Comment by raiyu | Hacker News Reader

Comment by raiyu | Hacker News Reader

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The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small. They are usually tremendously large datasets at gigantic companies, and they probably have to build in house expertise because it's hard to package this up into a product and resell it for various industries, datasets, etc.

Certainly something like autonomous driving needs machine learning to function, but again, these are going to be owned by large corporations, and even when a startup is successful, it's really about the layered technology on-top of machine learning that makes it interesting.

It's kind of like what Kelsey Hightower said about Kubernetes. It's interesting and great, but what will really matter is what service you put on top of it, so much so that whether you use Kubernetes becomes irrelevant.

So I think companies that are focusing on a specific problem, providing that value added service, building it through machine learning, can be successful. While just broadly deploying machine learning as a platform in and of itself can be very challenging.

And I think the autonomous driving space is a great example of that. They are building a value added service in a particular vertical, with tremendous investment, progress, and potentially life changing tech down the road. But as a consumer it's really the autonomous driving that is interesting, not whether they are using AI/machine learning to get there.

“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.”

Thankfully transfer learning and super convergence invalidates this claim.

Using pre-trained models + specific training techniques significantly reduces the amount of data you need, your training time and the cost to create near state of the art models.

Both Kaggle and google colab offer free GPU.

>Thankfully transfer learning and super convergence invalidates this claim.

IME it is nowhere near as universally successful as this suggests.

> Both Kaggle and google colab offer free GPU.

I think this sentence invalidates your argument against:

“The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small.”

In a hobbyist world, free GPU time is an amazing thing, and you can do a lot of fun and rewarding projects using transfer learning and other techniques that avoid heavy engineering and data processing. In a business world, where your product must consistently and accurately perform well, problems that may be solved by ML need to be heavily scrutinized and researched, because for most problems there are cheaper, faster, more robust solutions. Free GPU time doesn't weigh in at this scale.

Sure if ML/DL is your core business than yeah it doesn't make sense.

If ML/DL is an add-on to help augment your business (separate from the core value) then yes transfer learning and free GPU's will get you good returns.

How would you explain the rise (and success) of machine learning in science? A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.) and extremely finite data.

It's not clear there has been any deep impact actually, but there has been a lot of discussion (and grant proposals)

I've seen a lot of cross pollination of ML and AI techniques into various disciplines. A large percentage just didn't work at all, most of the rest were more "kind of interesting, but". Nothing earthshaking happened although pop sci press likes to talk about it a lot.

If you have more digital data than you used to, using modern free frameworks and toolkits to do basic (i.e. older, boring, but understood) ML stuff to understand it seems to have a reasonable return. Mostly I think this is because it becomes accessible to someone without much background in the area, and you can do reasonable things without having to put 6 months of reading and implementing together before starting.

How do you define success? Adoption? Because right now, writing "we will use machine learning to solve X" in a grant proposal is an easy way to increase chances of getting funding.

I'm not sure there is a rise. 'Science' is a huge domain. Machine learning if I had to guess maybe plays a role in < 1% of them, and that may be overstating it.

Also it's doubtful to even categorize machine-learning as science. The goal of science is to generate insight and knowledge, ML solves particular engineering problems or searches problem spaces, it doesn't build fundamental scientific models.

Can you elaborate on what you mean by "A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.)" ? I know a bunch of labs that apply machine learning to specific tasks, and the parts you list each can easily take up multiple people for years for a single task - not counting data acquisition, because data is definitely not "extremely finite", you need lots of quality data, and improving data is something that always gets improvements and can easily eat up more manpower than you can have budget, no matter what that budget is.

...because previously, the academics would use an army of undergrads to do the same data labeling that ML accomplishes.

(The dis-economy of scale hurts less if you're already starting from a point with the manual labor.)

Its now a lot cheaper in the 1980's than when I worked at the worlds leading hydrodynamics orgs.

I briefly looked at using neural nets to analyse data from an experiment - analysing the efficacy of toilet bowl designs.

The entry level hardware was £250k in 1981 - it was much cheaper to take photo's and have a research assistant count squares.

Now you could use fairly cheap commodity hardware to do it.

It would have been an amazing cutting edge project if we could have got some government funding we did have an in-house knowledge engineer.

It's interesting that the industry constantly has to relearn the idea that tech needs follow business needs, not the other way around. As you said, so many teams rushing to containerize, but if the services you run are piles of junk, do your users care about whether kubernetes can scale based on memory instead of cpu? Similarly, many effective "recommendation engines" are just inverted indexes and not fancy ML models, and are a hell of a lot cheaper.

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