10 ms·

Artificial Intelligence Still Isn’t All That Smart

93 points by benryon 8y ago

sgt101 8y ago
For an informed opinion see https://news.ycombinator.com/item?id=17766473 https://news.ycombinator.com/item?id=17766473
roenxi 8y ago
Take AlphaGo, because I know Go quite well. A computer is making better decisions than a human, using a pretty reasonable approximation of the same mechanism as a human but scaled up. It needs relatively more resources (gameplay time, expert attention, etc) to a achieve a more focused result than a human expert. At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. Human brains can evolve by chance. Nothing fundamental is stopping us from creating a synthetic one. Natural Intelligence isn't that smart either, realistically.
- leg100 8y ago
  > Human brains can evolve by chance. What do you mean by this?
  - rohanprabhu 8y ago
    Not OP, but I think they are referring to the basic premise of evolution. We still don't have a concrete theory on what causes mutation to occur, only one about which mutations end up surviving and sustaining across generations. As such the evolution of human intelligence is largely an enormous chain of favorable mutations having been coupled and survived over billions of years. And each mutation that did occur was owing to nothing more than chance.
    - leg100 8y ago
      Since, as you say, we don't have a concrete theory on what causes mutations to occur, how can you then go onto say the reason they occur is nothing more than chance?
  - vinn124 8y ago
    for a good introduction to order and complexity (including intelligence) arising from nothing, read "the origins of order" by stuart kauffman.
- tanilama 8y ago
  > At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. This is too early to call. NN acquires knowledge in a fundamentally different way than human. For example, to have a reasonable understanding of a certain language, like producing a sample of grammatically correct utterance, it definitely won't require a human to read the amount of text of Wikipedia, let alone multiple times.
  - nobody271 8y ago
    You're right but to be fair an individual human sits at the very top of billions of years of evolution. We don't have to have to read Wikipedia personally because most of the work was done by evolution, through our ancestors. We're really just hosts for our genetic code. It's the star of the show, not us.
- deleted 8y ago
  [deleted]
- xamuel 8y ago
  We're getting really good at climbing trees. We've got guys who can climb even the tallest trees, no problem. Any day now, we're gonna finally climb to the moon.
  - jf- 8y ago
    I love this comment. My hunch with regard to neural nets is that we’ve got something that looks like biology, and has a certain level of efficacy, but is missing a lot of the finer details that we’re unaware of in biology. Right now we’re just brute forcing problems with as much computing power and data as possible, biology obviously does better.
    - Pica_soO 8y ago
      True, i think the real feasible way to grow a AI is- to actually repeat the process that grows us. As in- grow a single neuron creature in a challenging environment, and by acceleration, environment acceleration and evolutionary algorithm, herd the smarts toward sentience.
    - currymj 8y ago
      it’s missing a lot of the finer details that we are aware of, too. a big one is timing — neurons in the real brain can delay firing depending on what signals they receive. there is research in training artificial neural networks with timing but it is not widely used for AI.
  - state_less 8y ago
    The tree climbers may well build a rocket and land on the moon anyhow. I think it’s a good idea not to add unwarranted mystery to the subject. We have good reason to believe our computers are underpowered for the task. This is a challenge, and while I think it’s going very fast, our collective sense of proportion of the timeline seems optimistic. In my view, if it happens this century, humans will have achieved an impressive feat. If we continue with geometric increases in flops/watt, maybe the rosy predictions will be closer than expected. I suppose what I’m implying is that nervous systems are doing computation, and not something mysterious that we don’t understand. The tree climbers knew they needed to climb higher and realized they’ll need another tool not a different understanding of spatial geometry.
- notahacker 8y ago
  Is it really "making better decisions than a human using a pretty reasonable approximation of the same mechanism as a human" though? To a layman, it looks rather more like an example of a high-powered calculator estimating Nash equilibria by crunching vast amounts of data. More human-like than Deep Blue, certainly, because it's not able to evaluate all possible game states so there's a bit of pattern recognition going on to cut down its search space, but it's succeeding in Go because with Go the higher level of abstractions humans work at is a coping mechanism for not having enough calculating power to simulate outcomes, not the only way of simulating possible outcomes. The issue that the real world is mostly not composed of optimisation problems with possible moves and win conditions as neatly and completely described in code and as replayable as Go is probably a thornier one for building AGI (as opposed to useful tools) through learning processes than the adequacy of the hardware...
- DanielGee 8y ago
