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Javier E

Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden Katz - The Atlantic - 1 views

www.theatlantic.com/...261637

chomsky science AI biology research brain science psychology

shared by Javier E on 27 Jun 13 - No Cached
  • Skinner's approach stressed the historical associations between a stimulus and the animal's response -- an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past.
  • Chomsky's conception of language, on the other hand, stressed the complexity of internal representations, encoded in the genome, and their maturation in light of the right data into a sophisticated computational system, one that cannot be usefully broken down into a set of associations.
  • Chomsky acknowledged that the statistical approach might have practical value, just as in the example of a useful search engine, and is enabled by the advent of fast computers capable of processing massive data. But as far as a science goes, Chomsky would argue it is inadequate, or more harshly, kind of shallow
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  • David Marr, a neuroscientist colleague of Chomsky's at MIT, defined a general framework for studying complex biological systems (like the brain) in his influential book Vision,
  • a complex biological system can be understood at three distinct levels. The first level ("computational level") describes the input and output to the system, which define the task the system is performing. In the case of the visual system, the input might be the image projected on our retina and the output might our brain's identification of the objects present in the image we had observed. The second level ("algorithmic level") describes the procedure by which an input is converted to an output, i.e. how the image on our retina can be processed to achieve the task described by the computational level. Finally, the third level ("implementation level") describes how our own biological hardware of cells implements the procedure described by the algorithmic level.
  • The emphasis here is on the internal structure of the system that enables it to perform a task, rather than on external association between past behavior of the system and the environment. The goal is to dig into the "black box" that drives the system and describe its inner workings, much like how a computer scientist would explain how a cleverly designed piece of software works and how it can be executed on a desktop computer.
  • As written today, the history of cognitive science is a story of the unequivocal triumph of an essentially Chomskyian approach over Skinner's behaviorist paradigm -- an achievement commonly referred to as the "cognitive revolution,"
  • While this may be a relatively accurate depiction in cognitive science and psychology, behaviorist thinking is far from dead in related disciplines. Behaviorist experimental paradigms and associationist explanations for animal behavior are used routinely by neuroscientists
  • Chomsky critiqued the field of AI for adopting an approach reminiscent of behaviorism, except in more modern, computationally sophisticated form. Chomsky argued that the field's heavy use of statistical techniques to pick regularities in masses of data is unlikely to yield the explanatory insight that science ought to offer. For Chomsky, the "new AI" -- focused on using statistical learning techniques to better mine and predict data -- is unlikely to yield general principles about the nature of intelligent beings or about cognition.
  • Behaviorist principles of associations could not explain the richness of linguistic knowledge, our endlessly creative use of it, or how quickly children acquire it with only minimal and imperfect exposure to language presented by their environment.
  • it has been argued in my view rather plausibly, though neuroscientists don't like it -- that neuroscience for the last couple hundred years has been on the wrong track.
  • Implicit in this endeavor is the assumption that with enough sophisticated statistical tools and a large enough collection of data, signals of interest can be weeded it out from the noise in large and poorly understood biological systems.
  • Brenner, a contemporary of Chomsky who also participated in the same symposium on AI, was equally skeptical about new systems approaches to understanding the brain. When describing an up-and-coming systems approach to mapping brain circuits called Connectomics, which seeks to map the wiring of all neurons in the brain (i.e. diagramming which nerve cells are connected to others), Brenner called it a "form of insanity."
  • These debates raise an old and general question in the philosophy of science: What makes a satisfying scientific theory or explanation, and how ought success be defined for science?
  • Ever since Isaiah Berlin's famous essay, it has become a favorite pastime of academics to place various thinkers and scientists on the "Hedgehog-Fox" continuum: the Hedgehog, a meticulous and specialized worker, driven by incremental progress in a clearly defined field versus the Fox, a flashier, ideas-driven thinker who jumps from question to question, ignoring field boundaries and applying his or her skills where they seem applicable.
  • Chomsky's work has had tremendous influence on a variety of fields outside his own, including computer science and philosophy, and he has not shied away from discussing and critiquing the influence of these ideas, making him a particularly interesting person to interview.
  • If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.
  • If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.
  • An unlikely pair, systems biology and artificial intelligence both face the same fundamental task of reverse-engineering a highly complex system whose inner workings are largely a mystery
  • neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.
Javier E

Scholarship and Politics - The Case of Noam Chomsky - NYTimes.com - 0 views

www.nytimes.com/...-the-case-of-noam-chomsky.html

scholarship politics chomsky language understanding epistemic political theory academic academy democracy

shared by Javier E on 10 Dec 13 - No Cached
  • (1) The academy is a world of its own, complete with rules, protocols, systems of evaluation, recognized achievements, agreed-on goals, a roster of heroes and a list of tasks yet to be done.
  • (2) Academic work proceeds within the confines of that world, within, that is, a professional, not a public, space, although its performance may be, and often is, public.
  • (3) academic work is only tangentially, not essentially, political; politics may attend the formation of academic units and the selection of academic personnel, but political concerns and pressures have no place in the unfolding of academic argument, except as objects of its distinctive forms of attention
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  • (4) The academic views of a professor are independent of his or her real-world political views; academic disputes don’t track partisan disputes or vice versa; you can’t reason from an academic’s disciplinary views to the positions he or she would take in the public sphere; they are independent variables.
  • The answer given in the first lecture — “What is Language?” — is that we are creatures with language, and that language as a uniquely human biological capacity appeared suddenly and quite late in the evolutionary story, perhaps 75,000 years ago.
  • Chomsky gave three lectures under the general title “What Kind of Creatures are We?”
  • Language, then, does not arise from the social/cultural environment, although the environment provides the stuff or input it works on. That input is “impoverished”; it can’t account for the creativity of language performance, which has its source not in the empirical world, but in an innate ability that is more powerful than the stimuli it utilizes and plays with. It follows that if you want to understand language, you shouldn’t look to linguistic behavior but to the internal mechanism — the Universal Grammar — of which particular linguistic behaviors are a non-exhaustive expression. (The capacity exceeds the empirical resources it might deploy.)
  • In his second lecture (“What Can We Understand?”), Chomsky took up the question of what humans are capable of understanding and his answer, generally, was that we can understand what we can understand, and that means that we can’t understand what is beyond our innate mental capacities
  • This does not mean, he said, that what we can’t understand is not real: “What is mysterious to me is not an argument that it does not exist.” It’s just that while language is powerful and creative, its power and creativity have limits; and since language is thought rather than an addition to or clothing of thought, the limits of language are the limits of what we can fruitfully think about
  • This is as good as it gets. There is “no evolution in our capacity for language.”
  • These assertions are offered as a counter to what Chomsky sees as the over-optimistic Enlightenment belief — common to many empiricist philosophies — that ours is a “limitless explanatory power” and that “we can do anything.”
  • In the third lecture (“What is the Common Good?”) Chomsky turned from the philosophy of mind and language to political philosophy and the question of what constitutes a truly democratic society
  • He likened dogmatic intellectual structures that interfere with free inquiry to coercive political structures that stifle the individual’s creative independence and fail to encourage humanity’s “richest diversity
  • He asserted that any institution marked by domination and hierarchy must rise to the challenge of justifying itself, and if it cannot meet the challenge, it should be dismantled.
  • He contrasted two accounts of democracy: one — associated by him with James Madison — distrusts the “unwashed” populace and puts its faith in representative government where those doing the representing (and the voting and the distributing of goods) constitute a moneyed and propertied elite
  • the other — associated by him with Adam Smith (in one of his moods), J. S. Mill, the 1960s and a tradition of anarchist writing — seeks to expand the franchise and multiply choices in the realms of thought, politics and economics. The impulse of this second, libertarian, strain of democracy, is “to free society from economic or theological guardianship,” and by “theological” Chomsky meant not formal religion as such but any assumed and frozen ideology that blocked inquiry and limited participation. There can’t, in short, be “too much democracy.”
  • It was thought of the highest order performed by a thinker, now 85 years old, who by and large eschewed rhetorical flourishes (he has called his own speaking style “boring” and says he likes it that way) and just did it, where ‘it” was the patient exploration of deep issues that had been explored before him by a succession of predecessors, fully acknowledged, in a conversation that is forever being continued and forever being replenished.
  • Yes, I said to myself, this is what we — those of us who bought a ticket on this particular train — do; we think about problems and puzzles and try to advance the understanding of them; and we do that kind of thinking because its pleasures are, in a strong sense, athletic and provide for us, at least on occasion, the experience of fully realizing whatever capabilities we might have. And we do it in order to have that experience, and to share it with colleagues and students of like mind, and not to make a moral or political point.
  • The term “master class” is a bit overused, but I feel no hesitation in using it here. It was a master class taught by a master, and if someone were to ask me what exactly is it that academics do, I would point to these lectures and say, simply, here it is, the thing itself.
silveiragu

