7 ms·

They have two different definitions of "artificial intelligence," which is where the schism seems to be arising from. Chomsky takes the academic approach - art

• phuff 14y ago
  Having studied Chomsky a fair bit in grad school, and also studied cognitive linguistics a fair bit in grad school, I think the idea that Chomsky's models will ever win anything is just wrong. Chomsky's central problem is that his modeling is not based on anything biological at all. His models don't correspond to reality. Some of them were based on some assumptions about how the brain works that were untestable in the 50s and 60s when all of his linguistic models were developed, and have since become testable and are not particularly evident in the way we currently understand the brain to work. Given this fact, I think your current best bet is Norvig as a modern approach to AI or anything linguistic-y. But this is only because it is slightly more grounded in reality rather than being something that Chomsky (who is a very smart guy) came up with on his own without the benefit of actual biological models of the brain. In the end, I think there will be (eventually, a long time from now) an actual model of how the brain processes language based on actual observations of working brains that throws away much of what Chomsky has proposed but probably uses some of it and that doesn't use huge Google-esque lookup tables but is highly influenced by statistics. But until we get to that point, statistics are probably your best bet since at least they're grounded in reality (unlike much of Chomsky's work).
  • sp332 14y ago
    Children learn language much faster than it seems possible for a "blank" neural network to learn. It seems that there is some "circuitry" hard-wired into the human brain that helps learning language. So the question is: can a computer learn language as well as a human, without simply hard-coding language into it?
    • debacle 14y ago
      Children are definitely not a 'blank' neural network. They spend ~6 months staring off into nowhere, looking, listening, and slowly developing the skills to respond to their environment.
      • sp332 14y ago
        By "blank" of course I mean that they begin blank and immediately start learning from their environment. You say they develop skills "slowly" but it's still much faster than you would expect, unless children have some innate skill at language built in instead of being "blank." Edit: sorry if this is vague, this is what I'm talking about https://en.wikipedia.org/wiki/Psychological_nativism https://en.wikipedia.org/wiki/Psychological_nativism
        • knowtheory 14y ago
          You are being way too vague. What is setting your expectations of "slowly"? What rate would you expect children to learn language at? Even if that were to be the case, your argument is essentially a god of the gaps argument. Not P therefor Q is not sound reasoning. The whole notion of the Universal Grammar and innate language faculties which instantiate subsets of the Universal Grammar is weird.
          • gnaritas 14y ago
            No, he's right, check out Pinkers the language instinct for the full treatment on the issue. Children learn at a rate impossible from just what they hear and imitate. We are pre-wired for language.
• jan_g 14y ago
  But what exactly is true artificial intelligence? For example, I consider Google search and Wolfram alpha very intelligent. They can do math, answer questions, rank information, follow current events, ...
  • debacle 14y ago
    They're still computers in the traditional sense - they only do what someone told them to do.
    • jan_g 14y ago
      I believe that you are then referring to sentience in addition to intelligence? Artificial sentient being is another thing entirely, I think. And tougher nut to crack :)
    • zumda 14y ago
      You are right, but to solve most problems, do machines really HAVE to think like humans?
      • debacle 14y ago
        For most problems, you don't need AI.
      • zumda 14y ago
        (can't answer you directly, so I'm doing it here) My point was, that, for example, language recognition doesn't need human intelligence, a statistical model is enough. Or driving around a confined space only requires a particle filter, not human intelligence.
  • waterlesscloud 14y ago
    I think statistical models are an 80% solution. They get a long way down the path very quickly, but then they hit a wall and don't advance much further. Search, translation, probably also autonomous vehicles. They get to a point with statistical approaches and then they stop advancing. The rapid success at first may be leading to a dead end. Having said that, there's a lot of value in these technologies as assistance to human intelligence, but I'm skeptical they're ever going to lead to full-on autonomous intelligence.
• olalonde 14y ago
  Isn't there a consensus that "passing the Turing test" <=> "true artificial intelligence"? Otherwise, what test do neurologists propose?
• power 14y ago
  “Norvig is taking the engineering approach - artificial intelligence needs only to pass the Turing test.” Passing the Turing test and the simulation of intelligence are supposed to be the same thing. Turing came up with the test to sidestep the definition of intelligence. I’m not sure what you mean by “but in order to have real intelligence you need an analytical engine (or brain, if you will) that can prioritize data without fiddling with bits. In the Norvig solution, someone will always have to be fiddling with the bits”. Do you mean bits as in pieces or as in ones and zeros ? If the former, haven’t Chomsky’s models required the addition of new parameters as exceptions to his rules are found ? If you mean the latter, how is a computer to prioritize data if it can’t look at bits ? There seems to be the misconception that Norvig does not use simple models. He does, just ones that use statistics for training and to learn. His approach strikes me as elegant, simple and more robust to changes in language over time than Chomsky’s.
