AI Research Blog

I'm working on a fairly detailed post about linguistic biases and how to formalize them as open-ended tasks, but it isn't quite ready yet. In the meantime, using this blog more as a journal entry, I'll try and give a summary of my thoughts and research directions. Ideally, what I want to do is create a series of curriculum that are open-ended and teach an agent "intelligence". Because that's pretty ambitious, I'm okay with simply some open ended curriculum that teach some important aspects of intelligence and help augment the curriculum of language models in various ways. There's a few reasons this is still my research direction. My main point is safety : (I'm still doing a lot of reading about AI safety and so my thoughts on these topics is constantly changing and I'm not on expert on them. This is just my current impressions) One way to create AI would be to feed a model very high resolution EEG signals and have it predict what the underlying...

  In general, I’ve been thinking about two main questions: Good ways of conducting research Formalizing the notion of “breaking language learning into synthetic tasks that capture most of the problem” 1 I’ve received a few pieces of advice for research processes that I’ve been reflecting on. There are two ways to go about “getting something to work”. One is to fix the task, then throw every tool you can think of until your model does what you want. The other way is to use a standard algorithm on fairly default settings, and keep adjusting the task, problem setup, etc. until things work. Either approach is warranted in different cases. The first makes more sense on clearly defined tasks we know we want to do well on but haven’t managed to do well yet. The second is much better for exploratory work (“trying to take a vague idea of behavior you want to see and condensing that into a formal setup”), as it helps you keep a clearer perspective and doesn’t muddy the waters of potential th...