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"Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. By the same token, the abili

"Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. By the same token, the ability of neural networks to learn interpretable word embeddings, say, does not remotely suggest that they are the right kind of tool for a human-level understanding of the world. It is impressive and surprising that these general-purpose, statistical models can learn meaningful relations from text alone, without any richer perception of the world, but this may speak much more about the unexpected ease of the task itself than it does about the capacity of the models. Just as checkers can be won through tree-search, so too can many semantic relations be learned from text statistics. Both produce impressive intelligent-seeming behaviour, but neither necessarily pave the way towards true machine intelligence." So true, and this is why I don't listen when Elon Musk or Stephen Hawkings spread fear about the impending AI disaster; they think because a neural network can recognize an image like a human can, that it's not a huge leap to say it will be able to soon think and act like a human, but in reality this is just not the case.

jbhatab 11y ago
I don't think they are afraid of current technology and neither am I. However, I am afraid of a breakthrough or new model of AI that does approach human intelligence. I don't know how, when, or why but I think it's reasonable in the next 400 years that we approach something like that. 400 years is not very long in terms of humanity but this technology could do things to humanity that we can't grasp right now.
- pjbrunet 11y ago
  Without getting into our personal, specific definitions of intelligence, I do agree the next 400 years could bring about something horribly disruptive that is not intelligent. Dumb and dangerous, discovered by fools, is the more likely threat to peace in the world.
  - jbhatab 11y ago
    Maybe, but I'm already afraid of dumb and dangerous tools. Nuclear weapons. Maybe we get stronger dumb and dangerous tools, but what scares me much more is a smart drone that has AI and the ability to learn things about me at a rate I can't comprehend.
    - otakucode 11y ago
      I think that fear is a very healthy one. I have had an idea for a website for a long time that I haven't gotten around to building yet, but may before too long. It would basically give people such a fear, in hopes that they become better informed and act not to stop the development of such knowledge (it has neutral and positive uses just like any tool which has negative ones) but to restrict its misuse. The site would be a simple one, and decidedly sensationalistic (I am not looking forward to media attention I would hope it would garner, but such is necessary to widely spread an idea these days). It would be machine learning system which has been trained on news articles (or, if I can find such a data set, information about their travel would actually be much better) about politicians and lists of politicians which were later shown to be secretly cheating on their spouses. And it would enable you to select your own politician of choice and see a prediction of how likely that person is to be secretly cheating on their spouse currently. All of the public discussion around the NSA, and every single last one of the statements by the government about the NSAs practices, centered EXCLUSIVELY on the idea of human analysts at the NSA directly reading the fulltext of peoples communications. Not one single time did they address the (as far as I am concerned) actual danger - the NSAs automated systems having access to peoples communications, providing "summaries" and "ratings" and "predictions." Being worried about some random stranger reading your personal, private communications should maybe be a little bit squicky. Being worried about some random machine learning system teasing out predictions of how likely you are to harbor thoughts that threaten the status quo should be pants-shittingly terrifying.
irremediable 11y ago
I think you've misrepresented their views. They doubtless know roughly what level the technology's at, even if it's not their speciality. They're worried about what level AI might go to in the next few decades/centuries. And you say they worry about an "impending AI disaster", but I don't think either of them has said they believe such a catastrophe to be imminent. There are good reasons to be at least a little worried about AI in the long-term future. (Not to mention the imminent social effects of replacing various categories of worker with AI -- many examples of this have already happened, or are happening right now.) Also, a minor nitpick, but you've misspelled Stephen Hawking's name. There's no "s" on the end.
- hacker_9 11y ago
  People were worried about losing their jobs back when computers first became widespread too, but actual statistical evidence suggests that the amount of jobs available actually rose.
  - an_cap 11y ago
    Snark about 'actual statistical evidence' notwithstanding, people losing their jobs is entirely compatible with an increase in the number of available jobs because, unsurprisingly, not all jobs are fungible.
    - bottled_poe 11y ago
      Assuming we were able to build a robot that looked and behaved identically to a human, which jobs could not be replaced?
      - bad_user 11y ago
        It doesn't even have to be identical, it just has to be acceptably humanoid. For example I imagine that in 5 stars restaurants human waiters will still be the norm for some time, but enterprises like McDonalds or Starbucks would probably not blink an eye in replacing people with machines if possible, as there is no human element in a McDrive. Would go hand in hand with the edible substances they sell as food.
sdenton4 11y ago
