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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#231

Post by sacado2 »

xamuel wrote: post #93
joshuamorton wrote: post #89

Earlier quoted context omitted.

Why does an ML-expert need to know the halting problem? Considering that ML is really a CS-oriented form of statistics, why would you expect a statistician to know CS theory?

Thinking more, it's the misleading names ("machine learning", "AI") that rustle my jimmies so much. Sure, you don't need to know the halting problem to approximately solve MNIST by fitting a million-parameter curve to a dataset. But you're misleading people if you're claiming to have any kind of insight into how computers can be made intelligent, or how computers can "learn", when you don't even know the halting prob…

This is a recent shift lead by the ML trend. Traditionally (like 5 years ago), ML and AI were two different things, AI being the term for symbol manipulation. Expert systems, inference engines, constraint programming, SAT solving for instance. These domains are typical CS stuff: inference, complexity classes, low-level representation of data, etc. You don't need that much knowledge in math/statistics to be proficient in those fields, but you rather know what the halting problem is.

I'm working in the symbolic AI field, and sometimes use ML techniques. They are complementary. To me, ML is about induction, AI is abut deduction. They don't solve the same kinds of problems and they tend to work pretty well together.

Re: Machine Learning Crash Course

#232

Post by sacado2 »

dannypgh wrote: post #209
xamuel wrote: post #93

Earlier quoted context omitted.

Thinking more, it's the misleading names ("machine learning", "AI") that rustle my jimmies so much. Sure, you don't need to know the halting problem to approximately solve MNIST by fitting a million-parameter curve to a dataset. But you're misleading people if you're claiming to have any kind of insight into how computers can be made intelligent, or how computers can "learn", when you don't even know the halting prob…

I disagree. Frankly, for a lot of people and a lot of contexts, I don't think the halting problem is particularly important. You're using understanding of it as a shibboleth for exposure to common curricula about theoretical computation. But you can even know a lot about practical computation and not know anything about the halting problem. Curious: has your knowledge of the halting problem ever actually saved you ti…

> Curious: has your knowledge of the halting problem ever actually saved you time or effort in your work? If so, how?

Not OP, but I'm working a lot with ontologies. Some ontologies representations are undecidable, while other languages are not very expressive but can be manipulated in polynomial time. Had I not known that, I would still be like "crap, why does it take so long? I must have a bug somewhere, maybe I should switch to C".

> AI and ML are being advanced more by people with more knowledge of linear algebra and statistics than computer science.

Just answered OP about that, but actually, symbolic AI is pure computer science. It does not get as much publicity as ML currently, but believe me, it's everywhere: at the core of almost all package managers, like debian's apt-get or maven, at the core of most advanced static code analyzers, etc.

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