L'automazione del linguaggio è una pratica antichissima. Senza andare indietro a Lullo, basti considerare come in Francia, quando Dahl scriveva il suo racconto, nasceva il concetto di letteratura potenziale, che ebbe nell’Oulipo (Ouvroir de littérature potentielle) e poi in ALAMO (Atelier de Littérature Assistée par la Mathématique et les Ordinateurs) i suoi quartieri generali. Raymond Queneau, con i suoi Cent mille milliards de poèmes (1961) fu il principale protagonista di quella stagione, ma si deve all’intelligenza e alla profondità di Italo Calvino la visione più interessante di ciò che si stava sperimentando. “L'uomo sta cominciando a capire come si smonta e come si rimonta la più complicata e la più imprevedibile di tutte le sue macchine: il linguaggio” scriveva nel suo Cibernetica e fantasmi (1967). Ma continuava: “La macchina letteraria può effettuare tutte le permutazioni possibili in un dato materiale; ma il risultato poetico sarà l’effetto particolare d’una di queste permutazioni sull’uomo dotato d’una coscienza e d’un inconscio, cioè sull’uomo empirico e storico, sarà lo shock che si verifica solo in quanto attorno alla macchina scrivente esistono i fantasmi nascosti dell’individuo e della società.” Calvino spostava dunque l’attenzione dalla poiesis (la generatività) all’ esthesis (la ricettività): si poteva apprezzare l’opera letteraria automatica non per le virtù degli algoritmi che l’avevano creata, ma in quanto evocatrice di qualcosa di latente nel soggetto e nella società. Il senso non è nella macchina che parla, ma nell’essere umano che l’ascolta. Se questa intuizione fosse recepita, compresa e condivisa, il discorso sull’IA generativa potrebbe assumere connotati più interessanti, secondo me.
G. Il Dom 1 Set 2024, 16:58 maurizio lana <maurizio.l...@uniupo.it> ha scritto: > The programmer Simon Willison has described the training for large > language models as “money laundering for copyrighted data,” which I find a > useful way to think about the appeal of generative-A.I. programs: they let > you engage in something like plagiarism, but there’s no guilt associated > with it because it’s not clear even to you that you’re copying. Some have > claimed that large language models are not laundering the texts they’re > trained on but, rather, learning from them, in the same way that human > writers learn from the books they’ve read. But a large language model is > not a writer; it’s not even a user of language. Language is, by definition, > a system of communication, and it requires an intention to communicate. > Your phone’s auto-complete may offer good suggestions or bad ones, but in > neither case is it trying to say anything to you or the person you’re > texting. The fact that ChatGPT can generate coherent sentences invites us > to imagine that it understands language in a way that your phone’s > auto-complete does not, but it has no more intention to communicate. > > forse l'ho già scritto, vedo un divario (preoccupante) tra la capacità di > analisi e di valutazione critica in opera in questo gruppo di persone che > parla in Nexa e la noncuranza/non conoscenza/timore/cupidigia con cui > intorno a noi (no) si pensa ai sistemi di IA. > questo articolo di Chiang mi pare che forse più di altri possa 'passare la > barriera cognitiva' di chi non vuole pensare o non sa pensare in modo > appropriatamente critico ai sistemi di IA. > ci vedo una caratteristica che non so descrivere, che mi fa pensare alle > spade laser di Dune, che devono colpire con appropriata lentezza per > passare gli scudi individuali. > Maurizio > > > Il 01/09/24 11:25, Daniela Tafani ha scritto: > > Why A.I. Isn’t Going to Make Art > To create a novel or a painting, an artist makes choices that are > fundamentally alien to artificial intelligence. > By Ted Chiang > August 31, 2024 > > > In 1953, Roald Dahl published “The Great Automatic Grammatizator,” a short > story about an electrical engineer who secretly desires to be a writer. One > day, after completing construction of the world’s fastest calculating > machine, the engineer realizes that “English grammar is governed by rules > that are almost mathematical in their strictness.” He constructs a > fiction-writing machine that can produce a five-thousand-word short story in > thirty seconds; a novel takes fifteen minutes and requires the operator to > manipulate handles and foot pedals, as if he were driving a car or playing an > organ, to regulate the levels of humor and pathos. The resulting novels are > so popular that, within a year, half the fiction published in English is a > product of the engineer’s invention. > > Is there anything about art that makes us think it can’t be created by > pushing a button, as in Dahl’s imagination? Right now, the fiction generated > by large language models like ChatGPT is terrible, but one can imagine that > such programs might improve in the future. How good could they get? Could > they get better than humans at writing fiction—or making paintings or > movies—in the same way that calculators are better at addition and > subtraction? > > Art is