[acdc4life]

Quantitative math, or applied math isn't based on fitting data to an arbitrary mathematical structure. It's looking at real life, and deriving the mathematical laws that govern what you see. You could have a neural net predict planetary motion. However, it doesn't know jack shit about physics.

>I have a neural net onboard my phone which automatically detects songs offline and tells me what they are.

MP3 uses something called psycho acoustics, which is a quantitative model on human perception, which is used to eliminate frequencies that can't be heard based on this model.

Your neural network doesn't tell you what features make songs distinct, it's not a quantitative model at all, but a black box heuristic on what the important features are superficially. If actual mathematicians worked on this problem, I guarantee you they'd do a better job, and their models would work on a commadore64, with real time training. Moreover it would tell you things like who is singing, if it's a live performance, which concert it was.

[sonnyblarney]

" If actual mathematicians worked on this problem, I guarantee you they'd do a better job"

No, this is wrong.

Some of the most brilliant people in the world have been working on image recognition, voice recognition etc. and AI is crushing all of their work.

"Your neural network doesn't tell you what features make songs distinct, it's not a quantitative model at all" - it doesn't matter at all if our objective is detecting the song. Neither does the mp3 compression algorithm.

[acdc4life]

>Some of the most brilliant people in the world have been working on image recognition, voice recognition etc. and AI is crushing all of their work.

This is very true. I take my stronger statements back, MAINSTREAM mathematicians attempting this problem are all wrong, and have been wrong for 50 years. But you do need the right theory, and the right math that realizes this theory.

"AI" is superficially beating the work in computer vision. Computer vision is complete bogus. The gabor filters, fourier transfroms etc. are all wrong conceptually. The known methods do abysmally on basic tasks like object recognition, texture segmentation etc. But they keep trying it.

I would take this one step further: computer vision, audio and NLP researchers have been stuck in a rut for the past 50 years. DL is beating THEIR math, but this is because of data and computation speed, not because of any insights. But DL is also wrong, and giving you an illusion of progress. Both of these things are doomed to go the way of GOFAI.

I can go into great detail and carefully explain why MAINSTREAM contemporary ideas in math for vision, audition and language are completely wrong, and have been wrong for 50 years. What is the right model? Like I mentioned before, the right ideas are emerging, neural networks will dominate, just not DL.

[wetmore]

Ok so who are the real, non-mainstream mathematicians who would do better?

[KidComputer]

> It's looking at real life

Collecting observations aka data.

> deriving the mathematical laws that govern what you see

Fitting a model.

> Your neural network doesn't tell you what features make songs distinct

It literally learns better features that you could ever come up with by hand. This is why CNNs do better in computer vision that hand engineered filters.

> I guarantee you they'd do a better job, and their models would work on a commadore64, with real time training.

LOL if you think that a room full of people can listen to TBs of audio data, decide what mathematical functions when combined together are better descriptors of that data than a DL model learning its features.

You don't have the slightest clue what you're talking about.

[bduerst]

>If actual mathematicians worked on this problem,

This is a No True Scotsman. Actual mathematicians did work on this problem, training the neural network to achieve it's target task of identifying songs using minimal power and storage consumption - which works.