One of the more useful heuristics I've heard is that when humans make split second decisions (like recognizing a face in an image or choosing the next word to say) there's only time for a few - like five to seven - layers of neurons to fire between impulse and action. These are the sort of tasks that neural networks are currently making great progress on. But things like long-term planning and goal formation aren't in this quick-reflex domain. (Though humans are arguably pretty bad at these sorts of activity too, on average.) We're also generally training neural nets to succeed at well-defined, bounded tasks, which don't really require long term planning so much. The video-game playing work at Deep Mind is actually a pretty important step forward, though; they're doing impressive stuff with domain-transfer (learning multiple games with a single network), and as one goes further with games, we might start seeing longer-term planning and concept formation. (eg, if we start seeing awesome StarCraft players...) There's a case to be made, though, that we humans just trained on a big, broad objective function - survive, thrive, and multiply - with training applied across millions of years. And a good deal of intense local training over the first ten years of life, using a massively parallel computer that far outstrips any data center. The question for strong AI seems to rest heavily on how much extraneous work there is done in biology, and whether we end up with a system so complex that it requires eighteen years of training time. If creating a single strong AI actually requires that much effort, we don't have to worry too much about skynet, methinks. But we certainly don't have to worry about them spontaneously arising when we train with simple objective functions.
- TeMPOraL 11y ago
  Note: even if training a single AI would require 18+ years, copying it will still be split-second. By this very token artificial minds have an advantage over meat ones.
  - monk_e_boy 11y ago
    And they learn in parallel, google self driving cars and a demo from Nvidia both report new experiences to the cloud which is then 'understood' or parsed I guess and lessons learned are passed down to the other cars. Lessons could also be passed from AI to AI (e.g. fro Google to Ford) by sending the input [training data] and a output [reasonable response to that data] for the other AI to ponder.
throwupper247 11y ago
The dangerous qualities of human thought are rather simple sometimes, the difference wouldn't be that big, probably.
blazespin 11y ago
Yeah, I'm not so sure. All it takes is an intelligent agent that has trained in a VR environment accidentally released into a RL environment and do something crazy - like kill someone. That will be a taste of things to come. Deep Mind is training computers to WIN in virtual environments right now. What if they train them against simulation of real world environments instead of video games? What if a computer discovers that blowing something up is a way to get a 'higher score'? What if they accidentally get pushed to production? It's war games / bostrom's paper clip monstrosity. it can happen. Arguably, it may have already happened in a flash crash. Create a simulation of a stock exchange, train a computer to win the market, it discovers that crashing the market to makes lots of money, gets pushed to production. Instant global chaos (though maybe the AI owner gets richer). The future is now, folks..
- hacker_9 11y ago
  1. In a virtual environment the computer knows about all the objects in the scene. It's a whole other level to have the computer interpret the world through light alone. 2. If you meant that it learnt to play 2d games through watching pixels, it still didn't interpret any meaning, and it's still a huge leap to go to interpreting the real world. 3. The real world is noisy. You don't see it because the brain handles all that part for you, but even something like different lighting or a smoke cloud can throw a computer off completely.
  - blazespin 11y ago
    1, No, that's not how Deep mind works. The games are unsupervised and the computer knows nothing about the game. 2. Not at all. Haven't you seen the videos of computers walking around recognizing things? This is already here. 3. The real world is noisy, yes, but a factory is not. It will have a limited set of components to be recognized.
    - argonaut 11y ago
      The training is most certainly supervised. The fact that people commonly draw a distinction between standard supervised learning and reinforcement learning isn't really relevant here (the distinction between a single datum and a sequence of observations), because the fact is the label is the score. If you read their DQN algorithm (or try to code an implementation, or look at any reinforcement learning algorithm, really) you would see that. https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf: In Algorithm 1, see variables r and y_j. Addendum: My broader implication is that if you misunderstand such a basic aspect of machine learning (and reinforcement learning), it makes me question whether we should lend credibility to your predictions about the field. Edited to address reply.
proc0 11y ago
At least Elon Musk is not really implying what technology will get us there. He is assuming that once we do, it has serious implications. It's hard to disagree with that. We will be sharing the planet with another intelligent creature for the first time ever.
- TheOtherHobbes 11y ago
  We've been sharing the planet with intelligent creatures since before evolution made us human. That's not the issue. The issue with AI is that we may be sharing the planet with an intelligence that's both nastier and more capable than we are. I still tend to see this as projection. There's a huge gap between wiring together neural networks and developing something with self-awareness, strategic agency, and some potential to expand both at a rate we can't imagine. Bostrom's paperclip monster is facile, and no worse than the kind of resource plunder we do already in the name of profit. Whatever challenges AI brings are going to be quite a few levels beyond that.
- jpmec 11y ago
  umm, except for the rest of the lifeforms on earth.
- deleted 11y ago
  [deleted]
- ekianjo 11y ago
  Wow are you actually saying all other lifeforms on earth have no intelligence. Thats a nice 19th century view of the world.
ujal 11y ago
No one knows yet how big the leap needs to be, but the most amazing thing about ANN's is that the feature layers a CNN arrives at may functionally resemble some of those found in the visual cortex: "The oft-cited resemblance of the imagery to LSD- and psilocybin-induced hallucinations is suggestive of a functional resemblance between artificial neural networks and particular layers of the visual cortex, a matter which merits further study." [0] Also see "How brains learn to see" from Pawan Sinha [1]. [0] https://en.wikipedia.org/wiki/DeepDream https://en.wikipedia.org/wiki/DeepDream [1] http://www.ted.com/talks/pawan_sinha_on_how_brains_learn_to_see http://www.ted.com/talks/pawan_sinha_on_how_brains_learn_to_...
kendallpark 11y ago
Well the thing with the field of "AI" is that it's a moving target. People used to call compilers artificial intelligence. As "AI" research is done, it ends up spawning new fields of study and then the new definition of AI = [previous definition of AI] - [new field of study].
rw 11y ago
