Pare non sia vero: https://www.repubblica.it/cultura/2024/06/18/news/la_fake_news_sulla_morte_di_noam_chomsky-423253469/?ref=RHLF-BG-P8-S1-T1
Saluti, Vera Il Mar 18 Giu 2024, 22:46 Daniela Tafani <[email protected]> ha scritto: > In memoria di Noam Chomsky, una sua intervista su IA, tecnica e scienza. > > Noam Chomsky Speaks on What ChatGPT Is Really Good For > Noam Chomsky Interviewed by C.J. Polychroniou > May 3, 2023 > > Artificial intelligence (AI) is sweeping the world. It is transforming > every walk of life and raising in the process major ethical concerns for > society and the future of humanity. ChatGPT, which is dominating social > media, is an AI-powered chatbot developed by OpenAI. It is a subset of > machine learning and relies on what is called Large Language Models that > can generate human-like responses. The potential application for such > technology is indeed enormous, which is why there are already calls to > regulate AI like ChatGPT. > > Can AI outsmart humans? Does it pose public threats? Indeed, can AI become > an existential threat? The world’s preeminent linguist Noam Chomsky, and > one of the most esteemed public intellectuals of all time, whose > intellectual stature has been compared to that of Galileo, Newton, and > Descartes, tackles these nagging questions in the interview that follows. > > C. J. Polychroniou: As a scientific discipline, artificial intelligence > (AI) dates back to the 1950s, but over the last couple of decades it has > been making inroads into all sort of fields, including banking, insurance, > auto manufacturing, music, and defense. In fact, the use of AI techniques > has been shown in some instance to surpass human capabilities, such as in a > game of chess. Are machines likely to become smarter than humans? > > Noam Chomsky: Just to clarify terminology, the term “machine” here means > program, basically a theory written in a notation that can be executed by a > computer–and an unusual kind of theory in interesting ways that we can put > aside here. > > We can make a rough distinction between pure engineering and science. > There is no sharp boundary, but it’s a useful first approximation. Pure > engineering seeks to produce a product that may be of some use. Science > seeks understanding. If the topic is human intelligence, or cognitive > capacities of other organisms, science seeks understanding of these > biological systems. > > As I understand them, the founders of AI–Alan Turing, Herbert Simon, > Marvin Minsky, and others–regarded it as science, part of the then-emerging > cognitive sciences, making use of new technologies and discoveries in the > mathematical theory of computation to advance understanding. Over the years > those concerns have faded and have largely been displaced by an engineering > orientation. The earlier concerns are now commonly dismissed, sometimes > condescendingly, as GOFAI–good old-fashioned AI. > > Continuing with the question, is it likely that programs will be devised > that surpass human capabilities? We have to be careful about the word > “capabilities,” for reasons to which I’ll return. But if we take the term > to refer to human performance, then the answer is: definitely yes. In fact, > they have long existed: the calculator in a laptop, for example. It can far > exceed what humans can do, if only because of lack of time and memory. For > closed systems like chess, it was well understood in the ‘50s that sooner > or later, with the advance of massive computing capacities and a long > period of preparation, a program could be devised to defeat a grandmaster > who is playing with a bound on memory and time. The achievement years later > was pretty much PR for IBM. Many biological organisms surpass human > cognitive capacities in much deeper ways. The desert ants in my backyard > have minuscule brains, but far exceed human navigational capacities, in > principle, not just performance. There is no Great Chain of Being with > humans at the top. > > The products of AI engineering are being used in many fields, for better > or for worse. Even simple and familiar ones can be quite useful: in the > language area, programs like autofill, live transcription, google > translate, among others. With vastly greater computing power and more > sophisticated programming, there should be other useful applications, in > the sciences as well. There already have been some: Assisting in the study > of protein folding is one recent case where massive and rapid search > technology has helped scientists to deal with a critical and recalcitrant > problem. > > Engineering projects can be useful, or harmful. Both questions arise in > the case of engineering AI. Current work with Large Language Models (LLMs), > including chatbots, provides tools for disinformation, defamation, and > misleading the uninformed. The threats are enhanced when they are combined > with artificial images and replication of voice. With different concerns in > mind, tens of thousands of AI researchers have recently called for a > moratorium on development because of potential dangers they perceive. > > As always, possible benefits of technology have to be weighed against > potential costs. > > Quite different questions arise when we turn to AI and science. Here > caution is necessary because of exorbitant and reckless claims, often > amplified in the media. To clarify the issues, let’s consider cases, some > hypothetical, some