Noam Chomsky Calls Postmodern Critiques of Science Over-Inflated "Polysyllabic Truisms"... - 0 views

www.openculture.com/...ated-polysyllabic-truisms.html

chomsky science

shared by silveiragu on 07 Jun 16 - No Cached
  • we recently featured an interview in which Noam Chomsky slams postmodernist intellectuals like Slavoj Zizek and Jacques Lacan as “charlatans” and posers.
  • The turn against postmodernism has been long in coming,
  • Chomsky characterizes leftist postmodern academics as “a category of intellectuals who are undoubtedly perfectly sincere”
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  • in his critique, such thinkers use “polysyllabic words and complicated constructions” to make claims that are “all very inflated” and which have “a terrible effect on the third world.
  • It’s considered very left wing, very advanced. Some of what appears in it sort of actually makes sense, but when you reproduce it in monosyllables, it turns out to be truisms. It’s perfectly true that when you look at scientists in the West, they’re mostly men, it’s perfectly true that women have had a hard time breaking into the scientific fields, and it’s perfectly true that there are institutional factors determining how science proceeds that reflect power structures.
  • you don’t get to be a respected intellectual by presenting truisms in monosyllables.
  • Chomsky’s cranky contrarianism is nothing new, and some of his polemic recalls the analytic case against “continental” philosophy or Karl Popper’s case against pseudo-science, although his investment is political as much as philosophical.
  • silveiragu on 07 Jun 16
     
    An interesting synopsis and analysis, linked to a relatively short interview with a great thinker.
Javier E

Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden Katz - The Atlantic - 0 views

