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The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite nar

The trouble is that people have been sold this idea that ML/AI can do amazing things, without properly being told that really the things it can do are quite narrowly-scoped. They've been sold the Star Trek computer idea. For example, years ago I was working on a prototype/proof-of-concept thing for instrumenting industrial machinery with stick-on small computers. Simple stuff - attach accelerators, temperature, humidity etc sensors to existing machines and collect the data and send it back. The management thought we'd be able to apply machine learning on the data to get "business insights" from the all-powerful machine. They didn't know what these insights might be, just that ML/AI would generate them and therefore make the business a fuck-ton of money because AI generated novel new "business insights" that no one had thought of before and so transform the business. They thought it was just a magic box that would generate unbounded magic answers for their needs by passing in just some temperature and humidity readings or whatever, and then it would tell them they need to make more brown bread and less bagels in the North East region etc. In reality, as I understand it, currently ML/AI requires us to know what the possible answers can be before we even begin training the network. So the classic example is it needs to know that the possible MNIST digits are 0-9, or that you are looking for one of 100 image classes etc. You cant train a network with the MNIST digits, and then have that network tell you what shares to buy or sell. Sure you can lop off the final layer and repurpose some of the middle layers, but you still need to train it to classify the inputs into categories you define up front. It won't give you a novel answer that you have not trained it for. ... at least that is how I understand it. Things may have changed over the past 5 years or so. That said, I do agree there have been some cool things lately like machine vision etc. I don't think it will be that huge an industry though - it feels like a lot of it is largely just commoditised now (which is good) and it will be just like any other library you pick - like picking a UI framework for a web app. Just pick up a pre-trained network from modelzoo and get on with your real business requirements for 99% of people using ML, while the other 1% (at FAANGs et al) and academia churn out new models.