  > Nothing fundamental is stopping us from creating a synthetic one. Ethical considerations are preventing us or at the very least limiting the speed of development. But mostly our lack of knowledge of the brain was what stopped us. Now that we are advancing on all fronts, I think we are going to be forced to deal with the ethics of neural/brain creation/destruction/experimentation. If scientists are able to keep disembodied brains "alive", then it will open up avenues for experimentation and ethical issues. https://news.ycombinator.com/item?id=16924764 https://news.ycombinator.com/item?id=16924764 There are even ethical issues of hybrid organ growth ( starting from the embryonic level ) where human organs are grown in pigs. The question is whether human cells can travel to pig's brains and cause pigs to be "more conscious". https://www.newsweek.com/it-ethical-grow-human-organs-pigs-465326 https://www.newsweek.com/it-ethical-grow-human-organs-pigs-4...
- YeGoblynQueenne 8y ago
  >> At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. Neural nets have fundamental limitations, particularly their inability to generalise outside a narrow band around their training set [1]. For instance- you will not find anything in the neural network bibliography about learning such a fundamental concept as counting, in the sense of finding the number n+1 that follows from a given number n, for arbitrary n. A neural net can certainly be trained on that task for numbers in a range [k,n] - but, given numbers outside this range its performance would "fall off a cliff". That's because neural networks can't learn general rules such as "x > y → y = x + k" etc. Add to this the fact that neural nets, while very good at specific tasks, must be trained anew for each new task- and you see that there is a big problem in getting anything approaching "general intelligence" (which might involve any number of wildly varying tasks) just with neural nets. For one thing- even if you could train a neural net model for something like a million tasks, there would remain the question of somehow stitching them all together in a coherent whole capable of performing the right task at the right time or combining decisions from multiple models. Finally, note that nobody is even trying to train a neural net to learn "general intelligence" end-to-end from examples; first of all, because we have no idea what constitutes general intelligence and therefore how to collect its examples. So it's not a matter of scale. More of a matter that there isn't anyone living today that has any idea how to get to AGI with any existing technique- including neural nets. ____________________ [1] https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html
  - YeGoblynQueenne 8y ago
    As an aside, in order to be able to speculate about how close we are to AGI, with neural networks or any other technique, it is important to first unerstand the state of the art in AI. As usual, I'll recommend the classic AI textbook, "AI - A modern approach" by Russel and Norvig: http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
    - bachbach 8y ago
      Are you really under attack for recommending the standard textbook? This website's community has got peculiar more and more over the last 3-5 years and not in a positive way. There were the assholes of yesteryear but even they seemed like more sincere assholes than present day assholes.
DanielGee 8y ago
Sigh. Here they go alternating from AI is a "super genius threat" to "it isn't that smart". Maybe an objective, honest and non-clickbait "Current State of AI" would mean they wouldn't have to swing from one extreme to the other every few months. However you define "smart", currently AI is mostly domain-specific "smart". Though there have been advances in general AI (non-domain specific AI), but we are nowhere close an autonomous generalized AI.
- freeone3000 8y ago
  The state of AI is such that it can be used to detect faces from a crowd to falsely arrest you, deny you a home loan based on nothing, cut you off from social media based on a misunderstanding, and still can't transfer knowledge between domains.
  - EdwardDiego 8y ago
    ...and still can't avoid running over pedestrians because they were pushing bikes which confused it.
- CPLX 8y ago
  > Here they go alternating from AI is a "super genius threat" to "it isn't that smart". It’s possible to be both you know. I can think of at least one example of a moron who is also a veritable genius at being threatening.
YeGoblynQueenne 8y ago
>> In the business world, machine learning often goes by the annoying moniker of “artificial intelligence.” That science-fiction buzzword evokes visions of godlike sentient computers, when in fact, the product is much closer to a statistical regression. Machine learning is about using algorithms to predict things — whether a web-security image contains a cat, what a Google user wants to search for, or whether a self-driving car should brake to avoid a crash. No one yet knows how to give a single computer system the mental flexibility to reason and learn like a human being. While the last senetence is true, the rest of the opening paragraph of this article commits the typical sin of promulgating a reductionist definition of "machine learning", probably because of a lack of historical perspective into the origins and the use of the term. Historically, then, the broadest definition of "machine learning" is the one given by Tom Mitchell, according to whom a machine learning system is one that can improve its behaviour over time. This covers machine learning algorithms that are really nothing like statistical regression- decision tree learners, or the wide array of algorithms and systems that learn logic-based representations that predominated in the early years of the discipline (and are still going, if I may, with Inductive Logic Programming). In short, neither prediction, nor statistical regression are necessary or sufficient characteristics of machine learning. Indeed, there is nothing stopping a system that would learn to reason from examples from being categorised as a "machine learning system". Nothing- except the incomplete understanding of experts from other disciplines, perhaps, like the author of this piece.