www.theatlantic.com/...261637

chomsky ai language brain science computer research methods science history

shared by Javier E on 30 Jun 13 - No Cached
  • If you take a look at the progress of science, the sciences are kind of a continuum, but they're broken up into fields. The greatest progress is in the sciences that study the simplest systems. So take, say physics -- greatest progress there. But one of the reasons is that the physicists have an advantage that no other branch of sciences has. If something gets too complicated, they hand it to someone else.
  • If a molecule is too big, you give it to the chemists. The chemists, for them, if the molecule is too big or the system gets too big, you give it to the biologists. And if it gets too big for them, they give it to the psychologists, and finally it ends up in the hands of the literary critic, and so on.
  • neuroscience for the last couple hundred years has been on the wrong track. There's a fairly recent book by a very good cognitive neuroscientist, Randy Gallistel and King, arguing -- in my view, plausibly -- that neuroscience developed kind of enthralled to associationism and related views of the way humans and animals work. And as a result they've been looking for things that have the properties of associationist psychology.
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  • in general what he argues is that if you take a look at animal cognition, human too, it's computational systems. Therefore, you want to look the units of computation. Think about a Turing machine, say, which is the simplest form of computation, you have to find units that have properties like "read", "write" and "address." That's the minimal computational unit, so you got to look in the brain for those. You're never going to find them if you look for strengthening of synaptic connections or field properties, and so on. You've got to start by looking for what's there and what's working and you see that from Marr's highest level.
  • it's basically in the spirit of Marr's analysis. So when you're studying vision, he argues, you first ask what kind of computational tasks is the visual system carrying out. And then you look for an algorithm that might carry out those computations and finally you search for mechanisms of the kind that would make the algorithm work. Otherwise, you may never find anything.
  • "Good Old Fashioned AI," as it's labeled now, made strong use of formalisms in the tradition of Gottlob Frege and Bertrand Russell, mathematical logic for example, or derivatives of it, like nonmonotonic reasoning and so on. It's interesting from a history of science perspective that even very recently, these approaches have been almost wiped out from the mainstream and have been largely replaced -- in the field that calls itself AI now -- by probabilistic and statistical models. My question is, what do you think explains that shift and is it a step in the right direction?
  • AI and robotics got to the point where you could actually do things that were useful, so it turned to the practical applications and somewhat, maybe not abandoned, but put to the side, the more fundamental scientific questions, just caught up in the success of the technology and achieving specific goals.
  • The approximating unanalyzed data kind is sort of a new approach, not totally, there's things like it in the past. It's basically a new approach that has been accelerated by the existence of massive memories, very rapid processing, which enables you to do things like this that you couldn't have done by hand. But I think, myself, that it is leading subjects like computational cognitive science into a direction of maybe some practical applicability... ..in engineering? Chomsky: ...But away from understanding.
  • I was very skeptical about the original work. I thought it was first of all way too optimistic, it was assuming you could achieve things that required real understanding of systems that were barely understood, and you just can't get to that understanding by throwing a complicated machine at it.
  • if success is defined as getting a fair approximation to a mass of chaotic unanalyzed data, then it's way better to do it this way than to do it the way the physicists do, you know, no thought experiments about frictionless planes and so on and so forth. But you won't get the kind of understanding that the sciences have always been aimed at -- what you'll get at is an approximation to what's happening.
  • Suppose you want to predict tomorrow's weather. One way to do it is okay I'll get my statistical priors, if you like, there's a high probability that tomorrow's weather here will be the same as it was yesterday in Cleveland, so I'll stick that in, and where the sun is will have some effect, so I'll stick that in, and you get a bunch of assumptions like that, you run the experiment, you look at it over and over again, you correct it by Bayesian methods, you get better priors. You get a pretty good approximation of what tomorrow's weather is going to be. That's not what meteorologists do -- they want to understand how it's working. And these are just two different concepts of what success means, of what achievement is.
  • if you get more and more data, and better and better statistics, you can get a better and better approximation to some immense corpus of text, like everything in The Wall Street Journal archives -- but you learn nothing about the language.
  • the right approach, is to try to see if you can understand what the fundamental principles are that deal with the core properties, and recognize that in the actual usage, there's going to be a thousand other variables intervening -- kind of like what's happening outside the window, and you'll sort of tack those on later on if you want better approximations, that's a different approach.
  • take a concrete example of a new field in neuroscience, called Connectomics, where the goal is to find the wiring diagram of very complex organisms, find the connectivity of all the neurons in say human cerebral cortex, or mouse cortex. This approach was criticized by Sidney Brenner, who in many ways is [historically] one of the originators of the approach. Advocates of this field don't stop to ask if the wiring diagram is the right level of abstraction -- maybe it's no
  • if you went to MIT in the 1960s, or now, it's completely different. No matter what engineering field you're in, you learn the same basic science and mathematics. And then maybe you learn a little bit about how to apply it. But that's a very different approach. And it resulted maybe from the fact that really for the first time in history, the basic sciences, like physics, had something really to tell engineers. And besides, technologies began to change very fast, so not very much point in learning the technologies of today if it's going to be different 10 years from now. So you have to learn the fundamental science that's going to be applicable to whatever comes along next. And the same thing pretty much happened in medicine.
  • that's the kind of transition from something like an art, that you learn how to practice -- an analog would be trying to match some data that you don't understand, in some fashion, maybe building something that will work -- to science, what happened in the modern period, roughly Galilean science.
  • it turns out that there actually are neural circuits which are reacting to particular kinds of rhythm, which happen to show up in language, like syllable length and so on. And there's some evidence that that's one of the first things that the infant brain is seeking -- rhythmic structures. And going back to Gallistel and Marr, its got some computational system inside which is saying "okay, here's what I do with these things" and say, by nine months, the typical infant has rejected -- eliminated from its repertoire -- the phonetic distinctions that aren't used in its own language.
  • people like Shimon Ullman discovered some pretty remarkable things like the rigidity principle. You're not going to find that by statistical analysis of data. But he did find it by carefully designed experiments. Then you look for the neurophysiology, and see if you can find something there that carries out these computations. I think it's the same in language, the same in studying our arithmetical capacity, planning, almost anything you look at. Just trying to deal with the unanalyzed chaotic data is unlikely to get you anywhere, just like as it wouldn't have gotten Galileo anywhere.
  • with regard to cognitive science, we're kind of pre-Galilean, just beginning to open up the subject
  • You can invent a world -- I don't think it's our world -- but you can invent a world in which nothing happens except random changes in objects and selection on the basis of external forces. I don't think that's the way our world works, I don't think it's the way any biologist thinks it is. There are all kind of ways in which natural law imposes channels within which selection can take place, and some things can happen and other things don't happen. Plenty of things that go on in the biology in organisms aren't like this. So take the first step, meiosis. Why do cells split into spheres and not cubes? It's not random mutation and natural selection; it's a law of physics. There's no reason to think that laws of physics stop there, they work all the way through. Well, they constrain the biology, sure. Chomsky: Okay, well then it's not just random mutation and selection. It's random mutation, selection, and everything that matters, like laws of physics.
  • What I think is valuable is the history of science. I think we learn a lot of things from the history of science that can be very valuable to the emerging sciences. Particularly when we realize that in say, the emerging cognitive sciences, we really are in a kind of pre-Galilean stage. We don't know wh
  • at we're looking for anymore than Galileo did, and there's a lot to learn from that.
Javier E

