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I feel like there’s a lot of people out there under the impression that we’re on the brink of human-like AI (in the self-driving car sphere, for example). Sure

I feel like there’s a lot of people out there under the impression that we’re on the brink of human-like AI (in the self-driving car sphere, for example). Surely there’s going to be some sort of crash when they collectively realise that’s not the case.

ben_w 8y ago
Hmm. Domain-specific AI is often already superhuman in performance, once trained. What we lack seems to be: (1) generalisation, as a trained AI is often useless in separate tasks; (2) efficient training, as AI take far more data to reach the same performance as a human (I have seen suggestions this is related to (1)); and (3) any idea at all what counts as self-awareness/conscious/qualia/etc., which may not be important from a performance point of view, but very much influences how people regard the AI and their future potential.
- YeGoblynQueenne 8y ago
  How can an AI system be "superhuman in performance" when it can't generalise outside its training set and requires vast amounts of data to have good predictive accuracy on that single training set? What is really the case is that some AI systems (in particular, deep neural net classifiers) have superhuman accuracy and that again only in classification tasks. Edit: I'm not being contrary for the sake of it. I think there is a very useful insight to draw from the success of deep neural network classifiers: that it is possible to perform classification without any kind of understanding of the objects, or their classes, at all. In the past, AI researchers operated under the assumption that reasoning and inference would be required to perform this task, but we know now that such abilities are not necessary if the task is only to classify objects. It's also clear that in some cases dumb classification can replace reasoning- as long as a reasoning problem can be reduced to dumb classification. However- humans clearly have the ability to reason and draw inferences (regardless of how often we do that successfully). There must be some explanation for this. Why do we have an intelligence that goes beyond simple object recognition? What is the point of having a broad intelligence? It must mean that there is more to intelligence than tasks that can be reduced to classification. So reducing "performance" to "accuracy" (of classification) risks fudging an important difference between the "superhuman" abilities of machines and human abilities. In the end, the question is what are humans good at and why can't machines do the same things, even though they can beat us roundly in other tasks? What is the difference between tasks that are easy for machines and tasks that are easy for humans?
  - amanaplanacanal 8y ago
    I think it is likely that there is more to animal/human intelligence than pattern matching. I don't think we can say for sure that there is more, though. If and when we finally understand how it all works, I wouldn't be completely surprised to find that it is just many layers of pattern matching and associated feedback loops. We just don't know enough yet.
  - ben_w 8y ago
    I think we broadly agree. What you’re raising in your argument seems to be examples of what I consider to be current weaknesses. However, I think you’re making a distinction without a difference going between classification accuracy and performance. That said, I am running on 1.5 hours less sleep than I need today — the only other important metric I can think of right now is the kind of skill which humans book-learn and which get implemented on a computer as an explicit and deliberate algorithm instead of being learned, and I simultaneously don’t count those as AI and think machines have beaten us for decades in that domain. I do have one other question though: do you regard Alpha(Go|Zero|Star) as nothing more than classification?
    - YeGoblynQueenne 8y ago
      Well, perhaps I'm making too fine a distinction, it wouldn't be the first time. I believe in AlphaZero etc the deep neural net component was used to identify moves with a high probability of leading to a winning board position. That's a good example of a problem we used to think would require inference or reasonging but that can, after all, be solved by classification. Although that's not to say that the same problems can't be solved by inference, plus powerful computation. That remains to be seen. Edit: Maybe I shouldn't have called it "dumb" classification; that sounds dismissive. There's no doubt that neural net classifiers are impressive in what they do. I mean it to say that they have no understading of their domain, or ability to reason etc.
  - pacala 8y ago
    s/classification/decision making/ AI systems are better than humans at making decisions. AI systems can scale to millions of decisions per second and beyond. AI systems don't need to spend 3/4 or their time sleeping or relaxing. AI systems don't need 20 years of training to become experts in their fields, they can be replicated almost instantly. While the scope of decisions the AIs can do is somewhat limited at the moment, the trend is clear: in the not so distant future AIs will make all economically relevant decisions.
electricslpnsld 8y ago
None of the ML people I’ve worked with (mostly at FB and Google, ymmv) have seriously believed we are anywhere close to human-like AI. The shortcomings of current ML systems are well understood — given metric tons of data, they are really great nonlinear regressors and data interpolators. They are absolutely terrible at generalizing, with no obvious solutions to this on the horizon.
- neuronic 8y ago
  I don't think ML people belong to the group that believes we are close before human-like AI. In fact, I think they are some of the few ones in touch about how far away it still is. As it should be. Even a few years ago, I worked as a student at a Big4 and these firms ran out for "technology consulting" and told their clients about AI this, AI that and it's gonna be robots taking it all over. Of course those were advertisements, ruses even, for getting audit & tax customers via the "compliance" angle (e.g. cloud compliance laws). But anyway, their is a true craze about AI and autonomous driving which holistically ignores all the major road blocks that we are facing now. It's buzzwords and marketing all over. It's the Gluten-intolerance of technology. According to general public media we are 2-5 years away from level 5 autonomy. Ask people at Waymo about the issues they face...
- marssaxman 8y ago
  I'm not sure that's a problem. Humans are terrific generalizers, and it would probably be a good thing for human civilization if human intelligence remained an essential part of the control loop. We can just continue developing ever-more-useful intelligence amplifiers and employing them as extensions of human cognition, instead of replacing ourselves by building minds like our own.
- ForHackernews 8y ago
  That one "singularity" guy is at Google, isn't he? Elon Musk claims to be worried about malicious super-intelligent AI. It seems like at least some people in the software and software-adjacent industries are talking like AGI is right around the corner.

ben_w 8y ago

Hmm. Domain-specific AI is often already superhuman in performance, once trained. What we lack seems to be: (1) generalisation, as a trained AI is often useless in separate tasks; (2) efficient training, as AI take far more data to reach the same performance as a human (I have seen suggestions this is related to (1)); and (3) any idea at all what counts as self-awareness/conscious/qualia/etc., which may not be important from a performance point of view, but very much influences how people regard the AI and their future potential.

