LeCun has a comment on this paper here: https://www.facebook.com/yann.lecun/posts/10153293764562143
Thanks for sharing that, he's got some interesting stuff to say about this topic.
Are ML and Statistics Complementary? [pdf]
11–20 of 33 posts
Re: Are ML and Statistics Complementary? [pdf]
#12I think feasibility to get an explanation for the results of modern machine learning is wishful thinking. I personally cannot explain my gut feelings. So why should we expect an explanation when machine deals with the same class of problems? Besides, it is easy to get wrong explanation and, as Vladimir Vapnik in his 3 metaphors for complex world observed, http://www.lancaster.ac.uk/users/esqn/windsor04/handouts/vap..…
Just because you can't explain your gut feelings doesn't mean they are unexplainable, or that you don't have any obligation to explain them.
Re: Are ML and Statistics Complementary? [pdf]
#13What is commonly understood as 'statistics' is just a specialized subset of machine learning. Machine learning generalizes statistics. The correct complement to machine learning is cryptography -- trying to intentionally build things that are provably intractable to reverse engineer.
Working with both statisticians and pure machine learners on the same task, I did notice some tendencies, presuppositions and modus operandi that were different (beyond being a specialized subset). Like said in this position paper, machine learners like to throw computation and parameters at the problem, where statisticians are more careful and sober. As an analogy, a statistician will approach a cliff very carefully, stomping the ground to make sure it is sturdy enough to carry a human. They'll approach the edge of the cliff 'till they have their p-measures and that is their model. Machine learners will jump head-first off the cliff and when you listen you can hear them yell: Cross-validatioooohhhh... as they plummet down.
I like the complement with cryptography. I would add another coding method: compression - Approximating the simplest model with explanatory power.
Re: Are ML and Statistics Complementary? [pdf]
#14What is commonly understood as 'statistics' is just a specialized subset of machine learning. Machine learning generalizes statistics. The correct complement to machine learning is cryptography -- trying to intentionally build things that are provably intractable to reverse engineer.
That's a strong and not-at-all obvious statement. Can you elaborate?
Re: Are ML and Statistics Complementary? [pdf]
#15I think feasibility to get an explanation for the results of modern machine learning is wishful thinking. I personally cannot explain my gut feelings. So why should we expect an explanation when machine deals with the same class of problems? Besides, it is easy to get wrong explanation and, as Vladimir Vapnik in his 3 metaphors for complex world observed, http://www.lancaster.ac.uk/users/esqn/windsor04/handouts/vap..…
As we start to use AI/ML for more tasks, the need for model interpretability rises. We expect doctors to explain their gut feelings, much like we expect computer vision models that detect disease to explain their findings and have a (theoretically sound) estimate of confidence. SVM's were so popular, pretty much because they had a firm theoretical basis on which they were designed (or "cute math" as deep learners may…
The article calls for an explanation not why some ML method works, but for an explanation of a particular ML result, like why a car drives this way or why a patient got a cancer. While I hopeful for the former, I just do not see the basis for the latter.
If some regulations shall require such explanation, the end result will be fake stories like parents tell to the children that Moon do not fall because it is nailed to the sky.
Re: Are ML and Statistics Complementary? [pdf]
#16Earlier quoted context omitted.
As we start to use AI/ML for more tasks, the need for model interpretability rises. We expect doctors to explain their gut feelings, much like we expect computer vision models that detect disease to explain their findings and have a (theoretically sound) estimate of confidence. SVM's were so popular, pretty much because they had a firm theoretical basis on which they were designed (or "cute math" as deep learners may…
The article calls for an explanation not why some ML method works, but for an explanation of a particular ML result, like why a car drives this way or why a patient got a cancer. While I hopeful for the former, I just do not see the basis for the latter. If some regulations shall require such explanation, the end result will be fake stories like parents tell to the children that Moon do not fall because it is nailed…
I don't think the paper asked for that. Relevant quote:
> machine learning is more concerned with making predictions, even if the prediction can not be explained very well (a.k.a. “a blackbox prediction”)
So in your example: an algo may explain that a car slows down, before taking a turn, because else it would likely crash. It may even get to a threshold ("under these weather conditions, anything over 55Mph is unsafe when taking a turn of such and such degree"). Statistics can help with that.
Welling is not asking for deep learning models to explain why a person got a cancer, but to explain its reasoning when it diagnoses a person with cancer ("I am confident, because in a random population of a 1000 other patients, these variables are within ..."). Statistics can help with that. It aligns with their mind set and tool set.
Regulations are cheated even with these kind of explanations, but that is for another story (black box models may provide some plausible deniability).
Re: Are ML and Statistics Complementary? [pdf]
#17What is commonly understood as 'statistics' is just a specialized subset of machine learning. Machine learning generalizes statistics. The correct complement to machine learning is cryptography -- trying to intentionally build things that are provably intractable to reverse engineer.
Working with both statisticians and pure machine learners on the same task, I did notice some tendencies, presuppositions and modus operandi that were different (beyond being a specialized subset). Like said in this position paper, machine learners like to throw computation and parameters at the problem, where statisticians are more careful and sober. As an analogy, a statistician will approach a cliff very carefully…
I have had the exact opposite experience with machine learning and statistics. In my experience, those who come from the 'statistics' side tend to use constructs, like null hypothesis significance testing, which are not consistent even from a theoretical point of view. And further, when they use them, they do awful things like p hacking, or using a direct comparison of t-stats as a model selection criterion, which are further rife with theoretical problems, not to mention lots of statistical biases and so forth.