I agree that the rate of progress in AI is unpredictable, which means we are probably not right on the cusp of superhuman AI. But what if we actually are on the precipice? How could you tell? You seem to be taking a bet on the following statement: "Before superhuman AI is developed, the techniques that make it possible will look dangerous." That's a risky proposition. There's so much to lose in this situation. Elon Musk, Stephen Hawking, and many other people are taking a different, more risk-averse bet: "Superhuman AI is extremely dangerous, so we need to be pessimistic about how good we are at predicting when it will happen." By that logic, general AI is to be feared and worried about right now, because our predictive abilities are imperfect. The AI trend is towards more danger, not less: there's a slight chance of a cataclysmic event happening today, and as time goes on, the likelihood of it happening will increase (due to ongoing R&D in AI). (As an aside, if you want to learn how hard the "AI Control Problem" is, I recommend the book "Superintelligence" by Nick Bostrum.)
- jbhatab 11y ago
  I agree with this completely and I think a simple board or coalition of companies and governments will be the answer. Regulation, policies, and awareness are the solutions to this problem, but thinking because it isn't a problem now that it won't be a potential problem at all is a scary thought.
- vonnik 11y ago
  The real cost of fearing an AGI now is an opportunity cost. The technology is a long ways away. Other events -- such as ecological collapse or nuclear disaster to name two -- are much more likely to irremediably affect our lives. So by hopping on Bostrom's fear wagon, you are making a bet on several levels: a) this matters now; b) this matters more than other dangers we could devote ourselves to minimizing; and c) something can actually be done about it. All three bets seem like bad ones to make to me.
  - rw 11y ago
    Fair comparison, but I didn't intend any monopoly on fear. All I'm saying is that the risk of GAI should be taken seriously, calibrated along with the other problems you mention. There's a lot of room between 'irrelevant' and 'the most important issue' :-)
Houshalter 11y ago
Elon Musk and Stephen Hawking are not talking about current AI at all. They are talking about the future, which could be decades away. But it is very likely we will have superintelligent AI in our lifetimes. Here's a survey of AI experts: http://www.nickbostrom.com/papers/survey.pdf http://www.nickbostrom.com/papers/survey.pdf >We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity
- panic 11y ago
  72 out of the 170 survey respondents were attendees of the conferences “Artificial General Intelligence” (AGI 12) and “Impacts and Risks of Artificial General Intelligence” (AGI Impacts 2012). It's not surprising that AGI researchers believe that AGI will be developed soon.
  - Houshalter 11y ago
    I don't understand why participants of a conference on artificial general intelligence should be excluded from predictions about artificial general intelligence. But whatever, even the "weak AI" researchers had similar opinions: The median predicted date for a 50% probability of AGI was 2050 by the top 100 AI researchers they surveyed. Only 10 years ahead of the more liberal predictions of the AGI group.
    - panic 11y ago
      Well, a survey of AI researchers in the 60's would have said we'd have AGI by now. Surveying researchers in a field is not a reliable method for determining truth.
      - mattmanser 11y ago
simonster 11y ago
To me, the most remarkable thing about recent neural network research is that a very substantial proportion of the human brain is dedicated to vision, and neural networks can now achieve human level performance at object recognition, and with similar, robust representations (see e.g. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003963 http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...). I see two possible conclusions that can be drawn here. The first possible possibility is that neural networks really are close enough to the way the brain works, that they learn the way the brain does, and that with some algorithmic tweaks and the right training data they can learn to everything the brain does. This is the view that Luke is rallying against, and I tend to agree that it's too soon to make this claim. On one hand, the frontal lobe is not all that different from the temporal lobe. If we can achieve the same things with a machine as with one giant chunk of 6-layer neocortex, it's not too hard to imagine that we'll be able to do the same with other pieces. The problems may be different, but the biological implementation is strikingly similar. On the other hand, as sdenton4 notes elsewhere in this thread, the big difference between sensation and cognition is that recurrent processing is that the former can (apparently!) be accomplished in an entirely feedforward manner while the latter requires (or is at least thought to require for efficient implementation) recurrent processing. And just because we have efficient ways to train one kind of neural network doesn't necessarily mean they'll generalize easily to others. But there is a second possibility, which is that neural networks don't learn or function quite like brains, but somehow both can find similar, nearly optimal strategies for encoding information in sensory domains. In this case, we're probably more than a few years away from machines that can think, but to us neuroscientists this is even more exciting. Vision scientists have been looking for decades to understand how the visual system works, and in this scenario, there's some underlying theory to vision and we have an easily observable system that implements it. Even if it turns out that vision is too different from cognition for these insights to generalize, the success of deep learning for object recognition still has vast implications for future understanding of the brain, and thus probably for future artificial intelligence. If we know the representations the brain uses, that makes figuring out the mechanism by which it computes those representations far easier than if we had to figure out both the mechanism and the representation from scratch. So my perspective here is that we don't know exactly how far recent neural network research is going to take us, but it's hard to deny its importance. Before I started graduate school, I didn't think that machines would achieve human-level performance at object recognition before we figured out how the brain does it, but here we are. But I suppose that historically, huge advances in AI research have been followed by stagnation and disappointment, so who knows how far we'll get this time.
ajuc 11y ago
The threat level is independent of the predicted time it happens. Also AIs won't think like human, and that's part of the problem (no shared value system).

