8 ms·

On Artificial Intelligence

49 points by ucha 12y ago

soup10 12y ago
tl;dr Human intelligence is special. Computer accomplishments like being good at chess is not real intelligence. In fact anything computers ever do is not real intelligence, because they aren't like us and possibly never will be because there is some divine truth and wisdom that only humans posses. How many times do we have to hear these arguments to realize they are just hot air? Either computers are capable of intelligence, or they are not. The answer to that question depends entirely on how you define intelligence. If you define it as the set of things humans are capable of and computers are not, then the answer will always be no. But just like humans, computers learn and are taught new things every day, and as time goes on the set of things human's are uniquely capable of grows smaller and smaller.
- oppositereally 12y ago
  I think that misunderstands the article (a forgivable offense: it is very long). tl;dr: AGI is possible, but we're going about it all wrong and won't create it until we philosophically understand it.
  - IvyMike 12y ago
    Deutsch discounts the possibility that we create AGI the same way the universe did: as a sum of emergent behaviors honed by an evolutionary process. Nobody developing an AGI is going to code every behavior, line by line. They're going to code an infrastructure within which an AGI could work.
    - thom 12y ago
      The interesting thing about this is that the AGI will probably have about as good an idea of how its brain works as we do. While I agree this is likely to be the way AGI comes about, I suspect it will impact its ability to improve itself. What are the ramifications of (let's say) human-level intelligence running on different hardware?
      - breuleux 12y ago
        Well, if AGI evolves from infrastructure that naturally fosters intelligence, then it is probable that most intelligence gains are actually a function of better infrastructure. This suggests that individual intelligences are actually dead ends: if a new, better infrastructure comes up, you can't just port an existing AGI to it because the whole of it is organized suboptimally: training a new AGI from scratch will always be faster than to try to improve the old one. It's similar to how new software projects can innovate more easily than existing ones because they don't have legacy burdens.
- java-man 12y ago
  Jeff Hawkins defines intelligence as the ability to predict. I think it's a good definition: it allows us to measure intelligence.
  - biomimic 12y ago
    I think Steven Pinker had good takes on mimicking human cognition in his book, How the Mind Works along with describing Computational Theory of the Mind (CTM) http://en.wikipedia.org/wiki/Computational_theory_of_mind http://en.wikipedia.org/wiki/Computational_theory_of_mind
  - thom 12y ago
    I've not read much about this definition, but it seems that a problem with focusing on the ability to predict is that to get useful work done, you have to reduce everything to a decision problem. You have an ideal fitness function, but how are you generating ideas, plans or theories to evaluate? I personally prefer (although it's certainly less measurable) Ben Goertzel's definition - the ability to achieve complex goals in complex environments. It's still wishy-washy enough for people to write off any given achievement of AI though, I guess.
  - chriskanan 12y ago
    The brain as a prediction and inference engine (the Bayesian brain) has been accepted by many in neuroscience. I think this article is novel because he rebels against this idea. I do think that many of the things that we consider intelligence are prediction, image recognition, speech comprehension, understanding other's theory of mind, and many games (e.g., chess). I think Deutsch's argument is that there is also creativity (and creative reasoning) that is omitted from today's approaches to AI.
- pesenti 12y ago
  Every time an AI system beats a human at a task that was previously thought hard (chess, jeopardy, face recognition, etc) many people immediately dismiss it as not reflecting real intelligence any longer.
  - dragonwriter 12y ago
    When that process is complete, and every tasks has been eliminated from those reflecting "real intelligence", we will be certain that we have created artificial intelligence by any measure that matters.
- 21echoes 12y ago
  The article stands in direct contradiction to your supposed summary of it. To wit: > Despite this long record of failure, AGI must be possible. And that is because of a deep property of the laws of physics, namely the universality of computation. > [Turing] concluded that a computer program whose repertoire included all the distinctive attributes of the human brain — feelings, free will, consciousness and all — could be written. This astounding claim split the intellectual world into two camps, one insisting that AGI was none the less impossible, and the other that it was imminent. Both were mistaken. etc etc etc. He literally spends more than 60% of the (very long) essay arguing against your "summary". Anyway, a real tl;dr is right at the top of the page: "Expecting to create an AGI without first understanding in detail how it works is like expecting skyscrapers to learn to fly if we build them tall enough." and then the last sentence: "it is plausible that just a single idea stands between us and the breakthrough. But it will have to be one of the best ideas ever." In other words, AGI is provably possible, but the author believes that we're going about it all wrong (behaviorist-inspired neural nets running on training sets, etc.) and need a philosophical (specifically: epistemological) breakthrough to move forward.
  - deeviant 12y ago
    The last thing that AGI needs is more philosophical thinking. Philosophy and the Turing Test have done enough damage to the field. Philosophers can not seem to comprehend how a mass of meat can create consciousness and take leaps to try to rule out the mass of meat, regardless of the fact that they all know that when bad things happen to said mass of meat bad things also happen to said consciousness. The Turing Test could not have been a more wrong direction to define AGI. Looking like something does not make you that thing, and it did not at all address capability, just the appearance of capability. Insight into intelligence will come slowly, and it will come from examining, categorizing, and eventually understanding the internal workings of the human brain.