Psychological nativism - Wikipedia - 0 views

en.wikipedia.org/...Psychological_nativism

psychological nativism psychology

shared by Javier E on 20 Feb 23 - No Cached
  • In the field of psychology, nativism is the view that certain skills or abilities are "native" or hard-wired into the brain at birth. This is in contrast to the "blank slate" or tabula rasa view, which states that the brain has inborn capabilities for learning from the environment but does not contain content such as innate beliefs.
  • Some nativists believe that specific beliefs or preferences are "hard-wired". For example, one might argue that some moral intuitions are innate or that color preferences are innate. A less established argument is that nature supplies the human mind with specialized learning devices. This latter view differs from empiricism only to the extent that the algorithms that translate experience into information may be more complex and specialized in nativist theories than in empiricist theories. However, empiricists largely remain open to the nature of learning algorithms and are by no means restricted to the historical associationist mechanisms of behaviorism.
  • Nativism has a history in philosophy, particularly as a reaction to the straightforward empiricist views of John Locke and David Hume. Hume had given persuasive logical arguments that people cannot infer causality from perceptual input. The most one could hope to infer is that two events happen in succession or simultaneously. One response to this argument involves positing that concepts not supplied by experience, such as causality, must exist prior to any experience and hence must be innate.
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  • The philosopher Immanuel Kant (1724–1804) argued in his Critique of Pure Reason that the human mind knows objects in innate, a priori ways. Kant claimed that humans, from birth, must experience all objects as being successive (time) and juxtaposed (space). His list of inborn categories describes predicates that the mind can attribute to any object in general. Arthur Schopenhauer (1788–1860) agreed with Kant, but reduced the number of innate categories to one—causality—which presupposes the others.
  • Modern nativism is most associated with the work of Jerry Fodor (1935–2017), Noam Chomsky (b. 1928), and Steven Pinker (b. 1954), who argue that humans from birth have certain cognitive modules (specialised genetically inherited psychological abilities) that allow them to learn and acquire certain skills, such as language.
  • For example, children demonstrate a facility for acquiring spoken language but require intensive training to learn to read and write. This poverty of the stimulus observation became a principal component of Chomsky's argument for a "language organ"—a genetically inherited neurological module that confers a somewhat universal understanding of syntax that all neurologically healthy humans are born with, which is fine-tuned by an individual's experience with their native language
  • In The Blank Slate (2002), Pinker similarly cites the linguistic capabilities of children, relative to the amount of direct instruction they receive, as evidence that humans have an inborn facility for speech acquisition (but not for literacy acquisition).
  • A number of other theorists[1][2][3] have disagreed with these claims. Instead, they have outlined alternative theories of how modularization might emerge over the course of development, as a result of a system gradually refining and fine-tuning its responses to environmental stimuli.[4]
  • Many empiricists are now also trying to apply modern learning models and techniques to the question of language acquisition, with marked success.[20] Similarity-based generalization marks another avenue of recent research, which suggests that children may be able to rapidly learn how to use new words by generalizing about the usage of similar words that they already know (see also the distributional hypothesis).[14][21][22][23]
  • The term universal grammar (or UG) is used for the purported innate biological properties of the human brain, whatever exactly they turn out to be, that are responsible for children's successful acquisition of a native language during the first few years of life. The person most strongly associated with the hypothesising of UG is Noam Chomsky, although the idea of Universal Grammar has clear historical antecedents at least as far back as the 1300s, in the form of the Speculative Grammar of Thomas of Erfurt.
  • This evidence is all the more impressive when one considers that most children do not receive reliable corrections for grammatical errors.[9] Indeed, even children who for medical reasons cannot produce speech, and therefore have no possibility of producing an error in the first place, have been found to master both the lexicon and the grammar of their community's language perfectly.[10] The fact that children succeed at language acquisition even when their linguistic input is severely impoverished, as it is when no corrective feedback is available, is related to the argument from the poverty of the stimulus, and is another claim for a central role of UG in child language acquisition.
  • Researchers at Blue Brain discovered a network of about fifty neurons which they believed were building blocks of more complex knowledge but contained basic innate knowledge that could be combined in different more complex ways to give way to acquired knowledge, like memory.[11
  • experience, the tests would bring about very different characteristics for each rat. However, the rats all displayed similar characteristics which suggest that their neuronal circuits must have been established previously to their experiences. The Blue Brain Project research suggests that some of the "building blocks" of knowledge are genetic and present at birth.[11]
  • modern nativist theory makes little in the way of specific falsifiable and testable predictions, and has been compared by some empiricists to a pseudoscience or nefarious brand of "psychological creationism". As influential psychologist Henry L. Roediger III remarked that "Chomsky was and is a rationalist; he had no uses for experimental analyses or data of any sort that pertained to language, and even experimental psycholinguistics was and is of little interest to him".[13]
  • , Chomsky's poverty of the stimulus argument is controversial within linguistics.[14][15][16][17][18][19]
  • Neither the five-year-old nor the adults in the community can easily articulate the principles of the grammar they are following. Experimental evidence shows that infants come equipped with presuppositions that allow them to acquire the rules of their language.[6]
  • Paul Griffiths, in "What is Innateness?", argues that innateness is too confusing a concept to be fruitfully employed as it confuses "empirically dissociated" concepts. In a previous paper, Griffiths argued that innateness specifically confuses these three distinct biological concepts: developmental fixity, species nature, and intended outcome. Developmental fixity refers to how insensitive a trait is to environmental input, species nature reflects what it is to be an organism of a certain kind, and the intended outcome is how an organism is meant to develop.[24]
Emily Freilich

Is the Man Who Is Tall Happy? - Movie Trailers - iTunes - 0 views

trailers.apple.com/...isthemanwhoistallhappy

knowledge

shared by Emily Freilich on 29 Dec 13 - No Cached
  • Emily Freilich on 29 Dec 13
     
    Trailer for a movie of a collection of interviews with linguist Noam Chomsky. Some thought provoking questions are presented in the trailer. 
Javier E

Opinion | Noam Chomsky: The False Promise of ChatGPT - The New York Times - 0 views

www.nytimes.com/...noam-chomsky-chatgpt-ai.html

chatbot AI abilities moral reasoning response limits

shared by Javier E on 08 Mar 23 - No Cached
  • we fear that the most popular and fashionable strain of A.I. — machine learning — will degrade our science and debase our ethics by incorporating into our technology a fundamentally flawed conception of language and knowledge.
  • OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney are marvels of machine learning. Roughly speaking, they take huge amounts of data, search for patterns in it and become increasingly proficient at generating statistically probable outputs — such as seemingly humanlike language and thought
  • if machine learning programs like ChatGPT continue to dominate the field of A.I
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  • , we know from the science of linguistics and the philosophy of knowledge that they differ profoundly from how humans reason and use language. These differences place significant limitations on what these programs can do, encoding them with ineradicable defects.
  • It is at once comic and tragic, as Borges might have noted, that so much money and attention should be concentrated on so little a thing — something so trivial when contrasted with the human mind, which by dint of language, in the words of Wilhelm von Humboldt, can make “infinite use of finite means,” creating ideas and theories with universal reach.
  • The human mind is not, like ChatGPT and its ilk, a lumbering statistical engine for pattern matching, gorging on hundreds of terabytes of data and extrapolating the most likely conversational response or most probable answer to a scientific question
  • the human mind is a surprisingly efficient and even elegant system that operates with small amounts of information; it seeks not to infer brute correlations among data points but to create explanations
  • such programs are stuck in a prehuman or nonhuman phase of cognitive evolution. Their deepest flaw is the absence of the most critical capacity of any intelligence: to say not only what is the case, what was the case and what will be the case — that’s description and prediction — but also what is not the case and what could and could not be the case
  • Those are the ingredients of explanation, the mark of true intelligence.
  • Here’s an example. Suppose you are holding an apple in your hand. Now you let the apple go. You observe the result and say, “The apple falls.” That is a description. A prediction might have been the statement “The apple will fall if I open my hand.”
  • an explanation is something more: It includes not only descriptions and predictions but also counterfactual conjectures like “Any such object would fall,” plus the additional clause “because of the force of gravity” or “because of the curvature of space-time” or whatever. That is a causal explanation: “The apple would not have fallen but for the force of gravity.” That is thinking.
  • The crux of machine learning is description and prediction; it does not posit any causal mechanisms or physical laws
  • any human-style explanation is not necessarily correct; we are fallible. But this is part of what it means to think: To be right, it must be possible to be wrong. Intelligence consists not only of creative conjectures but also of creative criticism. Human-style thought is based on possible explanations and error correction, a process that gradually limits what possibilities can be rationally considered.
  • ChatGPT and similar programs are, by design, unlimited in what they can “learn” (which is to say, memorize); they are incapable of distinguishing the possible from the impossible.
  • Whereas humans are limited in the kinds of explanations we can rationally conjecture, machine learning systems can learn both that the earth is flat and that the earth is round. They trade merely in probabilities that change over time.
  • For this reason, the predictions of machine learning systems will always be superficial and dubious.
  • some machine learning enthusiasts seem to be proud that their creations can generate correct “scientific” predictions (say, about the motion of physical bodies) without making use of explanations (involving, say, Newton’s laws of motion and universal gravitation). But this kind of prediction, even when successful, is pseudoscienc
  • While scientists certainly seek theories that have a high degree of empirical corroboration, as the philosopher Karl Popper noted, “we do not seek highly probable theories but explanations; that is to say, powerful and highly improbable theories.”
  • The theory that apples fall to earth because mass bends space-time (Einstein’s view) is highly improbable, but it actually tells you why they fall. True intelligence is demonstrated in the ability to think and express improbable but insightful things.
  • This means constraining the otherwise limitless creativity of our minds with a set of ethical principles that determines what ought and ought not to be (and of course subjecting those principles themselves to creative criticism)
  • True intelligence is also capable of moral thinking
  • To be useful, ChatGPT must be empowered to generate novel-looking output; to be acceptable to most of its users, it must steer clear of morally objectionable content
  • In 2016, for example, Microsoft’s Tay chatbot (a precursor to ChatGPT) flooded the internet with misogynistic and racist content, having been polluted by online trolls who filled it with offensive training data. How to solve the problem in the future? In the absence of a capacity to reason from moral principles, ChatGPT was crudely restricted by its programmers from contributing anything novel to controversial — that is, important — discussions. It sacrificed creativity for a kind of amorality.
  • Here, ChatGPT exhibits something like the banality of evil: plagiarism and apathy and obviation. It summarizes the standard arguments in the literature by a kind of super-autocomplete, refuses to take a stand on anything, pleads not merely ignorance but lack of intelligence and ultimately offers a “just following orders” defense, shifting responsibility to its creators.
  • In short, ChatGPT and its brethren are constitutionally unable to balance creativity with constraint. They either overgenerate (producing both truths and falsehoods, endorsing ethical and unethical decisions alike) or undergenerate (exhibiting noncommitment to any decisions and indifference to consequences). Given the amorality, faux science and linguistic incompetence of these systems, we can only laugh or cry at their popularity.
Javier E

Googling Is Believing: Trumping the Informed Citizen - The New York Times - 1 views

opinionator.blogs.nytimes.com/...-trumping-the-informed-citizen

psychology social media facts sources informed citizenry bias google democ

shared by Javier E on 09 Mar 16 - No Cached
  • Rubio’s Google gambit and Trump’s (non)reaction to it, reveals an interesting, and troubling, new change in attitude about a philosophical foundation of democracy: the ideal of an informed citizenry.
  • The idea is obvious: If citizens are going to make even indirect decisions about policy, we need to know the facts about the problem the policy is meant to rectify, and to be able to gain some understanding about how effective that policy would be.
  • Noam Chomsky argued in the 1980s that consent was being “manufactured” by Big Media — large consolidated content-delivery companies (like this newspaper) that could cause opinions to sway one way or the other at their whim.
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  • searching the Internet can get you to information that would back up almost any claim of fact, no matter how unfounded. It is both the world’s best fact-checker and the world’s best bias confirmer — often at the same time.
  • Nor is it a coincidence that people are increasingly following the election on social media, using it both as the source of their information and as the way to get their view out. Consent is still being manufactured, but the manufacturing is being done willingly by us, usually intended for consumption by other people with whom we already agree, facts or no facts.
  • It really isn’t a surprise that Rubio would ask us to Google for certain facts; that’s how you and I know almost everything we know nowadays — it is a way of knowing that is so embedded into the very fabric of our lives that we don’t even notice it
  • The problem of course is that having more information available, even more accurate information, isn’t what is required by the ideal.
  • What is required is that people actually know and understand that information, and there are reasons to think we are no closer to an informed citizenry understood in that way than we ever have been. Indeed, we might be further away.
  • The worry is no longer about who controls content. It is about who controls the flow of that content.
  • the flow of digital information is just as prone to manipulation as its content
  • No wonder Trump and his followers on Twitter immediately shrugged off Rubio’s inconvenient truths; there is nothing to fear from information when counterinformation is just as plentiful.
  • The real worry concerns our faith in the ideal of an informed citizenry itself. That worry, as I see it, has two faces.
  • First, as Jason Stanley and others have emphasized recently, appeals to ideals can be used to undermine those very ideals.
  • The very availability of information can make us think that the ideal of the informed citizen is more realized than it is — and that, in turn, can actually undermine the ideal, making us less informed, simply because we think we know all we need to know already.
  • Second, the danger is that increasing recognition of the fact that Googling can get you wherever you want to go can make us deeply cynical about the ideal of an informed citizenry — for the simple reason that what counts as an “informed” citizen is a matter of dispute. We no longer disagree just over values. Nor do we disagree just over the facts. We disagree over whose source — whose fountain of facts — is the right one.
  • And once disagreement reaches that far down, the daylight of reason seems very far away indeed.
Javier E

The advantage of ambiguity | MIT News - 1 views

news.mit.edu/...ambiguity-in-language-0119

language ambiguity meaning wittgenstein

shared by Javier E on 29 Nov 17 - No Cached
  • Why did language evolve? While the answer might seem obvious — as a way for individuals to exchange information — linguists and other students of communication have debated this question for years. Many prominent linguists, including MIT’s Noam Chomsky, have argued that language is, in fact, poorly designed for communication. Such a use, they say, is merely a byproduct of a system that probably evolved for other reasons — perhaps for structuring our own private thoughts.
  • In a new theory, they claim that ambiguity actually makes language more efficient, by allowing for the reuse of short, efficient sounds that listeners can easily disambiguate with the help of context.
  • “Various people have said that ambiguity is a problem for communication,” says Ted Gibson, an MIT professor of cognitive science and senior author of a paper describing the research to appear in the journal Cognition. "But the fact that context disambiguates has important ramifications for the re-use of potentially ambiguous forms. Ambiguity is no longer a problem — it's something that you can take advantage of, because you can reuse easy [words] in different contexts over and over again."
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  • virtually no speaker of English gets confused when he or she hears the word “mean.” That’s because the different senses of the word occur in such different contexts as to allow listeners to infer its meaning nearly automatically.
  • To understand why ambiguity makes a language more efficient rather than less so, think about the competing desires of the speaker and the listener. The speaker is interested in conveying as much as possible with the fewest possible words, while the listener is aiming to get a complete and specific understanding of what the speaker is trying to say.
  • it is “cognitively cheaper” to have the listener infer certain things from the context than to have the speaker spend time on longer and more complicated utterances. The result is a system that skews toward ambiguity, reusing the “easiest” words. Once context is considered, it’s clear that “ambiguity is actually something you would want in the communication system,” Piantadosi says.
  • “You would expect that since languages are constantly changing, they would evolve to get rid of ambiguity,” Wasow says. “But if you look at natural languages, they are massively ambiguous: Words have multiple meanings, there are multiple ways to parse strings of words. … This paper presents a really rigorous argument as to why that kind of ambiguity is actually functional for communicative purposes, rather than dysfunctional.”
  • “Ambiguity is only good for us [as humans] because we have these really sophisticated cognitive mechanisms for disambiguating,” he says. “It’s really difficult to work out the details of what those are, or even some sort of approximation that you could get a computer to use.”
caelengrubb

