Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Why is machine learning 'hard'? (2016)

ai.stanford.edu

Post reply on HN
Print view View original

101–110 of 144 posts

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

#101

Post by DeathArrow »

4death4 wrote: post #51

I used to work on an ML research team. In addition to what the author mentions, there is an entirely separate issue: whether or not what you're attempting to do is possible with the approach you've chosen. Consider making an iOS app. For the most part, an experienced software engineer can tell you if making a given app is possible, and they'll have a relatively clear idea about the steps required to realize the idea.…

>whether or not what you're attempting to do is possible with the approach you've chosen

Knowing that depends on your level of understanding the field and the math behind and also experience. If you just know how to make API calls, then it's hard.

What would be problematic if you want to do sentiment analysis for some product reviews? Result is the public perception within a margin of error, you have your data, you know what you want, you know how to get there.

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

#102

Post by DeathArrow »

As with everything, if you don't know the fundamentals, the basics, you are very limited on what you can achieve and find stuff difficult.

To use a parallel from software if you just know a programming language, how to call libraries and APIs you can't compare to a guy with a solid CS background who also understands algorithms, data structures, complexity, math and knows how computers work down to NAND gates.

Because I have studied some ML fundamentals during my batchelor and master degrees I know I am not competent in ML and AI. But I know how to become competent if I need or want to. Most people do believe finishing a bootcamp and calling some frameworks in Python makes them competent.

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

#103

Post by jurgenaut23 »

DeathArrow wrote: post #101
4death4 wrote: post #51

I used to work on an ML research team. In addition to what the author mentions, there is an entirely separate issue: whether or not what you're attempting to do is possible with the approach you've chosen. Consider making an iOS app. For the most part, an experienced software engineer can tell you if making a given app is possible, and they'll have a relatively clear idea about the steps required to realize the idea.…

>whether or not what you're attempting to do is possible with the approach you've chosen Knowing that depends on your level of understanding the field and the math behind and also experience. If you just know how to make API calls, then it's hard. What would be problematic if you want to do sentiment analysis for some product reviews? Result is the public perception within a margin of error, you have your data, you k…

Well, even with a high level of understanding, any sufficiently advanced use case will still have some uncertainty regarding its "feasibility". Of course, you might think that some problems are "solved", e.g., OCR, translation, (common) object recognition, but MANY other problems exist where, no matter how experienced and knowledgeable you are, you can only have an educated guess as to whether a given model can achieve a given performance without actually trying it out.

Where experience and knowledge really pays off is in telling apart model performance from bugs. There is a real know-how in troubleshooting ML pipelines and models in general.

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

#104

Post by DeathArrow »

janalsncm wrote: post #62
tbrownaw wrote: post #59

Earlier quoted context omitted.

> you don't often know if your data or model selection can produce the results you want. Like, not knowing if your data set actually contains anything predictive of what you're trying to predict?

Here’s an example of something similar. Say you have a baseline model with an AUC of 0.8. There’s a cool feature you’d like to add. After a week or two of software engineering to add it, you get it into your pipeline. AUC doesn’t budge. Is it because you added it in the wrong place? Is the feature too noisy? Is it because the feature is just a function of your existing features? Is it because your model isn’t big eno…

>AUC doesn’t budge. Is it because you added it in the wrong place? Is the feature too noisy? Is it because the feature is just a function of your existing features? Is it because your model isn’t big enough to learn the new feature? Is there a logical bug in your implementation?

Or is it because lack of expertise and experience and because someone tries stuff blindly without understanding a bit in the hope they will nail it with enough fiddling?

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

#105

Post by jurgenaut23 »

DeathArrow wrote: post #102

As with everything, if you don't know the fundamentals, the basics, you are very limited on what you can achieve and find stuff difficult. To use a parallel from software if you just know a programming language, how to call libraries and APIs you can't compare to a guy with a solid CS background who also understands algorithms, data structures, complexity, math and knows how computers work down to NAND gates. Because…

Yes, bootcamps are really a great way to score high on the Dunning-Kruger score, but not so much on any other metric of value. You need lots of sweat and tears to become a capable and competent ML engineer, especially to tell apart intrinsic limitations (i.e., no signal in your data) vs extrinsic issues (e.g., a bug anywhere between ETL and prediction, or a methodological mistake).

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

#106

Post by DeathArrow »

bugglebeetle wrote: post #67
raincole wrote: post #56

Earlier quoted context omitted.

But aren't all science basically like this? If you know your hypothsis works before you do the experiments, it's not science anymore.

Not really. It’s easy to tell relative to existing methods whether the size of data will solve the problem. For example, if you’re trying to solve a classification problem with a large number of labels, but only have a small amount of training data for some (or all) of them, it will probably never work.

>but only have a small amount of training data for some (or all) of them, it will probably never work.

Transfer learning might help in some cases.

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

#107

Post by DeathArrow »

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…

I would start with a solid understanding of probabilities, statistics, calculus.

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

#108

Post by wegfawefgawefg »

chefandy wrote: post #75
wegfawefgawefg wrote: post #61

Earlier quoted context omitted.

Sometimes its really hard to convince an organization that their strategy wont work. Especially if ego or higher ups are convinced.

Many developers also mistakenly think their mighty dev super brain genius powers make them capable of accurately evaluating and critiquing any profession. I’ve had many developers try to explain design to me, even knowing I’m an experienced, educated professional designer. I can see why some might roll their eyes.

While I can understand your point, I don't think it was fair to assume that my case is like that.

This wasn't an issue of design. It was an issue of them not making decisions without good information. Trying to throw an fconv network at a task that clearly requires positional invariance (convnet was appropriate). They had never done ml before and didnt know anything about it.

Another one was going crazy with docker, and costed them a good year and a half. Idk why the boss insisted everything be behind docker. and i mean everything. He just thought containers were a selling point

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

#109

Post by DeathArrow »

epgui wrote: post #52

I find it difficult to buy the argument that “it’s not difficult because of the math”, at least in the way the author meant. I do however literally agree with the author: ML is not difficult because of the math. It’s difficult because for some reason people think the math is not important. But ML is math.

ML without math is like trying to play 5th Symphony without knowing notes, chords or any musical theory.

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

#110

Post by posterboy »

mistrial9 wrote: post #36
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.

what is "this"?
Post reply on HN