7 ms·

Where is mathematics in ML today? Most of that are happening under the hood and all algorithms are just a black box.

verroq 8y ago
The ML field today is all about results. Get the high scores and figure out the math later. Not that there is anything wrong with this, we still at the stage where we're banging rocks together wondering what works and what doesn't, and the theories will come later.
- byebyetech 8y ago
  So what is the meaning of "ML experts" if all they do is trial and loss experiments! Is Math PhD just used for hiring signal rather than actual requirements to do ML projects?
  - Eridrus 8y ago
    Yes, there is no need to have a PhD to do ML, either applied or research. Not to say there isn't good research using advanced mathematical/statistical methods, but most of it is not. It's entirely a signaling game.
  - mensetmanusman 8y ago
    It’s an exciting time because the field is getting to the point where ‘complexity is unbounded’ I.e. in many materials or chemistry fields where you start operating with over 100 variables, you begin to develop a dark arts of understanding because what is being attempted is beyond the ability of computers to model. You can do chemistry without a PhD, but your ability to systematically try to address the complexity may be hindered without the training. Likewise with ‘ML experts’ (I hope they get a cool word some day to describe their profession).
    - DoctorOetker 8y ago
      the example you gave is unfortunately a counterexample! If we have a function f(x1, x2, ...) of 100 variables and you wonder about the gradient, theres multiple ways of calculating it. Theres symbolic differentiation, but due to the chain rule, the number of terms grows rapidly and the expression can not be stored. Then theres the finite difference method, whereby you calculate for each of 100 variables x_i: f(x1, x2, ..., (x_i +epsilon), ..., x100) - f(x1, ..., x100) the term on the right is the same constant so in total you need 100+1 forward function evaluations. And theres the issue of precision for small differences (mantissa). One of the main reasons machine learning took off is because of the mathematical realization (Automatic/Algorithmic Differentiation) on how a 1 forward and 1 backward pass is more mathematically rigorous (calculates gradient vs finite differences) and much more efficient. With the blackboxing of the algorithms, many endusers of the ML libraries end up using ML when they don't know the functional form of a map, but will refuse to apply automatic differentiation of a known complex function with large number of parameters. In contrast those endusers that made sure to understand Automatic Differentiation as a tool orthogonal to arbitrary function approximation (i.e. everyone who realizes the math part of ML is very important) will be able to apply AD (or any other tricks learnt through a mathematical perspective) in situatins where there is no need for arbitrary function approximation... EDIT: woops I thought you were arguing for blackboxing, against mathematical interpretation upvoted
  - ngcc_hk 8y ago
    May be Ml is an empirical subject more than a theoretical subject. It is more biology than physics. More astronomy ... even is the subject is created does not meant it follows rules. After all if intelligence comes out artificially, I hope it does not have rule.
    - galaxyLogic 8y ago
      Great point ML takes much of its inspiration from simulating brains. Therefore studying such artificial brains is much like studying biology and thus to a large degree empirical I would assume.
YjSe2GMQ 8y ago
There's a very neat and often forgotten piece of math in learning, VC dimension (unrelated to venture capital): https://en.m.wikipedia.org/wiki/Vapnik–Chervonenkis_dimension https://en.m.wikipedia.org/wiki/Vapnik–Chervonenkis_dimensio... Also, the reason why black box methods are such a big deal now is precisely because controlled/engineered methods turned out to be inferior (obvious example: image recognition; look no further than into the story of the dropout method and Alex Krizhevsky). Edit: s/basically forgotten/often forgotten/ in the first sentence.
- selimthegrim 8y ago
  It's not very forgotten if it's in the Learning from Data course, to say the least
- ginnungagap 8y ago
  There are some good combinatorics notes by Chernikov[0, pdf file] talking about VC theory if anyone is interested in learning more [0] http://www.math.ucla.edu/~chernikov/teaching/Combinatorics285N/CombinatoricsNotes.pdf http://www.math.ucla.edu/~chernikov/teaching/Combinatorics28...
SatvikBeri 8y ago
It's all around. The math portion just doesn't get as much hype. For example, Goodfellow and Bengio's book on Deep Learning talks a lot about connections to Bayesian Inference, Classical Statistics, Information Theory, and a bit about Topology. Christopher Oolah's blog gives plenty of great explanations of mathematical topics. Personally, I use Math in my work all the time. Thinking in terms of Information Theory has let me quantify and compare algorithms that seemed difficult to evaluate at first. And several times, I've seen a business make the wrong decision due to lack of Math/Stats background. I don't mean to be a Math snob – you can absolutely do a lot of valuable work treating algorithms as black boxes. But the Math is there, and it is being used.