p1esk 6y ago
Things may have changed over the past 5 years or so. Things may have changed over the past 5 weeks or so with GPT-3.
- Judgmentality 6y ago
  Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting democracy. I'm still waiting for AI to figure out self-driving cars, natural language processing, and other "holy grail" problems that researchers have been chipping away at for decades but we are still a long way from having "solved" them. Also in the case of self-driving cars, there is actually a very serious problem of it being difficult to guarantee safety. You can't tie every input to every output to demonstrate how it will behave (it is way, way, wayyyyyyyy too big for that), so you have to show statistically by driving so many miles that it's better. Which is still sound from an engineering perspective! But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times!
  - p1esk 6y ago
    Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and useful, should I care if it happened by accident, or without "understanding"? Can the knowledge encoded in the model parameters be interpreted as some form of understanding? If no, why not? What's missing exactly? What is different from how human scientists operate?
    - Judgmentality 6y ago
      Well you can randomly string words together and occasionally get lucky and make a meaningful argument, but that doesn't mean you've created a good method for constructing new ideas. It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.
01100011 6y ago
> currently ML/AI requires us to know what the possible answers can be before we even begin training the network Isn't that what unsupervised learning is for?
- bcrosby95 6y ago
  I took it more to mean that, e.g., when solving for 1+1 it won't be allowed to answer "tomatoes".
- HarHarVeryFunny 6y ago
  There's no such thing as truly unsupervised learning - any machine learning algorithm is defined by some guidelines you've provided (or built-in) for what output it has to generate. For example, you might think of data clustering as an unsupervised problem, but in reality you're still providing supervision in terms of what similarity measure to use and some control over how many clusters it should generate; and, at the end of the day, the output is always going to be a set of clusters, not, say, a stock tip or wry comment on the nature of your data!
YeGoblynQueenne 6y ago
This is a good explanation of how things work and things haven't changed much in the past 5 years. What changes the most is the amount of data and computing power ("compute") that are used in training neural nets. Architectures are also tweaked slightly although they continue to be largely based on the two architectures that started the current deep neural net boom, Long-Short Term Memory Networks (LSTMs) and Convolutional Neural Networks (CNNs), which are largely used for natural language processing and machine vision respectively. Every once in a while there is a bit of excitement about results that come out of adding a new parameter which is then given a fancy name like "attention" and generates much breathless copy about how neural nets will soon become as intelligent as a dog/ your three year old/ your personal assitant etc. Usually though it's not clear whether these new parameters or architectural tweaks are really responsible for the success of the proposed techniques and when one looks at the field as a whole it becomes more and more apparent that the most successful work is backed by the most data and the most computing power. To slightly er tweak your prediction, I think what'll happen is that, indeed, like you say, there will be libraries and APIs and so on (well, there already are) but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches). So in terms of academia perhaps we should expect a shift away from neural nets, to something that's easier to do research on.
- rpastuszak 6y ago
  > but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches). That makes me particularly sad. In a utilitarian sense, the ideal situation would involve new approaches being democratised. Instead, we're ending up with walled gardens. The current situation isn't dissimilar to what has happened to the Web Platform during past 5-10 years. And, similar predictions are made wrt VR and AR (both seem to attract different types of investors).
TrackerFF 6y ago
That's actually something ML is incredibly useful at, when it comes to machines with sensors - failure prediction / anomaly detection, etc. In the industry, (preventive) maintenance takes up a pretty huge chunk of resources. It's something techs need to do often, and it's often a laborious task, but it's obviously done to reduce downtime. So the business insight, as they like to call it, is to reduce costs tied up to repairs and maintenance. All critical applications have multiple levels of redundancy, so that a complete breakdown is very unlikely, but it's still a very expensive process if you're dealing with contractors. If you can get techs to swap out parts before the whole unit goes to sh!t, then that's often going to be a much cheaper alternative. But, in the end, it comes down to the quality of data, and the models being built. A lot of industrial businesses hire ML / AI engineers for this task alone, but expect some magic black-box that will warn x days / hours / minutes ahead that a machine/part is about to break down, and it's time to get it fixed. And they unfortunately expect a near-perfect accuracy, because someone in sales assured them that this is the future, and the future is now.
- nmyk 6y ago
  Yep, you and the user you're replying to are both right in different ways. One thing's for sure - machines don't generate "insights" on their own. Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making. In a supervised learning problem, the range of possible outputs is already known, meaning the model output will never be categorically different from what was in the training data. The knowledge obtained is meaningful as long as the training labels are meaningful, but it can never be new. Unsupervised learning doesn't have a notion of "training data" but that means an unsupervised model's output requires additional interpretation in order to be meaningful. It is possible to uncover new structures and identify anomalies in new ways, but this knowledge isn't meaningful until someone comes in and interprets it. Applied to the specific example where sensor data is used to try to generate insights about machine functionality: Either you can only predict the types of failures you've already seen, or you can identify states you've never seen but you wouldn't know whether they mean the system is likely to fail soon or not. It's the Roth/401(k) tradeoff. For model output to be useful, someone must pay an interpretation tax. The only choice is whether it is paid upon insight deposit or withdrawal.
dr_dshiv 6y ago
To look at a beautiful reframing of AI as a tool and not as a quasi-other agent, please enjoy this page: https://nooscope.ai/ https://nooscope.ai/
a1369209993 6y ago
> the possible MNIST digits are 0-9 Except - and this rather ties into your point - those are not the only possible digits; your network also has to deal with (ie reject) other possible digits such as "P", "E", "3̸̶", or "[Forlorn Sigil of Amon-Gül redacted]"[0], which look like, but are not, decimal digits. 0: https://www.youtube.com/watch?v=ajGX7odA87k https://www.youtube.com/watch?v=ajGX7odA87k
- HarHarVeryFunny 6y ago
  That depends on how you train it. If you only train it on examples of 0-9 then those are the only outputs it's going to give. If you fed a "P" into such a net then the outputs would be the degree of similarity of that "P" to each of the (0-9) digits it was trained on. You could of course threshold the output and ignore any prediction with confidence less than, e.g., 90%. If you wanted the net to do a better job of rejecting non-digits, or at least some specific ones, then you could include a bunch of non-digit examples in your training data (so now your net has 11 outputs: 0-9 and "non-digit"), then hopefully - but not necessarily - it's highest confidence prediction will be "non-digit" when presented with a non-digit input.
  - a1369209993 6y ago
    > If you only train it on examples of 0-9 then those are the only outputs it's going to give. Exactly. > 11 outputs: 0-9 and "non-digit" IIRC, this doesn't work (or not well) because the net tries to find similarities between the various members of the set "everything except these ten specific things", but you could just require low confidence for all digits on non-digit inputs as part of the gradient descent function. The problem is more that - if people with decision-making authority trust the AI to not be insane and evil by default - failure modes like this have to occur to you before the AI starts misbehaving in production.
HarHarVeryFunny 6y ago
The best way to think of today's AI capability is: automation. You train it with a lots of examples of "given this input, this is the output i want", and hopefully it learns to get the "correct" (similar input => similar output) output for new inputs that you feed it. i.e. you've now automated the process of figuring the correct output for a given input. There is also the "reinforcement learning" AI paradigm where the trained AI is choosing actions (from a given repertoire) in order to maximize an action outcome score based on some scoring function you provided. This is appropriate in a situation where you want the "AI" to do something more than just select the correct output, but again no magic - you're having to anticipate and score the potential outcomes.
- dumb1224 6y ago
  In my opinion this type of tasks is the typical AI tasks that get most exposed to the general public. The AI/ML model is set to attempt a human task. The aspiration is to get as good as a human (or faster/better more precision etc). Classic statistical inference/ models usually don't perform very well in these scenarios(or do they?). The typical AI methods train a model that utilises features that don't make much sense to humans (pixels/texture/word tokens etc). In contrast with traditional statistical modelling where each feature were given 'meaning' and their importance explored via investigating the observations (data) using properties of well studied mathematical models, the AI methods that often made the press don't put a lot of emphasis on these properties. The advancement is a push to utilise all available information/data to outstanding 'performance'. The explanation of the inner features within that math space is usually secondary (though I don't mean that authors of novel methods don't care about mathematical modelling). I might be too naive here but that's how I feel after trying out many methodologies in my field.
  - HarHarVeryFunny 6y ago
    The "AI" capabilities that are making headlines nowadays are mostly based on deep (multi-layer) neural networks with millions, or billions, or parameters. These nets do self-organize into a hierarchy of self-defined feature detectors followed by classifiers, but as you suggest for the most part they are best regarded as black boxes. You can do sensitivity analysis to determine what specific interval values are reacting to, and maybe glean some understanding that way, but by-nature the inner workings are not intended/expected to be meaningful - they are just a byproduct of the brute force output-error minimization process by which these nets are trained. Neural nets are mostly dominant in perceptual domains such as image or speech recognition, where the raw inputs represent a uniform sampling of data values (pixels, audio samples) over space and/or time. For classical business problems where the inputs are much richer and more varied, and already have individual meaning, then decision tree techniques such as random forests may be more appropriate and do provide explainability.
Balgair 6y ago
So, uh, how do you talk to the C_Os about this and tell them that it's GIGO? Asking for a friend.
- joejerryronnie 6y ago
  You get a co-worker to do it.
- HarHarVeryFunny 6y ago
  Tell them ML is so smart it's almost as smart as a C_O. So if they can solve the problem the ML probably can too ...