Does Language Influence Culture? - WSJ - 0 views

www.wsj.com/...748703467304575383131592767868

shared by caelengrubb on 07 Jun 21 - No Cached
  • These questions touch on all the major controversies in the study of mind, with important implications for politics, law and religion.
  • The idea that language might shape thought was for a long time considered untestable at best and more often simply crazy and wrong. Now, a flurry of new cognitive science research is showing that in fact, language does profoundly influence how we see the world.
  • Dr. Chomsky proposed that there is a universal grammar for all human languages—essentially, that languages don't really differ from one another in significant ways. And because languages didn't differ from one another, the theory went, it made no sense to ask whether linguistic differences led to differences in thinking.
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  • The search for linguistic universals yielded interesting data on languages, but after decades of work, not a single proposed universal has withstood scrutiny. Instead, as linguists probed deeper into the world's languages (7,000 or so, only a fraction of them analyzed), innumerable unpredictable differences emerged.
  • In the past decade, cognitive scientists have begun to measure not just how people talk, but also how they think, asking whether our understanding of even such fundamental domains of experience as space, time and causality could be constructed by language.
  • About a third of the world's languages (spoken in all kinds of physical environments) rely on absolute directions for space.
  • As a result of this constant linguistic training, speakers of such languages are remarkably good at staying oriented and keeping track of where they are, even in unfamiliar landscapes.
  • People rely on their spatial knowledge to build many other more complex or abstract representations including time, number, musical pitch, kinship relations, morality and emotions.
  • And many other ways to organize time exist in the world's languages. In Mandarin, the future can be below and the past above. In Aymara, spoken in South America, the future is behind and the past in front.
  • Beyond space, time and causality, patterns in language have been shown to shape many other domains of thought. Russian speakers, who make an extra distinction between light and dark blues in their language, are better able to visually discriminate shades of blue.
  • Patterns in language offer a window on a culture's dispositions and priorities.
  • Languages, of course, are human creations, tools we invent and hone to suit our needs
  • Simply showing that speakers of different languages think differently doesn't tell us whether it's language that shapes thought or the other way around. To demonstrate the causal role of language, what's needed are studies that directly manipulate language and look for effects in cognition.
Javier E

Technopoly-Chs. 9,10--Scientism, the great symbol drain - 0 views

scientism science social science narrative objectivity advertising morality authority coherence Postman book notes