The ML field today is all about results. Get the high scores and figure out the math later. Not that there is anything wrong with this, we still at the stage where we're banging rocks together wondering what works and what doesn't, and the theories will come later.

byebyetech 8y ago
So what is the meaning of "ML experts" if all they do is trial and loss experiments! Is Math PhD just used for hiring signal rather than actual requirements to do ML projects?
- Eridrus 8y ago
  Yes, there is no need to have a PhD to do ML, either applied or research. Not to say there isn't good research using advanced mathematical/statistical methods, but most of it is not. It's entirely a signaling game.
- mensetmanusman 8y ago
  It’s an exciting time because the field is getting to the point where ‘complexity is unbounded’ I.e. in many materials or chemistry fields where you start operating with over 100 variables, you begin to develop a dark arts of understanding because what is being attempted is beyond the ability of computers to model. You can do chemistry without a PhD, but your ability to systematically try to address the complexity may be hindered without the training. Likewise with ‘ML experts’ (I hope they get a cool word some day to describe their profession).
  - DoctorOetker 8y ago
    the example you gave is unfortunately a counterexample! If we have a function f(x1, x2, ...) of 100 variables and you wonder about the gradient, theres multiple ways of calculating it. Theres symbolic differentiation, but due to the chain rule, the number of terms grows rapidly and the expression can not be stored. Then theres the finite difference method, whereby you calculate for each of 100 variables x_i: f(x1, x2, ..., (x_i +epsilon), ..., x100) - f(x1, ..., x100) the term on the right is the same constant so in total you need 100+1 forward function evaluations. And theres the issue of precision for small differences (mantissa). One of the main reasons machine learning took off is because of the mathematical realization (Automatic/Algorithmic Differentiation) on how a 1 forward and 1 backward pass is more mathematically rigorous (calculates gradient vs finite differences) and much more efficient. With the blackboxing of the algorithms, many endusers of the ML libraries end up using ML when they don't know the functional form of a map, but will refuse to apply automatic differentiation of a known complex function with large number of parameters. In contrast those endusers that made sure to understand Automatic Differentiation as a tool orthogonal to arbitrary function approximation (i.e. everyone who realizes the math part of ML is very important) will be able to apply AD (or any other tricks learnt through a mathematical perspective) in situatins where there is no need for arbitrary function approximation... EDIT: woops I thought you were arguing for blackboxing, against mathematical interpretation upvoted
- ngcc_hk 8y ago
  May be Ml is an empirical subject more than a theoretical subject. It is more biology than physics. More astronomy ... even is the subject is created does not meant it follows rules. After all if intelligence comes out artificially, I hope it does not have rule.
  - galaxyLogic 8y ago
    Great point ML takes much of its inspiration from simulating brains. Therefore studying such artificial brains is much like studying biology and thus to a large degree empirical I would assume.

YjSe2GMQ 8y ago

There's a very neat and often forgotten piece of math in learning, VC dimension (unrelated to venture capital): https://en.m.wikipedia.org/wiki/Vapnik–Chervonenkis_dimension https://en.m.wikipedia.org/wiki/Vapnik–Chervonenkis_dimensio... Also, the reason why black box methods are such a big deal now is precisely because controlled/engineered methods turned out to be inferior (obvious example: image recognition; look no further than into the story of the dropout method and Alex Krizhevsky). Edit: s/basically forgotten/often forgotten/ in the first sentence.

selimthegrim 8y ago
It's not very forgotten if it's in the Learning from Data course, to say the least
ginnungagap 8y ago
There are some good combinatorics notes by Chernikov[0, pdf file] talking about VC theory if anyone is interested in learning more [0] http://www.math.ucla.edu/~chernikov/teaching/Combinatorics285N/CombinatoricsNotes.pdf http://www.math.ucla.edu/~chernikov/teaching/Combinatorics28...

SatvikBeri 8y ago

It's all around. The math portion just doesn't get as much hype. For example, Goodfellow and Bengio's book on Deep Learning talks a lot about connections to Bayesian Inference, Classical Statistics, Information Theory, and a bit about Topology. Christopher Oolah's blog gives plenty of great explanations of mathematical topics. Personally, I use Math in my work all the time. Thinking in terms of Information Theory has let me quantify and compare algorithms that seemed difficult to evaluate at first. And several times, I've seen a business make the wrong decision due to lack of Math/Stats background. I don't mean to be a Math snob – you can absolutely do a lot of valuable work treating algorithms as black boxes. But the Math is there, and it is being used.