Jakob Uszkoreit, Inceptive CEO and Transformer co-author, argues that scaling empirical deep learning is more practical than waiting for complete theoretical biological understanding.
Insight
Uszkoreit: Hardware efficiency is the only proven way to advance deep learning
“At the end of the day, in my mind, that's the one and only thing we know really works. If you want to push deep learning forward is to make it faster and more effective and more efficient on a given piece of Hardware.”
Insight
Uszkoreit: Transformer breakthrough was driven by accelerator hardware fit
“And if you want to look at, say, the biggest differences, for example, between the transformer, as it was described in the attention is all you need paper, and some of its ancestors, like this decomposable attention model, the big difference is just that the t…”
Opinion
Uszkoreit: GPUs are not at the sweet spot for large-scale deep learning
“I don't think GPUs are at the sweet spot when it comes to large-scale deep learning with respect to exactly those trade-offs, and so it may very well be that if we actually try these combinations, we might actually even quickly find something that's better.”
Insight
Uszkoreit: Community optimism drove Transformer adoption and success
“The other main contributor, I think, to the success of this architecture was optimism and hope. So suddenly you were in a situation where, for whatever reason, a bunch of things that people tried with this started to work, and then more started to work, and th…”
Insight
Uszkoreit: Training on synthetic data works by amortizing generation compute
“And ironically, and this comes back to a question that many people ask, I think around, does it make any sense to train on generated data? Because information theory, family information theory, very clearly says, nope, you're not going to get more information …”
Opinion
Uszkoreit: Test-time search is effective but clunky and hard to optimize
“I think it's super effective in test time search. I do think it's clunky because it's not something that you can easily end to end optimize. So, right, basically this is also what I'm, what I was trying to get at a little bit maybe with saying, well, some of t…”