- cs702 8y ago
  Exactly. I would add that the OP is motivated, in part, by Corporate America's current understanding of "AI" as little more than "glorified regression" and "glorified classification." There's a tremendous amount of hype, but really very little understanding outside of a few academic centers and Silicon Valley companies.
  - skywhopper 8y ago
    This is exactly the point of the article. If you can let go of your attachment to the academic meaning, the hype long ago was a done deal, and so the responsibility of the business press is to call BS on what the business world says about ML and AI.
Digit-Al 8y ago
I don't think we are that close to creating artificial intelligence for a very simple reason. To solve a problem you must first be able to define the problem to be solved. So, define intelligence. Take your time, this comment will wait for you. Done? Good. Now define it in a way that everyone else agrees with and that doesn't fall to pieces under scrutiny. Philosophers have been trying this for thousands of years and are still no closer to an answer. As far as I can tell we don't have any test for intelligence that is good enough to establish, for absolute certainty, that another human being is 'intelligent' let alone anything else. Define the problem and the requirements to consider the problem solved and only then do we have any hope of coming up with a solution.
- goolulusaurs 8y ago
  Well, this seems to be the definition Deepmind uses: https://arxiv.org/abs/0712.3329 https://arxiv.org/abs/0712.3329 . And there has been work to establish a quantitative measure of the extent to which an agents behavior is intelligent or mechanical: https://arxiv.org/abs/1805.12387 https://arxiv.org/abs/1805.12387 .
  - mi3law 8y ago
    Thanks for sharing such interesting papers! May I ask what makes you think that the first paper has Deepmind's definition of intelligence? Was it said so by Deepmind at some point?
    - goolulusaurs 8y ago
      Shane Legg, one of the authors of the paper, is also the co-founder of Deepmind and it is cited in several of their papers. There are some old videos of him discussing it here: https://www.youtube.com/playlist?list=PL985E81376A4D061E https://www.youtube.com/playlist?list=PL985E81376A4D061E .
- gnulinux 8y ago
  I agree with you but for a different reason. In some other sense you don't need to define intelligence. You can assume humans are intellugent and collect data as to how behave and cross validate your machine's bahevior with that. This should include talking, walking, feeling, problem solving, convincing etc... except in a simulated world.
deleted 8y ago
[deleted]
wazoox 8y ago
Even for very low-paid labour labelled as "low skilled", there are tons of intricate judgements call. I've read sociology articles on this matter, for instance a woman working in a factory at arranging cakes in boxes. She explained all of the small evaluations, complex decisions she had to make at the quick pace of the factory chain, like how to dispose different cakes to maximize box fill, of get sure that all cakes are properly seated and won't move in transport, etc. She deemed herself "unskilled", however at the same time concluded that it took her many months to master the skill of arranging cakes in boxes. Ditto for mailmen: now they're using a program call "GeoPost" to "optimize routes"; actually the program is unable to account for myriads of things that an actual, human postman must take into account, for instance the postman is supposed to deliver mail on the even side of the street first, than on the odd side on his return trip an hour later, according to the software. That works well for wide avenues, but in small residential areas people will get out of their house and call the postman if he delivers mail for the opposite-side neighbour but not them. Silly human, with their feelings :) In fact the craze for AI reminds me so much (once again) of good ol' grandpa Marx. As competition rages and given the tendency of profit to fall, companies try to get rid of living labour and replace it with "dead labour" (machine labour). But once again it meets the wall of value, which only comes from living labour. This all adds up perfectly.
Veedrac 8y ago
AI is a lot dumber than people expect... which is the scary thing. If you look at Leela Zero's training, you'll see a lot of huge flaws pointing to massive failures of reason and areas where it is frankly incapable of making basic generalizations. Yet variants of this same technology beat the best Go players, synthesize realistic voices, give sentence labels to images, drive cars, translate language, etc. Makes you wonder that, if such an unintelligent bit of math can fake so much, whether intelligence is really that mysterious or whether it just seems that way because we don't know how to write it.
skywhopper 8y ago
Glad we are finally seeing some gradual pushback on the ridiculous hype of the past few years. What is currently called “AI” can be useful in some very narrow scenarios but even then it’s dangerous when people put too much trust in it (same as any system—computerized, mechanical, or bureaucratic). Blindly trusting the algorithms that brought you the Facebook and Twitter timelines to make decisions about real things in human lives would be disastrous, and we already do far too much of it.
magwa101 8y ago
It doesn't have to be intelligent to be usefully applied broadly and upend our economies.

