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Machine Learning for Developers
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Re: Machine Learning for Developers
#62Earlier quoted context omitted.
And creating "fragile" models because they don't have the tools to reproduce their own experiments. How many authors of academic papers in ML could reproduce the exact same results a year later? I would guess around 10%.
This pisses me off so much. I'm not a mathematician, but I like to think I'm a pretty good programmer. I feel like I could pick up a mathematical concept described in a computer science paper more easily if I could actually see the damn code and run it myself. But most of the papers I've read haven't mentioned where to find the referenced source code or, if they do, it's either horribly written and only runs on the a…
From my interactions with researchers in ML, most of them are actually pretty good programmers. There just isn't an incentive to make your code clean:
1. There isn't much correlation between quantity or even quality of papers you publish and the quality of your code. Meaning, writing cleaner code is not going to help you get that postdoc or faculty position.
2. Doing research is full of stops and starts and branches that fail and approaches that get thrown out. It's a waste of time to write clean code since you know it'll most likely be thrown out. When you do get an approach that works, you publish your paper and move on.
Re: Machine Learning for Developers
#63Please do not disable zooming. It makes life unnecessarily difficult for those on iOS devices who do not have perfect eyesight.
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Re: Machine Learning for Developers
#64Earlier quoted context omitted.
This pisses me off so much. I'm not a mathematician, but I like to think I'm a pretty good programmer. I feel like I could pick up a mathematical concept described in a computer science paper more easily if I could actually see the damn code and run it myself. But most of the papers I've read haven't mentioned where to find the referenced source code or, if they do, it's either horribly written and only runs on the a…
From my interactions with researchers in ML, most of them are actually pretty good programmers. There just isn't an incentive to make your code clean: 1. There isn't much correlation between quantity or even quality of papers you publish and the quality of your code. Meaning, writing cleaner code is not going to help you get that postdoc or faculty position. 2. Doing research is full of stops and starts and branches…
Maybe you can expect more citations if other researchers can examine your code.
Re: Machine Learning for Developers
#65Earlier quoted context omitted.
The difference is that the software or the elevator will work but the statistical model is wrong and doesn't work. It is like the elevator only lift people above 120 and below 90 and for the others it just don't work or take you to the wrong floor.
> The difference is that the software... will work Lots of software doesn't work. Is there a substantial difference between putting an overfitting model in production, and putting a poorly tested program in production?
I think there might be. When ML fails the only individual capable of noticing is someone who understands the math. When code breaks often the "lay" user notices. The result is obvious to a novice. When ML fails it looks like a duck, quakes like a duck but after multiple years of study its immediately recognizable as an antelope. Though to disagree with my own point, security vulnerabilities have a similar profile. In essence, to all but the highly trained the difference is imperceptible.
Re: Machine Learning for Developers
#66Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.
>> doing "machine learning" without much understanding of mathematics and probability
In my understanding "machine learning" is just a buzz word for the good old fashioned data-mining, which is still a part of applied mathematics/statistics. Only because it involves computers it doesn't belongs to CS.
So what you have written sounds for me like "doing applied statistics without much understanding of mathematics and probability". And yes, I am worried about it.
Re: Machine Learning for Developers
#67Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.
Nah. The only topic I would be worried about is cryptography, when used in a non-learning context. That has a high potential to cause harm. Otherwise with machine learning, I don't see how it is necessarily more dangerous than any other software -- databases, network protocols and so on...
Databases, networking protocols and so forth are hardened, relatively speaking (less the occasional heart bleed or PoW-blockchain fork). If you have autonomous systems built on top of hardened infrastructure but behaving according to ML models, the impact of their wrong doings is exponentially higher. It's about top-level autonomy through ML models really: from flash crashes to (future) autopilots.
The same effect of severity vs. position in the control hierarchy goes for human organizations. A cashier can defraud for a couple of hundred $, the C-suite at Goldman Sachs / Enron for a multitude. The invention of the corporation, as much risk as it entails, was a milestone in human progress though. So yeah, let the predicting but in its entirety not quite predictable models run the world. It's worth it.
Re: Machine Learning for Developers
#68Earlier quoted context omitted.
A decent CS undergrad degree decade ago included abstract math concepts. I took Engineering math, Information Theory, Numerical analysis, Probability, Simulation in my sophomore and Junior years. NLP and AI were electives in Senior year. As a Junior, we were building toy programs that do Operations research type of work - solving linear equations via various matrix operations, design optimal queue processes based on…
In all fairness, that's pretty atypical of a standard CS degree. In my anecdotal experience (knowing people that went to Stanford/Berkeley/MIT/CMU), most people take at most 1 probability class, 1 linear algebra class, and maybe 1 AI/ML class. Info theory, NLP, numerical analysis, optimization, etc. are not at all common.
Or just got a CS degree too long ago. I got lots of discrete math - formal methods, automata theory, and number theory. All that stuff that's in Knuth. But no number-crunching beyond matrix inversion and Fourier transforms.
Re: Machine Learning for Developers
#69Is anyone else at least a bit worried about a bunch of developers running around doing "machine learning" without much understanding of mathematics and probability? E.g. consider the creation of fragile models that overfit data being used in finance, infrastructure, medicine, etc.
ML is the next commodity on the development stack. It is good to worry over the next few years, but after that, there should be a bunch of pretty solid tools out there for developers to work with. I am among the people that I believe are working on these tools.
Re: Machine Learning for Developers
#70Earlier quoted context omitted.
> The difference is that the software... will work Lots of software doesn't work. Is there a substantial difference between putting an overfitting model in production, and putting a poorly tested program in production?
I think there might be. When ML fails the only individual capable of noticing is someone who understands the math. When code breaks often the "lay" user notices. The result is obvious to a novice. When ML fails it looks like a duck, quakes like a duck but after multiple years of study its immediately recognizable as an antelope. Though to disagree with my own point, security vulnerabilities have a similar profile. In…
>"When code breaks often the "lay" user notices. The result is obvious to a novice."
That depends "how" it breaks. As a novice coder myself, I've had things go wrong that I don't notice or can't identify, and it looks like my program is running fine.
I think that's the parent's point: it might be stupid to implement crappy macho learning models into production, but it isn't worrisome. It's expected.