jbhatab 11y ago

I don't think they are afraid of current technology and neither am I. However, I am afraid of a breakthrough or new model of AI that does approach human intelligence. I don't know how, when, or why but I think it's reasonable in the next 400 years that we approach something like that. 400 years is not very long in terms of humanity but this technology could do things to humanity that we can't grasp right now.

pjbrunet 11y ago
Without getting into our personal, specific definitions of intelligence, I do agree the next 400 years could bring about something horribly disruptive that is not intelligent. Dumb and dangerous, discovered by fools, is the more likely threat to peace in the world.
- jbhatab 11y ago
  Maybe, but I'm already afraid of dumb and dangerous tools. Nuclear weapons. Maybe we get stronger dumb and dangerous tools, but what scares me much more is a smart drone that has AI and the ability to learn things about me at a rate I can't comprehend.
  - otakucode 11y ago
    I think that fear is a very healthy one. I have had an idea for a website for a long time that I haven't gotten around to building yet, but may before too long. It would basically give people such a fear, in hopes that they become better informed and act not to stop the development of such knowledge (it has neutral and positive uses just like any tool which has negative ones) but to restrict its misuse. The site would be a simple one, and decidedly sensationalistic (I am not looking forward to media attention I would hope it would garner, but such is necessary to widely spread an idea these days). It would be machine learning system which has been trained on news articles (or, if I can find such a data set, information about their travel would actually be much better) about politicians and lists of politicians which were later shown to be secretly cheating on their spouses. And it would enable you to select your own politician of choice and see a prediction of how likely that person is to be secretly cheating on their spouse currently. All of the public discussion around the NSA, and every single last one of the statements by the government about the NSAs practices, centered EXCLUSIVELY on the idea of human analysts at the NSA directly reading the fulltext of peoples communications. Not one single time did they address the (as far as I am concerned) actual danger - the NSAs automated systems having access to peoples communications, providing "summaries" and "ratings" and "predictions." Being worried about some random stranger reading your personal, private communications should maybe be a little bit squicky. Being worried about some random machine learning system teasing out predictions of how likely you are to harbor thoughts that threaten the status quo should be pants-shittingly terrifying.

