This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Or, as the author puts it, "we are at the tail end of the first wave of large language model-based AI... [it] ends somewhere in the next year or two with the kinds of limits people are running up against." I want to know only one thing, which is what gives him the confidence necessary to say that. If that one stat…
> This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Which is countered by...the assertion that it won't? LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots. > I want to know only one thing, which is what gives him the confide…
We found that extrapolating the performance given a few data points with smaller models is actually very accurate. That's how they determined hyper parameters, by tuning them on multiple smaller scale models and then extrapolating. So far, all those predictions were quite good.
Together with a bigger model, we also need more data to get better performance. If we add video and audio to the text data, we have still a lot more data we can use, so this is also not really a problem.
It would be very unexpected that those scaling laws are suddenly not true anymore for the next order of magnitude in model and data size.