    - pron 12y ago
      > Philosophers can not seem to comprehend how a mass of meat can create consciousness and take leaps to try to rule out the mass of meat, regardless of the fact that they all know that when bad things happen to said mass of meat bad things also happen to said consciousness. Neither can anyone else, so far. > Insight into intelligence will come slowly, and it will come from examining, categorizing, and eventually understanding the internal workings of the human brain. Insight -- yes. AI -- not so certain. Here's why: it's plausible that intelligence in the human mind emerges as a consequence of an intractable process. We might not be able to understand that intractable process. But suppose that instead of understanding it, we mimic it: we build a replica of the human brain, and it works! But this is not quite artificial intelligence but a replica of natural intelligence. This is not just a semantic difference: even if this mimicry works we may not be able to direct it in any way. For example, we won't necessarily be able to give this brain replica super-human intelligence, without, say, giving it a mental illness at the same time.
- ytturbed 12y ago
  >In fact anything computers ever do is not real intelligence No, he doesn't say that. On the contrary he names the principle ('Universality of Computation'; see paragraph 4) which guarantees that computers are capable of true intelligence, since, if programmed correctly, they can simulate the behaviour of any physical object, including human brains. >there is some divine truth and wisdom that only humans possess. He also explicitly repudiates supernatural explanations (see para immediately before the one mentioning John Searle).
- noiv 12y ago
  > is not real intelligence Why is it so hard to remove our self from that equation? Why should intelligence be a skill only humans acquire? It is not only about machines: There is a constant stream of papers revealing animals are not that dumb as thought either. We survived recognizing we are not the center of the universe, may be we are also not alone on the top of the intelligence pyramid. Latest edge question "What do you think about machines that think?" provides a good and very broad overview of many aspects. I go with George Church: "What do you care what other machines think?" http://edge.org/responses/what-do-you-think-about-machines-that-think http://edge.org/responses/what-do-you-think-about-machines-t...
- ForHackernews 12y ago
  Do you believe in consciousness? https://en.wikipedia.org/wiki/Philosophical_zombie https://en.wikipedia.org/wiki/Philosophical_zombie
robohamburger 12y ago
The idea that we ought to have a better philosophical underpinning for AGI makes a lot of sense. Unfortunately the author blows past this and starts making a lot of tortured claims that don't entirely make sense. The example of years that started with 20s seemed quite odd. Given the easiest way to understand numbers at least for me is inductively. Of course if you lop off a bunch of information such as the digits after 20 or 19 it sounds like an impossible problem.
breuleux 12y ago
I don't feel like the author is familiar with modern approaches to AI. For instance, he mentions "creativity" as a stumbling block for AGI, but there is a whole class of existing algorithms that exhibit prototypical creativity, namely generative models. Essentially, the idea is that "creativity" is the act of sampling from a distribution over the kind of thing you are trying to create. Learning algorithms like Boltzmann Machines will learn a distribution over the inputs they see. One thing you can do with such a distribution is checking the probability of a given input under it, which may be good to classify them. Another thing you might want to do is generate representative samples, i.e. generate an example E with probability X iff P(E) = X under the model. The latter is what I would call "creativity". Under this definition, creativity depends on both the learned distribution (which should only assign high probability to meaningful data) and of course on the sampling algorithm. As it turns out, it is very hard to write good sampling algorithms for non-trivial distributions (naive MCMC will often get stuck). So creativity is hard, but so are a lot of other tasks, so I don't think it's fair to single it out.
- chriskanan 12y ago
  I don't think generative models are sufficient to model the kind of creativity that the author is thinking about. See this quote from the article: The prevailing misconception is that by assuming that ‘the future will be like the past’, it can ‘derive’ (or ‘extrapolate’ or ‘generalise’) theories from repeated experiences by an alleged process called ‘induction’. But that is impossible. He is looking for solutions outside of the distribution that has been previously observed.
  - breuleux 12y ago
    What the author is looking for is a kind of induction. If I'm seeking a new theory of gravity, I'm not going to start looking at the colors of hats. Why not? Because I very strongly suspect it will lead absolutely nowhere. But if I think some ideas have potential and some others don't, isn't that a distribution of sorts? Theories that have relevance or potential obey a certain distribution, and it is this distribution that you are trying to sample from. Sure, theories may not be directly derived from the extrapolation of sense data, but they are nonetheless derived from the extrapolation of theories in general. So what you're looking for is not a fundamentally new paradigm, it's more like an additional level of indirection. But there's no point to experiment with multiple induction levels if we can't even make a single one work well enough.
dragonwriter 12y ago
