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No one would ever imagine that locking a baby in a featureless room with a giant stack of books would give them general intelligence. I don't understand why AI

No one would ever imagine that locking a baby in a featureless room with a giant stack of books would give them general intelligence. I don't understand why AI researchers think it will work for AIs. They need bodies that are biologically connected with the rest of the biosphere, with an intrinsic biological imperative, if they are ever to understand the world. I'm not saying they have to be exactly like us, but they will only be able to understand us to the extent that they have body parts and social experiences that are analogous to ours. This isn't an engineering problem, it's a philosophical problem: We are blind to most things. We can only see what we can relate to personally. We can use language and other symbol systems to expand our understanding by permuting and recombining our personal experiences, but everything is grounded in our interactive developmental trajectory. The kitten-in-a-cart experiment demonstrates this clearly: http://io9.gizmodo.com/the-seriously-creepy-two-kitten-experiment-1442107174 http://io9.gizmodo.com/the-seriously-creepy-two-kitten-exper... Interaction is crucial for perception. Sensation is not experience. And here's the rub: Once you give an AI an animal-like personal developmental trajectory to use for grounding the semantics of their symbol systems, you end up with something which is not particularly different or better than a human cyborg.

protomikron 10y ago
> This isn't an engineering problem, it's a philosophical problem [...] Indeed. However we can not rule out the possibility that we engineer a system without a "body" (I think you are referring to the Embodied mind thesis?). It is a complicated topic and discussions about it are futile without precise definitions of loaded terms, like "intelligence", "body", etc. A rather well-defined test is the classic Turing test and I wouldn't dismiss the possibility that it can be passed by a bodyless machine/program-thing.
- posterboy 10y ago
  I don't find the Turing Test convincing either, because someone smart enough to build it should be smart enough to recognize it from its answers. And if that depends on the intelligence of the questioner, whose intelligence is tested then, really? IIRC the test is a binary classifier, but intelligence is a spectrum that's fuzzy and therefore inherently hard to define. IE, how low is someone willing to assume the lack of intelligence in a human is not a good definition of general intelligence, as that's circular reasoning. I would suppose that babies possess general intelligence, but they lack the knowledge about the environment.
  - morgante 10y ago
    > I don't find the Turing Test convincing either, because someone smart enough to build it should be smart enough to recognize it from its answers. Why do you assume that? The creators of AlphaGo certainly couldn't beat it.
    - jackpirate 10y ago
      The Turing Test equivalent in the game of Go is not beating the computer. Instead, it is: given a history of moves, can you determine whether or not a computer was playing? Determining whether or not AlphaGo was playing seems like a relatively easy task to me for the designers. Since they have access to the AlphaGo system, they can just calculate the probability that each move corresponds to one AlphaGo would make.
      - morgante 10y ago
        > Instead, it is: given a history of moves, can you determine whether or not a computer was playing? No, it's really not. For the Turing Test, the AI is meant to be adversarial—it's objective is to convince you that it is human. AlphaGo's objective isn't to "play like a human," it is to win. If they gave it an objective of playing like a human, I'm sure AlphaGo could play in a way that would be indistinguishable from a human. > Since they have access to the AlphaGo system, they can just calculate the probability that each move corresponds to one AlphaGo would make. Peeking at the system/data is cheating. Obviously the person who sets up a Turing test knows which player is AI.
        posterboy 10y ago
        > If they gave it an objective of playing like a human, I'm sure AlphaGo could play in a way that would be indistinguishable from a human. It could just play unbelievably bad and appear like a beginner. That wouldn't prove intelligent. > Peeking at the system/data is cheating Someone ignorant of computers would hardly ever assume a machine. Of course the omission of this rule would leave someone smarter than the computer. If you talk statistics, IE the machine has to convince only a fair share of humans, the definition of the threshold is a problem. Intelligence would depend on the development of the society. I thought this is about an intrinsic value. It's an interesting thought experiment, but hardly conclusive, just observational.
- robotresearcher 10y ago
  > It is a complicated topic and discussions about it are futile without precise definitions of loaded terms, like "intelligence", "body", etc. On the contrary, to make progress we should give up on making precise definitions of non-technical terms such as 'body' and 'intelligence'. They are folk notions that don't have relevant precise definitions talking about them in an engineering context is distracting. You don't need a definition of 'beauty' to paint. You don't need a definition of 'justice' to practice law. You don't need a definition of 'intelligence' to build clever robots.
tener 10y ago
Why not simply simulate the body (or indeed multiple bodies) in a virtual world? Easier to build, parallelize, extend, maintain. Possibly somewhat safer too.
- erikpukinskis 10y ago
  That works, if your virtual world is good. But that's a big if. You've just taken one really hard problem (learning about the world) and turned it into an even harder problem (simulating the world).
  - tener 10y ago
    Some games are pretty convincing sand boxes and it's not obvious to me that you really need full range of senses to properly teach the AI.
    - deleted 10y ago
      [deleted]
- justinpombrio 10y ago
  This is exactly the premise of The Talos Principle :-)
digitalarborist 10y ago
It's probably a mistake to assume that just because it's the way we do it that it has to be the way machines do it. Although that's usually the initial assumption. In the early days of flight most attempts were based on birds, similarly submersible vehicles were based on fish. We know now it's better to use propellers. It could be we just haven't found what is analogous to a propeller for the AI world.