real. > > I mentioned insect navigation, which is an astonishing achievement. Insect > scientists have made much progress in studying how it is achieved, though > the neurophysiology, a very difficult matter, remains elusive, along with > evolution of the systems. The same is true of the amazing feats of birds > and sea turtles that travel thousands of miles and unerringly return to the > place of origin. > > Suppose Tom Jones, a proponent of engineering AI, comes along and says: > “Your work has all been refuted. The problem is solved. Commercial airline > pilots achieve the same or even better results all the time.” > > If even bothering to respond, we’d laugh. > > Take the case of the seafaring exploits of Polynesians, still alive among > Indigenous tribes, using stars, wind, currents to land their canoes at a > designated spot hundreds of miles away. This too has been the topic of much > research to find out how they do it. Tom Jones has the answer: “Stop > wasting your time; naval vessels do it all the time.” > > Same response. > > Let’s now turn to a real case, language acquisition. It’s been the topic > of extensive and highly illuminating research in recent years, showing that > infants have very rich knowledge of the ambient language (or languages), > far beyond what they exhibit in performance. It is achieved with little > evidence, and in some crucial cases none at all. At best, as careful > statistical studies have shown, available data are sparse, particularly > when rank-frequency (“Zipf’s law”) is taken into account. > > Enter Tom Jones: “You’ve been refuted. Paying no attention to your > discoveries, LLMs that scan astronomical amounts of data can find > statistical regularities that make it possible to simulate the data on > which they are trained, producing something that looks pretty much like > normal human behavior. Chatbots.” > > This case differs from the others. First, it is real. Second, people don’t > laugh; in fact, many are awed. Third, unlike the hypothetical cases, the > actual results are far from what’s claimed. > > These considerations bring up a minor problem with the current LLM > enthusiasm: its total absurdity, as in the hypothetical cases where we > recognize it at once. But there are much more serious problems than > absurdity. > > One is that the LLM systems are designed in such a way that they cannot > tell us anything about language, learning, or other aspects of cognition, a > matter of principle, irremediable. Double the terabytes of data scanned, > add another trillion parameters, use even more of California’s energy, and > the simulation of behavior will improve, while revealing more clearly the > failure in principle of the approach to yield any understanding. The reason > is elementary: The systems work just as well with impossible languages that > infants cannot acquire as with those they acquire quickly and virtually > reflexively. > > It’s as if a biologist were to say: “I have a great new theory of > organisms. It lists many that exist and many that can’t possibly exist, and > I can tell you nothing about the distinction.” > > Again, we’d laugh. Or should. > > Not Tom Jones–now referring to actual cases. Persisting in his radical > departure from science, Tom Jones responds: “How do you know any of this > until you’ve investigated all languages?” At this point the abandonment of > normal science becomes even clearer. By parity of argument, we can throw > out genetics and molecular biology, the theory of evolution, and the rest > of the biological sciences, which haven’t sampled more than a tiny fraction > of organisms. And for good measure, we can cast out all of physics. Why > believe in the laws of motion? How many objects have actually been observed > in motion? > > There is, furthermore, the small matter of burden of proof. Those who > propose a theory have the responsibility of showing that it makes some > sense, in this case, showing that it fails for impossible languages. It is > not the responsibility of others to refute the proposal, though in this > case it seems easy enough to do so. > > Let’s shift attention to normal science, where matters become interesting. > Even a single example of language acquisition can yield rich insight into > the distinction between possible and impossible languages. > > The reasons are straightforward, and familiar. All growth and development, > including what is called “learning,” is a process that begins with a state > of the organism and transforms it step-by-step to later stages. > > Acquisition of language is such a process. The initial state is the > biological endowment of the faculty of language, which obviously exists, > even if it is, as some believe, a particular combination of other > capacities. That’s highly unlikely for reasons long understood, but it’s > not relevant to our concerns here, so we can put it aside. Plainly there is > a biological endowment for the human faculty of language. The merest truism. > > Transition proceeds to a relatively stable state, changed only > superficially beyond: knowledge of the language. External data trigger and > partially shape the process. Studying the state attained (knowledge of the > language) and the external data, we can draw far-reaching conclusions about > the initial state, the biological endowment that makes language acquisition > possible. The conclusions about the initial state impose a distinction > between