15 ms·
On Chomsky and the Two Cultures of Statistical Learning
- gabelschlager 4y agoWell, Chomsky already dismissed corpus based linguistics in the 90s and 2000s, because a corpus (large collection of text documents, e.g., newspaper, blog post, literature or everything mixed together) is never a good enough approximation of the true underlying distribution of all words/constructs in a language. For example, a newspaper-based corpus might have frequent occurences of city names or names of politicians, whereas they might not occur that often in real everyday speech, because many people don't actually talk about those politicians all day long. Or, alternatively, names of small cities might have a frequency of 0. Naturally, he will, and does, also dismiss anything that occured in the ML field in the past decade. But I agree with the article. Dealing with language only in a theoretical/mathematical way, not even trying to evaluate your theories with real data, is just not very efficient and ignores that language models do seem to work to some degree.
- blululu 4y agoThis is a bit lateral, but there is a parallel where Marvin Minsky will most likely be best remembered for dismissing neural networks (a 1 layer perceptron can't even handle an xor!). We are now sufficiently removed from his heyday where I can't really recall anything he did besides the book Perceptrons with Seymour Papert (who went on to do some very interesting work in education). There is a chart out there about ML progress that makes a conjecture about how small the gap is between what we would consider that smartest and dumbest levels of human intelligence (in the grand scheme of information processing systems). It is a purely qualitative vibes sort of chart, but it is not unreasonable that even the smartest tenured professors at MIT might not be that much beyond the rest of us.
- masswerk 4y agoThis dismissal of Minsky misses that Minsky had actually extensive experience with neural nets (starting in the 1950s, with neural nets in hardware) and was around 1960 probably the most experienced person in the field. Also, in Jan 1961, he published “Steps Toward Artificial Intelligence” [0], where we not only find a description of gradient descend (then "hill climbing", compare sect. B in “Steps”, as this was still measured towards a success parameter and not against an error function), but also a summary of experiences with this. (Also, the eventual reversal of success into a quantifiable error function may provide some answer to the question of success in statistical models.) [0] Minsky, Marvin, “Steps Toward Artificial Intelligence”, Proceedings of the IRE, Volume: 49, Issue: 1, Jan. 1961: https://courses.csail.mit.edu/6.803/pdf/steps.pdf https://courses.csail.mit.edu/6.803/pdf/steps.pdf
- riku_iki 4y agoGradient descent was invented before Minsky. Imo, Minsky produced some vague writings, with no significant practical impact, but this is enough for some people to claim his founder's role in the field.
- masswerk 4y agoMinsky was actually a pioneer in the field, when it came to working with real networks. Compare [0] “A Neural-Analogue Calculator Based upon a Probability Model of Reinforcement”, Harvard University Psychological Laboratories, Cambridge, MA, January 8, 1952 [1] “Neural Nets and the Brain Model Problem”, Princeton Ph.D dissertation, 1954 In comparison, Frank Rosenblatt's Perceptron at Cornell was only built in 1958. Notably, Minsky's SNARC (1951) was the first learning neural network.
- riku_iki 4y ago> when it came to working with real networks. Compare my understanding is that that no one knows what that SNARK thing was, he built something on the grant, abandoned it shortly after that, and only many years later he and fanboys started using it as foundation of bold claims about his role in the field.
- cma 4y agoTake the amount of language a blind 6 year old has been exposed to. It is nothing like the scale of these corpsuses but they can develop a rich use of language. With current models if you increased parameters but gave it a similar amount of data it would overfit.
- riku_iki 4y agoIt could be because kids are gradually and structurally trained through trials, errors and manual corrections, which we somehow don't do with NN. He wouldn't be able learn language if only exercises he would be doing is to guess next word in sentence.
- aaroninsf 4y agoFor me this is a prototypical example of compounded cognitive error colliding with Dunning-Kruger. We (all of us) are very bad at non-linear reasoning, reasoning with orders of magnitude, and (by extension) have no valid intuition about emergent behaviors/properties in complex systems. In the case of scaled ML this is quite obvious in hindsight. There are many now-classic anecdotes about even those devising contemporary scale LLM being surprised and unsettled by what even their first versions were capable of. As we work away at optimizations and architectural features and expediencies which render certain classes of complex problem solving tractable by our ML, we would do well to intentionally filter for further emergent behavior. Whatever specific claims or notions any member has that may be right or wrong, the LessWrong folks are at least taking this seriously...
