@distributionat Kinda cool bc "試" means "try". The unembedding for "試" and "try" must be quite close, which is representationally/semantically desirable, but awkward for generation.
@ChuckBaggett@_beenkim I'm actually quite upset that there's not a <disable-link-clicking-unless-I-really-know-what-I'm-doing> functionality in gmail. That added bit of friction is a good forcing function for staying vigilant---especially when we're super distracted.
@prajdabre > "I'll get some problems labeled and fine tune the model" is a perfectly good starting answer
and a good research practice. Do the dumb simple thing first!
@eW8fkgAM52GS9xW@dwarkesh_sp I'm concerned about power inequality in a finite-resource world. While I may or may not care how wealthy someone else is (good for you btw!), I certainly do care how powerful people influence the world I live in. Thus my comment about agency and wanting my actions to matter c:
@wavage_ the analogy between GLP1 and LLM isn't perfect, but I'm in full support of deliberately overcoming <physically/cognitively painful problems> as a means of character-building and giving us, as Asimov puts it, a feeling of power c:
@andrewgwils NFL is useful!
It helps me sleep at night bc it's how I convince myself that finite capacity NN + shitty SGD optimization is a feature, not a bug c:
It's a great reminder that the universal function approximator narrative that was popular a ~decade ago is pure bait >:c
@willdepue@dwarkesh_sp@tszzl I think LLMs have no "taste" for what is a deep vs superficial insight. Imo a deep insight helps me decide next steps, etc. Talking to colleagues leads to more of such insights. But LLMs are great for translating my colleagues' deep insights into something my brain groks c: