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Is 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.
Is 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.
Is 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.
Is that any different from a bunch of developers plugging in magic numbers into a formula that they made up, which (to a first approximation) is roughly what happens now? Realistically, the outcome will be the same as it is now: those firms whose models don't reflect reality will blow up, those whose do will get bigger, a few will get too big to fail off some very confidently-expressed models and make a lot of people…
I'm picturing one of those dystopic films/novels where the main character is deleted/fired/jailed as a result of an algorithm error. Yes, in real life the trends will overcome the bad models. But just think of the potential consequences for harm on an individual basis!
Is 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.
Yes, but I also worry about a bunch of theorists writing substandard code that is unreadable and unmaintainable.
Earlier quoted context omitted.
Is that any different from a bunch of developers plugging in magic numbers into a formula that they made up, which (to a first approximation) is roughly what happens now? Realistically, the outcome will be the same as it is now: those firms whose models don't reflect reality will blow up, those whose do will get bigger, a few will get too big to fail off some very confidently-expressed models and make a lot of people…
> those firms whose models don't reflect reality will blow up, I'm picturing one of those dystopic films/novels where the main character is deleted/fired/jailed as a result of an algorithm error. Yes, in real life the trends will overcome the bad models. But just think of the potential consequences for harm on an individual basis!
It sucks, it's not fair or just, and everything would run more smoothly if we were omniscient beings living in a completely egalitarian society. Unfortunately, that's not the reality we live in. In the meantime, we accept it as simply fate or vulnerability, and muddle through as best as we can.
Java and Scala? Who uses that in ML? Python has long been the best language for ML, with some competition from Matlab.
Is 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.
Is there a decent introduction to scala as a language with real world ML? I've come across various ML primers that go into detail on PCA, Linear regression, etc. But not any that show real world ML usage i.e. if person listen to music of type X they'll also like Y. Face detection, etc.
Earlier quoted context omitted.
Yes, but I also worry about a bunch of theorists writing substandard code that is unreadable and unmaintainable.
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%.