- masswerk 4y ago"seem to work to some degree", appears much like a second order argument to this debate… ;-)
- aaroninsf 4y agoTo some degree is quite an understatement. :) My own hobby horse of late is that independent of its tethering to information about reality available through sensorium and testing, LLM are already doing more than building models of language qua language. Write up someone pointed me at: https://thegradient.pub/othello/ https://thegradient.pub/othello/
- bmitc 4y agoHow is an understatement? And what do people mean by language models working well? From what I can tell, these language models are able to form correct grammar quite surprisingly well. However, the content of such, is quite poor and often void of any understanding.
- getpost 4y agoThis has been posted many times before, but I thought of it yesterday when I saw this essay by Chomsky in the New York Times, "The False Promise of ChatGPT." https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chat...
- A4ET8a8uTh0 4y agoHe is very smart and obviously an authority in his chosen field, but I am not sure he is right here, because he misunderstands the promise in place ( possibly because he only used it for text generation ). edit: By that I mean the following: "Note, for all the seemingly sophisticated thought and language, the moral indifference born of unintelligence. Here, ChatGPT exhibits something like the banality of evil: plagiarism and apathy and obviation. " He does not seem to understand it is a feature of the system. He is absolutely correct about the level of hype though.
- ericpauley 4y agoThis was my biggest issue with this article as well. Chomsky (et al.) completely ignores the fact that ChatGPT has been "trained"/gaslit into thinking it is incapable of having an opinion. That ChatGPT returns an Open AI form letter for the questions they ask is almost akin to an exception that proves the rule: ChatGPT is so eager to espouse opinions that OpenAI had to nerf it so it doesn't. Typing the prompts from the article after the DAN (11.0) prompt caused GPT to immediately respond with its opinion. Chomsky's claims in the article are also weak because (as with many discussions about ChatGPT) they are non-falsifiable. There is seemingly no output ChatGPT could produce that would qualify as intelligent for Chomsky. Similar to the Chinese room argument, one can always claim the computer is just emulating understanding.
- katmannthree 4y ago> one can always claim the {entity} is just emulating understanding. I've yet to see a convincing argument that humans are any different. They're sometimes better at pretending to understand things, but at the end of the day both humans and ChatGPT have a small handful of things which they functionally understand[0] and a larger body of knowledge which is only partially integrated. Chomsky has disappeared up his own intestinal tract on this one. One can quibble about intelligence until the end of time, but the real question is that of utility -- which they certainly do have, in ever-increasing scope and measure. [0]: i.e. have synthesized the object and can properly explain and apply it in other contexts
- sampo 4y ago> At the Brains, Minds, and Machines symposium held during MIT's 150th birthday party So 12 years ago. (The page doesn't have dates, and the links are broken.)
- avgcorrection 4y agoQuite annoying. Goes to the trouble of a citation list but not a single date of authorship.
- simonw 4y agoYeah I had to dig around in the Internet Archive to figure out when this was written: https://web.archive.org/web/20110527123842/https://norvig.com/chomsky.html https://web.archive.org/web/20110527123842/https://norvig.co...
- VHRanger 4y agoSure, but we had Word2Vec and some primitive RNN-based language models 12 years ago. Arguably Word2Vec was the first big qualitative jump in NLP. Super modern LLMs (say, starting with BERT -- ChatGPT isn't really a qualitative jump from those as much as a quantitative one) are another one that's maybe 4 years old.
- jeffhwang 4y agoYes this bothered me too. I vaguely knew MIT was founded in the 1860s so I guessed the rough date of the talk. What clarified it for me was this quote “I then looked at all the titles and abstracts from the current issue of Science” where the link still works: https://www.science.org/toc/science/332/6032 https://www.science.org/toc/science/332/6032 May 2011
- antipaul 4y ago[2011] or thereabouts
- avgcorrection 4y agoThe part where he compares Chomsky to O’Reilly is a good laugh. (No, it’s not a good comparison.)
- dekhn 4y agoThe entire article is in fact an absolutely brilliant piece of criticism that can be read on a number of different levels. It's filled with tiny little bits of detail that reference entire vistas of thought that simply were not comprehensible before roughly ~2003.
- photochemsyn 4y agoSo far my favorite expository example on this subject involves whether any kind of machine learning model trained on Tycho Brahe's astronomical observational data (late 16th century) would be able to extract the equations of Johannes Kepler (1609) from them. It would likely be able to make good predictions of where planets would be in the future, but that wouldn't be based on simple equations that were easy for humans to look at and understand. (Those are: elliptic orbits, equal areas are swept in equal time by a line joining the orbiting bodies, and orbital periods (time) are proportional to the geometry of the ellipse (distance) by a square:cube ratio, and they're all more or less approximations in the solar system (which has many complicating interactions due to all the bodies involved).) Someone commented that most humans wouldn't be capable of doing that either, which is true enough... Perhaps if the machine learning model was also trained on a large set of equations as well as on a large set of astronomical data?
- mxwsn 4y agoThere's a subfield called symbolic regression that considers this problem. https://www.science.org/doi/10.1126/sciadv.aay2631 https://www.science.org/doi/10.1126/sciadv.aay2631 The Kepler equation is relatively simple compared to other equations re-discovered by ML.
- sampo 4y ago> The Kepler equation is relatively simple compared to other equations re-discovered by ML. But the ML was given, as input, the locations of the planets already in the solar system level Cartesian (x,y,z) coordinates. I am skeptical that any AI system would be able infer Kepler's model of elliptic orbitals around the Sun, if all they were given was Tycho Brahe's data as (altitude angle, azimuth angle, time) as seen from the point of view of an observer on the surface of Earth at the location of Denmark.
- aj7 4y agoThose “simple” equations underlie almost ALL scientific thought. They have implications far beyond the motions of the planets, which stimulated them. It’s hard to make a quantitative argument about how much more powerful they are than a statistical model, which I am sure is possible with today’s modeling, as you suggest. Ahh, but without Newton’s Laws, there would be no machines to run the models! It IS useful to have machines GUESS models in science, by the way. You might know that a consequence of the inverse square law of gravitation and electromagnetism implies that the surface integral of those fields over a closed surface enclosing mass or charge depends ONLY on mass and charge enclosed, and NOT on it’s detailed distribution therein. You have to have the model to actually prove this.
- aj7 4y ago“As another example, consider the Newtonian model of gravitational attraction, which says that the force between two objects of mass m1 and m2 a distance r apart is given by F = Gm1m2/r^2 where G is the universal gravitational constant. This is a trained model because the gravitational constant G is determined by statistical inference over the results of a series of experiments that contain stochastic experimental error.” No, it’s not. Period. It’s a THEORY that is supported “by statistical inference over the results of a series of experiments that contain stochastic experimental error.” It’s been checked hundreds of times and its accuracy has gotten better and better. The technology of the checking led to new science. By making it consistent with special relativity, and POSTULATING the principle of equivalence, a new THEORY was born, general relativity, also known as the theory of gravitation. IT has been checked hundreds of times, ”the technology of the checking has led to new science,” and it is a current very active field of RESEARCH, (not model training.) It’s a little horrifying that Norvig doesn’t seem to understand these nuances. The same arguments apply to the solid state physics underlying the machines that run large language models. That too, is a “THEORY. It has been checked hundreds of times, the technology of the checking has led to new science, and it is a current very active field of RESEARCH, (not model training.)”
- ckrapu 4y agoI disagree. It’s true that we are used to thinking of gravitation as a theory that was double checked with data, but it is also pretty clear that good physical arguments are strong priors. Then, with experimental data, we have a classic prior + data posterior update which does cast all of this as a statistical model.
- froh 4y ago(2011)
- sampo 4y ago> I take Chomsky's points to be the following: 1. Statistical language models have had engineering success, but that is irrelevant to science. Well, deep neural nets are not statistical models, so shouldn't Chomsky now be at least a little bit happier with ChatGPT?
- mxwsn 4y agoHow so? Deep neural nets and ChatGPT are very much statistical models.
- hackinthebochs 4y agoWhat does it mean to be a statistical model? Deep nets are deterministic, for example. We can use them to model probability distributions, but they are not intrinsically statistical.
- sampo 4y ago> How so? They don't fit the definition [1,2,3]. But I looked at the internet, and apparently many people consider neural nets to be statistical models, or "a kind of" statistical models. [1] https://en.wikipedia.org/wiki/Statistical_model https://en.wikipedia.org/wiki/Statistical_model [2] https://www.stat.uchicago.edu/~pmcc/pubs/AOS023.pdf https://www.stat.uchicago.edu/~pmcc/pubs/AOS023.pdf [3] https://www.statlect.com/glossary/statistical-model https://www.statlect.com/glossary/statistical-model
- Mattasher 4y ago> There is a notion of success ... which I think is novel in the history of science. It interprets success as approximating unanalyzed data. Yes! Yes! Yes! As I've been arguing for years, we've already plucked the low-hanging fruit of science. The vast majority of additional progress will have to be made by using what are essentially black box models, and the proof of a good model will be how well it approximates new data, not how nice the mathematical equation looks or how well it can be explained intuitively.
- ChatGTP 4y agoWe’ll that’s what you’re hoping for. However I think this is why self-driving cars aren’t necessarily going so well. In some ways they are, in other ways they fail catastrophically. Maybe statistically the self driving cars are better performers but form a product perspective, having a failure mode of death or serious injury isn’t a good thing. The black box has failure modes we don’t understand, like crashing into fire trucks. So black boxes are good for experiments, but please don’t hook up the black box to a nuclear missile solo, or airplane cockpit just yet thanks.
- EGreg 4y agoI'm regularly in touch with Chomsky. I've interviewed him several times, the latest being this for example with David Harvey: https://www.youtube.com/watch?v=ezf7wxJ7whA https://www.youtube.com/watch?v=ezf7wxJ7whA Earlier regarding Freedom of Speech and Capitalism: https://qbix.com/chomsky https://qbix.com/chomsky Sometimes I send him articles about how chimpanzees or others have "language". Or now they found bumblebees can teach each other. Chomsky famously maintains that only human are born with innate capabilities for language. Anyway, since Chomsky is a linguist and focused on language, it would make sense for him to say that. Of course, computers can develop their own languages (as Facebook's sales bots have done for example years ago: https://nypost.com/2017/08/01/creepy-facebook-bots-talked-to-each-other-in-a-secret-language/ https://nypost.com/2017/08/01/creepy-facebook-bots-talked-to...) I think that, in general, Chomsky is right that when it comes to language (unlike paintings or even photorealistic fakes etc.) the meaning will never be modeled perfectly, any more than, say modeling a Mandelbrot set on all levels of zoom can be done by a machine learning system that trains in the way generative LLMs train. Having said that, I think that logic itself is a "poor man's approximation" to what AI can do, in that it just uses a few parameters. I prefer Steven Wolfram's analysis. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/ https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-...
- initramfs 4y agoI don't think AI can ever learn the many dialects and slang words with quaternary meanings, since statistical methods are quite unreliable on the long tail. I once tried using Google Translate for a word known only in a specific region of a foreign country and got zero results. It's going to be quite complex, esp for non-English languages.
- cma 4y agoThis seems to show with scale one-shot stuff like hearing a slang word substitution one time can be done (within the other limitations of transformers) https://arxiv.org/abs/2303.03846 https://arxiv.org/abs/2303.03846
- calf 4y ago
- robg 4y agoI was in the audience during the panel and Chomsky was really trying to hold onto a view of science disconnected from the engineering needed to replicate the natural phenomenon. To take a simple example: Suppose you’re the Wright Brothers and you built a flying machine. Chomsky’s response would be: But you didn’t explain how birds fly. On the one hand, sure. Birds fly differently. On the other, the machine flies. Why does it need to explain all of flight? The same is true of language models. They don’t explain how we acquire language. But they replicate language uses in ways that look like language speakers.
- paganel 4y agoIt’s a valid criticism. What today’s “language” models do is that they have been able to scale up the implementation of the Mechanical Turk problem. They have done that quite well, give or take a few errors. The issue is that there’s a whole another world outside of the “answers” provided by Mechanicak Turk-like solutions, this is what guys like Chomsky allude to.
- BoardsOfCanada 4y agoI think that part of Chomsky's argument would be that language primarily is a tool for thinking and reasoning, and that the externalization and use for communication came later (I remember this from a lecture of his). In that view "language" of an AI is not the same as human language, while flight is flight regardless if it's mechanical or biological.
