[rsp1984]

This fits perfectly into the narrative of yesterday's discussion on HN [1].

Deep Neural Nets are somewhat of a brute force approach to machine learning. Training efficiency is horrible as compared with other ML approaches, but hey, as long as we can trade +5% of classification performance for +500% of NN complexity and throw more money at the problem, who cares?

I see a dystopian future where much better and much more efficient approaches to ML exist, but nobody's paying attention because we have Deep Neural Nets in hardware and decades of infrastructure supporting it.

[1] https://news.ycombinator.com/item?id=21929709

[justicezyx]

Well, if a better algorithm cannot beat DNN in a realistic product setting, then how can you say its better after all?

If the algorithm is indeed better, how can DNN dominates and turn into a dystonia...

[someguyorother]

What economists call path dependence.

The alternative algorithm would be better than DNN if the same amount of effort was put into creating special-purpose hardware, libraries, and so on; but in the dystopia, it's not fully refined DNN vs fully refined alternative algorithm, but fully refined DNN vs alternative algorithm with hardware and software optimized for DNN.

The alternative algorithm always looks unappealing because the playing field historically favors DNN, and so doesn't take off in the dystopia.

[justicezyx]

You are referring back to OP's own reasoning fallacy...

DNN emerges out from being an underdog. Its superiority was proven by technology and economy.

What you said is of course not wrong, but they can never be proven right. As immediately you switch the role, your argument then favors the other one.

[sbierwagen]

One example would be ternary logic, which more efficiently represents numbers: https://en.wikipedia.org/wiki/Radix_economy