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Why is machine learning 'hard'? (2016)

ai.stanford.edu

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

#61

Post by wegfawefgawefg »

Cacti wrote: post #42
barelyauser wrote: post #33

Earlier quoted context omitted.

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…

You let newcomers dick around for months with a net on an industrial vision problem? This stuff was solved two decades ago. Why didn’t you just tell them?

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

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

#62

Post by janalsncm »

tbrownaw wrote: post #59
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.…

> 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 enough to learn the new feature? Is there a logical bug in your implementation?

All of these hypotheses will take on the order of days to check.

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

#63

Post by wegfawefgawefg »

roenxi wrote: post #48

Machine learning isn't comparable to software development. It is a statistical modelling exercise. This is like asking why advertising is hard - if a non-expert wades in to a different domain then they may find it has different challenges than what they are used to! This is just a specific case of the normal things that analysts routinely deal with. The major challenges in this youthful field of machine learning are…

I dont feel like im doing statistical modeling when i do ml. Usually feels more like pipe alignment, followed by tremendous amounts of debugging.

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

#64

Post by janalsncm »

jolt42 wrote: post #35
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.

There isn’t only one correct answer. Quite the opposite actually, many configurations give you local maximas. The difficulty is that it can be hard to explain from first principles why one local maxima is good.

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

#65

Post by wegfawefgawefg »

MichaelRo wrote: post #27
ramesh31 wrote: post #11

Earlier quoted context omitted.

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

>> Seems like a prime startup opportunity. Sometimes it's just ... hard. Apply some thought maybe before blindly parroting "profit!" Reporter: "Why is it hard to cure cancer?". Crowd: "Would there be enough of a financial incentive to do so? Seems like a prime startup opportunity!" Reporter: "Why is it hard to end World poverty?". Crowd: "Would there be enough of a financial incentive to do so? Seems like a prime sta…

A cure for cancer would be terribly profitable. For a while.

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

#66

Post by nerdponx »

raincole wrote: post #56
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.…

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

Yes, and science is "hard" compared to software development in a lot of ways. Less certainty of success and poorly defined success criteria.

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

#67

Post by bugglebeetle »

raincole wrote: post #56
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.…

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.

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

#68

Post by salty_biscuits »

wegfawefgawefg wrote: post #63
roenxi wrote: post #48

Machine learning isn't comparable to software development. It is a statistical modelling exercise. This is like asking why advertising is hard - if a non-expert wades in to a different domain then they may find it has different challenges than what they are used to! This is just a specific case of the normal things that analysts routinely deal with. The major challenges in this youthful field of machine learning are…

I dont feel like im doing statistical modeling when i do ml. Usually feels more like pipe alignment, followed by tremendous amounts of debugging.

That is also what statistical modelling feels like. EDA and data cleaning.

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

#69

Post by shostack »

barelyauser wrote: post #33
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…

When I was working on a pet project to teach myself how to build a scoring model based on analysis of images on mobile I went down a whole rabbit hole on how to detect where the image is in a photo to draw a box around it and then compress to 500x500.

In reality, if I'm using a phone,I can just create a square frame for the user to center the image in and then compress.

Sometimes the simplest solution is the one you don't get to till after you slog through the harder approach. I'm glad I learned a bit about image processing with Pytorch along the way.

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

#70

Post by galaxyLogic »

tbrownaw wrote: post #59
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.…

> 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?

The Farmer is Turkey's best friend. Turkey believes so because every day Farmer gives Turkey food, lots of food. Farmer also keeps the Turkey warm and safe from predators. Turkey predicts that the Farmer will keep on being his best friend also tomorrow. Until one day, around Thanksgiving, the prediction goes wrong, awfully wrong.
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