[wijwp]

> Not to sound like a tin-foil hat but odds of achieving something like this increase by the day.

Where do you get this? The limitations of LLMs are becoming more clear by the day. Improvements are slowing down. Major improvements come from integrations, not major model improvements.

AGI likely can't be achieved with LLMs. That wasn't as clear a couple years ago.

[drodgers]

I don't know how someone could be following the technical progress in detail and hold this view. The progress is astonishing, and the benchmarks are becoming saturated so fast that it's hard to keep track.

Are there plenty of gaps left between here and most definitions of AGI? Absolutely. Nevertheless, how can you be sure that those gaps will remain given how many faculties these models have already been able to excel at (translation, maths, writing, code, chess, algorithm design etc.)?

It seems to me like we're down to a relatively sparse list of tasks and skills where the models aren't getting enough training data, or are missing tools and sub-components required to excel. Beyond that, it's just a matter of iterative improvement until 80th percentile coder becomes 99th percentile coder becomes superhuman coder, and ditto for maths, persuasion and everything else.

Maybe we hit some hard roadblocks, but room for those challenges to be hiding seems to be dwindling day by day.

[materiallie]

I think benchmark targeting is going to be a serious problem going forward. The recent Nate Silver podcast on poker performance is interesting. Basically, the LLM models still suck at playing poker.

Poker tests intelligence. So what gives? One interesting thing is that for whatever reason, poker performance isn't used a benchmark in the LLM showdown between big tech companies.

The models have definitely improved in the past few years. I'm skeptical that there's been a "break-through", and I'm growing more skeptical of the exponential growth theory. It looks to me like the big tech companies are just throwing huge compute and engineering budgets at the existing transformer tech, to improve benchmarks one by one.

I'm sure if Google allocated 10 engineers a dozen million dollars to improve Gemini's poker performance, it would increase. The idea before AGI and the exponential growth hypothesis is that you don't have to do that because the AI gets smarter in a general sense all on it's own.