shared by Javier E on 02 Oct 21 - No Cached
  • By Scientism, I mean three interrelated ideas that, taken together, stand as one of the pillars of Technopoly.
  • The first and indispensable idea is, as noted, that the methods of the natural sciences can be applied to the study of human behavior. This idea is the backbone of much of psychology and sociology as practiced at least in America, and largely accounts for the fact that social science, to quote F. A. Hayek, "has cont~ibuted scarcely anything to our understanding of social phenomena." 2
  • The second idea is, as also noted, that social science generates specific principles which can be used to organize society on a rational and humane basis. This implies that technical meansmostly "invisible technologies" supervised by experts-can be designed to control human behavior and set it on the proper course.
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  • The third idea is that faith in science can serve as a comprehensive belief system that gives meaning to life, as well. as a sense of well-being, morality, and even immortality.
  • the spirit behind this scientific ideal inspired several men to believe that the reliable and predictable knowledge that could be obtained about stars and atoms could also be obtained about human behavior.
  • Among the best known of these early "social scientists" were Claude-Henri de Saint-Simon, Prosper Enfantin, and, of course, Auguste Comte.
  • They held in common two beliefs to which T echnopoly is deeply indebted: that the natural sciences provide a method to unlock the secrets of both the human heart and the direction of social life; that society can be rationally and humanely reorganized according to principles that social science will uncover. It is with these men that the idea of "social engineering" begins and the seeds of Scientism are planted.
  • Information produced by counting may sometimes be valuable in helping a person get an idea, or, even more so, in providing support for an idea. But the mere activity of counting does not make science.
  • Nor does observing th_ings, though it is sometimes said that if one is empirical, one is scientific. To be empirical means to look at things before drawing conclusions. Everyone, therefore, is an empiricist, with the possible exception of paranoid schizophrenics.
  • What we may call science, then, is the quest to find the immutable and universal laws that govern processes, presuming that there are cause-and-effect relations among these processes. It follows that the quest to understand human behavior and feeling can in no sense except the most trivial be called science.
  • Scientists do strive to be empirical and where possible precise, but it is also basic to their enterprise that they maintain a high degree of objectivity, which means that they study things independently of what people think or do about them.
  • I do not say, incidentally, that the Oedipus complex and God do not exist. Nor do I say that to believe in them is harmful-far from it. I say only that, there being no tests that could, in principle, show them to be false, they fall outside the purview Scientism 151 of science, as do almost all theories that make up the content of "social science."
  • in the nineteenth centu~, novelists provided us with most of the powerful metaphors and images of our culture.
  • This fact relieves the scientist of inquiring into their values and motivations and for this reason alone separates science from what is called social science, consigning the methodology of the latter (to quote Gunnar Myrdal) to the status of the "metaphysical and pseudo-objective." 3
  • The status of social-science methods is further reduced by the fact that there are almost no experiments that will reveal a social-science theory to be false.
  • et us further suppose that Milgram had found that 100 percent of his 1 subjecl:s did what they were told, with or without Hannah Arendt. And now let us suppose that I tell you a story of a Scientism 153 group of people who in some real situation refused to comply with the orders of a legitimate authority-let us say, the Danes who in the face of Nazi occupation helped nine thousand Jews escape to Sweden. Would you say to me that this cannot be so because Milgram' s study proves otherwise? Or would you say that this overturns Milgram's work? Perhaps you would say that the Danish response is not relevant, since the Danes did not regard the Nazi occupation as constituting legitimate autho!ity. But then, how would we explain the cooperative response to Nazi authority of the French, the Poles, and the Lithuanians? I think you would say none of these things, because Milgram' s experiment qoes not confirm or falsify any theory that might be said to postulate a law of human nature. His study-which, incidentally, I find both fascinating and terrifying-is not science. It is something else entirely.
  • Freud, could not imagine how the book could be judged exemplary: it was science or it was nothing. Well, of course, Freud was wrong. His work is exemplary-indeed, monumental-but scarcely anyone believes today that Freud was doing science, any more than educated people believe that Marx was doing science, or Max Weber or Lewis Mumford or Bruno Bettelheim or Carl Jung or Margaret Mead or Arnold Toynbee. What these people were doing-and Stanley Milgram was doing-is documenting the behavior and feelings of people as they confront problems posed by their culture.
  • the stories of social r~searchers are much closer in structure and purpose to what is called imaginative literature; that is to say, both a social researcher and a novelist give unique interpretations to a set of human events and support their interpretations with examples in various forms. Their interpretations cannot be proved or disproved but will draw their appeal from the power of their language, the depth of their explanations, the relevance of their examples, and the credibility of their themes.
  • And all of this has, in both cases, an identifiable moral purpose.
  • The words "true" and "false" do not apply here in the sense that they are used in mathematics or science. For there is nothing universally and irrevocably true or false about these interpretations. There are no critical tests to confirm or falsify them. There are no natural laws from which they are derived. They are bound by time, by situation, and above all by the cultural prejudices of the researcher or writer.
  • Both the novelist and the social researcher construct their stories by the use of archetypes and metaphors.
  • Cervantes, for example, gave us the enduring archetype of the incurable dreamer and idealist in Don Quixote. The social historian Marx gave us the archetype of the ruthless and conspiring, though nameless, capitalist. Flaubert gave us the repressed b~urgeois romantic in Emma Bovary. And Margaret Mead gave us the carefree, guiltless Samoan adolescent. Kafka gave us the alienated urbanite driven to self-loathing. And Max Weber gave us hardworking men driven by a mythology he called the Protestant Ethic. Dostoevsky gave us the egomaniac redeemed by love and religious fervor. And B. F. Skinner gave us the automaton redeemed by a benign technology.
  • Why do such social researchers tell their stories? Essentially for didactic and moralistic purposes. These men and women tell their stories for the same reason the Buddha, Confucius, Hillel, and Jesus told their stories (and for the same reason D. H. Lawrence told his).
  • Moreover, in their quest for objectivity, scientists proceed on the assumption that the objects they study are indifferent to the fact that they are being studied.
  • If, indeed, the price of civilization is repressed sexuality, it was not Sigmund Freud who discovered it. If the consciousness of people is formed by their material circumstances, it was not Marx who discovered it. If the medium is the message, it was not McLuhan who discovered it. They have merely retold ancient stories in a modem style.
  • Unlike science, social research never discovers anything. It only rediscovers what people once were told and need to be told again.
  • Only in knowing ~omething of the reasons why they advocated education can we make sense of the means they suggest. But to understand their reas.ons we must also understand the narratives that governed their view of the world. By narrative, I mean a story of human history that gives meaning to the past, explains the present, and provides guidance for the future.
  • In Technopoly, it is not Scientism 159 enough to say, it is immoral and degrading to allow people to be homeless. You cannot get anywhere by asking a judge, a politician, or a bureaucrat to r~ad Les Miserables or Nana or, indeed, the New Testament. Y 01.i must show that statistics have produced data revealing the homeless to be unhappy and to be a drain on the economy. Neither Dostoevsky nor Freud, Dickens nor Weber, Twain nor Marx, is now a dispenser of legitimate knowledge. They are interesting; they are ''.worth reading"; they are artifacts of our past. But as for "truth," we must tum to "science."
  • In Technopoly, it is not enough for social research to rediscover ancient truths or to comment on and criticize the moral behavior of people. In T echnopoly, it is an insult to call someone a "moralizer." Nor is it sufficient for social research to put forward metaphors, images, and ideas that can help people live with some measure of understanding and dignity.
  • Such a program lacks the aura of certain knowledge that only science can provide. It becomes necessary, then, to transform psychology, sociology, and anthropology into "sciences," in which humanity itself becomes an object, much like plants, planets, or ice cubes.
  • That is why the commonplaces that people fear death and that children who come from stable families valuing scholarship will do well in school must be announced as "discoveries" of scientific enterprise. In this way, social resear~hers can see themselves, and can be seen, as scientists, researchers without bias or values, unburdened by mere opinion. In this way, social policies can be claimed to rest on objectively determined facts.
  • given the psychological, social, and material benefits that attach to the label "scientist," it is not hard to see why social researchers should find it hard to give it up.
  • Our social "s'cientists" have from the beginning been less tender of conscience, or less rigorous in their views of science, or perhaps just more confused about the questions their procedures can answer and those they cannot. In any case, they have not been squeamish about imputing to their "discoveries" and the rigor of their procedures the power to direct us in how we ought rightly to behave.
  • It is less easy to see why the rest of us have so willingly, even eagerly, cooperated in perpetuating the same illusion.
