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Machine Learning: The Great Stagnation

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Re: Machine Learning: The Great Stagnation

#51

Post by Tenoke »

funcDropShadow wrote: post #45
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)

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 brows…

I don't know but I live in Berlin, dont know German and use google translate on all sorts of official and non-official stuff (e.g. for my Anmeldung two weeks ago, reading up on Mietendeckel, news reports, random letters and shopping sites etc.) and it works almost always. Obviously not as perfect as a real translator but good enough > 99% of the time.

Re: Machine Learning: The Great Stagnation

#52

Post by eli_gottlieb »

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.

>If you could name three of them I'd be really grateful.

Latent Dirichlet Allocation, message-passing in Hidden Markov models, and Naive Bayes for spam filtering. Outside my subfield, there's always the basic handwriting recognition employed in ATMs.

Re: Machine Learning: The Great Stagnation

#53

Post by rolisz »

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…

Image tagging is not in that category. Google Photos, Synology Moments and other similar projects are valuable for end users. Nobody had time to manually tag hundreds of gigabytes of photos with even 10% of the items in them.

Re: Machine Learning: The Great Stagnation

#54

Post by marcinzm »

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…

>Soon investors and CEOs will realize that ML is effective in narrow ways and that not everything needs ML.

Sure but ML is also not being used in 90+% of the narrow use cases it is good for.

>They will also realize that 1 ML team + ML as a service (Azure ML, Sagemaker, Google AI platform) is cheaper and works more reliably.

These services replace part of the ML Ops component but not much else except in very narrow use cases. There's also already GUI based tools for building models but they're also not used much. I don't see this part of ML Ops as being the majority of what ML Engineers do so the majority of ML Engineers don't have to worry.

Re: Machine Learning: The Great Stagnation

#55

Post by _0ffh »

knuthsat wrote: post #9

There's a nice talk by Yann LeCun where he goes on to explain really well why deep learning has such fast progress. [0] He goes on to explain how theory always comes later. I thought that information theory came before practice but turns out it also came after. (there was a bunch of heuristics for sending messages with teletypes) A nice example is Roman technological advances in architecture and materials that predat…

I've once read a paper in the late 90s or so which openly said ML research was partly "experimental maths".

Re: Machine Learning: The Great Stagnation

#56

Post by api »

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…

Trying to use ML to get rid of programmers will just replace them with ML experts who also have to be programmers to implement the models and munge all the data. These people will in turn have to be paid more than the original programmers were.

Re: Machine Learning: The Great Stagnation

#57

Post by curiousllama »

mettamage wrote: post #35
knuthsat wrote: post #9

There's a nice talk by Yann LeCun where he goes on to explain really well why deep learning has such fast progress. [0] He goes on to explain how theory always comes later. I thought that information theory came before practice but turns out it also came after. (there was a bunch of heuristics for sending messages with teletypes) A nice example is Roman technological advances in architecture and materials that predat…

So in that sense forming a theory about something is integrating all the practical stuff you've seen before. To me that make sense, IMO that "practical stuff" are what experiments are, aren't they? Probably the experiments are far from perfect, if remotely good even, but that's how empiricism should work? I think? (hmm... not entirely sure)

It can go both ways. E.g. often, experimental physicists will try to prove/disprove a priori predictions from theoretical physicists.

Theory is just an explanation of hw the world works: you can come up with that explanation as a reason for observations, or as a logical consequence of other theories (which is then verified by observation).

Re: Machine Learning: The Great Stagnation

#58

Post by Jugurtha »

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.

We do machine learning for large organizations and business is good. We've been doing it for many years. We're even making our platform (https://iko.ai) to do it faster and more consistently as it takes care of all the menial tasks.

These projects have an impact in the real world [predictive maintenance on infrastructure that serves people, for example].

Re: Machine Learning: The Great Stagnation

#59

Post by mbeex »

Tenoke wrote: post #51
funcDropShadow wrote: post #45

Earlier quoted context omitted.

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 brows…

I don't know but I live in Berlin, dont know German and use google translate on all sorts of official and non-official stuff (e.g. for my Anmeldung two weeks ago, reading up on Mietendeckel, news reports, random letters and shopping sites etc.) and it works almost always. Obviously not as perfect as a real translator but good enough > 99% of the time.

In this case, give DeepL a try. Translations between English and German at least, are magnitudes above Google, IMO. Not without errors, of course.

Re: Machine Learning: The Great Stagnation

#60

Post by gbolcer »

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…

It's just another tool. I have this saying: all machine learning is just clever tricks and techniques until someone learns how to brute force the solution.

It's the same adoption/business technology tension that has existed since Frederick Taylor in the early 1900s or Vonnegut's Player Piano concept where they propose taking a recorder to automated human-adverse tasks by recording their movements. The hype is trying to replace people. Real-world adoption seems to take place where machine learning complements human activity to do things humans are not good at, not replaces it. It's not making them dumber, it's making them more enabled.

[reference]

In the early 1900’s Frederick Taylor called public attention to the problem of ‘national efficiency’ by proposing to eliminate ‘rule of thumb’ management techniques and replacing them with the principle of scientific management [157]. Scientific management reasons about the motions and activities of workers and states that wasted work can be eliminated through careful planning and optimization by managers. While Taylor was concerned about the inputs and outputs of manufacturing and material processes, computers and the information age brought about parallel concepts in the management and organization of information and its processes. Work efficiency could now be measured not by bricks or steel, but by their information flows.

F. W. Taylor, “Principles of Scientific Management”, Harper & Row, New York, NY, 1911.

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