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Why is machine learning 'hard'? (2016)

ai.stanford.edu

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Re: Why is machine learning 'hard'? (2016)

#31

Post by sackfield »

I think a big difference between ML and regular programming is how the components at scale make the systems viable. When I was learning computer science, it seemed quite intuitive to me that you would start out with assembly, then go to a C like compiler, then abstract that to a JIT/dynamic type language, and go from that to the UI. I could see how each step in the layer added value and presented its tradeoffs.

Contrast that to ML and even though I have done a large amount of work in it (in both university and in industry), I still can't fully appreciate how the building blocks interact to form an entire system. I find that I use intuition from other systems I have read about or implemented (e.g. decision trees and tabular data, ReLUs and images) to reason about the results in new systems and guess at better configurations and architectures.

Might say more about me, but I always found ML was a "start big and go backwards" deal whereas computer science was a "start small and go forwards" deal.

Re: Why is machine learning 'hard'? (2016)

#32

Post by naveen99 »

dang wrote: post #3

Discussed at the time: Why is machine learning ‘hard’? - https://news.ycombinator.com/item?id=12936891 - Nov 2016 (88 comments)

Similar:

Machine learning is still too hard for software engineers - https://news.ycombinator.com/item?id=30432987 - 151 comments

Machine learning is easier than it looks - https://news.ycombinator.com/item?id=6770785 167 comments

Re: Why is machine learning 'hard'? (2016)

#33

Post by barelyauser »

sackfield wrote: post #31

I think a big difference between ML and regular programming is how the components at scale make the systems viable. When I was learning computer science, it seemed quite intuitive to me that you would start out with assembly, then go to a C like compiler, then abstract that to a JIT/dynamic type language, and go from that to the UI. I could see how each step in the layer added value and presented its tradeoffs. Contr…

When building models it is useful to spend some time finding out what you already know about the problem. Things you yet don't know you know. This kind of knowledge will greatly simplify the model.

I see newcomers making this mistake very often. In industrial vision, for example, the newcomers like to create very complicated models. I then show them that the "box" you trained a entire model to recognize will actually always be there in this position for the camera, because it sits in a conveyor belt which restricts its lateral movement. The problem can simply be solved with simple image processing. Stuff like that happens all the time.

Re: Why is machine learning 'hard'? (2016)

#34

Post by Aunche »

I don't think machine learning is particularly hard. It just involves a lot of brute force work and most of the time, you get a middling result that isn't particularly exciting. One of my friends had no CS background whatsoever but managed to make a basic Runescape mining bot by manually labeling hundreds of the correct colored rocks. His account got banned after a couple days though.

Re: Why is machine learning 'hard'? (2016)

#35

Post by jolt42 »

doctorM wrote: post #5

I'm a bit sceptical of the exponentially harder debugging claim. First it looks polynomially harder for the given example :p. Second other engineering domains arguably have additional dimensions which correspond to the machine learning ones mentioned in the article. The choice of which high level algorithm to implement is another dimension to traditional software engineering that seemingly exists and corresponds to t…

The article also pretends that there is only one correct answer, which seems atypical of the domain. The 1 green spot should extend somewhat fuzzily in each dimension in the ML case.

Re: Why is machine learning 'hard'? (2016)

#36

Post by mistrial9 »

PaulHoule wrote: post #7

The #1 thing that makes it ‘hard’ in real life is that nobody wants to make training and test sets. So we have 50,000 papers on the NIST digits but no insight into ‘would this work for a different problem?’ (Ironically the latter might have been exactly what academics would have needed to understand why these algorithms work!)

no, this is routinely cited in introductory remarks these days, but ignores some practical aspects of the competitive context, among other things.

Re: Why is machine learning 'hard'? (2016)

#38

Post by wwarner »

jruohonen wrote: post #2

"An aspect of this difficulty involves building an intuition for what tool should be leveraged to solve a problem." While I agree with the good point about debugging, like many others, I am rather worried that we're increasingly deploying AI/ML where we shouldn't be deploying it. Hence, the above quote.

Want to agree with you, as so many ML apps seem to be solutions looking for problems. But I actually feel that we are rapidly deploying ML in a development context for vastly improved results. The way that good models are built relies on many ML steps, and when the results finally come together the result is superior to what could have been custom designed. Broad adoption of something like probabilistic programming is coming soon.

Re: Why is machine learning 'hard'? (2016)

#39

Post by Mltrw »

whiplash451 wrote: post #12
PaulHoule wrote: post #7

The #1 thing that makes it ‘hard’ in real life is that nobody wants to make training and test sets. So we have 50,000 papers on the NIST digits but no insight into ‘would this work for a different problem?’ (Ironically the latter might have been exactly what academics would have needed to understand why these algorithms work!)

You’re not paying tribute to MNIST-1D and many other datasets (including the massive segmentation dataset released by Meta with SAM). Read the literature before lecturing the community.

We still don't have enough data and people are still wasting their time with trying to extend algorithms instead of making better training data.

I've worked on a dozen ml projects two of them before Alex net came out and I've never gone wrong by spending 80% of my time creating a dataset specific to the problem and then using whatever algorithm is top dog right now.

Labelled data is king.

Re: Why is machine learning 'hard'? (2016)

#40

Post by Mltrw »

ramesh31 wrote: post #11
PaulHoule wrote: post #7

The #1 thing that makes it ‘hard’ in real life is that nobody wants to make training and test sets. So we have 50,000 papers on the NIST digits but no insight into ‘would this work for a different problem?’ (Ironically the latter might have been exactly what academics would have needed to understand why these algorithms work!)

Would there be enough of a financial incentive to do so? Seems like a prime startup opportunity.

There is plenty of money in it but you need to sell b2b and tp enterprise. That is not fun and as such no one is doing it.

Put another way,if I were trying to do a start up in this space I'd spend 50% of my budget on marketing 25% on a third world data labelling sweatshop, 20% on data pipeline engineering and 5% on sexy ml stuff.

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