  • When the new technologies and techniques and spirit of men like Galileo, Newton, and Bacon laid the foundations of natural science, they also discredited the authority of earlier accounts of the physical world, as found, for example, in the great tale of Genesis. By calling into question the truth of such accounts in one realm, science undermined the whole edifice of belief in sacred stories and ultimately swept away with it the source to which most humans had looked for moral authority. It is not too much to say, I think, that the desacralized world has been searching for an alternative source of moral authority ever since.
  • We welcome them gladly, and the claim explicitly made or implied, because we need so desperately to find some source outside the frail and shaky judgments of mortals like ourselves to authorize our moral decisions and behavior. And outside of the authority of brute force, which can scarcely be called moral, we seem to have little left but the authority of procedures.
  • It is not merely the misapplication of techniques such as quantification to questions where numbers have nothing to say; not merely the confusion of the material and social realms of human experience; not merely the claim of social researchers to be applying the aims and procedures of natural scien\:e to the human world.
  • This, then, is what I mean by Scientism.
  • It is the desperate hope, and wish, and ultimately the illusory belief that some standardized set of procedures called "science" can provide us with an unimpeachable source of moral authority, a suprahuman basis for answers to questions like "What is life, and when, and why?" "Why is death, and suffering?" 'What is right and wrong to do?" "What are good and evil ends?" "How ought we to think and feel and behave?
  • Science can tell us when a heart begins to beat, or movement begins, or what are the statistics on the survival of neonates of different gestational ages outside the womb. But science has no more authority than you do or I do to establish such criteria as the "true" definition of "life" or of human state or of personhood.
  • Social research can tell us how some people behave in the presence of what they believe to be legitimate authority. But it cannot tell us when authority is "legitimate" and when not, or how we must decide, or when it may be right or wrong to obey.
  • To ask of science, or expect of science, or accept unchallenged from science the answers to such questions is Scientism. And it is Technopoly's grand illusion.
  • In the institutional form it has taken in the United States, advertising is a symptom of a world-view 'that sees tradition as an obstacle to its claims. There can, of course, be no functioning sense of tradition without a measure of respect for symbols. Tradition is, in fact, nothing but the acknowledgment of the authority of symbols and the relevance of the narratives that gave birth to them. With the erosion of symbols there follows a loss of narrative, which is one of the most debilitating consequences of Technopoly' s power.
  • What the advertiser needs to know is not what is right about the product but what is wrong about the buyer. And so the balance of business expenditures shifts from product research to market research, which meahs orienting business away from making products of value and toward making consumers feel valuable. The business of business becomes pseudo-therapy; the consumer, a patient reassl.,lred by psychodramas.
  • At the moment, 1t 1s considered necessary to introduce computers to the classroom, as it once was thought necessary to bring closed-circuit television and film to the classroom. To the question "Why should we do this?" the answer is: "To make learning more efficient and more interesting." Such an answer is considered entirely adequate, since in T ~chnopoly efficiency and interest need no justification. It is, therefore, usually not noticed that this answer does not address the question "What is learning for?"
  • What this means is that somewhere near the core of Technopoly is a vast industry with license to use all available symbols to further the interests of commerce, by devouring the psyches of consumers.
  • In the twentieth century, such metaphors and images have come largely from the pens of social historians and researchers. ·Think of John Dewey, William James, Erik Erikson, Alfred Kinsey, Thorstein Veblen, Margaret Mead, Lewis Mumford, B. F. Skinner, Carl Rogers, Marshall McLuhan, Barbara Tuchman, Noam Chomsky, Robert Coles, even Stanley Milgram, and you must acknowledge that our ideas of what we are like and what kind of country we live in come from their stories to a far greater extent than from the stories of our most renowned novelists.
  • social idea that must be advanced through education.
  • Confucius advocated teaching "the Way" because in tradition he saw the best hope for social order. As our first systematic fascist, Plato wished education to produce philosopher kings. Cicero argued that education must free the student from the tyranny of the present. Jefferson thought the purpose of education is to teach the young how to protect their liberties. Rousseau wished education to free the young from the unnatural constraints of a wicked and arbitrary social order. And among John Dewey's aims was to help the student function without certainty in a world of constant change and puzzling· ambiguities.
  • The point is that cultures must have narratives and will find them where they will, even if they lead to catastrophe. The alternative is to live without meaning, the ultimate negation of life itself.
  • It is also to the point to say that each narrative is given its form and its emotional texture through a cluster of symbols that call for respect and allegiance, even devotion.
  • by definition, there can be no education philosophy that does not address what learning is for. Confucius, Plato, Quintilian, Cicero, Comenius, Erasmus, Locke, Rousseau, Jefferson, Russell, Montessori, Whitehead, and Dewey--each believed that there was some transcendent political, spiritual, or
  • The importance of the American Constitution is largely in its function as a symbol of the story of our origins. It is our political equivalent of Genesis. To mock it, to• ignore it, to circwnvent it is to declare the irrelevance of the story of the United States as a moral light unto the world. In like fashion, the Statue of Liberty is the key symbol of the story of America as the natural home of the teeming masses, from anywhere, yearning to be free.
  • There are those who believe--as did the great historian Arnold Toynbee-that without a comprehensive religious narrative at its center a culture must decline. Perhaps. There are, after all, other sources-mythology, politics, philosophy, and science; for example--but it is certain that no culture can flourish without narratives of transcendent orjgin and power.
  • This does not mean that the mere existence of such a narrative ensures a culture's stability and strength. There are destructive narratives. A narrative provides meaning, not necessarily survival-as, for example, the story provided by Adolf Hitler to the German nation in t:he 1930s.
  • What story does American education wish to tell now? In a growing Technopoly, what do we believe education is for?
  • The answers are discouraging, and one of. them can be inferred from any television commercial urging the young to stay in school. The commercial will either imply or state explicitly that education will help the persevering student to get a ·good job. And that's it. Well, not quite. There is also the idea that we educate ourselves to compete with the Japanese or the Germans in an economic struggle to be number one.
  • Young men, for example, will learn how to make lay-up shots when they play basketball. To be able to make them is part of the The Great Symbol Drain 177 definition of what good players are. But they do not play basketball for that purpose. There is usually a broader, deeper, and more meaningful reason for wanting to play-to assert their manhood, to please their fathers, to be acceptable to their peers, even for the sheer aesthetic pleasure of the game itself. What you have to do to be a success must be addressed only after you have found a reason to be successful.
  • Bloom's solution is that we go back to the basics of Western thought.
  • He wants us to teach our students what Plato, Aristotle, Cicero, Saint Augustine, and other luminaries have had to say on the great ethical and epistemological questions. He believes that by acquainting themselves with great books our students will acquire a moral and intellectual foundation that will give meaning and texture to their lives.
  • Hirsch's encyclopedic list is not a solution but a description of the problem of information glut. It is therefore essentially incoherent. But it also confuses a consequence of education with a purpose. Hirsch attempted to answer the question "What is an educated person?" He left unanswered the question "What is an education for?"
  • Those who reject Bloom's idea have offered several arguments against it. The first is that such a purpose for education is elitist: the mass of students would not find the great story of
  • Western civilization inspiring, are too deeply alienated from the past to find it so, and would therefore have difficulty connecting the "best that has been thought and said" to their own struggles to find q1eaning in their lives.
  • A second argument, coming from what is called a "leftist" perspective, is even more discouraging. In a sense, it offers a definition of what is meant by elitism. It asserts that the "story of Western civilization" is a partial, biased, and even oppressive one. It is not the story of blacks, American Indians, Hispanics, women, homosexuals-of any people who are not white heterosexual males of Judea-Christian heritage. This claim denies that there is or can be a national culture, a narrative of organizing power and inspiring symbols which all citizens can identify with and draw sustenance from. If this is true, it means nothing less than that our national symbols have been drained of their power to unite, and that education must become a tribal affair; that is, each subculture must find its own story and symbols, and use them as the moral basis of education.
  • nto this void comes the Technopoly story, with its emphasis on progress without limits, rights without responsibilities, and technology without cost. The T echnopoly story is without a moral center. It puts in its place efficiency, interest, and economic advance. It promises heaven on earth through the conveniences of technological progress. It casts aside all traditional narratives and symbols that· suggest stability and orderliness, and tells, instead, of a life of skills, technical expertise, and the ecstasy of consumption. Its purpose is to produce functionaries for an ongoing Technopoly.
  • It answers Bloom by saying that the story of Western civilization is irrelevant; it answers the political left by saying there is indeed a common culture whose name is T echnopoly and whose key symbol is now the computer, toward which there must be neither irreverence nor blasphemy. It even answers Hirsch by saying that there are items on his list that, if thought about too deeply and taken too seriously, will interfere with the progress of technology.
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