93 points by benryon 8y ago

For an informed opinion see https://news.ycombinator.com/item?id=17766473 https://news.ycombinator.com/item?id=17766473

Take AlphaGo, because I know Go quite well. A computer is making better decisions than a human, using a pretty reasonable approximation of the same mechanism as a human but scaled up. It needs relatively more resources (gameplay time, expert attention, etc) to a achieve a more focused result than a human expert. At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. Human brains can evolve by chance. Nothing fundamental is stopping us from creating a synthetic one. Natural Intelligence isn't that smart either, realistically.

leg100 8y ago
> Human brains can evolve by chance. What do you mean by this?
- rohanprabhu 8y ago
  Not OP, but I think they are referring to the basic premise of evolution. We still don't have a concrete theory on what causes mutation to occur, only one about which mutations end up surviving and sustaining across generations. As such the evolution of human intelligence is largely an enormous chain of favorable mutations having been coupled and survived over billions of years. And each mutation that did occur was owing to nothing more than chance.
  - leg100 8y ago
    Since, as you say, we don't have a concrete theory on what causes mutations to occur, how can you then go onto say the reason they occur is nothing more than chance?
- vinn124 8y ago
  for a good introduction to order and complexity (including intelligence) arising from nothing, read "the origins of order" by stuart kauffman.
tanilama 8y ago
> At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. This is too early to call. NN acquires knowledge in a fundamentally different way than human. For example, to have a reasonable understanding of a certain language, like producing a sample of grammatically correct utterance, it definitely won't require a human to read the amount of text of Wikipedia, let alone multiple times.
- nobody271 8y ago
  You're right but to be fair an individual human sits at the very top of billions of years of evolution. We don't have to have to read Wikipedia personally because most of the work was done by evolution, through our ancestors. We're really just hosts for our genetic code. It's the star of the show, not us.
deleted 8y ago
[deleted]
xamuel 8y ago
We're getting really good at climbing trees. We've got guys who can climb even the tallest trees, no problem. Any day now, we're gonna finally climb to the moon.
- jf- 8y ago
  I love this comment. My hunch with regard to neural nets is that we’ve got something that looks like biology, and has a certain level of efficacy, but is missing a lot of the finer details that we’re unaware of in biology. Right now we’re just brute forcing problems with as much computing power and data as possible, biology obviously does better.
  - Pica_soO 8y ago
    True, i think the real feasible way to grow a AI is- to actually repeat the process that grows us. As in- grow a single neuron creature in a challenging environment, and by acceleration, environment acceleration and evolutionary algorithm, herd the smarts toward sentience.
  - currymj 8y ago
    it’s missing a lot of the finer details that we are aware of, too. a big one is timing — neurons in the real brain can delay firing depending on what signals they receive. there is research in training artificial neural networks with timing but it is not widely used for AI.
- state_less 8y ago
  The tree climbers may well build a rocket and land on the moon anyhow. I think it’s a good idea not to add unwarranted mystery to the subject. We have good reason to believe our computers are underpowered for the task. This is a challenge, and while I think it’s going very fast, our collective sense of proportion of the timeline seems optimistic. In my view, if it happens this century, humans will have achieved an impressive feat. If we continue with geometric increases in flops/watt, maybe the rosy predictions will be closer than expected. I suppose what I’m implying is that nervous systems are doing computation, and not something mysterious that we don’t understand. The tree climbers knew they needed to climb higher and realized they’ll need another tool not a different understanding of spatial geometry.
notahacker 8y ago
