6 ms·

Human-Level Intelligence or Animal-Like Abilities? (2018)

59 points by Anon84 6y ago

mensetmanusman 6y ago
Surprised I haven’t come across this analogy before. Thinking about it now, ‘Animal-like abilities’ is a much better analogy in the context of the general public. It also helps ground scientists in thinking deeply about the difference between human and animal intelligence (some animals can use tools).
- throwaway_pdp09 6y ago
  This is vague but it was a while ago... some researcher was trying to make AI more accessible to the public by downplaying high expectations. She represented it as a cartoon dog, amiable, willing, but evidently none too bright. I believe it was no more than an experiment, though a clever one. Ring anyone's bell?
  - deleted 6y ago
    [deleted]
api 6y ago
Careful... you're on the verge here of realizing that intelligence is not a single-dimensional thing and that 'g' is bullshit.
- natch 6y ago
  Trollish comment above but I do wonder about this. I always hear people who think ‘g’ is bullshit giving some form of this argument: “AI can’t even x, how would you expect it to ever possibly do y?” It’s like saying: “How could a baby, which can not even tie its own shoes, spell a simple word, or fill a cup with water, ever possibly grow up to write a book, manage a corporation, or formulate a strategy for fighting a war? Impossible!” It’s almost as if they think babies can’t learn. The AI is not a baby though, they say. Yeah, it’s not. But it’s a constantly learning body of work that is not regressing and not slowing down. Why would it not get to ‘general’ eventually?
  - GuiA 6y ago
    We have billions of examples of babies growing up to learn to spell and tie their shoes, therefore it's very reasonable to assume that that's a skill most babies will get to. There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible. This is a very different case from e.g. "we built a prototype plane that can lift 15 ft off the ground for 10 seconds, we should be able to build one that can lift 10,000 feet for 10 hours", because in the former case we were beginning to have an understanding of aerodynamics that made that path plausible. We do not have such an understanding backing current ML work. That's well illustrated by the fact that only 6 years elapsed between the first manned flight and crossing the Atlantic by plane - practice caught up to theory really fast, as it tends to do in those cases. We are about 6 years in the current ML revolution - what is its equivalent to crossing the Atlantic? The logic you're advocating for here is more along the lines of "I taught my dog how to sit - shouldn't be too hard to get him to sit in front of a computer and start writing Python code now that I've got the first half figured out". (or - I taught my dog how to fetch - shouldn't be too hard to train him to be a full time food delivery employee, etc).
    - luckylion 6y ago
      > There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible. No reason? If you were an outside observer looking at the very first individuals of what you decided to call human, maybe. But after so many steps were made? You can argue that, before there was a plane, there was zero reason to thing that humans can ever create one. And you could've claimed that for pretty much anything else. And you'd generally see that they did get created. Why would artificial intelligence be fundamentally different? I'm not saying "we can do it in the next five years", but to say that there's no reason to believe that it will ever happen? > We do not have such an understanding backing current ML work. And we did not have the knowledge of aerodynamics before we started looking into throwing things, then keeping things in the air for longer than they would if they were thrown, and eventually flying them (yes, a bit simplified).
- SpicyLemonZest 6y ago
  Intelligence isn't a single-dimensional thing, but it seems pretty absurd to claim that there's no general factor at all. Could it really be meaningless to say that someone's smart?
  - api 6y ago
    It's like saying a car is fast. Sure, the car is fast. Is it also reliable, efficient, aesthetically pleasing, comfortable, or affordable? How does it handle? How does it do off-road? Can it fly? Can it operate underwater? Real world fitness landscapes are complex and multidimensional. My comment was a little snarky because while I like this site quite a bit for many reasons I do get a little fatigued by the prevalence of the IQ cult. The notion that there is a single metric on which brains can be sorted is sort of a sacred dogma around here, and one which to me is obviously quite wrong. All it takes is a casual glance at intelligence as it actually manifests in the only real world example of an intelligent system we have: biology. It's clearly a multidimensional quality that can be optimized for a large range of things just like anything else. If intelligence is multidimensional in all other living things, by what rationale would we conclude that human intelligence is one-dimensional? ... or ... If virtually every engineered or evolved capability is subject to multi-dimensional optimization, then why not also intelligence?
    - SpicyLemonZest 6y ago
      Absolutely true, but I'm not sure that's really a point of confusion. Almost everyone would agree that smart people aren't automatically good at athletics, or poetry, or woodworking. If you've seen people say that kind of thing I agree they're being silly.
    - luckylion 6y ago
      Yet we have a bunch of general attributes we expect when we say "car". Maybe "she's smart" is rather like "she has a car". You don't know whether it's a Porsche or a Truck, gas or electric, but you'll generally rely on her being able to cover larger distances than on foot, and at higher speeds, while also hauling some reasonable amount of luggage.
    - 6y ago
- chongli 6y ago
  It’s not bullshit to notice that a bunch of different abilities tend to be highly correlated. That’s all g is. What is bullshit is the formal theory of multiple intelligences, which is largely PC-inspired pseudoscience and does not account for the correlation between separate areas.
  - api 6y ago