p1esk 6y ago

Things may have changed over the past 5 years or so. Things may have changed over the past 5 weeks or so with GPT-3.

Judgmentality 6y ago
Until GPT-3 can write something meaningful, it's really just a showcase of the technology and a gimmick of a product. Sure it's cool, but what problem is it solving? As far as I can tell the only useful function it has is polluting the internet with pseudo-intellectual comments to promote some agenda (likely political). So now that I think about it, it actually would be incredibly valuable for things like subverting democracy. I'm still waiting for AI to figure out self-driving cars, natural language processing, and other "holy grail" problems that researchers have been chipping away at for decades but we are still a long way from having "solved" them. Also in the case of self-driving cars, there is actually a very serious problem of it being difficult to guarantee safety. You can't tie every input to every output to demonstrate how it will behave (it is way, way, wayyyyyyyy too big for that), so you have to show statistically by driving so many miles that it's better. Which is still sound from an engineering perspective! But it leads to things like Teslas driving into trucks parked sideways across the road because the engineers simply never predicted it would ever happen. And now it's happened multiple times!
- p1esk 6y ago
  Until GPT-3 can write something meaningful What is "meaningful"? Honest question. Isn't meaning assigned by a reader? If I'm reading poetry generated by GPT-3 and I like it just as much as poetry written by a human poet, does it make it meaningful? What if I finetune GPT-3 (or the bigger next gen version) on every scientific paper ever written, and as a result it generates a novel idea that turns out to be valid and useful, should I care if it happened by accident, or without "understanding"? Can the knowledge encoded in the model parameters be interpreted as some form of understanding? If no, why not? What's missing exactly? What is different from how human scientists operate?
  - Judgmentality 6y ago
    Well you can randomly string words together and occasionally get lucky and make a meaningful argument, but that doesn't mean you've created a good method for constructing new ideas. It seems to me the simplest way to decide whether or not something is meaningful (in this context) is whether or not the author (which is the algorithm GPT-3) can respond to criticisms against its own argument in a coherent way. In which case it has to get lucky twice, so it's that much less likely to happen randomly. If the author cannot respond to comments in a comprehensible manner, it's hard to defend the author.

