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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

#41

Post by xiphias2 »

alexcnwy wrote: post #31
swframe2 wrote: post #28

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.

Aleksa is great. Also check out Jeremy Howard from fast.ai - also no PhD but amazing teacher and contributes actively to research. Chris Olah (Google Brain, OpenAI, etc) go to university at all. PhD definitely not required.

I tried to build on top of fast.ai, and it was very easy to start, but all the hooks and magic in fast.ai 2 just made it extremely hard for me to understand and extend the code. I believe it went in a bad direction.

Re: Machine learning is still too hard for software engineers

#42

Post by rjsw »

fxtentacle wrote: post #29

"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 th…

If the data is garbage then it doesn't really matter how good your maths knowledge is, I challenge you to get a working "tank detection AI" when you are just training it on pictures of different cats.

The Nuclear Power industry is starting to think about stopping doing all designs on paper, maybe in a few decades they will have achieved this, sending a message that good data is the thing they should work on first isn't a bad idea.

Re: Machine learning is still too hard for software engineers

#43

Post by jacobr1 »

7h3kk1d wrote: post #8
yldedly wrote: post #7

Earlier quoted context omitted.

In my experience, both are true. I'm more on the ML side, and I can tell I don't have the kind of routine and habits that good software engineers have, though I'm learning. But on the other hand, and I've seen this from software engineers who've made the transition to ML, and clearly have a good handle on the concepts (in one case even published papers in ML journals), they don't seem to have the intuition that allow…

I think the above commenter is more critiquing the notion of relying on intuition all together.

Even in non-ML software engineering you still have architectural tradeoffs, that while you can in part make reasoned arguments about, you still are relying on the intuition of your technical leadership.

Re: Machine learning is still too hard for software engineers

#44

Post by surrTurr »

I‘d argue that integrating ML into a project isn‘t the hard problem, as it was described in this article. Most people will, after some research, be able to build some basic ML functionality using popular libraries and a bit of python. The hard thing about ML is, when you try to actually understand it. I‘ve met many „ML engineers“ who do their job but do not actually understand what they‘re doing. Understanding ML doesn‘t have to do a lot with coding. It‘s math and statistics.

Re: Machine learning is still too hard for software engineers

#45

Post by efitz »

fxtentacle wrote: post #29

"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 th…

Who cares if you overfitted? See, the model has 100% success rate vs the training set!

Who cares if it denies bail to minorities or hits a few pedestrians from time to time?

The problem isn’t that ML is too hard, it’s that it’s too easy. Crazy people keep connecting ML to systems that matter- that have real, irreversible impact to humans- and they don’t understand it.

I wish ML were 1000x harder/more expensive to integrate so the economics would drive away frivolity.

Re: Machine learning is still too hard for software engineers

#47

Post by jacobr1 »

monkeybutton wrote: post #22
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.

Bad pipeline is too strong. But good enough pipeline + good enough models > bad pipeline + great model

Re: Machine learning is still too hard for software engineers

#48

Post by uoaei »

jstx1 wrote: post #39
uoaei wrote: post #27

Earlier quoted context omitted.

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.

It's not an assumption, that's my main point - you need both skills in the same person. And it's true everywhere including in the biggest companies.

Any good tech lead will remove that need, so no, still not good general advice.

Re: Machine learning is still too hard for software engineers

#49

Post by picardo »

fxtentacle wrote: post #29

"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 th…

I agree with this. The emphasis of any product that wants to democratize ML should be on making it easy for lay people to train models, and to collaborate with ML experts.

Re: Machine learning is still too hard for software engineers

#50

Post by exdsq »

ramesh31 wrote: post #11

Can confirm. Data science team just schooled the entire engineering department in our Deep Racer tournament.

Makes sense though, right? Data team did data stuff better. If you had a different task with an engineering focus you'd see the opposite.
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