At least two potentially important algorithmic improvements had papers out this week. Both fall under ‘this is a well-known human trick, how about we use that?’ Tree of Thought is an upgrade to Chain of Thought, doing exactly what it metaphorically sounds like it would do. Incorporating world models, learning through interaction via a virtual world, into an LLM’s training is the other. Both claim impressive results. There seems to be this gigantic overhang of rather obvious, easy-to-implement ideas for improving performance and current capabilities, with the only limiting factor being that doing so takes a bit of time.
That’s scary. Who knows how much more is out there, or how far it can go? If it’s all about the algorithm and they’re largely open sourced, there’s no stopping it. Certainly we should be increasingly terrified of doing more larger training runs, and perhaps terrified even without them.
The regulation debate is in full swing. Altman and OpenAI issued a statement reiterating Altman’s congressional testimony, targeting exactly the one choke point we have available to us, which is large training runs, while warning not to ladder pull on the little guy. Now someone – this means you, my friend, yes you – need to get the damn thing written.
The rhetorical discussions about existential risk also continue, despite moral somewhat improving. As the weeks go by, those trying to explain why we might all die get slowly better at navigating the rhetoric and figuring out which approaches have a chance of working on which types of people with what background information, and in which contexts. Slowly, things are shifting in a saner direction, whether or not one thinks it might be enough. Whereas the rhetoric on the other side does not seem to be improving as quickly, which I think reflects the space being searched and also the algorithms being used to search that space.
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