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Machine Learning: The Great Stagnation

marksaroufim.substack.com

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Re: Machine Learning: The Great Stagnation

#41

Post by otabdeveloper4 »

Tenoke wrote: post #29
otabdeveloper4 wrote: post #19

Earlier quoted context omitted.

> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.

Here's 3 off the top of my head, but there's more especially when you get into less flashy territory. Translation (Google translate, DeepL) Automatically generated product descriptions, sometimes also edited by humans (Alibaba) Image Tagging (Facebook photos)

Okay, I should have worded my comment more carefully.

These applications seem to firmly fall into the "I'm willing to compromise on quality if I don't have to pay a living person a wage" niche, so they're value-destroying, not value-creating.

Are there examples of value-creating applications for ML? (From a business point of view; obviously the "shitty translations but at no cost" proposition creates value for the average Internet user.)

Re: Machine Learning: The Great Stagnation

#42

Post by belval »

otabdeveloper4 wrote: post #19
superbcarrot wrote: post #12

Earlier quoted context omitted.

That's unlikely. There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. The worst case for the field is that progress slows down, people realise that their expectations were unrealistic and the hype inevitably dies down. Which has to happen eventually. So even if ML isn't the hottest thing or a massively growing field, it will still be us…

> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.

https://docs.aws.amazon.com/whitepapers/latest/aws-overview/...

Most of these were developed woth actual business partners and are being used right now.

Re: Machine Learning: The Great Stagnation

#43

Post by b3kart »

Some good points in the article, but I disagree with the tone and the conclusion.

> we’ve rewarded and lauded incremental researchers as innovators, increased their budgets so they can do even more incremental research

There isn't a scientific field where every single paper is groundbreaking. It's a Brownian motion of small incremental innovations, until eventually we stumble upon something big (like deep learning). In no way is machine learning unique in this. Sounds like the author is simply disappointed that, like in any other profession, day-to-day of a researcher is a slog and not a perennial intellectual festival. We've been in an exciting deep learning craze for a while, but it's silly to expect it to last forever. Back to the grind now.

> Machine Learning Researchers can now engage in risk-free, high-income, high-prestige work

Not sure what author means by "risk-free". Yes, if you're not publishing enough you're most likely not going to starve. Is that a bad thing? Is the survival instinct the only good motivator for doing good research?

I would argue that there's plenty of risk, in that people who don't publish good research don't get very far in their academic careers, which in my view is good enough motivation. "They must do good research or starve" is a rather cynical take, especially from someone who seems to not be doing too badly for themselves.

I'd rather more fields provided similar benefits. Maybe then going into science wouldn't be associated with so much sacrifice, so more smart people would choose science over investment banking or such, and we'd make more scientific progress faster.

> CNNs use convolutions which are a generalization of matrix multiplication.

A nitpick: CNNs are most definitely not a generalization of matrix multiplication. In fact, the opposite: you can view CNNs as a matrix multiplication with a particular matrix structure.

Re: Machine Learning: The Great Stagnation

#44

Post by Tenoke »

otabdeveloper4 wrote: post #41
Tenoke wrote: post #29

Earlier quoted context omitted.

Here's 3 off the top of my head, but there's more especially when you get into less flashy territory. Translation (Google translate, DeepL) Automatically generated product descriptions, sometimes also edited by humans (Alibaba) Image Tagging (Facebook photos)

Okay, I should have worded my comment more carefully. These applications seem to firmly fall into the "I'm willing to compromise on quality if I don't have to pay a living person a wage" niche, so they're value-destroying, not value-creating. Are there examples of value-creating applications for ML? (From a business point of view; obviously the "shitty translations but at no cost" proposition creates value for the av…

I don't understand this. Do you think that e.g. the average engineer is value destroying because if the business hires a more expensive and experienced one they will do a better job?

In either case it is only 'value-destroying' if the business has unlimited resources.

Re: Machine Learning: The Great Stagnation

#45

Post by funcDropShadow »

Tenoke wrote: post #29
otabdeveloper4 wrote: post #19

Earlier quoted context omitted.

> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.

Here's 3 off the top of my head, but there's more especially when you get into less flashy territory. Translation (Google translate, DeepL) Automatically generated product descriptions, sometimes also edited by humans (Alibaba) Image Tagging (Facebook photos)

Google translate, good enough?

Today, I've received a package from Amazon containing router bits (for wood working not IT). It contains a so called "User Manual" which is obviously so badly translated, I assume automatically, that it will only fool a spell checker, that it is actually written in German.