irremediable 11y ago

I think you've misrepresented their views. They doubtless know roughly what level the technology's at, even if it's not their speciality. They're worried about what level AI might go to in the next few decades/centuries. And you say they worry about an "impending AI disaster", but I don't think either of them has said they believe such a catastrophe to be imminent. There are good reasons to be at least a little worried about AI in the long-term future. (Not to mention the imminent social effects of replacing various categories of worker with AI -- many examples of this have already happened, or are happening right now.) Also, a minor nitpick, but you've misspelled Stephen Hawking's name. There's no "s" on the end.

hacker_9 11y ago
People were worried about losing their jobs back when computers first became widespread too, but actual statistical evidence suggests that the amount of jobs available actually rose.
- an_cap 11y ago
  Snark about 'actual statistical evidence' notwithstanding, people losing their jobs is entirely compatible with an increase in the number of available jobs because, unsurprisingly, not all jobs are fungible.
  - bottled_poe 11y ago
    Assuming we were able to build a robot that looked and behaved identically to a human, which jobs could not be replaced?
    - bad_user 11y ago
      It doesn't even have to be identical, it just has to be acceptably humanoid. For example I imagine that in 5 stars restaurants human waiters will still be the norm for some time, but enterprises like McDonalds or Starbucks would probably not blink an eye in replacing people with machines if possible, as there is no human element in a McDrive. Would go hand in hand with the edible substances they sell as food.

sdenton4 11y ago

One of the more useful heuristics I've heard is that when humans make split second decisions (like recognizing a face in an image or choosing the next word to say) there's only time for a few - like five to seven - layers of neurons to fire between impulse and action. These are the sort of tasks that neural networks are currently making great progress on. But things like long-term planning and goal formation aren't in this quick-reflex domain. (Though humans are arguably pretty bad at these sorts of activity too, on average.) We're also generally training neural nets to succeed at well-defined, bounded tasks, which don't really require long term planning so much. The video-game playing work at Deep Mind is actually a pretty important step forward, though; they're doing impressive stuff with domain-transfer (learning multiple games with a single network), and as one goes further with games, we might start seeing longer-term planning and concept formation. (eg, if we start seeing awesome StarCraft players...) There's a case to be made, though, that we humans just trained on a big, broad objective function - survive, thrive, and multiply - with training applied across millions of years. And a good deal of intense local training over the first ten years of life, using a massively parallel computer that far outstrips any data center. The question for strong AI seems to rest heavily on how much extraneous work there is done in biology, and whether we end up with a system so complex that it requires eighteen years of training time. If creating a single strong AI actually requires that much effort, we don't have to worry too much about skynet, methinks. But we certainly don't have to worry about them spontaneously arising when we train with simple objective functions.

TeMPOraL 11y ago
Note: even if training a single AI would require 18+ years, copying it will still be split-second. By this very token artificial minds have an advantage over meat ones.
- monk_e_boy 11y ago
  And they learn in parallel, google self driving cars and a demo from Nvidia both report new experiences to the cloud which is then 'understood' or parsed I guess and lessons learned are passed down to the other cars. Lessons could also be passed from AI to AI (e.g. fro Google to Ford) by sending the input [training data] and a output [reasonable response to that data] for the other AI to ponder.

throwupper247 11y ago

The dangerous qualities of human thought are rather simple sometimes, the difference wouldn't be that big, probably.

blazespin 11y ago

Yeah, I'm not so sure. All it takes is an intelligent agent that has trained in a VR environment accidentally released into a RL environment and do something crazy - like kill someone. That will be a taste of things to come. Deep Mind is training computers to WIN in virtual environments right now. What if they train them against simulation of real world environments instead of video games? What if a computer discovers that blowing something up is a way to get a 'higher score'? What if they accidentally get pushed to production? It's war games / bostrom's paper clip monstrosity. it can happen. Arguably, it may have already happened in a flash crash. Create a simulation of a stock exchange, train a computer to win the market, it discovers that crashing the market to makes lots of money, gets pushed to production. Instant global chaos (though maybe the AI owner gets richer). The future is now, folks..

