zlacker

>>Tenoke+(OP)
The story is entertaining, but it has a big fallacy - progress is not a function of compute or model size alone. This kind of mistake is almost magical thinking. What matters most is the training set.

During the GPT-3 era there was plenty of organic text to scale into, and compute seemed to be the bottleneck. But we quickly exhausted it, and now we try other ideas - synthetic reasoning chains, or just plain synthetic text for example. But you can't do that fully in silico.

What is necessary in order to create new and valuable text is exploration and validation. LLMs can ideate very well, so we are covered on that side. But we can only automate validation in math and code, but not in other fields.

Real world validation thus becomes the bottleneck for progress. The world is jealously guarding its secrets and we need to spend exponentially more effort to pry them away, because the low hanging fruit has been picked long ago.

If I am right, it has implications on the speed of progress. Exponential friction of validation is opposing exponential scaling of compute. The story also says an AI could be created in secret, which is against the validation principle - we validate faster together, nobody can secretly outvalidate humanity. It's like blockchain, we depend on everyone else.

>>visarg+p23
Best reply in this entire thread, and I align with your thinking entirely. I also absolutely hate this idea amongst tech-oriented communities that because an AI can do some algebra and program an 8-bit video game quickly and without any mistakes, it's already overtaking humanity. Extrapolating from that idea to some future version of these models, they may be capable of solving grad school level physics problems and programming entire AAA video games, but again - that's not what _humanity_ is about. There is so much more to being human than fucking programming and science (and I'm saying this as an actual nuclear physicist). And so, just like you said, the AI arm's race is about getting it good at _known_ science/engineering, fields in which 'correctness' is very easy to validate. But most of human interaction exists in a grey zone.

Thanks for this.

>>nikisi+ed3
> that's not what _humanity_ is about

I've not spent too long thinking on the following, so I'm prepared for someone to say I'm totally wrong, but:

I feel like the services economy can be broadly broken down into: pleasure, progress and chores. Pleasure being poetry/literature, movies, hospitality, etc; progress being the examples you gave like science/engineering, mathematics; and chore being things humans need to coordinate or satisfy an obligation (accountants, lawyers, salesmen).

In this case, if we assume AI can deal with things not in the grey zone, then it can deal with 'progress' and many 'chores', which are massive chunks of human output. There's not much grey zone to them. (Well, there is, but there are many correct solutions; equivalent pieces of code that are acceptable, multiple versions of a tax return, each claiming different deductions, that would fly by the IRS, etc)

>>m11a+z44
I'm not sure where construction and physical work goes into your categories. Process and chores maybe. But I think AI will struggle in the physical domain - validation is difficult and repeated experiments to train on are either too risky, too costly or potentially too damaging (i.e. in the real world failure is often not an option unlike software where test benches can allow controlled failure in a simulated env).