- xj9 10y ago
  The only general intelligence we know of is us. It stands to reason that the first step towards creating AGI is to copy the one machine we know is capable of that type of processing. Why doesn't our research focus on understanding and copying biological brains? Numenta did, with good results, but it isn't an industry trend.
  - throwaway13337 10y ago
    The steps to learning to make aircraft didn't come from understanding how birds flap their wings. I can't imagine the kind of intelligence we consider general will be from study of how humans biologically think.
  - freyr 10y ago
    > Why doesn't our research focus on understanding and copying biological brains? There's lots of basic research being done to better understand the biological brain. Progress is slow and steady, but the brain remains poorly understood at this point in time. Most applied research has pursued more pragmatic methods because these methods have had faster progress and proven more useful in practice.
- djokkataja 10y ago
  I agree that machines won't necessarily have to do everything the same way we do things to be "intelligent". On the other hand, the concept of artificial general intelligence implies a machine that can perform all of the same functions a human can: "Artificial general intelligence (AGI) is the intelligence of a (hypothetical) machine that could successfully perform any intellectual task that a human being can."[0] And if you look at what humans do that we characterize as "intelligence", it's a substantial list of different functions: "Human intelligence is the intellectual capacity of humans, which is characterized by perception, consciousness, self-awareness, and volition. Through their intelligence, humans possess the cognitive abilities to learn, form concepts, understand, apply logic, and reason, including the capacities to recognize patterns, comprehend ideas, plan, problem solve, make decisions, retain information, and use language to communicate. Intelligence enables humans to experience and think."[1] If I take an engineering perspective and look at the list of functions that an AGI would have to perform to be an AGI, I would definitely want to give it more than a text-in, text-out interface so that it could have, for example, perception and volition. For example, imagine a theoretical "AGI" that exclusively deals with a stream of text in (as human language) and a stream of text out (again, as human language). If you ask it any questions about its physical surroundings, it's either going to make things up (which isn't perception and therefore fails both at being useful and meeting the definition of intelligence above), or it's going to get information about its physical surroundings via some proxy that feeds it a linguistic stream of information about its surroundings. But it doesn't matter if it's getting perceptual information from a proxy or from more directly embedded sensory interfaces; if it's getting perceptual information and able to use it sensibly, then it's performing the function of perception. And in that case the proxy source of perceptual information may as well be considered part of the "intelligent" system. These kinds of articles seem a bit silly to me, because they seem to imply that we should expect to be able to create a system which is "intelligent" but which only performs the function of understanding and producing meaningful language. But if you're only handling language, you're taking away all of the other functions that are part of the definition of "intelligence" above, which leaves you with a system which is far short of anything we'd consider "intelligent". [0] https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc... [1] https://en.wikipedia.org/wiki/Human_intelligence https://en.wikipedia.org/wiki/Human_intelligence
rbobby 10y ago
> locking a baby in a featureless room Strangely (or maybe horribly?) there are a number of studies of children raised in Romanian orphanages that somewhat cover this area. Under Nicolae Ceaușescu the government outlawed abortion (with some exceptions) in an attempt to increase the birth rate (https://en.wikipedia.org/wiki/Abortion_in_Romania https://en.wikipedia.org/wiki/Abortion_in_Romania). Coupled with a poor economy this lead to masses of infants and children being given over to government "care" in orphanages. These were pretty bleak places for infants and children, with infants often spending hours in a crib with little stimulation. Here's a really good article about the effects this sort of institutional "care" has on children: http://www.americanscientist.org/issues/feature/2009/3/the-deprived-human-brain/99999 http://www.americanscientist.org/issues/feature/2009/3/the-d... Here's the The Bucharest Early Intervention Project (tons of info about this subject area): http://www.bucharestearlyinterventionproject.org/ http://www.bucharestearlyinterventionproject.org/
Houshalter 10y ago
I believe we can get AI from just text. Obviously that won't work for babies, because babies get bored quickly looking at text. AIs can be forced to read billions of words in mere hours! Look at word2vec. By using simple dimensionality reduction on the frequency that words that occur near to each other in news articles, it can learn really interesting things about the meaning of words. The famous example is the vector "king" minus the vector for "man" plus "woman" equals "queen". It's learning the meaning of words, and the relationships between them. Recurrent NNs, using similar techniques, but with much more complexity, can learn to predict the next word in a sentence very well. They can learn to write responses that are almost indistinguishable from humans. And it's incredible this works at all, given RNNs have only a few thousand neurons at most, and a few days of training, compared to humans' billions of neurons trained over a lifetime. All of the information of our world is contained in text. Humans have produced billions of books, papers, articles, and internet comments. Billions of times more information than any human could read in their entire lifetime. Any information you can imagine is contained in text somewhere. I don't think it's necessary for AIs to be able to see, or interact with the world in any way. If you can predict the word a human would say next, with enough accuracy, then you could also produce answers indistinguishable from theirs. Meaning you could pass the Turing test, and perform any language task they could do just as well. So language prediction alone may be sufficient for AGI. This is the theory behind the Hutter Prize, which proposes that predicting (compressing) wikipedia's text is a measure of AI progress. The Hutter Prize isn't perfect (it's only a sample of wikipedia, which is very small compared to all the text humans have produced), but the idea is solid.
