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Comment by p1esk | Hacker News Reader

The list might grow slowly, but the last item on it - ML - grows like crazy right now. It's not unreasonable to expect that in 20 years the vast majority of all computation (from tiniest IoT devices to largest supercomputers) will be running ML models (from simplest classifiers to whole brain simulations).

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It's pretty unreasonable to expect that if you're aware of what AI winter is, and the fact that we're probably upon the cusp of another one: https://en.wikipedia.org/wiki/AI_winter

Once everyone realizes every practical use of their AI technology is more than adequately met by conventional code, and that there's no grand breakthrough into general artificial intelligence coming anytime soon, the hype cycle will end.

> Once everyone realizes every practical use of their AI technology is more than adequately met by conventional code

I'm all for skepticism for the current hyperbole, but there's no need to be hyperbolic in the other direction.

There are some applications in which deep learning really does work better than alternatives. The 2018 Gordon bell prize, after all, went to a team that did deep learning on Summit for climate analysis.

There is a nontrivial list of applications that you would have a hard time convincing experts that they would be better off with conventional code.

I think we're set for an AI fall, not winter. There are truly some techniques for which current ML implementations work quite well that cannot be adequately met using conventional code. Speech recognition, machine translation, and image processing are the main examples.

Can you name an existing, large business which depends on ML for its existence?

Google.

All of Google's market advantage happened before their ML kick, and none of their continued success can be ascribed to it.

You can even argue for the opposite, since they went all in into ML, the product quality has at best stagnated.

Their product isn’t search, it’s tsrgetted ads. Have the ads gotten worse?

They have not become better (in my experience). The ads are still mostly irrelevant for me. (With no anti-tracking attempted by me.)

I don't really want to get into an argument over definitions, but IMO pagerank is pretty obviously machine learning.

That was my intention when writing the reply, yeah.

It's like asking "name a large business which depends on the internet for its existence" back in 1995.

A better question to ask back then would have been "which large business will depend on the internet in 2015".

Note that the internet in 1995 was pretty bad (in terms of applications and from technical point of view), and the hype led to dot com crash 5 years later. Yet it's hard to overestimate the importance of the internet in today's world.

not for its existence, but the USPS would have a much harder time routing mail without ML based recognition of handwritten addresses.

Fair Isaac Corporation

This is the only acceptable answer here, and of course gradient boosted decision trees don't require special hardware, contra the original article on general purpose computing.

incidentally, AMD actually advertised their latest architecture as using "neural networks" for branch prediction. (Now IIRC it was actually just a linear model, aka a neural net with one layer.)

So if that technology were to catch on, a pedant could argue that most computing workloads really are machine learning.

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