Bellissimo questo reperto, mi chiedo se Balestrini conoscesse Christopher Strachey, che qualche anno prima sciveva implausibili lettere d'amore con tecniche molto simili (una moderna implementazione del suo algoritmo è qui: https://nickm.com/memslam/love_letters.html)
Anche a quel tempo, la letteratura cibernetica attirava gli strali di certi intellettuali (o presunti tali), che Calvino motteggiava così: "Ora qualcuno di voi si domanderà perché annuncio con aria tanto giuliva prospettive che alla maggior parte degli uomini di lettere suscitano lamentazioni lacrimose punteggiate da gridi d'esecrazione." Insomma ci sono corsi e ricorsi, dovremmo rileggere quel dibattito su estetica e teoria dell'informazione, forse tante cose sono state già dette, e meglio :-) G. Il Mar 3 Set 2024, 17:25 de petra giulio <giulio.depe...@gmail.com> ha scritto: > Aggiungo agli esempi utilmente forniti da Guido quello di un grande poeta > e scrittore italiano, Nanni Balestrini, uno dei principali animatori del > Gruppo 63. > In un articolo del 1962 (!) descrive i risultati di una originale > esperienza di utilizzo di un elaboratore elettronico (un IBM 360, > programmato con le schede perforate) per produrre testi poetici. > Nell’articolo sono descritti non solo i risultati dell’esperimento, ma > anche, passo per passo, la procedura di utilizzo dell’ elaboratore > elettronico. > Rileggendolo l’ho trovato, malgrado il tanto tempo passato, pertinente e > anche utile alla interessante discussione avviata da Daniela. > Nella prefazione dell’articolo si legge: > > *È necessario far notare la sostanziale differenza con altre prove sul > linguaggio svolte nell'ambito della cibernetica. Qui infatti non è stato > posto il problema di ottenere dalla macchina una imitazione di procedimenti > propriamente umani, ma sono state semplicemente sfruttate le capacità del > mezzo elettronico di risolvere con estrema rapidità alcune complesse > operazioni inerenti alla tecnica poetica.* > > Qui il link all’articolo di Balestrini > > > https://gammm.org/wp-content/uploads/2007/02/nanni-balestrini.-tape-mark-I.pdf > > > > > > > > > Il giorno dom 1 set 2024 alle 22:26 Guido Vetere <vetere.gu...@gmail.com> > ha scritto: > >> >> 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> >>> >>