• netcan 14y ago
  I'm going out on a limb because I don't really know much about neurology & I may be wrong about facts but.. I think an issue here is that we don't really know what "natural intelligence" is. For a significant part of the Scientific age we knew about genes in some sense without knowing much about them. We called them traits, observed & measured them. We got to know some "rules" about their inheritance. But it wasn't until genetics got to be a little better understood that we got to know their physical manifestation. We can explain the diference between genetic & cultural (memetic?) inheritance in these terms. A descendant's cooking habits are memetic and her hair colour is genetic. When it comes to neuroscience I think we're where we were a century ago in biology. Emotions, thoughts, memories. We don't know what their physical manifestation is. We dont know how they work. Since we don't know much about how natural intelligence works I think our common sense definition of intelligence is, to a certain extent: "stuff we can do that computers can't." I think that if we had a definition that was more functional than observational, you wouldn't be hesitant at all to use "mammalian" in your definition. Whatever processes result in observed human intelligence are almost certainly shared with other species. We'd probably also know what species to draw the lines at: reptiles? invertebrates? fungi? If apes have intelligence, goldfish don't but octopi do that suggests there multiple versions of natural intelligence.
• _delirium 14y ago
  A significant proportion of the "academic approach" is actually a third one: artificial intelligence is the analysis and implementation of rational decision-making. That approach tends to care neither about biological accuracy, nor believability in a Turing-test sense. Rather, it cares about whether its decisions are correct based on evidence available to the decision-maker. That's the kind of attitude you most often find in both statistical and logic-based AI circles. Actually, Russell & Norvig's AI textbook has a nice summary of these different approaches to AI in its intro chapter.
  • JabavuAdams 14y ago
    Too bad humans aren't rational.
    • _delirium 14y ago
      They aren't, but not all AI is aimed at mimicking human behavior. For example, if your goal is an AI system that can automatically control ship routing on the global Maersk shipping network (planning routes, rerouting to respond to contingencies, etc.), you might want "optimal" rather than "human-like" decision making.
      • JabavuAdams 14y ago
        Good point.
• bugsbunnyak 14y ago
  > Chomsky's approach, on the other hand, will result in a 'true' artificial intelligence, the way neurologists understand it. It's just going to take a lot longer to get there. High-level behavioral impressions taken by a neurologist are a convenient abstraction. That this high-level behavior is useful in monitoring mental state (outputs) says very little about the underlying 'hardware'. In fact, this is the `fundamental` debate in cognitive science: from whence does intelligence arise? Theories generally fall under two headings: 'top-down' and 'bottom-up', which roughly correspond to 'pre-programmed' and 'emergent'. The canonical bottom-up approach is the neural network, approximating cells with various equations that govern behavior (outputs) based on aggregate input (there are various levels at which this can be done). There are a variety of top-down approaches, a typical approach would take the form of logic engines (think Prolog), or generative rules (Chomsky) Statistical modelling approaches are closer to bottom-up, but depending on the model they may still incorporate domain knowledge that is emergent from the model input. Statistical approaches have momentum these days due to considerable success - thanks largely to Moore's law. However, they also have biological support: what is a neuron? It's an FPGA with a lot of electrical and chemical inputs. Small neural circuits can behave statistically, and it's an open question whether this gives rise to high-level behavior. A big reason it's an open question is that we don't yet have the spatial or temporal resolution to measure enough signals. That said, there is plenty of room for what I consider a happy medium: locally statistical behavior, but globally (and generationally) top-down organization driven by genetics.
• btilly 14y ago
  I personally believe that it will be possible to simulate a working brain in sufficient detail that it is able to "think" like a human does before humans understand said brain. In fact I doubt that the cognitive capacity of a human brain is enough to truly understand an operating human brain.
• rm999 14y ago
  This is exactly my take on it. They are talking about AI in different contexts, and therefore aren't really arguing with each other as much as past each other. Anyone who has any interesting in building something today would take a Norvig approach, and anyone who pictures AI 100 years from now should hope that the Chomsky approach eventually won out.
  • SudarshanP 14y ago
    Many confuse the Chomsky approach v/s Norvig approach as who is right and who is wrong. But that is an unnecessary distraction. Consider planes. The wright brothers studied planes without mastering Fluid dynamics. Their planes flew. That was awesome. But plane makers kept exploring and inventing. We achieved super sonic speeds and even made rockets. As time passed, we learnt more and more about fluid flows. While on the one hand atoms jiggling around with various velocities is all that we are studying. But the fluids demonstrate very rich and complex behaviours. Only a fool would stay contented that the aeroplane flew. Our understanding of fluid dynamics comes in handy to model wind turbines and weather patterns and a whole host of awesome areas. A lot of those great discoveries came from investments in aeronautical research made by profit hungry entrepreneurs. The science v/s technology debate is usually just funding politics. It is silly to argue whether the eye runs the marathon or the leg runs the marathon. Let us just accept that "the runner runs the marathon" and just move on. Chomsky may or may not be right. Just like aristotle was not right about many things. He was a genius and he contributed as well. He may have stirred up some trouble also. Over time if we are fortunate enough we may get a clear understanding of the core ideas like we understand the behavior of a fluid. If we have to settle with flying planes but don't understand wind, we cannot go supersonic or reach the moon.