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Comment by dagw | Hacker News Reader

2) would be awesome. If for example our hydrologists could easily take their extensive domain knowledge and experiment with layer of deep learning on top of that without having to go though the "high priests" that can only be a good thing in my mind.

I might be very much an oddity among programmers, but I genuinely believe that the more 'programming' that can be done by non-programmers, who actually understand the domain they are trying to model, the better. If nothing else it would free up more time for programmers to work on actual hard problems where they have more to contribute.

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My main concern is whether it deserves my time.

Investing in deep learning now costs considerable time and effort, whereas in the near future when deep learning is a commodity, that investment gives no real advantage.

Once it becomes a commodity there will be a bigger market for your services and a greater appreciation for the value you add (assuming you're actually good enough to add value).

There was a time when knowing how HTML tables worked was enough to get you a job, and knowing how to post a form to a cgi perl script made you an expert. It's getting easier and easier for more and more people to do more and more complicated things on the web every day, and still (and probably because of this) people whom are really good at it are commanding a higher and higher salary.

But my point is that we're reaching an asymptote, where knowing much more about deep learning gives only a marginal advantage.

So it's actually worse than web development.

knowing much more about deep learning gives only a marginal advantage.

Sure, but that's mostly because deep learning is so new and 'magical' that most people outside the field can't spot the difference between someone having PhD in the subject and someone having worked though a couple of Tensorflow tutorials. As the field matures and its practical applications to various fields and industries becomes clearer, so will the value of knowing more about deep learning than your peers.

Back when I took signal processing, I remember our professor making a very insightful comment. I sadly don't remember his exact words, but it went something like this.

"20 years back, everyone wanted CD's for music, today it is flash drives, the upcoming thing is streaming music from the internet. Technologies come and go. What I can say is that the Fourier transform will still be around 200 years from now as a valuable way of understanding the world."

I am of course in no real position to assert that this is a good idea (as I am still in graduate school), but this is the reason why I gravitate towards math and physics classes. Conditional on the assumption that we have a meaningful civilization decades from now (and none of the dangers outlined for instance in the excellent book "Here Be Dragons: Science, Technology and the Future of Humanity" by Olle Haggstrom) cause its utter collapse, I think it is a safe bet to assume "fundamental" topics like Maxwell's equations, Fourier transforms, etc will still be around, alive, and fruitfully studied and applied across a variety of domains. Part of the reason is simply historical: the Fourier transform has had over 200 years to prove its worth time and again across a variety of disciplines.

Maybe deep learning is here to stay, maybe it isn't. I don't know, and I am not willing or interested in making bets either way. I am willing to bet on a time proven framework of understanding, such as the examples above.

Indeed.

ML seems a great part, "Magic Function Machines". People make features that can be tracked, and more features, and more features. Then they splatter them against the wall, and hope for the best.

The best, are things like "Tell what type of bird it is"... But in the end, we have no clue HOW it got those results. Just that the Mystery Magic Function + data = results.

I can certainly see a middle path of being able to train a learning function, and then interrogating said function for the underlying things that make it true. Once we understand the primitives, then we can make ideal functions that do X, and do it well. Because right now, learning functions do it rather well but with exceptional overhead. And they're hard to tune without recrunching the whole dataset.

Investing in deep learning now costs considerable time and effort

Actually I'd argue that now deep learning requires the least investment of time and effort compared to potential payoff. Hell if you spend just a couple of weekends grinding through tensorflow tutorials you'll be able to impress the crap out of a huge number of people, many of them with far more money than sense.

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