01100011 6y ago

> currently ML/AI requires us to know what the possible answers can be before we even begin training the network Isn't that what unsupervised learning is for?

bcrosby95 6y ago
I took it more to mean that, e.g., when solving for 1+1 it won't be allowed to answer "tomatoes".
HarHarVeryFunny 6y ago
There's no such thing as truly unsupervised learning - any machine learning algorithm is defined by some guidelines you've provided (or built-in) for what output it has to generate. For example, you might think of data clustering as an unsupervised problem, but in reality you're still providing supervision in terms of what similarity measure to use and some control over how many clusters it should generate; and, at the end of the day, the output is always going to be a set of clusters, not, say, a stock tip or wry comment on the nature of your data!

YeGoblynQueenne 6y ago

This is a good explanation of how things work and things haven't changed much in the past 5 years. What changes the most is the amount of data and computing power ("compute") that are used in training neural nets. Architectures are also tweaked slightly although they continue to be largely based on the two architectures that started the current deep neural net boom, Long-Short Term Memory Networks (LSTMs) and Convolutional Neural Networks (CNNs), which are largely used for natural language processing and machine vision respectively. Every once in a while there is a bit of excitement about results that come out of adding a new parameter which is then given a fancy name like "attention" and generates much breathless copy about how neural nets will soon become as intelligent as a dog/ your three year old/ your personal assitant etc. Usually though it's not clear whether these new parameters or architectural tweaks are really responsible for the success of the proposed techniques and when one looks at the field as a whole it becomes more and more apparent that the most successful work is backed by the most data and the most computing power. To slightly er tweak your prediction, I think what'll happen is that, indeed, like you say, there will be libraries and APIs and so on (well, there already are) but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches). So in terms of academia perhaps we should expect a shift away from neural nets, to something that's easier to do research on.

rpastuszak 6y ago
> but all these will be controlled by large companies that can afford the ground work. So, I don't expect small outfits, research teams at universities or tiny startups, to make a big difference. Academia is fast losing the ability to produce work that beats the state-of-the-art anyway (that's from personal comms with other researches). That makes me particularly sad. In a utilitarian sense, the ideal situation would involve new approaches being democratised. Instead, we're ending up with walled gardens. The current situation isn't dissimilar to what has happened to the Web Platform during past 5-10 years. And, similar predictions are made wrt VR and AR (both seem to attract different types of investors).

TrackerFF 6y ago

That's actually something ML is incredibly useful at, when it comes to machines with sensors - failure prediction / anomaly detection, etc. In the industry, (preventive) maintenance takes up a pretty huge chunk of resources. It's something techs need to do often, and it's often a laborious task, but it's obviously done to reduce downtime. So the business insight, as they like to call it, is to reduce costs tied up to repairs and maintenance. All critical applications have multiple levels of redundancy, so that a complete breakdown is very unlikely, but it's still a very expensive process if you're dealing with contractors. If you can get techs to swap out parts before the whole unit goes to sh!t, then that's often going to be a much cheaper alternative. But, in the end, it comes down to the quality of data, and the models being built. A lot of industrial businesses hire ML / AI engineers for this task alone, but expect some magic black-box that will warn x days / hours / minutes ahead that a machine/part is about to break down, and it's time to get it fixed. And they unfortunately expect a near-perfect accuracy, because someone in sales assured them that this is the future, and the future is now.

nmyk 6y ago
Yep, you and the user you're replying to are both right in different ways. One thing's for sure - machines don't generate "insights" on their own. Let's define an "insight" as "new meaningful knowledge", just for fun. We could talk about what comprises "new" and "meaningful" but it would be beside the point I'm making. In a supervised learning problem, the range of possible outputs is already known, meaning the model output will never be categorically different from what was in the training data. The knowledge obtained is meaningful as long as the training labels are meaningful, but it can never be new. Unsupervised learning doesn't have a notion of "training data" but that means an unsupervised model's output requires additional interpretation in order to be meaningful. It is possible to uncover new structures and identify anomalies in new ways, but this knowledge isn't meaningful until someone comes in and interprets it. Applied to the specific example where sensor data is used to try to generate insights about machine functionality: Either you can only predict the types of failures you've already seen, or you can identify states you've never seen but you wouldn't know whether they mean the system is likely to fail soon or not. It's the Roth/401(k) tradeoff. For model output to be useful, someone must pay an interpretation tax. The only choice is whether it is paid upon insight deposit or withdrawal.

