5 ms·
Some might disagree with me but my best guesses are: - Probability math is confusing and difficult, and a base understanding is required to use PPLs in a way t
by smeeth 3y ago
Some might disagree with me but my best guesses are:
- Probability math is confusing and difficult, and a base understanding is required to use PPLs in a way that is not true of other ML/DL. Most CS PhDs will not be required to take enough of it to find PPLs intuitive, so to be familiar they will have had to opt into those classes. This is to say nothing of BS/MS practitioners, so the user base is naturally limited to the subset of people who studied Math/Stats is a rigorous way AND opted into the right classes or taught themselves later.
- Probabilistic models are often unique to the application. They require lots of bespoke code, modeling, and understanding. Contrast this with DL, where you throw your data in a blender and receive outputs.
- Uncertainty quantification often is not the most important outcome for sexy ML use cases. That is more frequently things like "accuracy," "residual error," or "wow that picture looks really good".
- PPL package tooling and documentation are often very confusing and don't work similarly to one another. This isn't necessarily the developer's fault, this stuff is hard, and the people with the domain knowledge needed to actually understand this stuff often have spent fewer hours in the open-source trenches.
- abeppu 3y agoRe your comment on CS PhDs not having probability background -- do you find that's true of ML researchers? I would understand that in a bunch of CS specialties, probability may not be a requirement, but in ML I would have expected otherwise.
- gh02t 3y agoNot OP but I deal with this a lot. In my experience a lot of folks working in mainstream ML haven't been exposed to it unless they specifically focused on it. It might just be a course load thing... getting the most out of these probabilistic PLs requires fairly deep expertise in both probability theory/Bayesian stats as well as in CS and you have a finite amount of courses you can take in school. Plus, a lot of the work in this area pre-dates the modern focus on deep learning or machine learning in general, so a lot of the knowledge tends to be held by professors/researchers that may not be as involved with the "new" ML courses. And of course, Math/Stats/CS departments don't always play nicely with each other and like to fight turf wars, though I've noticed cross-disciplinary research among the three becoming more accepted at the universities/institutes I work with. As a case study, I did most of my grad work on solving Bayesian inverse problems using probabilistic programming for applications in engineering, which is pretty cross-disciplinary. I now work mostly in ML, but I didn't really even touch anything in the ML domain until after I finished school. I could have, the courses were available, but they just weren't relevant to me at the time. Edit: I wouldn't be surprised if there was a considerable userbase in industries like finance, but in my experience those folks don't share much.
- seanmcdirmid 3y agoOne of the best ML researcher I know has a background in signal processing (and degrees in EE to go with). Not probability per se, but heavily uses probability and statistics.
- antegamisou 3y ago> has a background in signal processing Which largely counts as strong Linear Algebra and Probability Theory background.
- junipertea 3y agoWhile there are some exception, majority of published deep learning research barely mentions statistics at all, it's optimization all the way down
- uoaei 3y agoML is fundamentally not a CS specialty. It is a statistics/optimization (thus applied math) specialty. CS only comes into the picture at runtime. ML theory is divorced from computability until then.
- ke88y 3y agoThat's a weird game to play with those words.
- uoaei 3y agoCan you elaborate? The unreasonable effectiveness of approximate methods on discretized spaces doesn't change the fact that the theory underlying it is exact and continuous.
- ke88y 3y agoML is a sub field of CS, in practice. You don’t need a professional license to do math. Lots of computer scientists to harder and more interesting mathematics than their peers in the math dept. In that respect at least, the main substantive difference between the fields is about $40k/yr.
- uoaei 3y ago> ML is a sub field of CS, in practice. You're just stating things without justifying them. What else would you consider a subfield of CS? Finance? Accounting? Logistics? UI design? What is or isn't a subfield of a given science has nothing to do with the professional qualifications of those who practice it or how the tools may be implemented. We don't call pharmaceuticals "a subfield of robotics" because of how the factories are built.
- ke88y 3y agoAgain, this is such a weird game to play with words. I'm not sure what else to say.
- latenightcoding 3y agoML people nowadays barely know basic stats.
- Given_47 3y agoDisappointed black guy meme upon realizing a lot of “data science” is just calling some scikit learn module lol
- esafak 3y agoML is going mainstream. Most programmers don't know algorithms and data structures, I bet, if you consider all programmers around the world.
- uoaei 3y agoNormalcy is not a substitute for correctness.
- ur-whale 3y ago> ML people nowadays barely know basic stats. The same can unfortunately be said of many "statisticians", who use statistics as a big recipe book without understanding the first thing about the mathematical underpinnings of the topic. Don't believe me? Go ask the first statistician you run into to give you a half decent explanation of how the Chi-squared distribution and the Chi-squared test works, see what happens.