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Machine learning is still too hard for software engineers

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Re: Machine learning is still too hard for software engineers

#21

Post by lbriner »

tehsauce wrote: post #18

Another perspective, someone with no programming experience whatsoever today can in 1 minute create a new ML application for natural language tasks with performance that would blow most SOTA systems from 5 years ago out of the water, using things like the openai API. And this trend will almost certainly continue, where many tasks can be programmed by simply asking/describing the problem to a massive model and letting…

That might be true but my experience is that people are much better at building tooling than they are at using it properly.

So many organisations can't even get the basics of efficiency and custom service right but they still invest heavily into cutting edge tech. I can imagine plenty of companies joining the ML bandwagon and still not even really knowing what they do as a business.

Re: Machine learning is still too hard for software engineers

#22

Post by monkeybutton »

deepsun wrote: post #19

I've dived into ML (and DL) with 17 years of software development experience. I'd say it's much easier than software. Yes, there's A TON to learn and experiment with, but still much less than with software. I was able to feel confident enough after just 1.5 years learning and kaggling, and passed easily ML interviews to SF Bay Area companies (hint -- all data science people are extremely glad to see software experien…

Bad pipeline means bad data, and bad data means bad model. Its as simple as that.

Re: Machine learning is still too hard for software engineers

#23

Post by lbriner »

rchaves wrote: post #3

I really like the premise, and I really agree with it, but I don't think a SaaS is a solution, the solution is trying to find better abstractions that makes things simple and easier for the developers, using code, and without limiting their flexibility, but that's the hardest thing to do! As an experienced software developer who used to learn a new framework every week, I thought ML was going to be piece of cake. In…

The issue with abstractions is that ML isn't just a few variables or dissimilar systems to choose from, the whole problem domain that could be helped with ML has so many dimensions. Natural language, speech processing, pattern recognition etc. are all completely different. The only thing they have in common is that you might use computers for them.

I think that is why people find ML so hard. It isn't a single sausage machine to create insight from, it is a set of entire stacks from philosophy all the way down to the electronics.

Re: Machine learning is still too hard for software engineers

#24

Post by tiborsaas »

Irishsteve wrote: post #2

I'm doing ML a long time, practice makes perfect etc. If anything is too hard, it is front end.

As a senior frontend engineer, practice makes perfect, frontend is easy-peasy. ML/AI seems to have this thick wall of math around it, if I really want to understand what makes the models going.

Re: Machine learning is still too hard for software engineers

#25

Post by jstx1 »

uoaei wrote: post #16

Wow, it's almost like machine learning and software engineering are different disciplines entirely! Just because both involve coding doesn't mean software engineers should be expected to have the math chops (stats/prob, linalg, calc, etc.) to make machine learning work for them... Vice versa is a little more complicated, because ML/DS can be done very inefficiently without the proper coding practices, but understandi…

> machine learning and software engineering are different disciplines entirely

I don't think so. Or more precisely, they might look different academically but you need to have both as a skill to build something useful.

Re: Machine learning is still too hard for software engineers

#26

Post by dTal »

tehsauce wrote: post #18

Another perspective, someone with no programming experience whatsoever today can in 1 minute create a new ML application for natural language tasks with performance that would blow most SOTA systems from 5 years ago out of the water, using things like the openai API. And this trend will almost certainly continue, where many tasks can be programmed by simply asking/describing the problem to a massive model and letting…

This seems a trifle hyperbolic. No programming experience whatsoever? Remind me what the 'P' in 'API' stands for?

Re: Machine learning is still too hard for software engineers

#27

Post by uoaei »

jstx1 wrote: post #25
uoaei wrote: post #16

Wow, it's almost like machine learning and software engineering are different disciplines entirely! Just because both involve coding doesn't mean software engineers should be expected to have the math chops (stats/prob, linalg, calc, etc.) to make machine learning work for them... Vice versa is a little more complicated, because ML/DS can be done very inefficiently without the proper coding practices, but understandi…

> machine learning and software engineering are different disciplines entirely I don't think so. Or more precisely, they might look different academically but you need to have both as a skill to build something useful.

In-built is the assumption that one person must have both competencies. This may be true for cash-strapped startups but this hardly plays well as general advice.

Re: Machine learning is still too hard for software engineers

#28

Post by swframe2 »

In general, getting a phd is the best way to go but it is not the only way. "The AI Epiphany" channel by Aleksa Gordić is worth watching. Check out his origin story: https://www.youtube.com/watch?v=SgaN-4po_cA He works at DeepMind. He is self taught; without a phd.

Re: Machine learning is still too hard for software engineers

#29

Post by fxtentacle »

"Machine Learning is too difficult" ... says company selling "ML platform [which] can be used by anyone and it only takes minutes to train your first model."

No, actually, you're just being dishonest. Even if you hide TensorFlow and the keras models behind a nice GUI, people still need that mathematics knowledge to succeed. And yes, pre-training is great. But you need a shitload of stochastic analysis to make sure that the pre-trained embedding won't distort your results.

"For a software engineer, the hardest thing about developing Machine Learning functionality should be finding clean and representative ground-truth data, but it often isn’t."

That is (in my opinion) an entirely bogus request. Machine learning is a mathematical / statistical tool for modeling large unknown functions. I feel like this sentence is akin in usefulness to:

"For a nuclear power plant, the hardest thing about building one should be to draw how the finished building will look like in the press release".

Someone "doing" machine learning without the requisite math knowledge is effectively driving blind. And worse than that, they don't even know what they don't see, because they lack the skills to identify their blind spots. That's how you end up with a "tank detection AI" that in reality just classifies the weather into bright vs. dark. [1]

Companies like this who promise advanced mathematical algorithms with no prior skill or knowledge are how we unleash a plague of buggy unverified automatons upon the world.

[1] https://www.lesswrong.com/posts/5o3CxyvZ2XKawRB5w/machine-le...

Re: Machine learning is still too hard for software engineers

#30

Post by mrfusion »

deepsun wrote: post #19

I've dived into ML (and DL) with 17 years of software development experience. I'd say it's much easier than software. Yes, there's A TON to learn and experiment with, but still much less than with software. I was able to feel confident enough after just 1.5 years learning and kaggling, and passed easily ML interviews to SF Bay Area companies (hint -- all data science people are extremely glad to see software experien…

Can you share how you switched into ML? How and what did you study? Any tips?
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