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. Compare this to ML problems: you don't often know if your data or model selection can produce the results you want. On top of that, you don't know if you're not getting results because of a bug (i.e. the debugging issues mentioned by the author) or if because you have fundamental blocks elsewhere in the pipeline.
Why is machine learning 'hard'? (2016)
51–60 of 144 posts
Re: Why is machine learning 'hard'? (2016)
#52I 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.
Re: Why is machine learning 'hard'? (2016)
#53I think one of the issues is that fixing a problem is a lot harder in ML than in software engineering. You know that the model fails on this particular data point. If you have identified a bug in the code and wrote a fix did a pull request as long you are able to test the code for conditions you failed on you would have solved the problem. With modern ml especially with nueral nets as long as you don't have a way to…
Certainly resonates with me. When the problem is something like a list being the wrong shape or two lists not maintaining a parallel order its basically invisible unless you load the mental model of the code and think deeply about it. Not like you’re going to notice your list of length 2,340,383 should start with [0.12, 1.67, 0.66 instead of [0.412, 0.567, 0.23
Re: Why is machine learning 'hard'? (2016)
#54I 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 nailed where I currently stand. At my company I've been a jack of all trades but mostly software/dba work. My boss and I were very excited about ML when the hype cycle was taking off several years ago and completed a successful project. Fast forward to today, I got loaned out to another team that lost their data scientist, and for the first time in my career I'm having to say - "I don't think we can do what you want." To me the "science" part really stands out. I have a decent grasp of methodologies and tools, but after weeks of dissecting the issue my conclusion is that they just don't have enough useful data...
Re: Why is machine learning 'hard'? (2016)
#55I 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.…
i.e. there a lot more unknown unknowns, which takes a lot more effort and intelligence to not stumble into haphazardly then most other fields.
Re: Why is machine learning 'hard'? (2016)
#56I 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.
Re: Why is machine learning 'hard'? (2016)
#57Machine 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…
> Machine learning isn't comparable to software development. It is a statistical modelling exercise.
It's neither of the two. Machine learning isn't comparable to any other human endeavor because in many cases, much more value comes out of the models than (seemingly) goes in.
LLMs for example are punching way above their weight. The ideas underlying their software implementations are extremely simple compared to the incredibly complex behavior they produce. Take some neural networks that can be explained to a bright high schooler, add a few more relatively basic ML concepts, then push an unfiltered dump of half the Internet into it, and suddenly you get a machine that talks like a human.
Obviously I'm simplifying here, but consider that state-of-the-art LLM architectures are still simple enough that they can be completely understood through a 10-hour online course, and can be implemented in a few hundred lines of PyTorch code. That's absolutely bananas considering that the end result is something that can write a poem about airplanes in the style of Beowulf.
Re: Why is machine learning 'hard'? (2016)
#58I 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 nailed where I currently stand. At my company I've been a jack of all trades but mostly software/dba work. My boss and I were very excited about ML when the hype cycle was taking off several years ago and completed a successful project. Fast forward to today, I got loaned out to another team that lost their data scientist, and for the first time in my career I'm having to say - "I don't think we can do what you w…
The situation is not bad then. Can they collect more data? Can they generate more data?
Re: Why is machine learning 'hard'? (2016)
#59I 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?
Re: Why is machine learning 'hard'? (2016)
#60I 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.…
I have always thought of ML (not DL) as phenomena that can be modelled mathematically.
It turns out that not all problems have a great mathematical model like self driving cars for instance and so the search continues...