4 ms·

The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise, as the AI will figure it out during training. In fact, subjec

The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise, as the AI will figure it out during training. In fact, subject matter expertise can be a negative because it's a distraction from making the AI good enough to figure it out on its own. This assumption has proven to be very fragile, but I don't think the AI bigwigs have accepted that yet. Still flush from the success of things like AlphaZero, where this thesis was more true.

howlin 4y ago
There are a lot of problems that arise from lack of domain expertise, but they can be overcome with a multidisciplinary team. The biggest defeating problem for pure AI teams is that they don't understand the domain well enough to know if their data sets are representative. Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience. This affects dataset curation. When a naive ML system trains on this data, it won't appreciate the often implicit curation decisions that were made, and will thus be miscalibrated for the real world. A domain expert can offer a lot of benefits. They could know how to feature engineer in a way that is resilient to these saliency issues. They can immediately recognize when a system is making stupid decisions on out of sample data. And if the ML model allows for introspection, then the domain expert can assess whether the model's representations look sensible. I'm scenarios where datasets actually do accurately resemble the "real world", it is possible for ML to transcend human experts. Linguistics is a pretty good example of this.
- Avicebron 4y ago
  Unless of course the "domain experts" have fundamental disagreements or have equally limited knowledge of what should constitute what is important to extrapolate data beyond their own scope. E.g. like in comp sci, there might be multiple comparable ways to accomplish n, but which is best to reliably accomplish an unknown or unforseen n+1...depends.
- kspacewalk2 4y ago
  It makes sense to have a domain expert and an AI expert working together, but I'd offer two important modifications: 1) The AI expert is auxiliary here, and the domain expert is in the driver's seat. How can it be otherwise? You no more put the AI expert in charge than you'd put an electronic health record IT specialist in charge of the hospital's processes. The relationship needs to be outcome-focused, not technology-focused. 2) The end result is most likely to be a productivity tool which augments the abilities/accuracy/speed of human experts rather than replacing them. AGI being not that sciencey of a fiction, we aren't likely to be actually diagnosed by an AI radiologist in our lifetimes, nor will an AI scientist make an important scientific discovery. Ditch the hype and get to work on those productivity tools, because that's all you can do for the foreseeable future. That might seem like a disappointing reduction in ambition, but at least it's reality-based.
- raincom 4y ago
  > Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience This is called the frame problem in AI.
aidenn0 4y ago
An old story: “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” At that moment, Sussman was enlightened.
- dekhn 4y ago
  Every time I read this I appreciate its sublimity.
- samvher 4y ago
  Maybe I'm naive - but I don't get it. Closing your eyes doesn't make the room empty. And in the same way not programming preconceptions into the neural net doesn't make the preconceptions go away? I realize explaining a joke or something like this takes away some of the charm (sorry), but would love to get the point :)
  - just_testing 4y ago
    I interpret that as Minsky being... an unpleasurable person, to say the least.
  - usefulcat 4y ago
    I think you’ve exactly described the point.
  - PuppyTailWags 4y ago
    Randomly wiring a neural net doesn't remove preconceptions, it just makes it that you don't know them. Similarly, closing your eyes doesn't make a room empty, it just makes it that you can't see what's there. Minsky is pointing out that Sussman's fundamental assumptions on desiring to remove preconceptions is logically flawed.
    - samvher 4y ago
wpietri 4y ago
> The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise Not throwing any stones here, because I've been guilty of the same sort of arrogance in other contexts. But I think the same thing happened a ton during Bubble 1.0 and the software-is-eating-the-world thing. And it's hardly limited to tech: https://xkcd.com/793/ https://xkcd.com/793/ For me, at least, where this came from was ignorance and naivete. Three things cured me. One was getting deeper mastery of particular things, and experiencing a fair bit of frustration when dealing with people who didn't understand those things or respect my expertise. The second was truly recognizing there were plenty of equally smart people who'd spent just as long on other things. And the third was working in close, cross-functional team contexts with those people, where mutual listening and respect were vital to the team doing our best work. So here's hoping that the AI bigwigs learn that one way or another.
tarsinge 4y ago
Not only AlphaZero, but hadn't the whole field of computer vision (which LeCun is specialised in) had its major breakthrough with letting the AI figure out the features (i.e. CNNs)?
- fxtentacle 4y ago
  Yes, but they needed 2 billion images training data to get to the point where the AI usually draws the correct number of limbs... Any radiology AI that needs millions of training sets is useless in practice.
  - Karrot_Kream 4y ago
    > Any radiology AI that needs millions of training sets is useless in practice. Why? I have no doubt that radiology AI might not be that useful (though radiologist friends of mine say AI is making an increasing impact on their field.) But this logic doesn't make sense. So what if an AI needs a million training examples or even a million training sets? Once your topology and weights are set, that net can be copied/used by others and you get a ready-to-go AI. There's an argument to be made that if training scale is what's needed to get to AGI, then maybe AGI is unrealizable, but that's not the same as saying a domain-specific AI is useless because it needs a large training set.
- civilized 4y ago
  It helps that human scientific expertise in the topic of "recognizing objects" is limited.