- robg 4y agoJerry Fodor used to write of mentalese. It’s a fair criticism. And yet generative AI is showing that the models can be creative in ways that look like thinking outside of language.
- foobarqux 4y agoChomsky has explicitly made the distinction between the difference between a useful tool and science, which tries to explain things. In fact he does it almost every time he talks about this subject.
- reliableturing 4y agoAs someone who tends to side with Chomsky in these debates, I think Norvig makes some interesting points. However, I would like to pick one of his criticisms to disagree on: "In 1969 he [Chomsky] famously wrote: But it must be recognized that the notion of "probability of a sentence" is an entirely useless one, under any known interpretation of this term. His main argument being that, under any interpretation known to him, the probability of a novel sentence must be zero, and since novel sentences are in fact generated all the time, there is a contradiction. The resolution of this contradiction is of course that it is not necessary to assign a probability of zero to a novel sentence; in fact, with current probabilistic models it is well-known how to assign a non-zero probability to novel occurrences, so this criticism is invalid, but was very influential for decades." I think Norvig wrongly interprets Chomsky's "probability of a sentence is useless" as "the probability of a novel sentence must be zero". I agree that we've shown that it's possible to assign probabilities to sentences in certain contexts, but that doesn't mean that it can fully desribe a language and knowledge. This seems to me yet another case of 'the truth is somewhere in the middle' and would be weary of the false dichotomy that is put forward here. Yes we can assign probabilities to sentences and they can be useful, but it's not the whole story either.
- KKKKkkkk1 4y agoI'm sorry, but what Chomsky wrote is pseudo-scientific mumbo jumbo, and Norvig went to the trouble of wading through that mumbo jumbo and refuting it. Thank you, Peter Norvig.
- dekhn 4y agowhat's really funny is that I worked in DNA sequence analysis at a time when the chomsky hierarchy was primal and literally all my work was applying "probability of a sequence" concepts (specifically, everything from regular grammars to stochastic context free grammars). It's a remarkably powerful paradigm that has proven, time and time again, to be useful to scientists and engineers, much more so than rule systems constructed by humans. The probability of a sentence is vector-valued, not a scalar, and the probability can be expanded to include all sorts of details which address nearly all of Chomsky's complaints.
- robg 4y agoThese two cultures played out very early in neural networks, see Rumelhart and McClelland, for example [1]. For the language nativists like Chomsky the engineering will never appreciate all of the intricacies of language learning and uses. It’s a fair criticism that both forces the engineering to be better and misses the point. Engineering doesn’t need to replicate how the natural phenomenon “works” in order to provide a compelling set of uses for humanity. Choose a modality of sensing the world - vision, hearing, language, etc. If the engineering replicates and even improves upon the natural basis, we can continue to focus on improving the engineered solution. Nature is a wonderful source of inspiration but why be bound to its instantiation? The brain doesn’t record memories verbatim. Recording devices are better for that reason. [1] https://mitpress.mit.edu/9780262680530/parallel-distributed-processing/ https://mitpress.mit.edu/9780262680530/parallel-distributed-...
- foobarqux 4y agoThis is like a HN Groundhog Day article. Whenever this article comes up and people point out the flaws in the arguments, which everyone ignores when the next time the article is posted and the same specious reasoning is repeated.
- deleted 4y ago[deleted]
- QuadrupleA 4y ago"What did Chomsky mean", the eternal question. He, among with Alan Kay, are among the most (deliberately I suspect) cryptic obfuscatory bullshitters alive. They spout twisted, vague statements that can interpreted in dozens of conflicting ways, and through cult of personality people ascribe great wisdom and genius to them, thinking their grand thoughts must be just beyond the grasp of mere mortals like them. True genius can communicate things clearly I think. Probably not a popular opinion, and I'm a little cranky so grain of salt. But Chomsky and his ilk seem like some of the great intellectual hustlers of our time.
- denton-scratch 4y agoI think it's pretty unreasonable to call him a "hustler". He repudiated Transformational Grammar, which at the time was his towering intellectual achievement. That's like Wittgenstein repudiating the Tractatus; it's a mark of striking (and uncommon) intellectual honesty.