notoriously hard to define, and so are the differences between good > art and bad art. But let me offer a generalization: art is something that > results from making a lot of choices. This might be easiest to explain if we > use fiction writing as an example. When you are writing fiction, you > are—consciously or unconsciously—making a choice about almost every word you > type; to oversimplify, we can imagine that a ten-thousand-word short story > requires something on the order of ten thousand choices. When you give a > generative-A.I. program a prompt, you are making very few choices; if you > supply a hundred-word prompt, you have made on the order of a hundred choices. > > If an A.I. generates a ten-thousand-word story based on your prompt, it has > to fill in for all of the choices that you are not making. There are various > ways it can do this. One is to take an average of the choices that other > writers have made, as represented by text found on the Internet; that average > is equivalent to the least interesting choices possible, which is why > A.I.-generated text is often really bland. Another is to instruct the program > to engage in style mimicry, emulating the choices made by a specific writer, > which produces a highly derivative story. In neither case is it creating > interesting art. > > I think the same underlying principle applies to visual art, although it’s > harder to quantify the choices that a painter might make. Real paintings bear > the mark of an enormous number of decisions. By comparison, a person using a > text-to-image program like DALL-E enters a prompt such as “A knight in a suit > of armor fights a fire-breathing dragon,” and lets the program do the rest. > (The newest version of DALL-E accepts prompts of up to four thousand > characters—hundreds of words, but not enough to describe every detail of a > scene.) Most of the choices in the resulting image have to be borrowed from > similar paintings found online; the image might be exquisitely rendered, but > the person entering the prompt can’t claim credit for that. > > Some commentators imagine that image generators will affect visual culture as > much as the advent of photography once did. Although this might seem > superficially plausible, the idea that photography is similar to generative > A.I. deserves closer examination. When photography was first developed, I > suspect it didn’t seem like an artistic medium because it wasn’t apparent > that there were a lot of choices to be made; you just set up the camera and > start the exposure. But over time people realized that there were a vast > number of things you could do with cameras, and the artistry lies in the many > choices that a photographer makes. It might not always be easy to articulate > what the choices are, but when you compare an amateur’s photos to a > professional’s, you can see the difference. So then the question becomes: Is > there a similar opportunity to make a vast number of choices using a > text-to-image generator? I think the answer is no. An artist—whether working > digitally or with paint—implicitly makes far more decisions during the > process of making a painting than would fit into a text prompt of a few > hundred words. > > We can imagine a text-to-image generator that, over the course of many > sessions, lets you enter tens of thousands of words into its text box to > enable extremely fine-grained control over the image you’re producing; this > would be something analogous to Photoshop with a purely textual interface. > I’d say that a person could use such a program and still deserve to be called > an artist. The film director Bennett Miller has used DALL-E 2 to generate > some very striking images that have been exhibited at the Gagosian gallery; > to create them, he crafted detailed text prompts and then instructed DALL-E > to revise and manipulate the generated images again and again. He generated > more than a hundred thousand images to arrive at the twenty images in the > exhibit. But he has said that he hasn’t been able to obtain comparable > results on later releases of DALL-E. I suspect this might be because Miller > was using DALL-E for something it’s not intended to do; it’s as if he hacked > Microsoft Paint to make it behave like Photoshop, but as soon as a new > version of Paint was released, his hacks stopped working. OpenAI probably > isn’t trying to build a product to serve users like Miller, because a product > that requires a user to work for months to create an image isn’t appealing to > a wide audience. The company wants to offer a product that generates images > with little effort. > > It’s harder to imagine a program that, over many sessions, helps you write a > good novel. This hypothetical writing program might require you to enter a > hundred thousand words of prompts in order for it to generate an entirely > different hundred thousand words that make up the novel you’re envisioning. > It’s not clear to me what such a program would look like. Theoretically, if > such a program existed, the user could perhaps deserve to be called the > author. But, again, I don’t think companies like OpenAI want to create > versions of ChatGPT that require just as much effort from users as writing a > novel from scratch. The selling point of generative A.I. is that these > programs generate vastly more than you put into them, and that is precisely > what prevents them from being effective tools for artists. > > The companies promoting generative-A.I. programs claim that they will unleash > creativity. In essence, they are saying that art can be all inspiration and > no perspiration—but these things cannot be easily separated. I’m not saying > that art has to involve tedium. What I’m saying is that art requires making > choices at every scale; the countless small-scale choices made during > implementation are just as important to the final product as the few > large-scale choices made during the conception. It is a mistake to equate > “large-scale” with “important” when it comes to the choices made when > creating art; the interrelationship between the large scale and the small > scale is where the artistry lies. > > Believing that inspiration outweighs everything else is, I suspect, a sign > that someone is unfamiliar with the medium. I contend that this is true even > if one’s goal is to create entertainment rather than high art. People often > underestimate the effort required to entertain; a thriller novel may not live > up to Kafka’s ideal of a book—an “axe for the frozen sea within us”—but it > can still be as finely crafted as a Swiss watch. And an effective thriller is > more than its premise or its plot. I doubt you could replace every sentence > in a thriller with one that is semantically equivalent and have the resulting > novel be as entertaining. This means that its sentences—and the small-scale > choices they represent—help to determine the thriller’s effectiveness. > > > Many novelists have had the experience of being approached by someone > convinced that they have a great idea for a novel, which they are willing to > share in exchange for a fifty-fifty split of the proceeds. Such a person > inadvertently reveals that they think formulating sentences is a nuisance > rather than a fundamental part of storytelling in prose. Generative A.I. > appeals to people who think they can express themselves in a medium without > actually working in that medium. But the creators of traditional novels, > paintings, and films are drawn to those art forms because they see the unique > expressive potential that each medium affords. It is their eagerness to take > full advantage of those potentialities that makes their work satisfying, > whether as entertainment or as art. > > Of course, most pieces of writing, whether articles or reports or e-mails, do > not come with the expectation that they embody thousands of choices. In such > cases, is there any harm in automating the task? Let me offer another > generalization: any writing that deserves your attention as a reader is the > result of effort expended by the person who wrote it. Effort during the > writing process doesn’t guarantee the end product is worth reading, but > worthwhile work cannot be made without it. The type of attention you pay when > reading a personal e-mail is different from the type you pay when reading a > business report, but in both cases it is only warranted when the writer put > some thought into it. > > Recently, Google aired a commercial during the Paris Olympics for Gemini, its > competitor to OpenAI’s GPT-4. The ad shows a father using Gemini to compose a > fan letter, which his daughter will send to an Olympic athlete who inspires > her. Google pulled the commercial after widespread backlash from viewers; a > media professor called it “one of the most disturbing commercials I’ve ever > seen.” It’s notable that people reacted this way, even though artistic > creativity wasn’t the attribute being supplanted. No one expects a child’s > fan letter to an athlete to be extraordinary; if the young girl had written > the letter herself, it would likely have been indistinguishable from > countless others. The significance of a child’s fan letter—both to the child > who writes it and to the athlete who receives it—comes from its being > heartfelt rather than from its being eloquent. > > Many of us have sent store-bought greeting cards, knowing that it will be > clear to the recipient that we didn’t compose the words ourselves. We don’t > copy the words from a Hallmark card in our own handwriting, because that > would feel dishonest. The programmer Simon Willison has described the > training for large language models as “money laundering for copyrighted > data,” which I find a useful way to think about the appeal of generative-A.I. > programs: they let you engage in something like plagiarism, but there’s no > guilt associated with it because it’s not clear even to you that you’re > copying. > > Some have claimed that large language models are not laundering the texts > they’re trained on but, rather, learning from them, in the same way that > human writers learn from the books they’ve read. But a large