, it should still have scaling laws. If we could do this, we could arbitrarily scale our models to continually increase capabilities, and it wouldn't be limited by the presence of data. Yet, in some ways this is non-sensical. The set of all distributions is very large, so a single model cannot "learn" them. Thus, it must focus on learning a subset of all distributions. Somehow we'd need a way of specifying a distribution that isn't too large to be intractable, while still containing human language and most of the things we care about. And even if we did that, th