- AsyncAwait 10y ago
  If we ever want 'true AI' then interacting with the world would certainly be a crucial component of building one. Text is a crucial component, but ultimately being able to 'see' gives one an entirely new perspective on what words mean. There's a reason after all, for why toddlers touch hot surfaces even after repeatedly being told that it will hurt - until they experience it for themselves, the word 'hot' doesn't quite connect even if they heard it dozens of times and have a very good abstract sense of what it means.
- kafkaesq 10y ago
wrongc0ntinent 10y ago
In fact it is a very hard engineering problem, which doesn't exclude a "philosophical" one. There's a line of research surrounding anthropomimetic robots with the specific task of studying cognition, going back at least a decade.
posterboy 10y ago
Computers are programmed only using text, even if the text has just two symbols. Sensor interfaces use digital signals, again symbol streams. That would beget the question if there could be machines mightier than a Turing Complete one. I'm sure that's missing your point.
- scythe 10y ago
  If you could simply program a human's behavior into a machine, that would be fine, but humans aren't capable of encoding their neural circuitry in a programming language; in fact, the combined efforts of all humans have only begun to shed light on what human behavior is. As such, generating formalized information (that describes behavior) requires some process (other than human hands) to do it -- and in the case of a machine that interacts with the world, "turing machine" is not a complete description of any such process.
  - posterboy 10y ago
    By Shannon's information theory, everything is bits of information. Completely irrelevant for the mechanism of a Turing Machine is, who writes the initial bits on the tape. For all I care, the world is the tape and the computer is the head being moved through it. It's the old fallacy seeing the brain as a computer, since we've build computers after structures from our brains. Hence I wondered, what more there should be.
nawitus 10y ago
Ben Goertzel of OpenCog fame has argued for this for a long time.
gerbilly 10y ago
Reminds me of a quote I once heard, for which I can't find the source: The cheapest way to make a brain is still the old way.
hacker42 10y ago
Here is the reason why I don't believe in that: First of all, it seems that mind space is huge, i.e. there are very different programs that can lead to general intelligence, many of which will be very different from humans (this along is evidenced by the fact how strongly human characters and intellects vary, e.g. highly functioning mental conditions). A lot in machine learning points to that possibility. There are for example many ways to get supervision signals e.g. reconstruction error, prediction error, adversaries, intrinsic motivation (reducing the number of bits required for a representation), compression, sparseness etc. We basically just need a system that comes up with efficient representations of the world such that it can reason about it, i.e. that it can tell you which hypotheses about the world are likely true given some data. This computation allows you to make predictions and predictions are really at the heart of intelligence. If you can follow a hypothetical trajectory of generated, hallucinated or simulated samples of reality, i.e. samples that likely correspond to what actually happens in the world (and in the agent's own brain), then you can actually perform actions that are targeted at some purpose (e.g. maximizing reward signals). However, there are many sources of data that essentially give you the same information. Whether you create a representation by directly interacting with the world or just watch many examples of how the world generally evolves over time and how different entities interact with one another, you essentially get the same idea about how the world works (except in the first case you also learn a motor system). I think the anthropomorphism is really misplaced here, because computer systems are not dependent on actually performing in the real world. Since computers have near unlimited, noiseless memory and have super fast access to that memory, they can actually plan interactions by careful reasoning on the fly instead of needing to learn e.g. motor skills for manipulating objects, eating and handwriting before one can get anywhere near the performance of computers wrt. access to reliable external memory. A computer system also does not have hormone and neuromodulator levels that need to be met for healthy development (e.g. dopamine), therefore the intuition that deprivation of interaction with the world prevents learning is extremely misleading.
robotresearcher 10y ago
> you end up with something which is not particularly different or better than a human cyborg. Even if that were true, which I am not sure about, such a robot will have a very different moral status, as an artifact, and one that can be reproduced cheaply and indefinitely. This is very useful indeed, so on this axis it could be counted as 'better'. The idea of development being important for AI is an old one, but it hasn't had much concrete success. Brook's Cog robot at MIT is a prominent example of a robot that didn't do very much, despite this approach being taken in a good faith effort by talented and well-supported people.
- wangchow 10y ago
  Humans seem to be created cheaply and indefinitely too. Population is growing out of control. We could attach them to pods and harvest energy from their souls!
dragonwriter 10y ago
> They need bodies that are biologically connected with the rest of the biosphere On what basis do you conclude that "biologically" is important here? There may be some reason to suspect that human-like intelligence requires human-like ability to sense and interact with the outside world, but I see less reason to suspect that it is important that the mechanism of the sensors or manipulators must be biological.
mannykannot 10y ago
Helen Keller's life argues against the proposition that a machine requires the same sort of interaction with its environment that the average human experiences, before it can achieve intelligence.
- djokkataja 10y ago
