zlacker

>>Tenoke+(OP)
I think we've actually had capable AIs for long enough now to see that this kind of exponential advance to AGI in 2 years is extremely unlikely. The AI we have today isn't radically different from the AI we had in 2023. They are much better at the thing they are good at, and there are some new capabilities that are big, but they are still fundamentally next-token predictors. They still fail at larger scope longer term tasks in mostly the same way, and they are still much worse at learning from small amounts of data than humans. Despite their ability to write decent code, we haven't seen the signs of a runaway singularity as some thought was likely.

I see people saying that these kinds of things are happening behind closed doors, but I haven't seen any convincing evidence of it, and there is enormous propensity for AI speculation to run rampant.

>>Vegeno+xv3
Disagree. We know it _can_ learn out of distribution capabilities based on similarities to other distributions. Like the TikZ Unicorn[1] (which was not in training data anywhere) or my code (which has variable names and methods/ideas probably not seen 1:1 in training).

IMO this out of distribution learning is all we need to scale to AGI. Sure there are still issues, it doesn't always know which distribution to pick from. Neither do we, hence car crashes.

[1]: https://arxiv.org/pdf/2303.12712 or on YT https://www.youtube.com/watch?v=qbIk7-JPB2c