The argument that artificial intelligence requires understanding how intelligence works is an argument that natural intelligence requires Intelligent Design. (Its also an argument that fortuitous discoveries--such as of pharmaceuticals with utility in treating conditions they were not designed for and whose mechanisms we do not understand--cannot occur.) Obviously, understanding intelligence better would promote more effective directed research toward artificial intelligence. But if we can identify it (which the Turing Test is about), then it is quite possible that we can develop it -- and know that we have -- without understanding it. (And it may only be through developing it that we end up understanding how it works.)
- dsacco 12y ago
  I upvoted you for the insightful reasoning but I disagree with your stated premise that > The argument that artificial intelligence requires understanding how intelligence works is an argument that natural intelligence requires Intelligent Design. I think that statement makes sense when phrased that way, so it is an attractive idea. However, I don't think it's true. From an evolutionary standpoint, biological intelligence developed naturally because biological components are natural. Furthermore, machines do not develop when left in isolation, while biological organisms do. If you leave a large population of simple machines running in an environment, this is overwhelmingly not likely to result in a machine intelligence millions of years later. Machines were developed by humans and do not develop in the same way; comparing the two as in your Intelligent Design argument doesn't make much sense. I do not think artificial intelligence can arise naturally because its components are not natural. This discussion could also foster a discussion on two other interesting questions: 1. Does intelligence require organic components that operate in a deterministic way (i.e. the brain) to transcend it from merely a "machine"? 2. If intelligence requires biology, where do you draw the line between creating an intelligence through natural human reproduction and creating an artificial intelligence another way? Personally, I believe artificial intelligence does not require biological components, and I believe that under certain circumstances, it could develop unintentionally from a relatively advanced computer, but that is not the same as naturally.
  - dragonwriter 12y ago
bitdiddle 12y ago
Brian Eno makes a good argument[1] that it's already here. [1] http://edge.org/responses/q2015 http://edge.org/responses/q2015
ajuc 12y ago
Completely missing the point of analogy, I know, but skyscrappers would fly if they were tall enough, right? The center of gravity far enough should make them sattelites, if they were made from something that can withstand the forces involved?
- fchollet 12y ago
  Your "skyscraper" would need to have its top in geostationary orbit, or else it would be wrapping up around the earth as its top would be in LEO or HEO and thus would be moving at a different speed than its base. I.e. your skyscraper (or cable) would be crashing down. So that's a space elevator. But can a space elevator be said to be "flying"? It stays above a single spot the whole time.
  - ajuc 12y ago
    But then you can take one brick from the bottom, throw it out, and you launch into space.
  - bjornsing 12y ago
    BS. ;) First of all before a (hypothetical) "skyscraper" could fly it would have to have its top beyond geostationary orbit. How so? Because a skyscraper is geostationary and if its center of mass is below geostationary orbit then it's simply not orbiting. It doesn't have enough kinetic energy. Secondly, there's no law that an object revolving around earth must do so at "orbit" speed/altitude. You and I are perfect examples of that: we're going far too slow. Only consequence of that is that earths gravitational pull must constantly be counteracted by a supporting force. If an object were to revolve around earth at a speed faster than "orbit" that's also fine, as long as there's a force keeping it down. So, no, this "skyscraper" could have its top well below geostationary orbit as long as it rests firmly on mother earth. Once its center of mass is beyond geostationary orbit though it would need to be held down.
- fchollet 12y ago
  In any case, I think that's a point very much relevant to the analogy: when you do more of the same, but at a scale a few orders of magnitude larger, you do get qualitatively different results. That's very much applicable to AI. As an illustration, when we started to use GPUs for training neural nets with the exact same algorithms as a decade earlier but with an order of magnitude more parameters, we got dramatically better results (see Ciresan et al. 2010 [1]). Building the same dumb skyscrapers but making them 100 times taller might in fact get them to fly. [1] http://arxiv.org/abs/1003.0358 http://arxiv.org/abs/1003.0358
ForHackernews 12y ago
This author makes a good case that current approaches toward producing AGI are misguided: > The Skynet misconception likewise informs the hope that AGI is merely an emergent property of complexity, or that increased computer power will bring it forth (as if someone had already written an AGI program but it takes a year to utter each sentence). It is behind the notion that the unique abilities of the brain are due to its ‘massive parallelism’ or to its neuronal architecture, two ideas that violate computational universality. But I don't think he's done a very good job of supporting his assertion that thinking (in the AGI sense) is a computational process. The closest he comes is: > But that’s not a metaphor: the universality of computation follows from the known laws of physics. That's it? Because physics?
dcre 12y ago
> Unfortunately, what we know about epistemology is contained largely in the work of the philosopher Karl Popper and is almost universally underrated and misunderstood (even — or perhaps especially — by philosophers). For example, it is still taken for granted by almost every authority that knowledge consists of justified, true beliefs and that, therefore, an AGI’s thinking must include some process during which it justifies some of its theories as true, or probable, while rejecting others as false or improbable. This is where I gave up. Deutsch dismisses thousands of years of thought in a sentence. Not to mention that "justified true belief" is a phrase you find much more often in a textbook or an encyclopedia article than in a real work of philosophy.