dr_dshiv 6y ago

To look at a beautiful reframing of AI as a tool and not as a quasi-other agent, please enjoy this page: https://nooscope.ai/ https://nooscope.ai/

a1369209993 6y ago

> the possible MNIST digits are 0-9 Except - and this rather ties into your point - those are not the only possible digits; your network also has to deal with (ie reject) other possible digits such as "P", "E", "3̸̶", or "[Forlorn Sigil of Amon-Gül redacted]"[0], which look like, but are not, decimal digits. 0: https://www.youtube.com/watch?v=ajGX7odA87k https://www.youtube.com/watch?v=ajGX7odA87k

HarHarVeryFunny 6y ago
That depends on how you train it. If you only train it on examples of 0-9 then those are the only outputs it's going to give. If you fed a "P" into such a net then the outputs would be the degree of similarity of that "P" to each of the (0-9) digits it was trained on. You could of course threshold the output and ignore any prediction with confidence less than, e.g., 90%. If you wanted the net to do a better job of rejecting non-digits, or at least some specific ones, then you could include a bunch of non-digit examples in your training data (so now your net has 11 outputs: 0-9 and "non-digit"), then hopefully - but not necessarily - it's highest confidence prediction will be "non-digit" when presented with a non-digit input.
- a1369209993 6y ago
  > If you only train it on examples of 0-9 then those are the only outputs it's going to give. Exactly. > 11 outputs: 0-9 and "non-digit" IIRC, this doesn't work (or not well) because the net tries to find similarities between the various members of the set "everything except these ten specific things", but you could just require low confidence for all digits on non-digit inputs as part of the gradient descent function. The problem is more that - if people with decision-making authority trust the AI to not be insane and evil by default - failure modes like this have to occur to you before the AI starts misbehaving in production.

HarHarVeryFunny 6y ago

The best way to think of today's AI capability is: automation. You train it with a lots of examples of "given this input, this is the output i want", and hopefully it learns to get the "correct" (similar input => similar output) output for new inputs that you feed it. i.e. you've now automated the process of figuring the correct output for a given input. There is also the "reinforcement learning" AI paradigm where the trained AI is choosing actions (from a given repertoire) in order to maximize an action outcome score based on some scoring function you provided. This is appropriate in a situation where you want the "AI" to do something more than just select the correct output, but again no magic - you're having to anticipate and score the potential outcomes.

dumb1224 6y ago
In my opinion this type of tasks is the typical AI tasks that get most exposed to the general public. The AI/ML model is set to attempt a human task. The aspiration is to get as good as a human (or faster/better more precision etc). Classic statistical inference/ models usually don't perform very well in these scenarios(or do they?). The typical AI methods train a model that utilises features that don't make much sense to humans (pixels/texture/word tokens etc). In contrast with traditional statistical modelling where each feature were given 'meaning' and their importance explored via investigating the observations (data) using properties of well studied mathematical models, the AI methods that often made the press don't put a lot of emphasis on these properties. The advancement is a push to utilise all available information/data to outstanding 'performance'. The explanation of the inner features within that math space is usually secondary (though I don't mean that authors of novel methods don't care about mathematical modelling). I might be too naive here but that's how I feel after trying out many methodologies in my field.
- HarHarVeryFunny 6y ago
  The "AI" capabilities that are making headlines nowadays are mostly based on deep (multi-layer) neural networks with millions, or billions, or parameters. These nets do self-organize into a hierarchy of self-defined feature detectors followed by classifiers, but as you suggest for the most part they are best regarded as black boxes. You can do sensitivity analysis to determine what specific interval values are reacting to, and maybe glean some understanding that way, but by-nature the inner workings are not intended/expected to be meaningful - they are just a byproduct of the brute force output-error minimization process by which these nets are trained. Neural nets are mostly dominant in perceptual domains such as image or speech recognition, where the raw inputs represent a uniform sampling of data values (pixels, audio samples) over space and/or time. For classical business problems where the inputs are much richer and more varied, and already have individual meaning, then decision tree techniques such as random forests may be more appropriate and do provide explainability.

Balgair 6y ago

So, uh, how do you talk to the C_Os about this and tell them that it's GIGO? Asking for a friend.

joejerryronnie 6y ago
You get a co-worker to do it.
HarHarVeryFunny 6y ago
Tell them ML is so smart it's almost as smart as a C_O. So if they can solve the problem the ML probably can too ...