YeGoblynQueenne 8y ago
How can an AI system be "superhuman in performance" when it can't generalise outside its training set and requires vast amounts of data to have good predictive accuracy on that single training set? What is really the case is that some AI systems (in particular, deep neural net classifiers) have superhuman accuracy and that again only in classification tasks. Edit: I'm not being contrary for the sake of it. I think there is a very useful insight to draw from the success of deep neural network classifiers: that it is possible to perform classification without any kind of understanding of the objects, or their classes, at all. In the past, AI researchers operated under the assumption that reasoning and inference would be required to perform this task, but we know now that such abilities are not necessary if the task is only to classify objects. It's also clear that in some cases dumb classification can replace reasoning- as long as a reasoning problem can be reduced to dumb classification. However- humans clearly have the ability to reason and draw inferences (regardless of how often we do that successfully). There must be some explanation for this. Why do we have an intelligence that goes beyond simple object recognition? What is the point of having a broad intelligence? It must mean that there is more to intelligence than tasks that can be reduced to classification. So reducing "performance" to "accuracy" (of classification) risks fudging an important difference between the "superhuman" abilities of machines and human abilities. In the end, the question is what are humans good at and why can't machines do the same things, even though they can beat us roundly in other tasks? What is the difference between tasks that are easy for machines and tasks that are easy for humans?
- amanaplanacanal 8y ago
  I think it is likely that there is more to animal/human intelligence than pattern matching. I don't think we can say for sure that there is more, though. If and when we finally understand how it all works, I wouldn't be completely surprised to find that it is just many layers of pattern matching and associated feedback loops. We just don't know enough yet.
- ben_w 8y ago
  I think we broadly agree. What you’re raising in your argument seems to be examples of what I consider to be current weaknesses. However, I think you’re making a distinction without a difference going between classification accuracy and performance. That said, I am running on 1.5 hours less sleep than I need today — the only other important metric I can think of right now is the kind of skill which humans book-learn and which get implemented on a computer as an explicit and deliberate algorithm instead of being learned, and I simultaneously don’t count those as AI and think machines have beaten us for decades in that domain. I do have one other question though: do you regard Alpha(Go|Zero|Star) as nothing more than classification?
  - YeGoblynQueenne 8y ago
    Well, perhaps I'm making too fine a distinction, it wouldn't be the first time. I believe in AlphaZero etc the deep neural net component was used to identify moves with a high probability of leading to a winning board position. That's a good example of a problem we used to think would require inference or reasonging but that can, after all, be solved by classification. Although that's not to say that the same problems can't be solved by inference, plus powerful computation. That remains to be seen. Edit: Maybe I shouldn't have called it "dumb" classification; that sounds dismissive. There's no doubt that neural net classifiers are impressive in what they do. I mean it to say that they have no understading of their domain, or ability to reason etc.
- pacala 8y ago
  s/classification/decision making/ AI systems are better than humans at making decisions. AI systems can scale to millions of decisions per second and beyond. AI systems don't need to spend 3/4 or their time sleeping or relaxing. AI systems don't need 20 years of training to become experts in their fields, they can be replicated almost instantly. While the scope of decisions the AIs can do is somewhat limited at the moment, the trend is clear: in the not so distant future AIs will make all economically relevant decisions.

electricslpnsld 8y ago

None of the ML people I’ve worked with (mostly at FB and Google, ymmv) have seriously believed we are anywhere close to human-like AI. The shortcomings of current ML systems are well understood — given metric tons of data, they are really great nonlinear regressors and data interpolators. They are absolutely terrible at generalizing, with no obvious solutions to this on the horizon.

neuronic 8y ago
I don't think ML people belong to the group that believes we are close before human-like AI. In fact, I think they are some of the few ones in touch about how far away it still is. As it should be. Even a few years ago, I worked as a student at a Big4 and these firms ran out for "technology consulting" and told their clients about AI this, AI that and it's gonna be robots taking it all over. Of course those were advertisements, ruses even, for getting audit & tax customers via the "compliance" angle (e.g. cloud compliance laws). But anyway, their is a true craze about AI and autonomous driving which holistically ignores all the major road blocks that we are facing now. It's buzzwords and marketing all over. It's the Gluten-intolerance of technology. According to general public media we are 2-5 years away from level 5 autonomy. Ask people at Waymo about the issues they face...
marssaxman 8y ago
I'm not sure that's a problem. Humans are terrific generalizers, and it would probably be a good thing for human civilization if human intelligence remained an essential part of the control loop. We can just continue developing ever-more-useful intelligence amplifiers and employing them as extensions of human cognition, instead of replacing ourselves by building minds like our own.
ForHackernews 8y ago
That one "singularity" guy is at Google, isn't he? Elon Musk claims to be worried about malicious super-intelligent AI. It seems like at least some people in the software and software-adjacent industries are talking like AGI is right around the corner.