howlin 4y ago

There are a lot of problems that arise from lack of domain expertise, but they can be overcome with a multidisciplinary team. The biggest defeating problem for pure AI teams is that they don't understand the domain well enough to know if their data sets are representative. Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience. This affects dataset curation. When a naive ML system trains on this data, it won't appreciate the often implicit curation decisions that were made, and will thus be miscalibrated for the real world. A domain expert can offer a lot of benefits. They could know how to feature engineer in a way that is resilient to these saliency issues. They can immediately recognize when a system is making stupid decisions on out of sample data. And if the ML model allows for introspection, then the domain expert can assess whether the model's representations look sensible. I'm scenarios where datasets actually do accurately resemble the "real world", it is possible for ML to transcend human experts. Linguistics is a pretty good example of this.

Avicebron 4y ago
Unless of course the "domain experts" have fundamental disagreements or have equally limited knowledge of what should constitute what is important to extrapolate data beyond their own scope. E.g. like in comp sci, there might be multiple comparable ways to accomplish n, but which is best to reliably accomplish an unknown or unforseen n+1...depends.
kspacewalk2 4y ago
It makes sense to have a domain expert and an AI expert working together, but I'd offer two important modifications: 1) The AI expert is auxiliary here, and the domain expert is in the driver's seat. How can it be otherwise? You no more put the AI expert in charge than you'd put an electronic health record IT specialist in charge of the hospital's processes. The relationship needs to be outcome-focused, not technology-focused. 2) The end result is most likely to be a productivity tool which augments the abilities/accuracy/speed of human experts rather than replacing them. AGI being not that sciencey of a fiction, we aren't likely to be actually diagnosed by an AI radiologist in our lifetimes, nor will an AI scientist make an important scientific discovery. Ditch the hype and get to work on those productivity tools, because that's all you can do for the foreseeable future. That might seem like a disappointing reduction in ambition, but at least it's reality-based.
raincom 4y ago
> Humans are great at salience assessments, and can ignore tons of the examples and features they witness when using their experience This is called the frame problem in AI.

aidenn0 4y ago

An old story: “What are you doing?”, asked Minsky. “I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied. “Why is the net wired randomly?”, asked Minsky. “I do not want it to have any preconceptions of how to play”, Sussman said. Minsky then shut his eyes. “Why do you close your eyes?”, Sussman asked his teacher. “So that the room will be empty.” At that moment, Sussman was enlightened.

dekhn 4y ago
Every time I read this I appreciate its sublimity.
samvher 4y ago
Maybe I'm naive - but I don't get it. Closing your eyes doesn't make the room empty. And in the same way not programming preconceptions into the neural net doesn't make the preconceptions go away? I realize explaining a joke or something like this takes away some of the charm (sorry), but would love to get the point :)
- just_testing 4y ago
  I interpret that as Minsky being... an unpleasurable person, to say the least.
- usefulcat 4y ago
  I think you’ve exactly described the point.
- PuppyTailWags 4y ago
  Randomly wiring a neural net doesn't remove preconceptions, it just makes it that you don't know them. Similarly, closing your eyes doesn't make a room empty, it just makes it that you can't see what's there. Minsky is pointing out that Sussman's fundamental assumptions on desiring to remove preconceptions is logically flawed.
  - samvher 4y ago

wpietri 4y ago

> The tribal thesis in the AI world seems to be that AI workers don't need subject matter expertise Not throwing any stones here, because I've been guilty of the same sort of arrogance in other contexts. But I think the same thing happened a ton during Bubble 1.0 and the software-is-eating-the-world thing. And it's hardly limited to tech: https://xkcd.com/793/ https://xkcd.com/793/ For me, at least, where this came from was ignorance and naivete. Three things cured me. One was getting deeper mastery of particular things, and experiencing a fair bit of frustration when dealing with people who didn't understand those things or respect my expertise. The second was truly recognizing there were plenty of equally smart people who'd spent just as long on other things. And the third was working in close, cross-functional team contexts with those people, where mutual listening and respect were vital to the team doing our best work. So here's hoping that the AI bigwigs learn that one way or another.

tarsinge 4y ago

Not only AlphaZero, but hadn't the whole field of computer vision (which LeCun is specialised in) had its major breakthrough with letting the AI figure out the features (i.e. CNNs)?

fxtentacle 4y ago
Yes, but they needed 2 billion images training data to get to the point where the AI usually draws the correct number of limbs... Any radiology AI that needs millions of training sets is useless in practice.
- Karrot_Kream 4y ago
  > Any radiology AI that needs millions of training sets is useless in practice. Why? I have no doubt that radiology AI might not be that useful (though radiologist friends of mine say AI is making an increasing impact on their field.) But this logic doesn't make sense. So what if an AI needs a million training examples or even a million training sets? Once your topology and weights are set, that net can be copied/used by others and you get a ready-to-go AI. There's an argument to be made that if training scale is what's needed to get to AGI, then maybe AGI is unrealizable, but that's not the same as saying a domain-specific AI is useless because it needs a large training set.
civilized 4y ago
It helps that human scientific expertise in the topic of "recognizing objects" is limited.