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).
"Children learn language much faster than it seems possible for a "blank" neural network to learn." This is a very strong statement that has no mathematical or computational proof AFAIK. It had no proof when Chomksy first put down that thesis either, it was an axiom of his. BTW, belief in a specialized language faculty was not universal, even in the past, esp. some philosophers of language disagreed with this view.
PS: I am far from the first person to point that out. At this point they are treating it as an axiom because they continue to believe it despite the disproof of their premises.
We have certain built in behaviors and reflexes.
To put it another way - most people think they are rational. Most people act irrationally. To me it is animal instinct that is cause of greed, war, social hierarchy, etc. and it is so ingrained in society that we don't question it's root cause which most likely boils down to atavistic tendencies.
Edit: sorry if this is vague, this is what I'm talking about https://en.wikipedia.org/wiki/Psychological_nativism
The whole notion of the Universal Grammar and innate language faculties which instantiate subsets of the Universal Grammar is weird.
PS: Language acquisition starts before birth, and continues 24/7 after that until we 'learn' it.
Having read the book, his arguments are far more convincing than your assertions.
If you just want a compelling argument: Biology is a vary important component in the creation and evolution of language, because the fine motor control required to say "linguistic" vs "mommy" or "stop" has a lot to do with how languages are learned and evolve. As baby's practice how to say thing babble converts to simple words but in doing so there are pattern as to which sounds are easier to produce and therefore enable them to probe their environment by reproducing. This paralls the evolution of language where the most important and simplist ideas where the first to be communicated and therefore take up the 'root' address space in the language with more complex words and ideas like 'chemist' being tacked on over time.
PS: Sure, it sounds great. But how many assumptions am I stringing together in just those few sentences.
PS: Think of it like this A -> B, B -> C therefore A -> C is all well in good most of the time. A -> B .... Y -> Z therefore A -> Z only really works with math, build a chain that long and it's unlikely for all those steps to be accurate.
If I where to attack the book I could say something like: "In chapter 2 'Chatterboxes' he states humans are the only animal that uses language which is complex issue by it's self. He goes further and says every group of humans in remote areas we encountered have had complex language. He then runs with that line of reasoning. However this ignores not just other animals that use simple forms of language but human ancestors that where close to us anatomically and probably also used language. If homo sapiens's ancestors also used language then you would expect the earliest humans to also use language therefore language would spread from it's origins to all those remote areas vs. being created from scratch in those remote areas."
Now, I could get 10 PHD's to say the same thing and use quotes etc, but what's important is the accuracy of the statement not who says it. http://lesswrong.com/lw/jl/what_is_evidence/
PS: You clearly lack the courage of your convictions to actually leave an argument when you say 'Goodbye'. However, I realize trying to reason with a fool is a waste of time, so best of luck and 'Goodbye'.
PS: You're still an ass, and you don't know what appeal to authority means. We have to be debating and me relying on him to make a point for it to be an appeal to authority. As I clearly indicated I wasn't interested in debating the subject, you can't accuse me of logical fallacies, a point I made previously but you failed to grok because you're an ass.
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.
Actually, Russell & Norvig's AI textbook has a nice summary of these different approaches to AI in its intro chapter.
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.
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.
There is a lot more involved than just language. It seems possible to solve language and still not create "strong AI." See: Watson kicking ass at Jeopardy. That is pretty sophisticated language comprehension; it even got clues that involved puns and wordplay.
From that it would seem totally possible to adapt this sort of intelligence to taking an IQ test. Just provide the right corpus to search and give the algorithms time to learn and be tuned. What if Watson tests out at a 150 IQ? Is he "intelligent"? After all it is an "intelligence quotient."
I think most people would say no, the issue is the Turing Test. That involves language, but I think the real point is an artificial personality: a computer that can hold a conversation and evince a discernible will and opinion. It seems to me that to do that, the machine must have emotions. I'm no expert in the field, but from my layman's position I don't see nearly as many stories about studying and replicating emotions as I do about human language.
Which is funny because any dog owner knows that dogs, despite having almost no formal language, are clearly willful, independent, emotional living beings. Can we even simulate a dog's emotional state with a computer? How about a bug's emotional state? I haven't heard much about that. But there is a ton of heat around language.
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.
I think that you are also getting at the _sentience_. I think this is what most people refer to when speaking about true AI. You know, having consciousness, desires, social skills, etc.
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.
In fact I doubt that the cognitive capacity of a human brain is enough to truly understand an operating human brain.
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.
Chomsky takes the academic approach - artificial intelligence is the simulation of humanlike (or even possibly mammalian) intelligence.
Norvig is taking the engineering approach - artificial intelligence needs only to pass the Turing test.
They're both right, both approaches have value, and they both are bound by our limited technology at the moment.
In the end, though, Norvig will lose out. Sure, he'll make the finish line first - an AI capable of 'passing' the Turing test, 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.
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.