Is it really "making better decisions than a human using a pretty reasonable approximation of the same mechanism as a human" though? To a layman, it looks rather more like an example of a high-powered calculator estimating Nash equilibria by crunching vast amounts of data. More human-like than Deep Blue, certainly, because it's not able to evaluate all possible game states so there's a bit of pattern recognition going on to cut down its search space, but it's succeeding in Go because with Go the higher level of abstractions humans work at is a coping mechanism for not having enough calculating power to simulate outcomes, not the only way of simulating possible outcomes. The issue that the real world is mostly not composed of optimisation problems with possible moves and win conditions as neatly and completely described in code and as replayable as Go is probably a thornier one for building AGI (as opposed to useful tools) through learning processes than the adequacy of the hardware...
DanielGee 8y ago
> Nothing fundamental is stopping us from creating a synthetic one. Ethical considerations are preventing us or at the very least limiting the speed of development. But mostly our lack of knowledge of the brain was what stopped us. Now that we are advancing on all fronts, I think we are going to be forced to deal with the ethics of neural/brain creation/destruction/experimentation. If scientists are able to keep disembodied brains "alive", then it will open up avenues for experimentation and ethical issues. https://news.ycombinator.com/item?id=16924764 https://news.ycombinator.com/item?id=16924764 There are even ethical issues of hybrid organ growth ( starting from the embryonic level ) where human organs are grown in pigs. The question is whether human cells can travel to pig's brains and cause pigs to be "more conscious". https://www.newsweek.com/it-ethical-grow-human-organs-pigs-465326 https://www.newsweek.com/it-ethical-grow-human-organs-pigs-4...
YeGoblynQueenne 8y ago
>> At the moment, it is very easy to suspect that it is only the economics of training a neural net (hardware, specialist attention, data gathering an processing) that is holding us back from AGI. Neural nets have fundamental limitations, particularly their inability to generalise outside a narrow band around their training set [1]. For instance- you will not find anything in the neural network bibliography about learning such a fundamental concept as counting, in the sense of finding the number n+1 that follows from a given number n, for arbitrary n. A neural net can certainly be trained on that task for numbers in a range [k,n] - but, given numbers outside this range its performance would "fall off a cliff". That's because neural networks can't learn general rules such as "x > y → y = x + k" etc. Add to this the fact that neural nets, while very good at specific tasks, must be trained anew for each new task- and you see that there is a big problem in getting anything approaching "general intelligence" (which might involve any number of wildly varying tasks) just with neural nets. For one thing- even if you could train a neural net model for something like a million tasks, there would remain the question of somehow stitching them all together in a coherent whole capable of performing the right task at the right time or combining decisions from multiple models. Finally, note that nobody is even trying to train a neural net to learn "general intelligence" end-to-end from examples; first of all, because we have no idea what constitutes general intelligence and therefore how to collect its examples. So it's not a matter of scale. More of a matter that there isn't anyone living today that has any idea how to get to AGI with any existing technique- including neural nets. ____________________ [1] https://blog.keras.io/the-limitations-of-deep-learning.html https://blog.keras.io/the-limitations-of-deep-learning.html
- YeGoblynQueenne 8y ago
  As an aside, in order to be able to speculate about how close we are to AGI, with neural networks or any other technique, it is important to first unerstand the state of the art in AI. As usual, I'll recommend the classic AI textbook, "AI - A modern approach" by Russel and Norvig: http://aima.cs.berkeley.edu/ http://aima.cs.berkeley.edu/
  - bachbach 8y ago
    Are you really under attack for recommending the standard textbook? This website's community has got peculiar more and more over the last 3-5 years and not in a positive way. There were the assholes of yesteryear but even they seemed like more sincere assholes than present day assholes.