    Athletic ability (real time kinetic intelligence) and math ability are highly correlated? A car's speed and its ability to drive off-road are highly correlated? Rocket thrust and specific impulse are highly correlated? (Hint: these are inversely correlated.) I didn't come to this through PC. I came to it through a basic understanding of learning and optimization.
    - chongli 6y ago
      Athletic ability isn't intelligence. Intelligence may be a part of it (c.f. Wayne Gretzky's quote "skate to where the puck is going, not where it's been") but strength, speed, and stamina aren't. Burmese pythons are way stronger than humans yet I doubt anyone would call them more intelligent. Mantis shrimp have way faster reflexes and their vision is way more sophisticated than ours yet they're not more intelligent as well. There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things. We do all of these things using the same "muscle", our brain. It turns out that some people's brains are faster at being able to learn all of these myriad tasks. That's all there is to it.
      - api 6y ago
        > That's all there is to it. I think that's the main point where we disagree. I think there's a hell of a lot more to it. I remember noticing when I was in school that while I could learn a variety of things, there were definitely subjects that were a lot easier for me and others in which I'd struggle no matter how hard I worked. I also observed other people with very different profiles of easy and hard subjects. I barely passed organic chemistry. I studied hard, but it just would not "take." I had a friend who breezed through it. Anything that involved writing though? I'd breeze through it. I could get As in any class that involved understanding concepts and writing about them with almost no effort. This same friend struggled to write an essay. I actually dropped out of vertebrate anatomy. Tons and tons of clades and names for discrete things. I found a way to adjust my major requirements to avoid taking it. Otherwise I would have had to switch majors. There is no way I could have passed that class with any amount of tutoring. It's like I have a photographic memory for concepts, but can't recall discrete facts. I kept looking for the underlying patterns in organic chemistry but it turns out we don't understand it well enough to have those... we just know a ton of reactions that do things. I flounder badly in subjects whose facts can't be attached to an abstract conceptual framework, but I can read about a concept once and I understand it forever. As a programmer I really love IDEs with "code insight" and other features that basically consult the documentation for you in real time. I get everything about the concepts and algorithms but can never remember the order of arguments on memcpy() even after using it for 20 years. I know memcpy() is there and what it does conceptually though, so it's fine since the IDE will show me which argument goes first. My point is that different people who are "smart" can differ radically in their ability profile.
- joshualedbetter 6y ago
  "Chimps have better short term memory than humans, even on human oriented tasks... Short term memory is an important dimension of intelligence. By Kelly's argument, humans are not smarter than chimps; indeed, he would claim that "smarter than chimpanzee" is a meaningless concept. This is cold comfort to the chimps whose species survive only because we deign to allow it. It is colder comfort still to all those species we have already wiped out. It's also cold comfort to humans who might be worried about being wiped out by machines." -- Human Compatible by Stuart Russell, page 148
- dang 6y ago
  Please don't post shallow flamebait, a.k.a. troll, in HN threads. Especially not on classic flamewar topics. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
deleted 6y ago
[deleted]
op03 6y ago
Ant level intelligence I'd be happy with. "Colony you may now clean up my kitchen table and do the dishes. Thanks"
- TeMPOraL 6y ago
  And then you keep thinking about how fast cereal is disappearing in your house, wondering whether you're just so sleepy in the morning you don't remember eating more and more of it - until one night you spot a string of AInts stealing your cereal, piece by piece. By the time you've realized your mistake, the colony has grown 100x in size and already spread to the two neighboring houses, being held back by the front line of an ongoing war with an AInt colony of your friend one street down.
  - op03 6y ago
    :) that wld entertain me, having witnessed a couple ant-termite wars in the garden.
- 29athrowaway 6y ago
  Ant swarm intelligence is not perfect. Sometimes they fall into infinite loops killing the entire colony: Ant death spirals. https://www.youtube.com/watch?v=N0HoqjxfvJ4 https://www.youtube.com/watch?v=N0HoqjxfvJ4
  - op03 6y ago
    Cool clip thx. Very interesting and hadn't heard of it. Could be a feature tho to get rid of the dumbest ants.
    - 29athrowaway 6y ago
      You might need to extract ant pheromones and draw a circle with it, so that ants keep reinforcing that circular path attracting more ants, forming a death pit.
YeGoblynQueenne 6y ago
The title of the article does an awful job of summarising it. The main subject of the article is not about the level of intelligence of current or future AI, as the title suggsts. Instead, the article is a reflection on the progress in the field in the last few years and a discussion of the degree to which progress in deep learning has benefited, or harmed, AI research in general. This is best summarised by the "Key Insights" box on top of the article: > the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today > In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits > We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history. The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc. The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests. On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another. In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.
andra1 6y ago
Francois Chollet tries to address some of these problems by tackling how intelligence is measured. Most AI systems are measured by task performance, but he argues that intelligence is about "skill-acquisition efficiency". https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547