possible and impossible languages. The distinction holds for all > those who share the initial state–all humans, as far as is known; there > seems to be no difference in capacity to acquire language among existing > human groups. > > All of this is normal science, and it has achieved many results. > > Experiment has shown that the stable state is substantially obtained very > early, by three to four years of age. It’s also well-established that the > faculty of language has basic properties specific to humans, hence that it > is a true species property: common to human groups and in fundamental ways > a unique human attribute. > > A lot is left out in this schematic account, notably the role of natural > law in growth and development: in the case of a computational system like > language, principles of computational efficiency. But this is the essence > of the matter. Again, normal science. > > It is important to be clear about Aristotle’s distinction between > possession of knowledge and use of knowledge (in contemporary terminology, > competence and performance). In the language case, the stable state > obtained is possession of knowledge, coded in the brain. The internal > system determines an unbounded array of structured expressions, each of > which we can regard as formulating a thought, each externalizable in some > sensorimotor system, usually sound though it could be sign or even (with > difficulty) touch. > > The internally coded system is accessed in use of knowledge (performance). > Performance includes the internal use of language in thought: reflection, > planning, recollection, and a great deal more. Statistically speaking that > is by far the overwhelming use of language. It is inaccessible to > introspection, though we can learn a lot about it by the normal methods of > science, from “outside,” metaphorically speaking. What is called “inner > speech” is, in fact, fragments of externalized language with the > articulatory apparatus muted. It is only a remote reflection of the > internal use of language, important matters I cannot pursue here. > > Other forms of use of language are perception (parsing) and production, > the latter crucially involving properties that remain as mysterious to us > today as when they were regarded with awe and amazement by Galileo and his > contemporaries at the dawn of modern science. > > The principal goal of science is to discover the internal system, both in > its initial state in the human faculty of language and in the particular > forms it assumes in acquisition. To the extent that this internal system is > understood, we can proceed to investigate how it enters into performance, > interacting with many other factors that enter into use of language. > > Data of performance provide evidence about the nature of the internal > system, particularly so when they are refined by experiment, as in standard > field work. But even the most massive collection of data is necessarily > misleading in crucial ways. It keeps to what is normally produced, not the > knowledge of the language coded in the brain, the primary object under > investigation for those who want to understand the nature of language and > its use. That internal object determines infinitely many possibilities of a > kind that will not be used in normal behavior because of factors irrelevant > to language, like short-term memory constraints, topics studied 60 years > ago. Observed data will also include much that lies outside the system > coded in the brain, often conscious use of language in ways that violate > the rules for rhetorical purposes. These are truisms known to all field > workers, who rely on elicitation techniques with informants, basically > experiments, to yield a refined corpus that excludes irrelevant > restrictions and deviant expressions. The same is true when linguists use > themselves as informants, a perfectly sensible and normal procedure, common > in the history of psychology up to the present. > > Proceeding further with normal science, we find that the internal > processes and elements of the language cannot be detected by inspection of > observed phenomena. Often these elements do not even appear in speech (or > writing), though their effects, often subtle, can be detected. That is yet > another reason why restriction to observed phenomena, as in LLM approaches, > sharply limits understanding of the internal processes that are the core > objects of inquiry into the nature of language, its acquisition and use. > But that is not relevant if concern for science and understanding have been > abandoned in favor of other goals. > > More generally in the sciences, for millennia, conclusions have been > reached by experiments–often thought experiments–each a radical abstraction > from phenomena. Experiments are theory-driven, seeking to discard the > innumerable irrelevant factors that enter into observed phenomena–like > linguistic performance. All of this is so elementary that it’s rarely even > discussed. And familiar. As noted, the basic distinction goes back to > Aristotle’s distinction between possession of knowledge and use of > knowledge. The former is the central object of study. Secondary (and quite > serious) studies investigate how the internally stored system of knowledge > is used in performance, along with the many non-linguistic factors than > enter into what is directly observed. > > We might also recall an observation of