49 points by ucha 12y ago

tl;dr Human intelligence is special. Computer accomplishments like being good at chess is not real intelligence. In fact anything computers ever do is not real intelligence, because they aren't like us and possibly never will be because there is some divine truth and wisdom that only humans posses. How many times do we have to hear these arguments to realize they are just hot air? Either computers are capable of intelligence, or they are not. The answer to that question depends entirely on how you define intelligence. If you define it as the set of things humans are capable of and computers are not, then the answer will always be no. But just like humans, computers learn and are taught new things every day, and as time goes on the set of things human's are uniquely capable of grows smaller and smaller.

oppositereally 12y ago
I think that misunderstands the article (a forgivable offense: it is very long). tl;dr: AGI is possible, but we're going about it all wrong and won't create it until we philosophically understand it.
- IvyMike 12y ago
  Deutsch discounts the possibility that we create AGI the same way the universe did: as a sum of emergent behaviors honed by an evolutionary process. Nobody developing an AGI is going to code every behavior, line by line. They're going to code an infrastructure within which an AGI could work.
  - thom 12y ago
    The interesting thing about this is that the AGI will probably have about as good an idea of how its brain works as we do. While I agree this is likely to be the way AGI comes about, I suspect it will impact its ability to improve itself. What are the ramifications of (let's say) human-level intelligence running on different hardware?
    - breuleux 12y ago
      Well, if AGI evolves from infrastructure that naturally fosters intelligence, then it is probable that most intelligence gains are actually a function of better infrastructure. This suggests that individual intelligences are actually dead ends: if a new, better infrastructure comes up, you can't just port an existing AGI to it because the whole of it is organized suboptimally: training a new AGI from scratch will always be faster than to try to improve the old one. It's similar to how new software projects can innovate more easily than existing ones because they don't have legacy burdens.
java-man 12y ago
Jeff Hawkins defines intelligence as the ability to predict. I think it's a good definition: it allows us to measure intelligence.
- biomimic 12y ago
  I think Steven Pinker had good takes on mimicking human cognition in his book, How the Mind Works along with describing Computational Theory of the Mind (CTM) http://en.wikipedia.org/wiki/Computational_theory_of_mind http://en.wikipedia.org/wiki/Computational_theory_of_mind
- thom 12y ago
  I've not read much about this definition, but it seems that a problem with focusing on the ability to predict is that to get useful work done, you have to reduce everything to a decision problem. You have an ideal fitness function, but how are you generating ideas, plans or theories to evaluate? I personally prefer (although it's certainly less measurable) Ben Goertzel's definition - the ability to achieve complex goals in complex environments. It's still wishy-washy enough for people to write off any given achievement of AI though, I guess.
- chriskanan 12y ago
  The brain as a prediction and inference engine (the Bayesian brain) has been accepted by many in neuroscience. I think this article is novel because he rebels against this idea. I do think that many of the things that we consider intelligence are prediction, image recognition, speech comprehension, understanding other's theory of mind, and many games (e.g., chess). I think Deutsch's argument is that there is also creativity (and creative reasoning) that is omitted from today's approaches to AI.
pesenti 12y ago
Every time an AI system beats a human at a task that was previously thought hard (chess, jeopardy, face recognition, etc) many people immediately dismiss it as not reflecting real intelligence any longer.
- dragonwriter 12y ago
  When that process is complete, and every tasks has been eliminated from those reflecting "real intelligence", we will be certain that we have created artificial intelligence by any measure that matters.
21echoes 12y ago