hacker_9 11y ago
1. In a virtual environment the computer knows about all the objects in the scene. It's a whole other level to have the computer interpret the world through light alone. 2. If you meant that it learnt to play 2d games through watching pixels, it still didn't interpret any meaning, and it's still a huge leap to go to interpreting the real world. 3. The real world is noisy. You don't see it because the brain handles all that part for you, but even something like different lighting or a smoke cloud can throw a computer off completely.
- blazespin 11y ago
  1, No, that's not how Deep mind works. The games are unsupervised and the computer knows nothing about the game. 2. Not at all. Haven't you seen the videos of computers walking around recognizing things? This is already here. 3. The real world is noisy, yes, but a factory is not. It will have a limited set of components to be recognized.
  - argonaut 11y ago
    The training is most certainly supervised. The fact that people commonly draw a distinction between standard supervised learning and reinforcement learning isn't really relevant here (the distinction between a single datum and a sequence of observations), because the fact is the label is the score. If you read their DQN algorithm (or try to code an implementation, or look at any reinforcement learning algorithm, really) you would see that. https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf: In Algorithm 1, see variables r and y_j. Addendum: My broader implication is that if you misunderstand such a basic aspect of machine learning (and reinforcement learning), it makes me question whether we should lend credibility to your predictions about the field. Edited to address reply.

proc0 11y ago

At least Elon Musk is not really implying what technology will get us there. He is assuming that once we do, it has serious implications. It's hard to disagree with that. We will be sharing the planet with another intelligent creature for the first time ever.

TheOtherHobbes 11y ago
We've been sharing the planet with intelligent creatures since before evolution made us human. That's not the issue. The issue with AI is that we may be sharing the planet with an intelligence that's both nastier and more capable than we are. I still tend to see this as projection. There's a huge gap between wiring together neural networks and developing something with self-awareness, strategic agency, and some potential to expand both at a rate we can't imagine. Bostrom's paperclip monster is facile, and no worse than the kind of resource plunder we do already in the name of profit. Whatever challenges AI brings are going to be quite a few levels beyond that.
jpmec 11y ago
umm, except for the rest of the lifeforms on earth.
deleted 11y ago
[deleted]
ekianjo 11y ago
Wow are you actually saying all other lifeforms on earth have no intelligence. Thats a nice 19th century view of the world.

ujal 11y ago

No one knows yet how big the leap needs to be, but the most amazing thing about ANN's is that the feature layers a CNN arrives at may functionally resemble some of those found in the visual cortex: "The oft-cited resemblance of the imagery to LSD- and psilocybin-induced hallucinations is suggestive of a functional resemblance between artificial neural networks and particular layers of the visual cortex, a matter which merits further study." [0] Also see "How brains learn to see" from Pawan Sinha [1]. [0] https://en.wikipedia.org/wiki/DeepDream https://en.wikipedia.org/wiki/DeepDream [1] http://www.ted.com/talks/pawan_sinha_on_how_brains_learn_to_see http://www.ted.com/talks/pawan_sinha_on_how_brains_learn_to_...

kendallpark 11y ago

Well the thing with the field of "AI" is that it's a moving target. People used to call compilers artificial intelligence. As "AI" research is done, it ends up spawning new fields of study and then the new definition of AI = [previous definition of AI] - [new field of study].

rw 11y ago

I agree that the rate of progress in AI is unpredictable, which means we are probably not right on the cusp of superhuman AI. But what if we actually are on the precipice? How could you tell? You seem to be taking a bet on the following statement: "Before superhuman AI is developed, the techniques that make it possible will look dangerous." That's a risky proposition. There's so much to lose in this situation. Elon Musk, Stephen Hawking, and many other people are taking a different, more risk-averse bet: "Superhuman AI is extremely dangerous, so we need to be pessimistic about how good we are at predicting when it will happen." By that logic, general AI is to be feared and worried about right now, because our predictive abilities are imperfect. The AI trend is towards more danger, not less: there's a slight chance of a cataclysmic event happening today, and as time goes on, the likelihood of it happening will increase (due to ongoing R&D in AI). (As an aside, if you want to learn how hard the "AI Control Problem" is, I recommend the book "Superintelligence" by Nick Bostrum.)

