[brucephillips]

The big tech companies are demonstrably using deep learning to solve previously unsolvable problems. It's a significant advance.

What's yet to be seen is if startups can profit from this advance, since it depends on massive data and compute.

[comstock]

AlphaGo is interesting. But what big new problems have been solved? (rather than incrementally improved).

[joe_the_user]

The big results as far as I understand:

- Image recognition

- Winning the games computers hadn't won already

- Incremental progress on translation. Plus translation that doesn't need as many domain experts

- Self-driving cars (with related automation applications)

Of image there, image recognition stands out as the big leap and the rest are relatively incremental. One of the things with the other applications is that they provide a recipe format that's more systematic than previous approaches. A lot of vision approaches pre-deep-learning were very hit-or-miss. Deep learning has a lot of black art involved in effective training and a lot of time investment but my impression it is more reliable than what came before.

Any other examples welcome

[sanxiyn]

You forgot speech recognition.

[eksemplar]

This isn't related to alpha go as such, but we can now predict which citizens are going to need help raising their children by having a machine cross reference their case history with public records.

It's not legal yet, but it will be, because it will potentially save lives (and money).

[mfukar]

Well, "big new" sounds like a destructive qualification. Why can't it be "big" and "old"? https://arxiv.org/pdf/1712.01208.pdf

[brucephillips]

https://m.cacm.acm.org/magazines/2017/6/217734-deep-learning...

[comstock]

Looks like great incremental progess. Have you seen the state of Japanese<->English translation? It’s almost completely useless.

I really don’t see this as a huge win for deep learning, anything else?

[brucephillips]

Whether progress is incremental is an ill defined question. I don't consider "super human translation" to be incremental. The key point here is that deep learning has produced significant results. I'm not sure why you care to argue semantics.

[comstock]

Well, I’m interested in understanding how valuable deep learning is and if lives up to the hype.

Better translation of European languages (which wasn’t a totally unsolved problem anyway) doesn’t seem to be something that really lives up to the hype.

Particularly as the article cited doesn’t seem to back up its statements very well.

So... anything else?

[brucephillips]

If super human translation doesn't impress you, what will?