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> That is not to say ML isn't a powerful tool in the right context, but I feel it is grossly over-hyped and over-used. And that seems to be a controversial stan

> That is not to say ML isn't a powerful tool in the right context, but I feel it is grossly over-hyped and over-used. And that seems to be a controversial stance. Even within start-up circles I've encountered push-back when suggesting going the simpler route and saving ML for much much later. Exactly. Many times when people are looking for an intelligent solution to a hard problem, Machine Learning is actually the wrong solution...but ML enthusiasts (as opposed to experts) are so eager to use it that they end up prescribing it as a solution to every problem, and that can really mess with non-technical managers heads. I recently advised a high level non-technical manager who was looking for a solution to a very combinatorial optimization problem, and I advised to him that he should be looking to hire some expertise from the field of Operations Research. But he also had a dozen software engineers that were chomping at the bit to put some machine learning on their resumes, who were all advising that he needed a machine learning solution. And when the reqs went out, they were all for data scientists...and when the first data scientist came on board, he quit after three months and told the manager that he didn't need machine learning, but that he needed a Gurobi license and someone who could model Integer Programming problems...also known as an Operations Researcher. I know I know, /r/ThatHappened. But it's a real problem and ML enthusiasts are actually feeding the next AI winter by overprescribing it where it is the least applicable solution to the problem, ultimately generating disappointment in ML. Sometimes you don't need Machine Learning...sometimes you need Linear Programming, sometimes Econometrics, sometimes Statistics, sometimes Constraint Programming, and sometimes you just need an Excel Pivot Table.

EricMausler 6y ago
I have a bachelor's in operations research (labeled Information & Systems Engineering) and have been finding myself looking up ML certifications because the jobs posted in relation to the problems I am trained to solve are asking for it. I have been all-in on the data wave since picking my major in 2010, and honestly most of the time all you need is SQL and a pivot table. I already learned python to smooth some edges on what people think the job is about. To bite the bullet on ML makes me worried I am going to spread myself too thin. I spent hours researching in my own time and it is so vast. Like you said, you need to be committed as a data scientist at that point. Has operations research become a specialization of data science now?
klmadfejno 6y ago
When I was at MIT, the Op. Research Center was working on globally optimal decision trees. That is, a simple, single decision tree, that is devised by an optimization model such that it is going to be the best possible tree you could make with a given training set (vs. regular trees which are trained in a dumb greedy way). That always felt like the holy grail of actual useful business "ML" to me. But either way, you're often correct. For anything but the simplest decisions to be made, the bulk of the value will come from better decision making. ML can help refine the estimates of the value of each decision, but making the final choose is an easily overlooked and super important step.
twelfthnight 6y ago
> but ML enthusiasts (as opposed to experts) are so eager to use it that they end up prescribing it as a solution to every problem Interesting you say "as opposed to experts". I think that's right on. It's been my experience on data science teams that the data scientists are often the ones pushing back on ML solutions and it's leadership who want GPT-3 or whatever latest ML model in the stack. I had one team where we were told we needed to call our ML work "AI" because if we didn't, leadership would think we weren't cutting edge and spin-up a competing team who was doing "AI".
edot 6y ago
I absolutely believe that happened. OR is criminally underrated by businesses today, in favor of ML and AI hype. For proof, search for an OR-titled job and see how many come up, compared to ML-titled. Lots of “ML” and “AI” is prettied up 50 year old OR techniques.

EricMausler 6y ago

I have a bachelor's in operations research (labeled Information & Systems Engineering) and have been finding myself looking up ML certifications because the jobs posted in relation to the problems I am trained to solve are asking for it. I have been all-in on the data wave since picking my major in 2010, and honestly most of the time all you need is SQL and a pivot table. I already learned python to smooth some edges on what people think the job is about. To bite the bullet on ML makes me worried I am going to spread myself too thin. I spent hours researching in my own time and it is so vast. Like you said, you need to be committed as a data scientist at that point. Has operations research become a specialization of data science now?

klmadfejno 6y ago

When I was at MIT, the Op. Research Center was working on globally optimal decision trees. That is, a simple, single decision tree, that is devised by an optimization model such that it is going to be the best possible tree you could make with a given training set (vs. regular trees which are trained in a dumb greedy way). That always felt like the holy grail of actual useful business "ML" to me. But either way, you're often correct. For anything but the simplest decisions to be made, the bulk of the value will come from better decision making. ML can help refine the estimates of the value of each decision, but making the final choose is an easily overlooked and super important step.

twelfthnight 6y ago

> but ML enthusiasts (as opposed to experts) are so eager to use it that they end up prescribing it as a solution to every problem Interesting you say "as opposed to experts". I think that's right on. It's been my experience on data science teams that the data scientists are often the ones pushing back on ML solutions and it's leadership who want GPT-3 or whatever latest ML model in the stack. I had one team where we were told we needed to call our ML work "AI" because if we didn't, leadership would think we weren't cutting edge and spin-up a competing team who was doing "AI".

edot 6y ago

I absolutely believe that happened. OR is criminally underrated by businesses today, in favor of ML and AI hype. For proof, search for an OR-titled job and see how many come up, compared to ML-titled. Lots of “ML” and “AI” is prettied up 50 year old OR techniques.