language model > is not a writer; it’s not even a user of language. Language is, by > definition, a system of communication, and it requires an intention to > communicate. Your phone’s auto-complete may offer good suggestions or bad > ones, but in neither case is it trying to say anything to you or the person > you’re texting. The fact that ChatGPT can generate coherent sentences invites > us to imagine that it understands language in a way that your phone’s > auto-complete does not, but it has no more intention to communicate. > > It is very easy to get ChatGPT to emit a series of words such as “I am happy > to see you.” There are many things we don’t understand about how large > language models work, but one thing we can be sure of is that ChatGPT is not > happy to see you. A dog can communicate that it is happy to see you, and so > can a prelinguistic child, even though both lack the capability to use words. > ChatGPT feels nothing and desires nothing, and this lack of intention is why > ChatGPT is not actually using language. What makes the words “I’m happy to > see you” a linguistic utterance is not that the sequence of text tokens that > it is made up of are well formed; what makes it a linguistic utterance is the > intention to communicate something. > > Because language comes so easily to us, it’s easy to forget that it lies on > top of these other experiences of subjective feeling and of wanting to > communicate that feeling. We’re tempted to project those experiences onto a > large language model when it emits coherent sentences, but to do so is to > fall prey to mimicry; it’s the same phenomenon as when butterflies evolve > large dark spots on their wings that can fool birds into thinking they’re > predators with big eyes. There is a context in which the dark spots are > sufficient; birds are less likely to eat a butterfly that has them, and the > butterfly doesn’t really care why it’s not being eaten, as long as it gets to > live. But there is a big difference between a butterfly and a predator that > poses a threat to a bird. > > A person using generative A.I. to help them write might claim that they are > drawing inspiration from the texts the model was trained on, but I would > again argue that this differs from what we usually mean when we say one > writer draws inspiration from another. Consider a college student who turns > in a paper that consists solely of a five-page quotation from a book, stating > that this quotation conveys exactly what she wanted to say, better than she > could say it herself. Even if the student is completely candid with the > instructor about what she’s done, it’s not accurate to say that she is > drawing inspiration from the book she’s citing. The fact that a large > language model can reword the quotation enough that the source is > unidentifiable doesn’t change the fundamental nature of what’s going on. > > As the linguist Emily M. Bender has noted, teachers don’t ask students to > write essays because the world needs more student essays. The point of > writing essays is to strengthen students’ critical-thinking skills; in the > same way that lifting weights is useful no matter what sport an athlete > plays, writing essays develops skills necessary for whatever job a college > student will eventually get. Using ChatGPT to complete assignments is like > bringing a forklift into the weight room; you will never improve your > cognitive fitness that way. > > Not all writing needs to be creative, or heartfelt, or even particularly > good; sometimes it simply needs to exist. Such writing might support other > goals, such as attracting views for advertising or satisfying bureaucratic > requirements. When people are required to produce such text, we can hardly > blame them for using whatever tools are available to accelerate the process. > But is the world better off with more documents that have had minimal effort > expended on them? It would be unrealistic to claim that if we refuse to use > large language models, then the requirements to create low-quality text will > disappear. However, I think it is inevitable that the more we use large > language models to fulfill those requirements, the greater those requirements > will eventually become. We are entering an era where someone might use a > large language model to generate a document out of a bulleted list, and send > it to a person who will use a large language model to condense that document > into a bulleted list. Can anyone seriously argue that this is an improvement? > > It’s not impossible that one day we will have computer programs that can do > anything a human being can do, but, contrary to the claims of the companies > promoting A.I., that is not something we’ll see in the next few years. Even > in domains that have absolutely nothing to do with creativity, current A.I. > programs have profound limitations that give us legitimate reasons to > question whether they deserve to be called intelligent at all. > > The computer scientist François Chollet has proposed the following > distinction: skill is how well you