We aren’t even mining asteroids near Earth’s orbit. Space colonization is a ketamine dream. There’s no extraterrestrial economy. Earth is all we have. One pie.
A pie that includes sand which is now turned into GPUs that can solve complex problems described in English. Value that was unlocked fairly recently from “one pie”.
Of course: Everything is resource-constrained. That’s why it’s called economics.
The question was whether the “pie”—total economic output—has a meaningful upper bound on growth because we only have a whole planet full of resources to exploit as our minds and capabilities allow.
We don’t boost any authors/publishers/pieces. We don’t have any specific plan to monetize right now, but many AI-based products seem to work well as paid subscriptions vs using an advertising model.
In the United States, the working poor are considered deserving of their burdens in an immutable, moralizing, Calvinist way.
“They make bad choices.”
“They have bad culture.”
“They have bad genes.”
If there were theory that led to directly useful results (like, telling you the right hyperparameters to use for your data in a simple way, or giving you a new kind of regularization that you can drop in to dramatically improve learning) then deep learning practitioners would love it. As it currently stands, such theories don't really exist.
This is way too rigorous. You can absolutely have theories that lead to useful results even if they aren't as predictive as you describe. Theory of evolution for an obvious counterpoint.
There are strong incentives to leave theory as technical debt and keep charging forward. I don't think it's resentment of theory, everyone would love a theory if one were available but very few are willing to forgoe the near term rewards to pursue theory. Also it's really hard.
There are many reasons to believe a theory may not be forthcoming, or that if it is available may not be useful.
For instance, we do not have consensus on what a theory should accomplish - should it provide convergence bounds/capability bounds? Should it predict optimal parameter counts/shapes? Should it allow more efficient calculation of optimal weights? Does it need to do these tasks in linear time?
Even materials science in metals is still cycling through theoretical models after thousands of years of making steel and other alloys.
Many mathematicians are (rightly, IMO) allergic to assertions that certain branches are not useful (explicit in OP) and especially so if they are dismissive of attempts to understand complicated real world phenomema (implicit in OP, if you ask me).
Who is proud? What you are seeing in some cases is eye rolling. And it's fair eye rolling.
There is an enormous amount of theory used in the various parts of building models, there just isn't an overarching theory at the very most convenient level of abstraction.
It almost has to be this way. If there was some neat theory, people would use it and build even more complex things on top of it in an experimental way and then so on.
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