The article stands in direct contradiction to your supposed summary of it. To wit: > Despite this long record of failure, AGI must be possible. And that is because of a deep property of the laws of physics, namely the universality of computation. > [Turing] concluded that a computer program whose repertoire included all the distinctive attributes of the human brain — feelings, free will, consciousness and all — could be written. This astounding claim split the intellectual world into two camps, one insisting that AGI was none the less impossible, and the other that it was imminent. Both were mistaken. etc etc etc. He literally spends more than 60% of the (very long) essay arguing against your "summary". Anyway, a real tl;dr is right at the top of the page: "Expecting to create an AGI without first understanding in detail how it works is like expecting skyscrapers to learn to fly if we build them tall enough." and then the last sentence: "it is plausible that just a single idea stands between us and the breakthrough. But it will have to be one of the best ideas ever." In other words, AGI is provably possible, but the author believes that we're going about it all wrong (behaviorist-inspired neural nets running on training sets, etc.) and need a philosophical (specifically: epistemological) breakthrough to move forward.
- deeviant 12y ago
  The last thing that AGI needs is more philosophical thinking. Philosophy and the Turing Test have done enough damage to the field. Philosophers can not seem to comprehend how a mass of meat can create consciousness and take leaps to try to rule out the mass of meat, regardless of the fact that they all know that when bad things happen to said mass of meat bad things also happen to said consciousness. The Turing Test could not have been a more wrong direction to define AGI. Looking like something does not make you that thing, and it did not at all address capability, just the appearance of capability. Insight into intelligence will come slowly, and it will come from examining, categorizing, and eventually understanding the internal workings of the human brain.
  - pron 12y ago
    > Philosophers can not seem to comprehend how a mass of meat can create consciousness and take leaps to try to rule out the mass of meat, regardless of the fact that they all know that when bad things happen to said mass of meat bad things also happen to said consciousness. Neither can anyone else, so far. > Insight into intelligence will come slowly, and it will come from examining, categorizing, and eventually understanding the internal workings of the human brain. Insight -- yes. AI -- not so certain. Here's why: it's plausible that intelligence in the human mind emerges as a consequence of an intractable process. We might not be able to understand that intractable process. But suppose that instead of understanding it, we mimic it: we build a replica of the human brain, and it works! But this is not quite artificial intelligence but a replica of natural intelligence. This is not just a semantic difference: even if this mimicry works we may not be able to direct it in any way. For example, we won't necessarily be able to give this brain replica super-human intelligence, without, say, giving it a mental illness at the same time.
ytturbed 12y ago
>In fact anything computers ever do is not real intelligence No, he doesn't say that. On the contrary he names the principle ('Universality of Computation'; see paragraph 4) which guarantees that computers are capable of true intelligence, since, if programmed correctly, they can simulate the behaviour of any physical object, including human brains. >there is some divine truth and wisdom that only humans possess. He also explicitly repudiates supernatural explanations (see para immediately before the one mentioning John Searle).
noiv 12y ago
> is not real intelligence Why is it so hard to remove our self from that equation? Why should intelligence be a skill only humans acquire? It is not only about machines: There is a constant stream of papers revealing animals are not that dumb as thought either. We survived recognizing we are not the center of the universe, may be we are also not alone on the top of the intelligence pyramid. Latest edge question "What do you think about machines that think?" provides a good and very broad overview of many aspects. I go with George Church: "What do you care what other machines think?" http://edge.org/responses/what-do-you-think-about-machines-that-think http://edge.org/responses/what-do-you-think-about-machines-t...
ForHackernews 12y ago
Do you believe in consciousness? https://en.wikipedia.org/wiki/Philosophical_zombie https://en.wikipedia.org/wiki/Philosophical_zombie