  She was blind and deaf, but she still had enormous amounts of tactile information and the ability to physically interact with her environment. Moreover, those sensory elements were critical for her to finally be able to start learning language--she finally caught on that the signs another person was making in one hand represented the water being run over her other hand. And she didn't make that breakthrough until she was 7 years old, and only then began to learn with the persistent help of an instructor.[0] I would say that Helen Keller's life argues that a intelligent machine must be able to have experiences and the capacity to associate experiences with language. The machine probably doesn't need all of the perceptual modalities that we have, as Helen Keller demonstrated, but it should probably have some similarities with our own so that there would be common ground for initiating communication about experiences. A machine with just a text in / text out interface has nothing in common with us. [0] https://en.wikipedia.org/wiki/Helen_Keller#Early_childhood_and_illness https://en.wikipedia.org/wiki/Helen_Keller#Early_childhood_a...
  - mannykannot 10y ago
    So Keller's "enormous amounts of tactile information and the ability to physically interact with her environment" means that her achievements were not that remarkable? Speaking personally, if I were to lose my sight and hearing, I imagine I would find life extremely daunting, even though I have what seems to me the very considerable advantage of having learned language. Anne Sullivan's equally remarkable (IMHO) role as a teacher is not really an issue here, as training is also an option for AI, though it might be evidence in a rather different discussion about whether unsupervised learning alone, particularly as practiced today, is likely to get us to AI (clearly, the evolution of intelligence can be cast as unsupervised learning, but that is a very long and uncertain process...)
jonkho 10y ago
I second this. To further illustrate that semantics is far more than syntax and that emotions are an inseparable part of understanding language, do the following experiment: take any word or concept and look up its definition in a dictionary. Take note of the words used in the definition and look up the meaning of those words. Repeat recursively and if you go far enough down the tree you will find that all words lead to self-recursive definitions that state "it is a feeling of" this or that. This is proof that semantics of language - and intelligence required to grasp it's semantics - is inseparable from subjective states.
- Houshalter 10y ago
  What does the experiment prove? Of course dictionaries are circular. But that doesn't mean they don't contain huge amounts of information about the world, and about language. Information an AI could infer without any interaction with the outside world at all.
  - jonkho 10y ago
    This means that the semantics of the language is rooted in subjective states. Restated, this means humans "understand" language because of humans' emotions. Computers may "understand" language too, but it surely will not be due to the subjective states as it is with humans. If we define AI as a computer that must "understand" language the same way as humans do, then by definition, AI is not possible.
    - Houshalter 10y ago
      This is why Turing invented his famous test. At the time people were arguing about what it would take to prove a machine is intelligent. People argued that the internal properties of the machine mattered, that it needed to feel emotions and qualia just like humans. But Turing argued that if the machine could be shown to do the same tasks that humans can do, and act indistinguishable from a real human, then surely it's intelligent. Its internal properties don't matter, at least not to judge intelligence.
jonkho 10y ago
I second this. To further illustrate that semantics is far more than syntax and that emotions are an inseparable part of understanding language, do the following experiment: take any word or concept and look up its definition in a dictionary. Take note of the words used in the definition and look up the meaning of those words. Repeat recursively and if you go far enough down the tree you will find that all words lead to self-recursive definitions that state "it is a feeling of" this or that. This is proof that semantics of language - and intelligence required to grasp it's semantics - is inseparable from subjective states.
sullyj3 10y ago
This sort of anthropomorphic intuition pump is counter production. It would be much easier to believe that a baby could do that if we designed the baby's brain from scratch to be able to.
dlukes 10y ago
In this vein, have you read about the work done by people mostly from the AI lab at the Vrije Universiteit in Brussels (Belgium)? (They're also affiliated with the Sony CSL in Paris: http://csl.sony.fr/language.php http://csl.sony.fr/language.php) They're precisely interested in the philosophical problem of how a grounded language emerges and is perpetuated among a population of embodied agents, as opposed to the engineering problem of, say, understanding complex, context-dependent natural language queries. There's a great book which gives an overview of this field, The Talking Heads Experiment: Origins of Words and Meanings by Luc Steels, which discusses many of the advances made in this field (including for instance how having a grammar, as opposed to just stringing words related to what you want to say at random, is an evolutionary advantage because it boosts communicative success). It's published as open access, so go grab your free copy! :) http://langsci-press.org/catalog/book/49 http://langsci-press.org/catalog/book/49 Chapter 4 in particular has a very interesting discussion of what's problematic with the machine learning approach -- that it takes a lot of training examples for a classifier to start making interesting decisions -- and presents a selectionist alternative to that, where distinctions (as in e.g. nodes in decision trees) are grown randomly and they're reinforced / pruned based on feedback. Crucially, the categories (semantic distinctions) are not labels given at the outset, but they emerge along with the language, based on the environment the agents encounter and the tasks they're using language for. In general, I'd recommend Chapters 1 and 2 for a quick introduction, but in a pinch, I attempted to give a 50,000-foot summary in an essay I wrote (look under the heading Evolutionary Linguistics): http://dlukes.github.io/cathedral-and-bazaar.html http://dlukes.github.io/cathedral-and-bazaar.html I realize that engineering applications of these ideas might be a long way off (and perhaps they'll never materialize), but boy are these exciting discoveries about the very fabric of language :)