- CRConrad 4y ago> I think it's pretty unreasonable to call him a "hustler" [...] a mark of striking (and uncommon) intellectual honesty. No, given his towering intellectual dishonesty in political writings, he is a hustler. If his actual scientific work isn't a hustle, too bad for him: Having revealed his character in one arena, he's shot his reputation and disqualified himself from the benefit of the doubt in all arenas.
- denton-scratch 4y ago> his towering intellectual dishonesty in political writings Citation needed. You can disagree with his political opinions; he takes positions that are controversial. That's not the same as intellectual dishonesty or hustling.
- CRConrad 4y ago> Citation needed. https://youtu.be/VCcX_xTLDIY https://youtu.be/VCcX_xTLDIY
- scythe 4y agoChomsky's argument is not terribly long, nor is it difficult to understand, nor is it nearly as harsh as some people (Aaronson and Norvig, possibly among others) seem to be taking it, nor does he spend much oxygen talking about his own work or preferred approaches (although Aaronson and Norvig seem interested on emphasizing that he mentioned it at all). It is probably worth just reading it: http://languagelog.ldc.upenn.edu/myl/PinkerChomskyMIT.html http://languagelog.ldc.upenn.edu/myl/PinkerChomskyMIT.html Really this is the core of what Chomsky said: >There is a succ- notion of success which has developed in uh computational cognitive science in recent years which I think is novel in the history of science. It interprets success as uh approximating unanalyzed data. Uh so for example if your were say to study bee communication this way, instead of doing the complex experiments that bee scientists do, you know like uh having fly to an island to see if they leave an odor trail and this sort of thing, if you simply did extensive videotaping of bees swarming, OK, and you did you know a lot of statistical analysis of it, uh you would get a pretty good prediction for what bees are likely to do next time they swarm, actually you'd get a better prediction than bee scientists do, and they wouldn't care because they're not trying to do that. Uh but and you can make it a better and better approximation by more video tapes and more statistics and so on. Uh I mean actually you could do physics this way, uh instead of studying things like balls rolling down frictionless planes, which can't happen in nature, uh if you uh uh took a ton of video tapes of what's happening outside my office window, let's say, you know, leaves flying and various things, and you did an extensive analysis of them, uh you would get some kind of prediction of what's likely to happen next, certainly way better than anybody in the physics department could do. Well that's a notion of success which is I think novel, I don't know of anything like it in the history of science. Now this contrasts powerfully with Norvig's "Galileo" picture. Copernicus and Galileo were excellent mathematicians by contemporary standards. They had data, models, predictions, and criteria for the validity of those models. How do we measure the success of GPT-3? That is the key question Chomsky raises. Galileo's conviction, and his willingness to die defending the truth, was not based merely on the profound experience of looking through a telescope.
- AlbertCory 4y ago"Every time I fire a linguist, the speech recognition system gets more accurate!" (https://www.intefrankly.com/articles/Every-time-I-fire-a-linguist-the-speech-recognition-system-gets-more-accurate/0992487dd573 https://www.intefrankly.com/articles/Every-time-I-fire-a-lin...) The fact is, linguists failed. They might have beautiful theories, but the theories have no utilitarian value (unlike the laws of gravity).
- thomasahle 4y ago> the theories have no utilitarian value If you've ever used a compiler, you've benefitted from "linguist theories" like regular or context free grammars.
- mtlmtlmtlmtl 4y agoPeople who dismiss an entire field of science as useless invariably know little to nothing about the field, I've found. Whenever I feel like doing that I try to go read some on the field instead. Usually I find applications I didn't know about.
- AlbertCory 4y agotell us about all these applications we didn't know about! You know, the ones that'll translate 40+ human languages into (vaguely) readable English or vice versa. Give us the URL.
- mtlmtlmtlmtl 4y agoDo you actually think Linguistics is only about language translation?
- AlbertCory 4y ago> Do you actually think... No, I don't, but a working translation is proof that the theory does understand human language as it's actually used. Or in this case, that it doesn't.
- foobarqux 4y agoI wish there would be a debate at a major venue between Chomsky and the best proponent of LLMs --- over text or video --- but it's not clear who that opponent would even be. There really isn't a towering pro-LLM intellectual figure, one who can also talk a bit about philosophy, cognitive science or linguistics.