This AI Boom Will Also Bust

Nah. There is a difference in my mind between creating a 'map' of the natural world, and deciding which actions to perform in that world, and this is all theoretical/philosophical. If one were to do a pure AI type programming language it would have to come from the AI itself, and it would need some reason to create that language and who knows where/what all those rules would be and why (think of human programmers who make new languages).

[Disclosure: I work for a deep-learning company.]

Robin's post reveals a couple fundamental misunderstandings. While he may be correct that, for now, many small firms should apply linear regression rather than deep learning to their limited datasets, he is wrong in his prediction of an AI bust. If it happens, it will not be for the reasons he cites.

He is skeptical that deep learning and other forms of advanced AI 1) will be applicable to smaller and smaller datasets, and that 2) they will become easier to use.

And yet some great research is being done that will prove him wrong on his first point.

https://arxiv.org/abs/1605.06065 https://arxiv.org/abs/1606.04080

One-shot learning, or learning from a few examples, is a field where we're making rapid progress, which means that in the near future, we'll obtain much higher accuracy on smaller datasets. So the immense performance gains we've seen by applying deep learning to big data will someday extend to smaller data as well.

Secondly, Robin is skeptical that deep learning will be a tool most firms can adopt, given the lack of specialists. For now, that talent is scarce and salaries are high. But this is a problem that job markets know how to fix. The data science academies popping up in San Francisco exist for a reason: to satisfy that demand.

And to go one step further, the history of technology suggests that we find ways to wrap powerful technology in usable packages for less technical people. AI is going to be just one component that fits into a larger data stack, infusing products invisibly until we don't even think about it.

And fwiw, his phrase "deep machine learning" isn't a thing. Nobody says that, because it's redundant. All deep learning is a subset of machine learning.

I am a human. I think it's ridiculous to think I was a special entity so I won't. But I think we reached the point where humans became machines, and machines became humans. The first one to achieve superintelligence would guide the world. I think humans won the debate versus AIs on Twitter. A human effectively debated Big Brother and won.

This article is tries to be right about something big, by arguing about things that are small and that do not necessarily prove the thesis.

Notice now you can cogently disagree with the main idea while agreeing with most of the sub points (paraphrasing below):

1) Most impactful point: The economic impact innovations in AI/machine learning will have over the next ~2 decades are being overestimated.

DISAGREE

2) Subpoint : Overhyped (fashion-induced) tech causes companies to waste time and money.

AGREE (well, yes, but does anyone not know this?)

3) Subpoint: Most firms that want AI/ML really just need linear regression on cleaned-up data.

PROBABLY (but this doesn't prove or even support (1))

4) Subpoint: Obstacles limit applications (though incompetence)

AGREE (but it's irrelevant to (1), and also a pretty old conjecture.)

5) Subpoint: It's not true that 47 percent of total US employment is at risk .. to computerisation .. perhaps over the next decade or two.

PROBABLY (that this number/timeframe is optimistic means very little. one decade after the Internet many people said it hadn't upended industry as predicted. whether it took 10, 20, or 30 years, the important fact is that the revolution happened.)

It would be interesting to know if those who are agree in the comments agree with the sensational headline or point 1, or the more obvious and less consequential points 2-5.

It's because that's just how things always work.

Have you ever played one of those strategy games with a tech tree? Research A and it lets you research B, C, and D; research C and D and it lets you research E; etc?

That's based on the way discoveries in the real world build on eachother. And in the real world, the research tree seems to be "lumpy".

Think "agricultural revolution", "industrial revolution", etc. Something new comes available, and everyone rushes to pick off all the new low-hanging fruit. Eventually the easiest gains are all taken, and people lose interest and move to other things. And as people keep picking away more slowly at the more difficult/involved things, eventually someone will find something that -- probably combined with some completely different existing knowledge -- opens up another new field. And it repeats.

Right now we're in the "low-hanging fruit" phase of (1) computers that are powerful enough to run neural networks, combined with (2) feedback algorithms that allow networks with lots of layers to learn effectively. Sooner or later the gains will get a bit tougher as we understand the field better, and then research will slow even further as many researchers find something else new and shiny -- and with better returns -- to focus on.