jbhatab 11y ago
I agree with this completely and I think a simple board or coalition of companies and governments will be the answer. Regulation, policies, and awareness are the solutions to this problem, but thinking because it isn't a problem now that it won't be a potential problem at all is a scary thought.
vonnik 11y ago
The real cost of fearing an AGI now is an opportunity cost. The technology is a long ways away. Other events -- such as ecological collapse or nuclear disaster to name two -- are much more likely to irremediably affect our lives. So by hopping on Bostrom's fear wagon, you are making a bet on several levels: a) this matters now; b) this matters more than other dangers we could devote ourselves to minimizing; and c) something can actually be done about it. All three bets seem like bad ones to make to me.
- rw 11y ago
  Fair comparison, but I didn't intend any monopoly on fear. All I'm saying is that the risk of GAI should be taken seriously, calibrated along with the other problems you mention. There's a lot of room between 'irrelevant' and 'the most important issue' :-)

Houshalter 11y ago

Elon Musk and Stephen Hawking are not talking about current AI at all. They are talking about the future, which could be decades away. But it is very likely we will have superintelligent AI in our lifetimes. Here's a survey of AI experts: http://www.nickbostrom.com/papers/survey.pdf http://www.nickbostrom.com/papers/survey.pdf >We thus designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity

panic 11y ago
72 out of the 170 survey respondents were attendees of the conferences “Artificial General Intelligence” (AGI 12) and “Impacts and Risks of Artificial General Intelligence” (AGI Impacts 2012). It's not surprising that AGI researchers believe that AGI will be developed soon.
- Houshalter 11y ago
  I don't understand why participants of a conference on artificial general intelligence should be excluded from predictions about artificial general intelligence. But whatever, even the "weak AI" researchers had similar opinions: The median predicted date for a 50% probability of AGI was 2050 by the top 100 AI researchers they surveyed. Only 10 years ahead of the more liberal predictions of the AGI group.
  - panic 11y ago
    Well, a survey of AI researchers in the 60's would have said we'd have AGI by now. Surveying researchers in a field is not a reliable method for determining truth.
    - mattmanser 11y ago

simonster 11y ago

To me, the most remarkable thing about recent neural network research is that a very substantial proportion of the human brain is dedicated to vision, and neural networks can now achieve human level performance at object recognition, and with similar, robust representations (see e.g. http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003963 http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...). I see two possible conclusions that can be drawn here. The first possible possibility is that neural networks really are close enough to the way the brain works, that they learn the way the brain does, and that with some algorithmic tweaks and the right training data they can learn to everything the brain does. This is the view that Luke is rallying against, and I tend to agree that it's too soon to make this claim. On one hand, the frontal lobe is not all that different from the temporal lobe. If we can achieve the same things with a machine as with one giant chunk of 6-layer neocortex, it's not too hard to imagine that we'll be able to do the same with other pieces. The problems may be different, but the biological implementation is strikingly similar. On the other hand, as sdenton4 notes elsewhere in this thread, the big difference between sensation and cognition is that recurrent processing is that the former can (apparently!) be accomplished in an entirely feedforward manner while the latter requires (or is at least thought to require for efficient implementation) recurrent processing. And just because we have efficient ways to train one kind of neural network doesn't necessarily mean they'll generalize easily to others. But there is a second possibility, which is that neural networks don't learn or function quite like brains, but somehow both can find similar, nearly optimal strategies for encoding information in sensory domains. In this case, we're probably more than a few years away from machines that can think, but to us neuroscientists this is even more exciting. Vision scientists have been looking for decades to understand how the visual system works, and in this scenario, there's some underlying theory to vision and we have an easily observable system that implements it. Even if it turns out that vision is too different from cognition for these insights to generalize, the success of deep learning for object recognition still has vast implications for future understanding of the brain, and thus probably for future artificial intelligence. If we know the representations the brain uses, that makes figuring out the mechanism by which it computes those representations far easier than if we had to figure out both the mechanism and the representation from scratch. So my perspective here is that we don't know exactly how far recent neural network research is going to take us, but it's hard to deny its importance. Before I started graduate school, I didn't think that machines would achieve human-level performance at object recognition before we figured out how the brain does it, but here we are. But I suppose that historically, huge advances in AI research have been followed by stagnation and disappointment, so who knows how far we'll get this time.

ajuc 11y ago

The threat level is independent of the predicted time it happens. Also AIs won't think like human, and that's part of the problem (no shared value system).