circuithunter

This is a really nice paper which asks some critical questions for the future of NAS research.

However, it's important to note that this paper doesn't show that NAS algorithms as originally designed, with completely independent training of each proposed architecture, are equivalent to random search. Rather, it shows that weight sharing, a technique introduced by ENAS [1] which tries to minimize necessary compute by training multiple models simultaneously with shared weights, doesn't outperform random baselines. Intuitively, this makes sense: weight sharing dramatically reduces the number of independent evaluations, and thereby leads to far less signal for the controller, which proposes architectures.

The paper itself makes this fairly clear, but I think it's easy to misinterpret this distinction from the abstract.

[1] ] H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean. Efficient neural architecture search via parameter sharing. ICML, 2018.

Machine translation is a big one. See the recent NY Times feature [1] and the arxiv paper [2]. Automated image captioning is another (used extensively by Facebook).

[1] https://www.nytimes.com/2016/12/14/magazine/the-great-ai-awa...

[2] https://arxiv.org/abs/1609.08144

There's actually quite a bit of evidence suggesting that brains, both behaviorally and mechanistically, are Bayesian [0].

As for your second point, assuming that humans are Bayesian, there are many reasons why people would have variability in their mathematical ability, including different priors and differences in the ability to estimate posteriors.

[0] https://scholar.google.com/scholar?q=brain+bayesian&hl=en&bt...