evolutionary biologist Theodosius > Dobzhansky, famous primarily for his work with Drosophila: Each species is > unique, and humans are the uniquest of all. If we are interested in > understanding what kind of creatures we are–following the injunction of the > Delphic Oracle 2,500 years ago–we will be primarily concerned with what > makes humans the uniquest of all, primarily language and thought, closely > intertwined, as recognized in a rich tradition going back to classical > Greece and India. Most behavior is fairly routine, hence to some extent > predictable. What provides real insight into what makes us unique is what > is not routine, which we do find, sometimes by experiment, sometimes by > observation, from normal children to great artists and scientists. > > One final comment in this connection. Society has been plagued for a > century by massive corporate campaigns to encourage disdain for science, > topics well studied by Naomi Oreskes among others. It began with > corporations whose products are murderous: lead, tobacco, asbestos, later > fossil fuels. Their motives are understandable. The goal of a business in a > capitalist society is profit, not human welfare. That’s an institutional > fact: Don’t play the game and you’re out, replaced by someone who will. > > The corporate PR departments recognized early on that it would be a > mistake to deny the mounting scientific evidence of the lethal effects of > their products. That would be easily refuted. Better to sow doubt, > encourage uncertainty, contempt for these pointy-headed suits who have > never painted a house but come down from Washington to tell me not to use > lead paint, destroying my business (a real case, easily multiplied). That > has worked all too well. Right now it is leading us on a path to > destruction of organized human life on earth. > > In intellectual circles, similar effects have been produced by the > postmodern critique of science, dismantled by Jean Bricmont and Alan Sokal, > but still much alive in some circles. > > It may be unkind to suggest the question, but it is, I think, fair to ask > whether the Tom Joneses and those who uncritically repeat and even amplify > their careless proclamations are contributing to the same baleful > tendencies. > > CJP: ChatGPT is a natural-language-driven chatbot that uses artificial > intelligence to allow human-like conversations. In a recent article in The > New York Times, in conjunction with two other authors, you shut down the > new chatbots as a hype because they simply cannot match the linguistic > competence of humans. Isn’t it however possible that future innovations in > AI can produce engineering projects that will match and perhaps even > surpass human capabilities? > > NC: Credit for the article should be given to the actual author, Jeffrey > Watumull, a fine mathematician-linguist-philosopher. The two listed > co-authors were consultants, who agree with the article but did not write > it. > > It’s true that chatbots cannot in principle match the linguistic > competence of humans, for the reasons repeated above. Their basic design > prevents them from reaching the minimal condition of adequacy for a theory > of human language: distinguishing possible from impossible languages. Since > that is a property of the design, it cannot be overcome by future > innovations in this kind of AI. However, it is quite possible that future > engineering projects will match and even surpass human capabilities, if we > mean human capacity to act, performance. As mentioned above, some have long > done so: automatic calculators for example. More interestingly, as > mentioned, insects with minuscule brains surpass human capacities > understood as competence. > > CJP: In the aforementioned article, it was also observed that today’s AI > projects do not possess a human moral faculty. Does this obvious fact make > AI robots less of a threat to the human race? I reckon the argument can be > that it makes them perhaps even more so. > > NC: It is indeed an obvious fact, understanding “moral faculty” broadly. > Unless carefully controlled, AI engineering can pose severe threats. > Suppose, for example, that care of patients was automated. The inevitable > errors that would be overcome by human judgment could produce a horror > story. Or suppose that humans were removed from evaluation of the threats > determined by automated missile-defense systems. As a shocking historical > record informs us, that would be the end of human civilization. > > CJP: Regulators and law enforcement agencies in Europe are raising > concerns about the spread of ChatGPT while a recently submitted piece of > European Union legislation is trying to deal with AI by classifying such > tools according to their perceived level of risk. Do you agree with those > who are concerned that ChatGPT poses a serious public threat? Moreover, do > you really think that the further development of AI tools can be halted > until safeguards can be introduced? > > NC: I can easily sympathize with efforts to try to control the threats > posed by advanced technology, including this case. I am, however, skeptical > about the possibility of doing so. I suspect that the genie is out of the > bottle. Malicious actors–institutional or individual–can probably find ways > to evade safeguards. Such suspicions are of course no reason not to try, > and to exercise vigilance. > > https://chomsky.info/20230503-2/