59 points by Anon84 6y ago

Surprised I haven’t come across this analogy before. Thinking about it now, ‘Animal-like abilities’ is a much better analogy in the context of the general public. It also helps ground scientists in thinking deeply about the difference between human and animal intelligence (some animals can use tools).

throwaway_pdp09 6y ago
This is vague but it was a while ago... some researcher was trying to make AI more accessible to the public by downplaying high expectations. She represented it as a cartoon dog, amiable, willing, but evidently none too bright. I believe it was no more than an experiment, though a clever one. Ring anyone's bell?
- deleted 6y ago
  [deleted]

api 6y ago

Careful... you're on the verge here of realizing that intelligence is not a single-dimensional thing and that 'g' is bullshit.

natch 6y ago
Trollish comment above but I do wonder about this. I always hear people who think ‘g’ is bullshit giving some form of this argument: “AI can’t even x, how would you expect it to ever possibly do y?” It’s like saying: “How could a baby, which can not even tie its own shoes, spell a simple word, or fill a cup with water, ever possibly grow up to write a book, manage a corporation, or formulate a strategy for fighting a war? Impossible!” It’s almost as if they think babies can’t learn. The AI is not a baby though, they say. Yeah, it’s not. But it’s a constantly learning body of work that is not regressing and not slowing down. Why would it not get to ‘general’ eventually?
- GuiA 6y ago
  We have billions of examples of babies growing up to learn to spell and tie their shoes, therefore it's very reasonable to assume that that's a skill most babies will get to. There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible. This is a very different case from e.g. "we built a prototype plane that can lift 15 ft off the ground for 10 seconds, we should be able to build one that can lift 10,000 feet for 10 hours", because in the former case we were beginning to have an understanding of aerodynamics that made that path plausible. We do not have such an understanding backing current ML work. That's well illustrated by the fact that only 6 years elapsed between the first manned flight and crossing the Atlantic by plane - practice caught up to theory really fast, as it tends to do in those cases. We are about 6 years in the current ML revolution - what is its equivalent to crossing the Atlantic? The logic you're advocating for here is more along the lines of "I taught my dog how to sit - shouldn't be too hard to get him to sit in front of a computer and start writing Python code now that I've got the first half figured out". (or - I taught my dog how to fetch - shouldn't be too hard to train him to be a full time food delivery employee, etc).
  - luckylion 6y ago
    > There are 0 examples of a man made construct reaching "general" intelligence (whatever fuzzy meaning you may attach to it); there is no reason to believe that because we can reach some arbitrary human milestone today ("be a world class Go player"), any other human milestone ("start and run a company, and be a mentor and inspiration to others") is plausible. No reason? If you were an outside observer looking at the very first individuals of what you decided to call human, maybe. But after so many steps were made? You can argue that, before there was a plane, there was zero reason to thing that humans can ever create one. And you could've claimed that for pretty much anything else. And you'd generally see that they did get created. Why would artificial intelligence be fundamentally different? I'm not saying "we can do it in the next five years", but to say that there's no reason to believe that it will ever happen? > We do not have such an understanding backing current ML work. And we did not have the knowledge of aerodynamics before we started looking into throwing things, then keeping things in the air for longer than they would if they were thrown, and eventually flying them (yes, a bit simplified).
SpicyLemonZest 6y ago
Intelligence isn't a single-dimensional thing, but it seems pretty absurd to claim that there's no general factor at all. Could it really be meaningless to say that someone's smart?
- api 6y ago
  It's like saying a car is fast. Sure, the car is fast. Is it also reliable, efficient, aesthetically pleasing, comfortable, or affordable? How does it handle? How does it do off-road? Can it fly? Can it operate underwater? Real world fitness landscapes are complex and multidimensional. My comment was a little snarky because while I like this site quite a bit for many reasons I do get a little fatigued by the prevalence of the IQ cult. The notion that there is a single metric on which brains can be sorted is sort of a sacred dogma around here, and one which to me is obviously quite wrong. All it takes is a casual glance at intelligence as it actually manifests in the only real world example of an intelligent system we have: biology. It's clearly a multidimensional quality that can be optimized for a large range of things just like anything else. If intelligence is multidimensional in all other living things, by what rationale would we conclude that human intelligence is one-dimensional? ... or ... If virtually every engineered or evolved capability is subject to multi-dimensional optimization, then why not also intelligence?