protomikron 10y ago

> This isn't an engineering problem, it's a philosophical problem [...] Indeed. However we can not rule out the possibility that we engineer a system without a "body" (I think you are referring to the Embodied mind thesis?). It is a complicated topic and discussions about it are futile without precise definitions of loaded terms, like "intelligence", "body", etc. A rather well-defined test is the classic Turing test and I wouldn't dismiss the possibility that it can be passed by a bodyless machine/program-thing.

posterboy 10y ago
I don't find the Turing Test convincing either, because someone smart enough to build it should be smart enough to recognize it from its answers. And if that depends on the intelligence of the questioner, whose intelligence is tested then, really? IIRC the test is a binary classifier, but intelligence is a spectrum that's fuzzy and therefore inherently hard to define. IE, how low is someone willing to assume the lack of intelligence in a human is not a good definition of general intelligence, as that's circular reasoning. I would suppose that babies possess general intelligence, but they lack the knowledge about the environment.
- morgante 10y ago
  > I don't find the Turing Test convincing either, because someone smart enough to build it should be smart enough to recognize it from its answers. Why do you assume that? The creators of AlphaGo certainly couldn't beat it.
  - jackpirate 10y ago
    The Turing Test equivalent in the game of Go is not beating the computer. Instead, it is: given a history of moves, can you determine whether or not a computer was playing? Determining whether or not AlphaGo was playing seems like a relatively easy task to me for the designers. Since they have access to the AlphaGo system, they can just calculate the probability that each move corresponds to one AlphaGo would make.
    - morgante 10y ago
      > Instead, it is: given a history of moves, can you determine whether or not a computer was playing? No, it's really not. For the Turing Test, the AI is meant to be adversarial—it's objective is to convince you that it is human. AlphaGo's objective isn't to "play like a human," it is to win. If they gave it an objective of playing like a human, I'm sure AlphaGo could play in a way that would be indistinguishable from a human. > Since they have access to the AlphaGo system, they can just calculate the probability that each move corresponds to one AlphaGo would make. Peeking at the system/data is cheating. Obviously the person who sets up a Turing test knows which player is AI.
      - posterboy 10y ago
        > If they gave it an objective of playing like a human, I'm sure AlphaGo could play in a way that would be indistinguishable from a human. It could just play unbelievably bad and appear like a beginner. That wouldn't prove intelligent. > Peeking at the system/data is cheating Someone ignorant of computers would hardly ever assume a machine. Of course the omission of this rule would leave someone smarter than the computer. If you talk statistics, IE the machine has to convince only a fair share of humans, the definition of the threshold is a problem. Intelligence would depend on the development of the society. I thought this is about an intrinsic value. It's an interesting thought experiment, but hardly conclusive, just observational.
robotresearcher 10y ago
> It is a complicated topic and discussions about it are futile without precise definitions of loaded terms, like "intelligence", "body", etc. On the contrary, to make progress we should give up on making precise definitions of non-technical terms such as 'body' and 'intelligence'. They are folk notions that don't have relevant precise definitions talking about them in an engineering context is distracting. You don't need a definition of 'beauty' to paint. You don't need a definition of 'justice' to practice law. You don't need a definition of 'intelligence' to build clever robots.

tener 10y ago

Why not simply simulate the body (or indeed multiple bodies) in a virtual world? Easier to build, parallelize, extend, maintain. Possibly somewhat safer too.

erikpukinskis 10y ago
That works, if your virtual world is good. But that's a big if. You've just taken one really hard problem (learning about the world) and turned it into an even harder problem (simulating the world).
- tener 10y ago
  Some games are pretty convincing sand boxes and it's not obvious to me that you really need full range of senses to properly teach the AI.
  - deleted 10y ago
    [deleted]
justinpombrio 10y ago
This is exactly the premise of The Talos Principle :-)

digitalarborist 10y ago

It's probably a mistake to assume that just because it's the way we do it that it has to be the way machines do it. Although that's usually the initial assumption. In the early days of flight most attempts were based on birds, similarly submersible vehicles were based on fish. We know now it's better to use propellers. It could be we just haven't found what is analogous to a propeller for the AI world.