DanielGee 8y ago

Sigh. Here they go alternating from AI is a "super genius threat" to "it isn't that smart". Maybe an objective, honest and non-clickbait "Current State of AI" would mean they wouldn't have to swing from one extreme to the other every few months. However you define "smart", currently AI is mostly domain-specific "smart". Though there have been advances in general AI (non-domain specific AI), but we are nowhere close an autonomous generalized AI.

freeone3000 8y ago
The state of AI is such that it can be used to detect faces from a crowd to falsely arrest you, deny you a home loan based on nothing, cut you off from social media based on a misunderstanding, and still can't transfer knowledge between domains.
- EdwardDiego 8y ago
  ...and still can't avoid running over pedestrians because they were pushing bikes which confused it.
CPLX 8y ago
> Here they go alternating from AI is a "super genius threat" to "it isn't that smart". It’s possible to be both you know. I can think of at least one example of a moron who is also a veritable genius at being threatening.

YeGoblynQueenne 8y ago

>> In the business world, machine learning often goes by the annoying moniker of “artificial intelligence.” That science-fiction buzzword evokes visions of godlike sentient computers, when in fact, the product is much closer to a statistical regression. Machine learning is about using algorithms to predict things — whether a web-security image contains a cat, what a Google user wants to search for, or whether a self-driving car should brake to avoid a crash. No one yet knows how to give a single computer system the mental flexibility to reason and learn like a human being. While the last senetence is true, the rest of the opening paragraph of this article commits the typical sin of promulgating a reductionist definition of "machine learning", probably because of a lack of historical perspective into the origins and the use of the term. Historically, then, the broadest definition of "machine learning" is the one given by Tom Mitchell, according to whom a machine learning system is one that can improve its behaviour over time. This covers machine learning algorithms that are really nothing like statistical regression- decision tree learners, or the wide array of algorithms and systems that learn logic-based representations that predominated in the early years of the discipline (and are still going, if I may, with Inductive Logic Programming). In short, neither prediction, nor statistical regression are necessary or sufficient characteristics of machine learning. Indeed, there is nothing stopping a system that would learn to reason from examples from being categorised as a "machine learning system". Nothing- except the incomplete understanding of experts from other disciplines, perhaps, like the author of this piece.

cs702 8y ago
Exactly. I would add that the OP is motivated, in part, by Corporate America's current understanding of "AI" as little more than "glorified regression" and "glorified classification." There's a tremendous amount of hype, but really very little understanding outside of a few academic centers and Silicon Valley companies.
- skywhopper 8y ago
  This is exactly the point of the article. If you can let go of your attachment to the academic meaning, the hype long ago was a done deal, and so the responsibility of the business press is to call BS on what the business world says about ML and AI.