  - SpicyLemonZest 6y ago
    Absolutely true, but I'm not sure that's really a point of confusion. Almost everyone would agree that smart people aren't automatically good at athletics, or poetry, or woodworking. If you've seen people say that kind of thing I agree they're being silly.
  - luckylion 6y ago
    Yet we have a bunch of general attributes we expect when we say "car". Maybe "she's smart" is rather like "she has a car". You don't know whether it's a Porsche or a Truck, gas or electric, but you'll generally rely on her being able to cover larger distances than on foot, and at higher speeds, while also hauling some reasonable amount of luggage.
  - 6y ago
chongli 6y ago
It’s not bullshit to notice that a bunch of different abilities tend to be highly correlated. That’s all g is. What is bullshit is the formal theory of multiple intelligences, which is largely PC-inspired pseudoscience and does not account for the correlation between separate areas.
- api 6y ago
  Athletic ability (real time kinetic intelligence) and math ability are highly correlated? A car's speed and its ability to drive off-road are highly correlated? Rocket thrust and specific impulse are highly correlated? (Hint: these are inversely correlated.) I didn't come to this through PC. I came to it through a basic understanding of learning and optimization.
  - chongli 6y ago
    Athletic ability isn't intelligence. Intelligence may be a part of it (c.f. Wayne Gretzky's quote "skate to where the puck is going, not where it's been") but strength, speed, and stamina aren't. Burmese pythons are way stronger than humans yet I doubt anyone would call them more intelligent. Mantis shrimp have way faster reflexes and their vision is way more sophisticated than ours yet they're not more intelligent as well. There is a lot of debate over the definition of intelligence but one thing most definitions have in common is that it is general. A human being can play the piano, solve an integral, write a novel, compose a photograph, and many many more things. We do all of these things using the same "muscle", our brain. It turns out that some people's brains are faster at being able to learn all of these myriad tasks. That's all there is to it.
    - api 6y ago
      > That's all there is to it. I think that's the main point where we disagree. I think there's a hell of a lot more to it. I remember noticing when I was in school that while I could learn a variety of things, there were definitely subjects that were a lot easier for me and others in which I'd struggle no matter how hard I worked. I also observed other people with very different profiles of easy and hard subjects. I barely passed organic chemistry. I studied hard, but it just would not "take." I had a friend who breezed through it. Anything that involved writing though? I'd breeze through it. I could get As in any class that involved understanding concepts and writing about them with almost no effort. This same friend struggled to write an essay. I actually dropped out of vertebrate anatomy. Tons and tons of clades and names for discrete things. I found a way to adjust my major requirements to avoid taking it. Otherwise I would have had to switch majors. There is no way I could have passed that class with any amount of tutoring. It's like I have a photographic memory for concepts, but can't recall discrete facts. I kept looking for the underlying patterns in organic chemistry but it turns out we don't understand it well enough to have those... we just know a ton of reactions that do things. I flounder badly in subjects whose facts can't be attached to an abstract conceptual framework, but I can read about a concept once and I understand it forever. As a programmer I really love IDEs with "code insight" and other features that basically consult the documentation for you in real time. I get everything about the concepts and algorithms but can never remember the order of arguments on memcpy() even after using it for 20 years. I know memcpy() is there and what it does conceptually though, so it's fine since the IDE will show me which argument goes first. My point is that different people who are "smart" can differ radically in their ability profile.
joshualedbetter 6y ago
"Chimps have better short term memory than humans, even on human oriented tasks... Short term memory is an important dimension of intelligence. By Kelly's argument, humans are not smarter than chimps; indeed, he would claim that "smarter than chimpanzee" is a meaningless concept. This is cold comfort to the chimps whose species survive only because we deign to allow it. It is colder comfort still to all those species we have already wiped out. It's also cold comfort to humans who might be worried about being wiped out by machines." -- Human Compatible by Stuart Russell, page 148
dang 6y ago
Please don't post shallow flamebait, a.k.a. troll, in HN threads. Especially not on classic flamewar topics. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html

