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> cleverly choosing your fitting function One benefit of ML is being able to replace clever subject matter experts (who may be expensive and hard to identify)

> cleverly choosing your fitting function One benefit of ML is being able to replace clever subject matter experts (who may be expensive and hard to identify) with ML generalists.

counters 6y ago
But is it a benefit or a liability? Reminds me a lot of the arguments in Susskind and Susskind's "The Future of the Professions."
ssivark 6y ago
While that’s the poster dream, I wonder whether it actually works in practice, rather than being a liability, as a sibling comment points out. IME, imposing domain knowledge (usually directly into the model, or sometimes also in the training data distribution) is crucial to getting things working in an efficient and robust manner.
- smallnamespace 6y ago
  Deep NNs are already competitive with specialized pipelines for translation and speech. The primary downside is that they require more data to train.
  - ssivark 6y ago
    Sure, but that’s such a tiny corner among generic problems. To generalize from there seems unwarranted.
  - peterwoerner 6y ago
    more importantly, thanks to the internet there is a huge amount of available data that is reasonably available. Many of the science problems I have dealt with have something like 11 data "points". And experiments may take a month to perform.
sesuximo 6y ago
And then the ML generalists (sometimes painfully) re learn the same lessons that the subject matter experts have learned over generations
peterwoerner 6y ago
Frequently, you still need a clever subject matter expert to figure out what form of input you need to insert into the neural network. E.G. someone has to figure out how to take crystal structure and put it in a neural network. Most obvious choices (e.g. voxel models) are intractable without huge amounts of computational resources which aren't generally available Your data needs to be very cheap. And you don't have to be that clever. For periodic function sines and cosines. For potential energy quadratics, polynomials and exponents do a great job. One of the key components which makes neural networks generally inferior is that you need enough data to learn the governing equations (always true physics) as well as the constitutive law (part of the physics which applies only to your problem) as opposed to just the constitutive law. Of course there are notable exeptions. Neural networks have been doing a great job of processing microscopy data and building 3d models from a series of 2d images.

> cleverly choosing your fitting function One benefit of ML is being able to replace clever subject matter experts (who may be expensive and hard to identify) with ML generalists.

counters 6y ago

But is it a benefit or a liability? Reminds me a lot of the arguments in Susskind and Susskind's "The Future of the Professions."

ssivark 6y ago

While that’s the poster dream, I wonder whether it actually works in practice, rather than being a liability, as a sibling comment points out. IME, imposing domain knowledge (usually directly into the model, or sometimes also in the training data distribution) is crucial to getting things working in an efficient and robust manner.

smallnamespace 6y ago
Deep NNs are already competitive with specialized pipelines for translation and speech. The primary downside is that they require more data to train.
- ssivark 6y ago
  Sure, but that’s such a tiny corner among generic problems. To generalize from there seems unwarranted.
- peterwoerner 6y ago
  more importantly, thanks to the internet there is a huge amount of available data that is reasonably available. Many of the science problems I have dealt with have something like 11 data "points". And experiments may take a month to perform.

sesuximo 6y ago

And then the ML generalists (sometimes painfully) re learn the same lessons that the subject matter experts have learned over generations

peterwoerner 6y ago

Frequently, you still need a clever subject matter expert to figure out what form of input you need to insert into the neural network. E.G. someone has to figure out how to take crystal structure and put it in a neural network. Most obvious choices (e.g. voxel models) are intractable without huge amounts of computational resources which aren't generally available Your data needs to be very cheap. And you don't have to be that clever. For periodic function sines and cosines. For potential energy quadratics, polynomials and exponents do a great job. One of the key components which makes neural networks generally inferior is that you need enough data to learn the governing equations (always true physics) as well as the constitutive law (part of the physics which applies only to your problem) as opposed to just the constitutive law. Of course there are notable exeptions. Neural networks have been doing a great job of processing microscopy data and building 3d models from a series of 2d images.