perform at a task, while intelligence is > how efficiently you gain new skills. I think this reflects our intuitions > about human beings pretty well. Most people can learn a new skill given > sufficient practice, but the faster the person picks up the skill, the more > intelligent we think the person is. What’s interesting about this definition > is that—unlike I.Q. tests—it’s also applicable to nonhuman entities; when a > dog learns a new trick quickly, we consider that a sign of intelligence. > > In 2019, researchers conducted an experiment in which they taught rats how to > drive. They put the rats in little plastic containers with three copper-wire > bars; when the mice put their paws on one of these bars, the container would > either go forward, or turn left or turn right. The rats could see a plate of > food on the other side of the room and tried to get their vehicles to go > toward it. The researchers trained the rats for five minutes at a time, and > after twenty-four practice sessions, the rats had become proficient at > driving. Twenty-four trials were enough to master a task that no rat had > likely ever encountered before in the evolutionary history of the species. I > think that’s a good demonstration of intelligence. > > Now consider the current A.I. programs that are widely acclaimed for their > performance. AlphaZero, a program developed by Google’s DeepMind, plays chess > better than any human player, but during its training it played forty-four > million games, far more than any human can play in a lifetime. For it to > master a new game, it will have to undergo a similarly enormous amount of > training. By Chollet’s definition, programs like AlphaZero are highly > skilled, but they aren’t particularly intelligent, because they aren’t > efficient at gaining new skills. It is currently impossible to write a > computer program capable of learning even a simple task in only twenty-four > trials, if the programmer is not given information about the task beforehand. > > Self-driving cars trained on millions of miles of driving can still crash > into an overturned trailer truck, because such things are not commonly found > in their training data, whereas humans taking their first driving class will > know to stop. More than our ability to solve algebraic equations, our ability > to cope with unfamiliar situations is a fundamental part of why we consider > humans intelligent. Computers will not be able to replace humans until they > acquire that type of competence, and that is still a long way off; for the > time being, we’re just looking for jobs that can be done with turbocharged > auto-complete. > > Despite years of hype, the ability of generative A.I. to dramatically > increase economic productivity remains theoretical. (Earlier this year, > Goldman Sachs released a report titled “Gen AI: Too Much Spend, Too Little > Benefit?”) The task that generative A.I. has been most successful at is > lowering our expectations, both of the things we read and of ourselves when > we write anything for others to read. It is a fundamentally dehumanizing > technology because it treats us as less than what we are: creators and > apprehenders of meaning. It reduces the amount of intention in the world. > > Some individuals have defended large language models by saying that most of > what human beings say or write isn’t particularly original. That is true, but > it’s also irrelevant. When someone says “I’m sorry” to you, it doesn’t matter > that other people have said sorry in the past; it doesn’t matter that “I’m > sorry” is a string of text that is statistically unremarkable. If someone is > being sincere, their apology is valuable and meaningful, even though > apologies have previously been uttered. Likewise, when you tell someone that > you’re happy to see them, you are saying something meaningful, even if it > lacks novelty. > > Something similar holds true for art. Whether you are creating a novel or a > painting or a film, you are engaged in an act of communication between you > and your audience. What you create doesn’t have to be utterly unlike every > prior piece of art in human history to be valuable; the fact that you’re the > one who is saying it, the fact that it derives from your unique life > experience and arrives at a particular moment in the life of whoever is > seeing your work, is what makes it new. We are all products of what has come > before us, but it’s by living our lives in interaction with others that we > bring meaning into the world. That is something that an auto-complete > algorithm can never do, and don’t let anyone tell you otherwise. ? > https://www.newyorker.com/culture/the-weekend-essay/why-ai-isnt-going-to-make-art > > > > ------------------------------ > > we don’t need more ‘responsibly built’ weapons or surveillance technology > sarah myers > > ------------------------------ > Maurizio Lana > Università del Piemonte Orientale > Dipartimento di Studi Umanistici > Piazza Roma 36 - 13100 Vercelli > <https://www.google.com/maps/search/Piazza+Roma+36+-+13100+Vercelli?entry=gmail&source=g> >