robohamburger 12y ago

The idea that we ought to have a better philosophical underpinning for AGI makes a lot of sense. Unfortunately the author blows past this and starts making a lot of tortured claims that don't entirely make sense. The example of years that started with 20s seemed quite odd. Given the easiest way to understand numbers at least for me is inductively. Of course if you lop off a bunch of information such as the digits after 20 or 19 it sounds like an impossible problem.

breuleux 12y ago

I don't feel like the author is familiar with modern approaches to AI. For instance, he mentions "creativity" as a stumbling block for AGI, but there is a whole class of existing algorithms that exhibit prototypical creativity, namely generative models. Essentially, the idea is that "creativity" is the act of sampling from a distribution over the kind of thing you are trying to create. Learning algorithms like Boltzmann Machines will learn a distribution over the inputs they see. One thing you can do with such a distribution is checking the probability of a given input under it, which may be good to classify them. Another thing you might want to do is generate representative samples, i.e. generate an example E with probability X iff P(E) = X under the model. The latter is what I would call "creativity". Under this definition, creativity depends on both the learned distribution (which should only assign high probability to meaningful data) and of course on the sampling algorithm. As it turns out, it is very hard to write good sampling algorithms for non-trivial distributions (naive MCMC will often get stuck). So creativity is hard, but so are a lot of other tasks, so I don't think it's fair to single it out.

chriskanan 12y ago
I don't think generative models are sufficient to model the kind of creativity that the author is thinking about. See this quote from the article: The prevailing misconception is that by assuming that ‘the future will be like the past’, it can ‘derive’ (or ‘extrapolate’ or ‘generalise’) theories from repeated experiences by an alleged process called ‘induction’. But that is impossible. He is looking for solutions outside of the distribution that has been previously observed.
- breuleux 12y ago
  What the author is looking for is a kind of induction. If I'm seeking a new theory of gravity, I'm not going to start looking at the colors of hats. Why not? Because I very strongly suspect it will lead absolutely nowhere. But if I think some ideas have potential and some others don't, isn't that a distribution of sorts? Theories that have relevance or potential obey a certain distribution, and it is this distribution that you are trying to sample from. Sure, theories may not be directly derived from the extrapolation of sense data, but they are nonetheless derived from the extrapolation of theories in general. So what you're looking for is not a fundamentally new paradigm, it's more like an additional level of indirection. But there's no point to experiment with multiple induction levels if we can't even make a single one work well enough.

dragonwriter 12y ago

The argument that artificial intelligence requires understanding how intelligence works is an argument that natural intelligence requires Intelligent Design. (Its also an argument that fortuitous discoveries--such as of pharmaceuticals with utility in treating conditions they were not designed for and whose mechanisms we do not understand--cannot occur.) Obviously, understanding intelligence better would promote more effective directed research toward artificial intelligence. But if we can identify it (which the Turing Test is about), then it is quite possible that we can develop it -- and know that we have -- without understanding it. (And it may only be through developing it that we end up understanding how it works.)