xj9 10y ago
The only general intelligence we know of is us. It stands to reason that the first step towards creating AGI is to copy the one machine we know is capable of that type of processing. Why doesn't our research focus on understanding and copying biological brains? Numenta did, with good results, but it isn't an industry trend.
- throwaway13337 10y ago
  The steps to learning to make aircraft didn't come from understanding how birds flap their wings. I can't imagine the kind of intelligence we consider general will be from study of how humans biologically think.
- freyr 10y ago
  > Why doesn't our research focus on understanding and copying biological brains? There's lots of basic research being done to better understand the biological brain. Progress is slow and steady, but the brain remains poorly understood at this point in time. Most applied research has pursued more pragmatic methods because these methods have had faster progress and proven more useful in practice.
djokkataja 10y ago
I agree that machines won't necessarily have to do everything the same way we do things to be "intelligent". On the other hand, the concept of artificial general intelligence implies a machine that can perform all of the same functions a human can: "Artificial general intelligence (AGI) is the intelligence of a (hypothetical) machine that could successfully perform any intellectual task that a human being can."[0] And if you look at what humans do that we characterize as "intelligence", it's a substantial list of different functions: "Human intelligence is the intellectual capacity of humans, which is characterized by perception, consciousness, self-awareness, and volition. Through their intelligence, humans possess the cognitive abilities to learn, form concepts, understand, apply logic, and reason, including the capacities to recognize patterns, comprehend ideas, plan, problem solve, make decisions, retain information, and use language to communicate. Intelligence enables humans to experience and think."[1] If I take an engineering perspective and look at the list of functions that an AGI would have to perform to be an AGI, I would definitely want to give it more than a text-in, text-out interface so that it could have, for example, perception and volition. For example, imagine a theoretical "AGI" that exclusively deals with a stream of text in (as human language) and a stream of text out (again, as human language). If you ask it any questions about its physical surroundings, it's either going to make things up (which isn't perception and therefore fails both at being useful and meeting the definition of intelligence above), or it's going to get information about its physical surroundings via some proxy that feeds it a linguistic stream of information about its surroundings. But it doesn't matter if it's getting perceptual information from a proxy or from more directly embedded sensory interfaces; if it's getting perceptual information and able to use it sensibly, then it's performing the function of perception. And in that case the proxy source of perceptual information may as well be considered part of the "intelligent" system. These kinds of articles seem a bit silly to me, because they seem to imply that we should expect to be able to create a system which is "intelligent" but which only performs the function of understanding and producing meaningful language. But if you're only handling language, you're taking away all of the other functions that are part of the definition of "intelligence" above, which leaves you with a system which is far short of anything we'd consider "intelligent". [0] https://en.wikipedia.org/wiki/Artificial_general_intelligence https://en.wikipedia.org/wiki/Artificial_general_intelligenc... [1] https://en.wikipedia.org/wiki/Human_intelligence https://en.wikipedia.org/wiki/Human_intelligence

rbobby 10y ago

> locking a baby in a featureless room Strangely (or maybe horribly?) there are a number of studies of children raised in Romanian orphanages that somewhat cover this area. Under Nicolae Ceaușescu the government outlawed abortion (with some exceptions) in an attempt to increase the birth rate (https://en.wikipedia.org/wiki/Abortion_in_Romania https://en.wikipedia.org/wiki/Abortion_in_Romania). Coupled with a poor economy this lead to masses of infants and children being given over to government "care" in orphanages. These were pretty bleak places for infants and children, with infants often spending hours in a crib with little stimulation. Here's a really good article about the effects this sort of institutional "care" has on children: http://www.americanscientist.org/issues/feature/2009/3/the-deprived-human-brain/99999 http://www.americanscientist.org/issues/feature/2009/3/the-d... Here's the The Bucharest Early Intervention Project (tons of info about this subject area): http://www.bucharestearlyinterventionproject.org/ http://www.bucharestearlyinterventionproject.org/

Houshalter 10y ago

I believe we can get AI from just text. Obviously that won't work for babies, because babies get bored quickly looking at text. AIs can be forced to read billions of words in mere hours! Look at word2vec. By using simple dimensionality reduction on the frequency that words that occur near to each other in news articles, it can learn really interesting things about the meaning of words. The famous example is the vector "king" minus the vector for "man" plus "woman" equals "queen". It's learning the meaning of words, and the relationships between them. Recurrent NNs, using similar techniques, but with much more complexity, can learn to predict the next word in a sentence very well. They can learn to write responses that are almost indistinguishable from humans. And it's incredible this works at all, given RNNs have only a few thousand neurons at most, and a few days of training, compared to humans' billions of neurons trained over a lifetime. All of the information of our world is contained in text. Humans have produced billions of books, papers, articles, and internet comments. Billions of times more information than any human could read in their entire lifetime. Any information you can imagine is contained in text somewhere. I don't think it's necessary for AIs to be able to see, or interact with the world in any way. If you can predict the word a human would say next, with enough accuracy, then you could also produce answers indistinguishable from theirs. Meaning you could pass the Turing test, and perform any language task they could do just as well. So language prediction alone may be sufficient for AGI. This is the theory behind the Hutter Prize, which proposes that predicting (compressing) wikipedia's text is a measure of AI progress. The Hutter Prize isn't perfect (it's only a sample of wikipedia, which is very small compared to all the text humans have produced), but the idea is solid.

