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Yeah, I think you can't just throw money randomly at problems and expect results unless you know a line of attack that can get you all the way. OpenAI chose the
by rakejake 19d ago
Yeah, I think you can't just throw money randomly at problems and expect results unless you know a line of attack that can get you all the way. OpenAI chose the line of attack only after it became known to them via rumors. They "front-ran" the researchers.
- dcre 19d agoWorth noting they claim they did not choose the line of attack. Of course we don’t know whether that is true.
- rakejake 19d agoPlausible deniability - The line of attack is in their sessions/prompts data. Just make the prompt pointed enough that the search space is tractable and use your ginormous compute. > "Of course we don’t know whether that is true" Yep. Who is verifying these claims? We all know how trustworthy Altman & Co are.
- freejazz 19d agoYeah, they didn't choose the line of attack, the person they copied it from did..
- cmiles8 19d agoYes. What the headlines hailed as an AGI discovery the facts show more to be someone spending years mining for gold, rumor gets to OpenAI that there might be gold in this specific place, they mine there and instantly discover gold, then tell the world they’ve developed the worlds best gold finding/mining machine. Separate from all the allegations of more nefarious actions and ethical issues, that’s the most charitable version of what happened here.
- Romario77 19d agothey threw it on all the millenial math problems (I think there are 6 at this point unsolved, well, 5 now). And according to them at some point they saw that one was close to being solved, so they pointed all the agents at it. The same thing happens to humans - at this time there are no simple problems left, so solving the hard ones requires using prior knowledge and attempts at solving things.
- nrdvana 19d agoYeah but the one they decided the AI was close to solving may have been so because the researchers' progress on this problem became part of the training data for that AI...
- whimsicalism 19d agobut the researchers were also largely relying on AI
- pphysch 19d agoIn the same way you rely on a keyboard or touchscreen to type this comment. It doesn't mean the tool is the brain behind the work.
- whimsicalism 19d agofrankly don’t know how to reply to these sorts of comments anymore
- pphysch 19d agoThat's usually a good sign you are on shaky ground!
- logancbrown 19d agoObvious false analogy in your earlier argument.
- buellerbueller 19d agoNo so obvious to this guy.
- whimsicalism 19d agoit is truly not obvious to you why keyboard isn’t a good analogy for LLM?
- vouaobrasil 19d agoKeyboards don't suggest chains of reasoning or words to type. When I press the K key, I know exactly what will happen. It's just a translation layer that gives an output known ahead of time and thus does not impinge upon the creativity of putting words together. A better example would be playing chess against a player slightly stronger than me and using a chess computer to suggest some good moves. I could win, but it certianly wouldn't be just my brain that wins. It would be an amalgamation of my brain with a machine that suggests good moves. One cannot simply reason by analogy.
- eieje1 19d agoYou can’t do anything novel with these models from scratch and let it fly. I’ve observed something over the past few months Work on something novel -> llm is kinda useless and low value-add -> Keep at it and in the process feed it more information -> keep doing this periodically -> a few months go by and you realise the model outputs are almost like-for-like regurgitations of what was inputted in some prior period. Once it’s accumulated new info can it produce something automated that is somewhat useful? Sure. But by itself - absolutely not. I clearly see humans will be needed - the best ones that is. For ‘rote work’ and stuff that is not IP sensitive firms will be ok with employees putting that as inputs into models. But I’d wary about trusting the labs. They will push the letter of the law to the max. Personally I’ve stopped doing anything novel with these models. If I do use a model on something adjacent but not totally novel I have to craft the inputs in a strategic way not to give much away. I’d wager firms will soon realise this and that growth rate of revenues of the frontier labs will become questionable. The economic cost that firms have brought out thus far is only financial. There’s a whole bunch of other costs people aren’t talking about.
- vonneumannstan 19d agoThis doesn't follow for me. There are what, Dozens or Erdos tier problems that got solved with no progress for decades? How does that factor in to your view?
- fc417fc802 19d agoIIUC the argument is that while unsolved there was much work done on them that shows up in the training data. The idea being that the LLM is limited to a small amount of inference over externally supplied data.
- eieje1 19d ago[dead]
- vonneumannstan 17d agoAnd yet the best humans could not use that same available data to solve the problems.
- chermi 19d agoAlmost but not quite I think. You can throw money at parts of problems. I think it's helpful to think it kind of like supercomputer MD/MC or electronic structure calculations. A tool that can get you valuable answers but not necessarily aid understanding. Simulations can be used to aid understanding also, and are integral to theory development. In the same way the approach to this result is.