michael_nielsen

In science, the most important progress often comes from better questions and better ideas, not better equipment (in this case, more computational power and data).

In biology, the government-run genome project cost 10 times as much as Venter's private project. Much of the reason for the cost difference is that Venter adopted a clever hack (pairwise end shotgun sequencing) the government project didn't use until the end.

Why is big science so seductive? In part, because it seems guaranteed: you can plan, you can see success from the start. That's much less nerve-inducing (and _seems_ less uncertain) than needing to have clever ideas along the way.

Back to neural nets: a danger in scaling up your computational power is that you start to focus _only_ on questions that require that computational power. You hire specialists who thrive in that environment, but who aren't so good at playing with basic, fundamental questions...

... and your culture starts to tilt that way, driving out people who do like to play with basic, fundamental questions.

Take all this with a grain of salt. Neural nets are a side interest, not my main interest. Maybe I'm wrong. But I don't think so. This dynamic has played out in genome sequencing, in particle physics, & in many other areas. Big science is attractive, but often small science wins

Does this mean computational power or big data is useless? No, of course not. There are important questions that can likely only be addressed that way. But if you want to work on AI, it seems to me a mistake to be too focused on the need for lots of data and lots of compute.