There's different layers that I could use to describe it.
Mathimatically it's basically the problem of determining how much one thing amoung a group of things is a factor for an outcome, but doing it while realizing there is interference from other factors and also recognizing your outcome might not be a single scalar value. (Also doing this based on sample data rather than a model). It turns out when you solve this problem you solve some aspects of animal like learning in general.
So like if I gave you some fft data and wanted you to diagnose the issue with an engine this would be a good tool to use if you already have a library of past engine sounds and diagnosises. This isn't just identifying a pitch that indicates a certain problem, although this system can do that, but if you have different engine types with different baseline ffts how do you listen to one property and have that inform the interpretation of another?
No, neural networks are the standard now. I took his class in coursera and it's amazing what has happened in those few years.
Why is it the standard. Because it's smart enough to generally be a catch all even with novice configuration. Know you have a machine learning problem but not sure what to do about it. Plug in a nueral network and see what happens.
5 comments
1 u/Spar7an2 23 Mar 2016 23:38
what is this exactly?
2 u/luckyguy 24 Mar 2016 02:33
There's different layers that I could use to describe it.
Mathimatically it's basically the problem of determining how much one thing amoung a group of things is a factor for an outcome, but doing it while realizing there is interference from other factors and also recognizing your outcome might not be a single scalar value. (Also doing this based on sample data rather than a model). It turns out when you solve this problem you solve some aspects of animal like learning in general.
So like if I gave you some fft data and wanted you to diagnose the issue with an engine this would be a good tool to use if you already have a library of past engine sounds and diagnosises. This isn't just identifying a pitch that indicates a certain problem, although this system can do that, but if you have different engine types with different baseline ffts how do you listen to one property and have that inform the interpretation of another?
0 u/Tecktonik 23 Mar 2016 22:28
Conclusion at the end: "And that's the end of classical AI."
Neural networks FTW!
It will take 20 years for this to be accepted.
1 u/luckyguy 24 Mar 2016 02:25
No, neural networks are the standard now. I took his class in coursera and it's amazing what has happened in those few years.
Why is it the standard. Because it's smart enough to generally be a catch all even with novice configuration. Know you have a machine learning problem but not sure what to do about it. Plug in a nueral network and see what happens.
0 u/Tecktonik 24 Mar 2016 20:57
FTW - For The Win