I often hear and read good things about Google Translate but every time I read something from it, e.g. when a browser or webpage helpfully decides that I would prefer a butchered salad of German words instead of an English web page, I am repulsed.

Re: Machine Learning: The Great Stagnation

#46

Post by curiousllama »

> We’ve gamified and standardized the process so much that it’s starting to resemble case studies at consulting interviews.

This is precisely true, as someone who has passed both screens for competitive jobs.

Cracking the coding interview Case in Point

Live coding Do 3-digit multiplication in your head (eg 347 * 469)

Sorting algorithms M&A Evaluation Frameworks

I could go on...

You just memorize a bunch of crap that's vaguely (but not really) applicable but is super random, and then you just keep asking "do you want me to keep going" in various tones until they tell you to stop.

Re: Machine Learning: The Great Stagnation

#47

Post by wrinkl3 »

otabdeveloper4 wrote: post #19
superbcarrot wrote: post #12

Earlier quoted context omitted.

That's unlikely. There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. The worst case for the field is that progress slows down, people realise that their expectations were unrealistic and the hype inevitably dies down. Which has to happen eventually. So even if ML isn't the hottest thing or a massively growing field, it will still be us…

> There are many machine learning applications that have been shown to be good enough for commercial use and they aren't going anywhere. If you could name three of them I'd be really grateful. Serious question; everything surrounding ML seems to be only good for (non-monetizable) art projects. As art it is amazing, not going to lie, but "commercial use" seems like a huge stretch.

Do you consider voice assistants, DLSS and protein folding predictors to be non-monetizable art projects?

Re: Machine Learning: The Great Stagnation

#49

Post by Ericson2314 »

mlthoughts2018 wrote: post #37
Ericson2314 wrote: post #25

I still feel like much of AI is a plot to dumb down the modern economy. We want our business people to be just as effective as our quants; we want nothing to require real intellectual labor. The idea that you traditionally have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and all the more so because you are utterly dependent…

> “ The idea that you have these programmers who spout mumbo-jumo all day, cost a lot of money, and seem to always be planning stuff behind your back is threatening, and this breaks their control over the means of production.” That is a very bizarre description of ML engineers. In every company I’ve worked at, ML is a team or teams that partners with product managers and other engineering teams to learn about problem…

> That is a very bizarre description of ML engineers

No no no, that's a description of regular programmers.

There's so much more arcana in the field of programming and computer science as a whole, especially with the piss-poor job we've done deprecating bad old interfaces etc. (the monster that is modern Unix grows without bound). And of course there are the various langauge and other fads. All that is a nightmare for a traditional business person, whether they know it or not, and the regular programmers probably feel like an extortion racket of sorts.

Re: Machine Learning: The Great Stagnation

#50

Post by mlthoughts2018 »

screye wrote: post #32

There is about to be a 'great pivot' in ML. There has been a rabid frenzy of throwing money at anything that has ML in it. Soon investors and CEOs will realize that ML is effective in narrow ways and that not everything needs ML. They will also realize that 1 ML team + ML as a service (Azure ML, Sagemaker, Google AI platform) is cheaper and works more reliably. The services will keep improving and an underpaid medioc…

I am an experienced ML manager in a large ecommerce company, and I mostly agree with you, and I can’t wait for this to happen - and I think people just entering college or grad school for ML should not fear it. It’s a good thing.

Right now, there is so much misunderstanding about what ML is, what resources it needs, and how it works that the corporate environment is very stressful.

ML jobs are well paid, but they are NOT fun. No one understands ML devops & the infra needs to enable tight experimentation loops. Existing observability and telemetry systems are wildly bad for model training, reproducibility or any form of online or semi-online learning. As an ML engineer you’ll have to take on huge workloads of devops, infra, tooling, data munging. I’ve seen more than a few brilliant ML engineers burnout and quit because of this.

As ML becomes better understood as a boring technology, and decisions around ML projects, team structure and especially ops support start to get more standardized, I think this will get better.

The pivot you mention means a thinning out of the headcount on the pure ML research side. But it also means opening up more positions in ML engineering, infra & devops.

If people choose their specialization appropriately and remain open to being less on the research side of this, then I think there will continue to be lots of opportunities for high-paying jobs, and people will know their required responsibilities more unambiguously and probably will be happier, rather than dredging through the endless series of bait and switch jobs that exist today, promising a focus on ML research but typically forcing you more into ML devops & data platform management.

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