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Comment by _bxg1 | Hacker News Reader

Personally I think deep-learning is a bubble, and it will soon collapse to its natural place in computer science. Which is not to say that it's a fad that will disappear, only that it will retreat to being just a regular tool among the many tools we have for solving different kinds of problems. Its inscrutable nature is definitely problematic for some use-cases, and not so problematic for others.

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I've been doing the data thing for a while. During one of my defenses of R, someone brought up that R was a black hole. That if you programmed in R, you were a user who just filled in the correct function arguments and it just spit out the answer. And that was when my thoughts on machine learning changed.

The vast majority of us are users. We massage the data to be in a certain shape, then feed it through a machine that someone else created. We can change the parameters. We can change the data. But few of us are going to look in to the code of a random forest function.

I've switched tracks and started doing web development. Playing with the hyper parameters in machine learning is no different than changing the feel of a drop down by changing the colors, fonts and other things to fit a certain aesthetic.

I could be wrong, but I have yet to meet anyone that has done anything besides use packages created by others to call themselves data scientists. I think that opens it up to becoming just another tool no different than Excel.

years ago on the first ML hype wave I completed the excellent MOOC by Andrew Ng. In that course, he did go through the math and it was helpful to me to understand what was going on under the hood, but even then the value of ML wasn't understanding what it was doing, but understanding if your model was doing something well. I think your take that using packages created by others will be mostly what we do moving forward, and that's also true of pretty much all software development.

I consider R to be one of the lower-level ML/DS languages, in that people that use R typically are fairly intentional about what they are doing.

I've been working in this space for a long time and recently started reading up on a particular ML technique which gained a lot of popularity over the past five years. What strikes me about 95% of the material available is how over-hyped and uninformative it is, to the point of just being wrong.

While I agree with your sentiment regarding ML engineers - they are just another kind of devs, and that's where it will go - I think DL is not just a tool like any other from the software toolbox. It's more like a paradigm changer, like the print, the engine, electricity, communication and computing. It tends to eat the world.

> It's more like a paradigm changer, like the print, the engine, electricity, communication and computing.

Either we really disagree about deep learning, or you vastly underestimate the influence of the other technologies that you've listed.

I'd maybe buy it if you broadened the claim to "quantification" or something. It's undoubtedly true that aggressively collecting and analyzing data has transformed society a lot: Taylorism, mass production, bureaucracies, science (not just data), even Guinness. However, this has been going on for ~150 years already.

As for deep learning specifically...meh.

That's exactly what I'm arguing against: I think the "eating the world" part is a hype-cycle. I think DL has truly revolutionized a handful of very narrow cases - computer vision and speech recognition/synthesis, for example - but that people are vastly over-estimating how "paradigm-changing" it actually is.

yes, but reliable facial recognition alone has huge social implications, never mind all the other potential applications for cognitive automation.

Take just computer vision alone. It has applications in manufacturing, robotics, SDCs, medical scans, cartography, agriculture, and many others. It's like the motor - a universal tool.

> Its inscrutable nature is definitely problematic for some use-cases, and not so problematic for others.

It's a problem whereever reliable operation is required, or analytic tractability (explanation) is required, or where resources available for data labeling are limited.

Its niche appears quite small, unless and until solid mathematical foundations are developed for it.

It shines the most in "soft computing"; computer vision, etc. These also tend to be the least-important areas to explain or audit, partly just because they're so trivial to verify with nothing but human intuition.

Where it becomes problematic - and where DL isn't actually very well-suited anyway - is making "real decisions"; things that would normally be backed by rigid logic.

NNs are "computer science" only insofar as numerical algorithms are. Which is to say, beyond the question of big-O, it's all math.

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