I find the machine learning approach is far more humble. It starts out by saying that I, as a domain expert or a statistician, probably don't know any better than a lay person what is going to work for prediction or how to best attribute efficacy for explanation. Instead of coming at the problem from a position of hubris, that me and my stats background know what to do, I will instead try to arrive at an algorithmic solution that has provable inference properties, and then allow it to work and commit to it.
Either side can lead to failings if you just try to throw an off-the-shelf method at a problem without thinking, but there's a difference between criticizing the naivety with which a given practitioner uses the method versus criticizing the method itself.
When we look at the methods themselves I see much more care, humility, and carefulness to avoid statistical fallacies in the machine learning world. I see a lot of sloppy hacks and from-first-principles-invalid (like NHST) approaches in the 'statistics' side. And even when we consider how practioners use them, both sides are pretty much equally as guilty of trying to just throw methods at a problem like a black box. Machine learning is no more of a black box than a garbage-can regression from which t-stats will be used for model selection. However, all of the notorious misuses of p-values and conflation over policy questions (questions for which a conditional posterior is necessarily required, but for which likelihood functions are substituted as a proxy for the posterior) seem very uniquely problematic for only the 'statistics' side.
Three papers that I recommend for this sort of discussion are:
[1] "Bayesian estimation supersedes the t-test" by Kruschke, http://www.indiana.edu/~kruschke/BEST/BEST.pdf
[2] "Statistical Modeling: The Two Cultures" by Breiman, https://projecteuclid.org/euclid.ss/1009213726
[3] "Let's put the garbage-can regressions and garbage-can probits where they belong" by Achen, http://www.columbia.edu/~gjw10/achen04.pdf
Re: Are ML and Statistics Complementary? [pdf]
#18What is commonly understood as 'statistics' is just a specialized subset of machine learning. Machine learning generalizes statistics. The correct complement to machine learning is cryptography -- trying to intentionally build things that are provably intractable to reverse engineer.
[deleted]
Re: Are ML and Statistics Complementary? [pdf]
#19Earlier quoted context omitted.
The article calls for an explanation not why some ML method works, but for an explanation of a particular ML result, like why a car drives this way or why a patient got a cancer. While I hopeful for the former, I just do not see the basis for the latter. If some regulations shall require such explanation, the end result will be fake stories like parents tell to the children that Moon do not fall because it is nailed…
I don't think the paper asked for that. Relevant quote: > machine learning is more concerned with making predictions, even if the prediction can not be explained very well (a.k.a. “a blackbox prediction”) So in your example: an algo may explain that a car slows down, before taking a turn, because else it would likely crash. It may even get to a threshold ("under these weather conditions, anything over 55Mph is unsaf…
I am referring to this fragment:
> Thus, for many applications, in order to successfully interact with humans, machines will need to explain their reasoning, including some quantification of confidence, to humans.
No doubtful there are cases when an explanation is easy. Often this is because we have a very solid model like physics of a car. In fact since we know the model, we do not need an explanation, we must demand that the algorithm follows the model or declare it unfit.
But how can we expect an explanation for a behavior in a critical situation on a road that was not explicitly programmed and when the algorithm decided to turn to a particular degree bases on a non-trivial inference? Similarly, when an algorithm decides if a patient needs an emergency operation or if they can wait, why can we expect an simple explanation especially for the patient with rare conditions when algorithm again must perform an inference, not a deduction from 1000 very similar cases?
Re: Are ML and Statistics Complementary? [pdf]
#20I think feasibility to get an explanation for the results of modern machine learning is wishful thinking. I personally cannot explain my gut feelings. So why should we expect an explanation when machine deals with the same class of problems? Besides, it is easy to get wrong explanation and, as Vladimir Vapnik in his 3 metaphors for complex world observed, http://www.lancaster.ac.uk/users/esqn/windsor04/handouts/vap..…
I hear what you're saying, but in terms of usefulness for business decisions, what leaders at a company would be satisfied with someone providing Vapnik's quote? Certainly machine learning and statistics have applications outside of business, but when it comes to realities in industry settings, very often an explanation of results is necessary, in addition to an explanation of why a particular machine learning approa…
The problem is not about how to take a scientific conclusion and make it suitably less scientific to serve as a business explanation.
The problem is to replace inept employees who believe "business decisions" are not scientific questions, so that over time there is a convergence to using the scientific method, with legitimate statistical rigor, when making a so-called business decision.
Generally speaking, the only people who want for there to be a distinction between a "business question" and a "scientific question" are people who can profit from the political manipulation that becomes possible once a question is decoupled from technological and statistical rigor. Once that decoupling happens, you can use almost anything as the basis of a decision, and you can secure blame insurance against almost any outcome.
This is why many of the experiments testing whether prediction markets, when used internally to a company, can force projects to be completed on time and under budget are generally met with extreme resistance from managers even when they are resounding successes.
The managers don't care if the projects are delivered on time or under budget. What they care about is being able to use political tools to argue for bonuses, create pockets of job security, backstab colleagues, block opposing coalitions within the firm. You can't do that stuff if everyone is expected to be scientific, so you have to introduce the arbitrary buzzword "business" into the mix, and start demanding nonsense stuff like "actionable insight" -- things that are intentionally not scientifically rigorous to ensure there is room for pliable political manipulation for self-serving and/or rent-seeking executives, all with plausible deniability that it's supposed to be "quantitative."