Current projects

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 in more text-like and system-like directions and somehow get around the issue of interpretability (the fact you can easily see what the creatures are doing is one of the main reasons he currently focuses on locomotion, wheras settings with language, programs, and theorems get really hard to understand what's going on, which makes it hard to diagnose).

The two directions I'm currently pushing in are:

- An economy/industry sim, where individual products are theorems (using metamath). The goal is to try and have some open-ended economy type behavior that matches many of the empirical laws of innovation. The main goal is to have drift: I don't actually really care which particular theorems it focuses on, I just want the economy sim to value different things and continually produce and invent new things and never really stagnate. 

- Discovering interesting systems: The goal is have an agent working in Conways Game of Life that tries to discover interesting things. To do this, I'm having one agent try and produce things where it's difficult to predict what it'll do, and another agent try and predict what'll happen. This is tricky to balance right because if the agent is too expressive it can just learn the rules, so it has to be forced to compress it in some way, but I think there's ways to get around this. If I can get agents discovering interesting things, that acts as a good measure for an interesting system. Then I can generate lots of systems (mostly cellular automata at first), and give them to an agent, and see how it reacts to those systems. 

Part of this also involves prototyping systems to get a better feel for the design space, because I'm still a bit of a game design noob. I've also been reading Rules of Play and have found it pretty informative.

Comments

Post a Comment

Popular posts from this blog

Starting up this blog again (MATS)

I’ll be attending MATS , studying mechanistic interpretability with  Adrià Garriga-Alonso  as my mentor. I’ve been spending the last few months skilling up on this, and the program starts on Monday. I think doing a weekly/biweekly blog post would be useful for documenting what I learn. Broadly, it’s been interesting learning about mech interp. It’s a field where you rarely have concrete proofs, instead you just find lines of evidence for/against a claim and try to build arguments based on those. Those arguments may be later overturned, that’s okay. This means that your “results” are less what theories you have, and more about what evidence you found. The main areas I’m interested in (subject to change) are: (I’ll talk about and explain these more in later posts if I end up working on them) - Mechanistic Interpretability of the steering vectors learned by Contrastive Activation Adding - Implementing Activation Adding in some of the popular chatbot uis (for example, emotion cont...
Read more

Open-ended concept creation through scaffolding

(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...
Read more