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0 pointsmumblemumble5y ago0 comments

My sense is that the original sin here is conflating data science with machine learning.

A good data scientist might choose to use machine learning to accomplish their job. Or they might find that classical statistical inference is the better tool for the task at hand. A good data scientist, having built this model, might choose to put it into production. Or they might find that a simple if-statement could do the job almost as effectively but not nearly as expensively. A good data scientist, having decided to productionize a model, will also provide some information about how it might break down - for example, describing shifts in customer behavior, or changes in how some input signal is generated, or feedback effects that might invalidate the model.

OTOH, if your job has been framed in terms of cutting-edge machine learning, then you may well know - at a gut level, if not consciously - that your job is basically just a pissing match to see who can deploy the most bleeding-edge or expensive technology the fastest. It's like the modern hospital childbirth scene in Monty Python's The Meaning of Life, where the doctor is more interested in showing off the machine that goes, "ping!" in order to impress the other doctors than he is in paying attention to the mother.

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morvita5y ago

This matches my experience exactly.

About 5 years ago I was coming out of academia with a PhD in chemistry wanting to get into tech. Someone pointed me toward data science and I was immediately pulled in by the potential for deep insights and working with interesting data sets. After getting into an industry research position, I was quickly disillusioned by the talk from our leadership of how we needed to become an "AI company" and discovered that was I really wanted to be doing was classic algorithm development, not data science.

Now, I've see so much poorly implemented, unnecessary machine learning applied to problems that didn't need it that I first assume that any machine learning project is a bad technical decision until proven otherwise. I've happily moved into an engineering role building interesting pipelines.

aspaceman5y ago

There’s people who consider classical inference and the like to be machine learning just as much as neural nets are. I like that perspective.

mumblemumbleOP5y ago

There are some things, like OLS and logistic regression, that are commonly used for both purposes. But there's a sort of moral distinction between machine learning and statistical inference, driven by whether you consider your key deliverable to be y-hat or beta-hat, that ends up having implications.

For example, I can get pretty preoccupied with multicollinearity or heteroskedasticity when I'm wearing my statistician hat, while they barely qualify as passing diversions when I'm wearing my machine learning engineer hat. If I'm doing ML, I'll happily deliberately bias the model. That would be anathema if I were doing statistical inference.

aspaceman5y ago

Oh gotcha. That’s an interesting way to draw the line and I appreciate the distinction.

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0 pointsmumblemumble5y ago0 comments

My sense is that the original sin here is conflating data science with machine learning.

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morvita5y ago

This matches my experience exactly.

aspaceman5y ago

There’s people who consider classical inference and the like to be machine learning just as much as neural nets are. I like that perspective.

mumblemumbleOP5y ago

aspaceman5y ago

Oh gotcha. That’s an interesting way to draw the line and I appreciate the distinction.

j / k navigate · click thread line to collapse