[darth_avocado]

Correct. That is what I was trying to hint at. Yes, massive compute is needed to train ai, but it isn’t the only thing. A lot of research and experimentation goes into moving the marker just a little bit. Innovation can’t be forced into weekly sprints, it takes its own time.

[paytonjjones]

Research and experimentation on neural nets has been going on since the 70s (arguably much earlier even), but the lions share of capability changes has all been in the last couple years.

Scale was really the unlock; the new pre and post training techniques and architectures are very cool and useful but they definitely aren't the differentiators when comparing to the previous era of NLP.

[ehnto]

I think the unlock only happens once though. I think that's where people are misled at the moment, the technology was there but required huge compute and data ingest to show improvement, but we have done that now. What's next for a giant leap is not more compute, and what new data we can provide now pales in comparison to that first ingest.

[emp17344]

I think the “unlock” is that AI firms were given trillions of dollars to discover new techniques. In fact, there are very few industries where a sudden influx of that much money would not lead to rapid advancements. It’s not really unique to the AI field.

[paytonjjones]

Your timeline is a bit backwards: they were given trillions of dollars only after they'd demonstrated a few pretty remarkable advancements.

The fact that their advancement suggested that pouring more compute would continue working was also especially attractive to investors: it made a massive R&D budget feel like less of a risk.

[8note]

which non-transformer neural networks are matching frontier performance using compute scale?

[paytonjjones]

BERT is a transformer! The unlock happened within transformers, yes, but they were not exactly super new or innovative architectures at that time. The scale was the main innovation that brought us from BERT to today's frontier.