Digit-Al 8y ago

I don't think we are that close to creating artificial intelligence for a very simple reason. To solve a problem you must first be able to define the problem to be solved. So, define intelligence. Take your time, this comment will wait for you. Done? Good. Now define it in a way that everyone else agrees with and that doesn't fall to pieces under scrutiny. Philosophers have been trying this for thousands of years and are still no closer to an answer. As far as I can tell we don't have any test for intelligence that is good enough to establish, for absolute certainty, that another human being is 'intelligent' let alone anything else. Define the problem and the requirements to consider the problem solved and only then do we have any hope of coming up with a solution.

goolulusaurs 8y ago
Well, this seems to be the definition Deepmind uses: https://arxiv.org/abs/0712.3329 https://arxiv.org/abs/0712.3329 . And there has been work to establish a quantitative measure of the extent to which an agents behavior is intelligent or mechanical: https://arxiv.org/abs/1805.12387 https://arxiv.org/abs/1805.12387 .
- mi3law 8y ago
  Thanks for sharing such interesting papers! May I ask what makes you think that the first paper has Deepmind's definition of intelligence? Was it said so by Deepmind at some point?
  - goolulusaurs 8y ago
    Shane Legg, one of the authors of the paper, is also the co-founder of Deepmind and it is cited in several of their papers. There are some old videos of him discussing it here: https://www.youtube.com/playlist?list=PL985E81376A4D061E https://www.youtube.com/playlist?list=PL985E81376A4D061E .
gnulinux 8y ago
I agree with you but for a different reason. In some other sense you don't need to define intelligence. You can assume humans are intellugent and collect data as to how behave and cross validate your machine's bahevior with that. This should include talking, walking, feeling, problem solving, convincing etc... except in a simulated world.

deleted 8y ago

[deleted]

wazoox 8y ago

Even for very low-paid labour labelled as "low skilled", there are tons of intricate judgements call. I've read sociology articles on this matter, for instance a woman working in a factory at arranging cakes in boxes. She explained all of the small evaluations, complex decisions she had to make at the quick pace of the factory chain, like how to dispose different cakes to maximize box fill, of get sure that all cakes are properly seated and won't move in transport, etc. She deemed herself "unskilled", however at the same time concluded that it took her many months to master the skill of arranging cakes in boxes. Ditto for mailmen: now they're using a program call "GeoPost" to "optimize routes"; actually the program is unable to account for myriads of things that an actual, human postman must take into account, for instance the postman is supposed to deliver mail on the even side of the street first, than on the odd side on his return trip an hour later, according to the software. That works well for wide avenues, but in small residential areas people will get out of their house and call the postman if he delivers mail for the opposite-side neighbour but not them. Silly human, with their feelings :) In fact the craze for AI reminds me so much (once again) of good ol' grandpa Marx. As competition rages and given the tendency of profit to fall, companies try to get rid of living labour and replace it with "dead labour" (machine labour). But once again it meets the wall of value, which only comes from living labour. This all adds up perfectly.

Veedrac 8y ago

AI is a lot dumber than people expect... which is the scary thing. If you look at Leela Zero's training, you'll see a lot of huge flaws pointing to massive failures of reason and areas where it is frankly incapable of making basic generalizations. Yet variants of this same technology beat the best Go players, synthesize realistic voices, give sentence labels to images, drive cars, translate language, etc. Makes you wonder that, if such an unintelligent bit of math can fake so much, whether intelligence is really that mysterious or whether it just seems that way because we don't know how to write it.

skywhopper 8y ago

Glad we are finally seeing some gradual pushback on the ridiculous hype of the past few years. What is currently called “AI” can be useful in some very narrow scenarios but even then it’s dangerous when people put too much trust in it (same as any system—computerized, mechanical, or bureaucratic). Blindly trusting the algorithms that brought you the Facebook and Twitter timelines to make decisions about real things in human lives would be disastrous, and we already do far too much of it.

magwa101 8y ago

It doesn't have to be intelligent to be usefully applied broadly and upend our economies.