mannigfaltig

I doubt this will be a real problem because I've seen brilliant people excel in provincial universities with very little means and talent around. Internet education has gotten pretty good and probably reflects most of the experience of studying at a top university with the best researchers in the field. People might be 5% worse prepared. Nothing to be concerned about.

I still find it incredibly hard to tell whether this is overblown hype or legit scientific progress. There is no indication whatsoever that this approach scales to deep feature hierarchies and that is likely what you need to compete on hard tasks like classification on ImageNet. Given the amount of money at play (several hundred millions of dollars), writing 70 pages, making code publishable is certainly an obvious way to get the most out of the hype.

My guess would be that symmetry is a simple heuristic measure of physical fitness. Visual attraction is basically a strong regularizer that restricts the search space to phenotypes with particular traits. Asymmetry means that the joints wear out more quickly and muscles might not coordinate optimally leading to less strength and a reduced ability to hunt and to fight predators. AFAIK it is also a quite robust predictor of all kinds of diseases because it often means that the growth signalling is out of tune throughout the system. Visual selection basically performs environmental selection more immediately and more effectively: an asymmetric person might still survive, but its offspring has a lower overall chance to survive. The teaching signals of that are much weaker.

The problem is that anxiety is warranted in this case. You can’t do anything about solving the problem for everybody, but you can potentially save your own life:

- Work like a madman to get into the 1%.

- Move to a region that is self-sustaining and is little impacted by global warming.

- Buy guns, tools and a farm.

> evolution of evolvability and then a seemingly unrelated subject: the evolution of robustness.

Isn't evolvability only about robustness? What other criteria would improve evolution? Mutations just happen, so the question is how well the phenotype can deal with these mutations. If it can incorporate mutations well, it can tunnel to different useful phenotypes and therefore is robust.

I think, the main effect of sexual reproduction is that, much like GANs and competitive self-play, it creates species-internal competition: Both sexes need to impress, which makes cheating an obvious strategy (makeup, steroids, Shakespeare quotes, LISP etc., but many such examples can be found in the animal world), and hence both sexes also need to be able to detect cheating. Some species are rather asymmetric in that regard. For example, in humans it is mainly women who attract (they masquerade as fruits [make up is likely a cross-cultural phenomenon; and, well, breasts] tapping into male food gathering circuitry); men compete in hierarchies trying to impress and women select men from the top of the hierarchy. Complex dynamics emerging from this likely lead to the immense growth of the human cortex.

Sexual reproduction basically outsources some of the selection effort to the cognitive apparatus of the species itself, thereby introducing a massive amount of additional selection signals (mainly by the much increased necessity to model other minds, namely minds of the opposite sex). Many of these signals promote traits that are useful for survival (mainly intelligence and health).

That seems like a strange/unnecessary way to put it because DO is noise.

You can also call it just subgraph; not all of them, but exponentially many.

Wait, that can’t be wrong because that is literally what DO does. It is a convex hull regularizer around the network activations using noise. That is also why dropout does not solve susceptibility to adversarial examples: It merely extends the regions that the NN generalizes to outward; but that is limited because high-dimensional spaces are counter-intuitively large and the noise required to cover a descent fraction of the “unmapped” space would completely prevent learning. AFAIK, Yarin Gal merely provides a Bayesian interpretation of the noise.