deleted 6y ago

[deleted]

op03 6y ago

Ant level intelligence I'd be happy with. "Colony you may now clean up my kitchen table and do the dishes. Thanks"

TeMPOraL 6y ago
And then you keep thinking about how fast cereal is disappearing in your house, wondering whether you're just so sleepy in the morning you don't remember eating more and more of it - until one night you spot a string of AInts stealing your cereal, piece by piece. By the time you've realized your mistake, the colony has grown 100x in size and already spread to the two neighboring houses, being held back by the front line of an ongoing war with an AInt colony of your friend one street down.
- op03 6y ago
  :) that wld entertain me, having witnessed a couple ant-termite wars in the garden.
29athrowaway 6y ago
Ant swarm intelligence is not perfect. Sometimes they fall into infinite loops killing the entire colony: Ant death spirals. https://www.youtube.com/watch?v=N0HoqjxfvJ4 https://www.youtube.com/watch?v=N0HoqjxfvJ4
- op03 6y ago
  Cool clip thx. Very interesting and hadn't heard of it. Could be a feature tho to get rid of the dumbest ants.
  - 29athrowaway 6y ago
    You might need to extract ant pheromones and draw a circle with it, so that ants keep reinforcing that circular path attracting more ants, forming a death pit.

YeGoblynQueenne 6y ago

The title of the article does an awful job of summarising it. The main subject of the article is not about the level of intelligence of current or future AI, as the title suggsts. Instead, the article is a reflection on the progress in the field in the last few years and a discussion of the degree to which progress in deep learning has benefited, or harmed, AI research in general. This is best summarised by the "Key Insights" box on top of the article: > the recent successes of deep learning have revealed something very interesting about the structure of our world, yet this seems to be the least pursued and talked about topic today > In AI, the key question today is not whether we should use model-based or function-based approaches but how to integrate and fuse them so we can realise their objective benefits > We need a new generation of AI researchers who are well versed in an appreciate classical AI, machine learning, and computer science more broadly while also being informed about AI history. The first "key point" refers to classes of functions that can be seen as "cognitive functions". For example, mapping a set of inputs to outputs can reasonably be considered as approximating some aspect of cognition when the inputs are regions of images and the subjects their labels, so that the function performs object recognition, a task that AI research has long considered an aspect of cognition. Understanding how such functions work has the potential to contribute to our understanding of human cognition, that has long been a major goal of AI research. Yet, in recent years, interest has shifted from understanding such results to applying them at the level of phone apps, etc. The final key point is a call to arms. We can't make progress as a field by throwing out everything we've done before, everytime we achieve some success in a narrow range of tasks. The author witnessed this happenning in the 1980's with the rise and fall of expert systems -and the AI winter that followed. In modern times, the success of deep learning has all but eclipsed the deep knowledge that researchers in the field once possessed about symbolic logic and important avenues of research are impossible to follow because the younger generation of researchers simply don't have the necessary background - and are "bullied by the success" of neural networks into directing their careers towards neural network research, whatever their true interests. On a personal level, not summarising the article anymore, the latter is the most disturbing development. Neural networks can perform "perceptual" tasks, but are wholly incapable of reasoning. Symbolic AI had reasoning down pat- and not in approximate fashion (as a recent trend in deep learning research attempts to perform it). Yet, we seem to have regressed and lost one ability to perform one set of cognitive tasks in the process of figuring out how to perform another. In the past, AI researchers were well-rounded polymaths, versed in CS but also (continuous) mathematics, physics, psychology, linguistics... Nowadays, researchers seem to be optimising for a narrow band of knowledge and ignoring everything else. This cannot end well.

andra1 6y ago

Francois Chollet tries to address some of these problems by tackling how intelligence is measured. Most AI systems are measured by task performance, but he argues that intelligence is about "skill-acquisition efficiency". https://arxiv.org/abs/1911.01547 https://arxiv.org/abs/1911.01547