dsacco 12y ago
I upvoted you for the insightful reasoning but I disagree with your stated premise that > The argument that artificial intelligence requires understanding how intelligence works is an argument that natural intelligence requires Intelligent Design. I think that statement makes sense when phrased that way, so it is an attractive idea. However, I don't think it's true. From an evolutionary standpoint, biological intelligence developed naturally because biological components are natural. Furthermore, machines do not develop when left in isolation, while biological organisms do. If you leave a large population of simple machines running in an environment, this is overwhelmingly not likely to result in a machine intelligence millions of years later. Machines were developed by humans and do not develop in the same way; comparing the two as in your Intelligent Design argument doesn't make much sense. I do not think artificial intelligence can arise naturally because its components are not natural. This discussion could also foster a discussion on two other interesting questions: 1. Does intelligence require organic components that operate in a deterministic way (i.e. the brain) to transcend it from merely a "machine"? 2. If intelligence requires biology, where do you draw the line between creating an intelligence through natural human reproduction and creating an artificial intelligence another way? Personally, I believe artificial intelligence does not require biological components, and I believe that under certain circumstances, it could develop unintentionally from a relatively advanced computer, but that is not the same as naturally.
- dragonwriter 12y ago

bitdiddle 12y ago

Brian Eno makes a good argument[1] that it's already here. [1] http://edge.org/responses/q2015 http://edge.org/responses/q2015

ajuc 12y ago

Completely missing the point of analogy, I know, but skyscrappers would fly if they were tall enough, right? The center of gravity far enough should make them sattelites, if they were made from something that can withstand the forces involved?

fchollet 12y ago
Your "skyscraper" would need to have its top in geostationary orbit, or else it would be wrapping up around the earth as its top would be in LEO or HEO and thus would be moving at a different speed than its base. I.e. your skyscraper (or cable) would be crashing down. So that's a space elevator. But can a space elevator be said to be "flying"? It stays above a single spot the whole time.
- ajuc 12y ago
  But then you can take one brick from the bottom, throw it out, and you launch into space.
- bjornsing 12y ago
  BS. ;) First of all before a (hypothetical) "skyscraper" could fly it would have to have its top beyond geostationary orbit. How so? Because a skyscraper is geostationary and if its center of mass is below geostationary orbit then it's simply not orbiting. It doesn't have enough kinetic energy. Secondly, there's no law that an object revolving around earth must do so at "orbit" speed/altitude. You and I are perfect examples of that: we're going far too slow. Only consequence of that is that earths gravitational pull must constantly be counteracted by a supporting force. If an object were to revolve around earth at a speed faster than "orbit" that's also fine, as long as there's a force keeping it down. So, no, this "skyscraper" could have its top well below geostationary orbit as long as it rests firmly on mother earth. Once its center of mass is beyond geostationary orbit though it would need to be held down.
fchollet 12y ago
In any case, I think that's a point very much relevant to the analogy: when you do more of the same, but at a scale a few orders of magnitude larger, you do get qualitatively different results. That's very much applicable to AI. As an illustration, when we started to use GPUs for training neural nets with the exact same algorithms as a decade earlier but with an order of magnitude more parameters, we got dramatically better results (see Ciresan et al. 2010 [1]). Building the same dumb skyscrapers but making them 100 times taller might in fact get them to fly. [1] http://arxiv.org/abs/1003.0358 http://arxiv.org/abs/1003.0358

ForHackernews 12y ago

This author makes a good case that current approaches toward producing AGI are misguided: > The Skynet misconception likewise informs the hope that AGI is merely an emergent property of complexity, or that increased computer power will bring it forth (as if someone had already written an AGI program but it takes a year to utter each sentence). It is behind the notion that the unique abilities of the brain are due to its ‘massive parallelism’ or to its neuronal architecture, two ideas that violate computational universality. But I don't think he's done a very good job of supporting his assertion that thinking (in the AGI sense) is a computational process. The closest he comes is: > But that’s not a metaphor: the universality of computation follows from the known laws of physics. That's it? Because physics?

dcre 12y ago

> Unfortunately, what we know about epistemology is contained largely in the work of the philosopher Karl Popper and is almost universally underrated and misunderstood (even — or perhaps especially — by philosophers). For example, it is still taken for granted by almost every authority that knowledge consists of justified, true beliefs and that, therefore, an AGI’s thinking must include some process during which it justifies some of its theories as true, or probable, while rejecting others as false or improbable. This is where I gave up. Deutsch dismisses thousands of years of thought in a sentence. Not to mention that "justified true belief" is a phrase you find much more often in a textbook or an encyclopedia article than in a real work of philosophy.