AsyncAwait 10y ago
If we ever want 'true AI' then interacting with the world would certainly be a crucial component of building one. Text is a crucial component, but ultimately being able to 'see' gives one an entirely new perspective on what words mean. There's a reason after all, for why toddlers touch hot surfaces even after repeatedly being told that it will hurt - until they experience it for themselves, the word 'hot' doesn't quite connect even if they heard it dozens of times and have a very good abstract sense of what it means.
kafkaesq 10y ago

wrongc0ntinent 10y ago

In fact it is a very hard engineering problem, which doesn't exclude a "philosophical" one. There's a line of research surrounding anthropomimetic robots with the specific task of studying cognition, going back at least a decade.

posterboy 10y ago

Computers are programmed only using text, even if the text has just two symbols. Sensor interfaces use digital signals, again symbol streams. That would beget the question if there could be machines mightier than a Turing Complete one. I'm sure that's missing your point.

scythe 10y ago
If you could simply program a human's behavior into a machine, that would be fine, but humans aren't capable of encoding their neural circuitry in a programming language; in fact, the combined efforts of all humans have only begun to shed light on what human behavior is. As such, generating formalized information (that describes behavior) requires some process (other than human hands) to do it -- and in the case of a machine that interacts with the world, "turing machine" is not a complete description of any such process.
- posterboy 10y ago
  By Shannon's information theory, everything is bits of information. Completely irrelevant for the mechanism of a Turing Machine is, who writes the initial bits on the tape. For all I care, the world is the tape and the computer is the head being moved through it. It's the old fallacy seeing the brain as a computer, since we've build computers after structures from our brains. Hence I wondered, what more there should be.

nawitus 10y ago

Ben Goertzel of OpenCog fame has argued for this for a long time.

gerbilly 10y ago

Reminds me of a quote I once heard, for which I can't find the source: The cheapest way to make a brain is still the old way.

hacker42 10y ago

Here is the reason why I don't believe in that: First of all, it seems that mind space is huge, i.e. there are very different programs that can lead to general intelligence, many of which will be very different from humans (this along is evidenced by the fact how strongly human characters and intellects vary, e.g. highly functioning mental conditions). A lot in machine learning points to that possibility. There are for example many ways to get supervision signals e.g. reconstruction error, prediction error, adversaries, intrinsic motivation (reducing the number of bits required for a representation), compression, sparseness etc. We basically just need a system that comes up with efficient representations of the world such that it can reason about it, i.e. that it can tell you which hypotheses about the world are likely true given some data. This computation allows you to make predictions and predictions are really at the heart of intelligence. If you can follow a hypothetical trajectory of generated, hallucinated or simulated samples of reality, i.e. samples that likely correspond to what actually happens in the world (and in the agent's own brain), then you can actually perform actions that are targeted at some purpose (e.g. maximizing reward signals). However, there are many sources of data that essentially give you the same information. Whether you create a representation by directly interacting with the world or just watch many examples of how the world generally evolves over time and how different entities interact with one another, you essentially get the same idea about how the world works (except in the first case you also learn a motor system). I think the anthropomorphism is really misplaced here, because computer systems are not dependent on actually performing in the real world. Since computers have near unlimited, noiseless memory and have super fast access to that memory, they can actually plan interactions by careful reasoning on the fly instead of needing to learn e.g. motor skills for manipulating objects, eating and handwriting before one can get anywhere near the performance of computers wrt. access to reliable external memory. A computer system also does not have hormone and neuromodulator levels that need to be met for healthy development (e.g. dopamine), therefore the intuition that deprivation of interaction with the world prevents learning is extremely misleading.

robotresearcher 10y ago

> you end up with something which is not particularly different or better than a human cyborg. Even if that were true, which I am not sure about, such a robot will have a very different moral status, as an artifact, and one that can be reproduced cheaply and indefinitely. This is very useful indeed, so on this axis it could be counted as 'better'. The idea of development being important for AI is an old one, but it hasn't had much concrete success. Brook's Cog robot at MIT is a prominent example of a robot that didn't do very much, despite this approach being taken in a good faith effort by talented and well-supported people.

wangchow 10y ago
Humans seem to be created cheaply and indefinitely too. Population is growing out of control. We could attach them to pods and harvest energy from their souls!

dragonwriter 10y ago

> They need bodies that are biologically connected with the rest of the biosphere On what basis do you conclude that "biologically" is important here? There may be some reason to suspect that human-like intelligence requires human-like ability to sense and interact with the outside world, but I see less reason to suspect that it is important that the mechanism of the sensors or manipulators must be biological.

mannykannot 10y ago

Helen Keller's life argues against the proposition that a machine requires the same sort of interaction with its environment that the average human experiences, before it can achieve intelligence.

djokkataja 10y ago
She was blind and deaf, but she still had enormous amounts of tactile information and the ability to physically interact with her environment. Moreover, those sensory elements were critical for her to finally be able to start learning language--she finally caught on that the signs another person was making in one hand represented the water being run over her other hand. And she didn't make that breakthrough until she was 7 years old, and only then began to learn with the persistent help of an instructor.[0] I would say that Helen Keller's life argues that a intelligent machine must be able to have experiences and the capacity to associate experiences with language. The machine probably doesn't need all of the perceptual modalities that we have, as Helen Keller demonstrated, but it should probably have some similarities with our own so that there would be common ground for initiating communication about experiences. A machine with just a text in / text out interface has nothing in common with us. [0] https://en.wikipedia.org/wiki/Helen_Keller#Early_childhood_and_illness https://en.wikipedia.org/wiki/Helen_Keller#Early_childhood_a...
- mannykannot 10y ago
  So Keller's "enormous amounts of tactile information and the ability to physically interact with her environment" means that her achievements were not that remarkable? Speaking personally, if I were to lose my sight and hearing, I imagine I would find life extremely daunting, even though I have what seems to me the very considerable advantage of having learned language. Anne Sullivan's equally remarkable (IMHO) role as a teacher is not really an issue here, as training is also an option for AI, though it might be evidence in a rather different discussion about whether unsupervised learning alone, particularly as practiced today, is likely to get us to AI (clearly, the evolution of intelligence can be cast as unsupervised learning, but that is a very long and uncertain process...)

jonkho 10y ago

I second this. To further illustrate that semantics is far more than syntax and that emotions are an inseparable part of understanding language, do the following experiment: take any word or concept and look up its definition in a dictionary. Take note of the words used in the definition and look up the meaning of those words. Repeat recursively and if you go far enough down the tree you will find that all words lead to self-recursive definitions that state "it is a feeling of" this or that. This is proof that semantics of language - and intelligence required to grasp it's semantics - is inseparable from subjective states.

Houshalter 10y ago
What does the experiment prove? Of course dictionaries are circular. But that doesn't mean they don't contain huge amounts of information about the world, and about language. Information an AI could infer without any interaction with the outside world at all.
- jonkho 10y ago
  This means that the semantics of the language is rooted in subjective states. Restated, this means humans "understand" language because of humans' emotions. Computers may "understand" language too, but it surely will not be due to the subjective states as it is with humans. If we define AI as a computer that must "understand" language the same way as humans do, then by definition, AI is not possible.
  - Houshalter 10y ago
    This is why Turing invented his famous test. At the time people were arguing about what it would take to prove a machine is intelligent. People argued that the internal properties of the machine mattered, that it needed to feel emotions and qualia just like humans. But Turing argued that if the machine could be shown to do the same tasks that humans can do, and act indistinguishable from a real human, then surely it's intelligent. Its internal properties don't matter, at least not to judge intelligence.

jonkho 10y ago

sullyj3 10y ago

This sort of anthropomorphic intuition pump is counter production. It would be much easier to believe that a baby could do that if we designed the baby's brain from scratch to be able to.

dlukes 10y ago

In this vein, have you read about the work done by people mostly from the AI lab at the Vrije Universiteit in Brussels (Belgium)? (They're also affiliated with the Sony CSL in Paris: http://csl.sony.fr/language.php http://csl.sony.fr/language.php) They're precisely interested in the philosophical problem of how a grounded language emerges and is perpetuated among a population of embodied agents, as opposed to the engineering problem of, say, understanding complex, context-dependent natural language queries. There's a great book which gives an overview of this field, The Talking Heads Experiment: Origins of Words and Meanings by Luc Steels, which discusses many of the advances made in this field (including for instance how having a grammar, as opposed to just stringing words related to what you want to say at random, is an evolutionary advantage because it boosts communicative success). It's published as open access, so go grab your free copy! :) http://langsci-press.org/catalog/book/49 http://langsci-press.org/catalog/book/49 Chapter 4 in particular has a very interesting discussion of what's problematic with the machine learning approach -- that it takes a lot of training examples for a classifier to start making interesting decisions -- and presents a selectionist alternative to that, where distinctions (as in e.g. nodes in decision trees) are grown randomly and they're reinforced / pruned based on feedback. Crucially, the categories (semantic distinctions) are not labels given at the outset, but they emerge along with the language, based on the environment the agents encounter and the tasks they're using language for. In general, I'd recommend Chapters 1 and 2 for a quick introduction, but in a pinch, I attempted to give a 50,000-foot summary in an essay I wrote (look under the heading Evolutionary Linguistics): http://dlukes.github.io/cathedral-and-bazaar.html http://dlukes.github.io/cathedral-and-bazaar.html I realize that engineering applications of these ideas might be a long way off (and perhaps they'll never materialize), but boy are these exciting discoveries about the very fabric of language :)