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September 2024

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Re: [nexa] AI Training is Copyright Infringement

by maurizio lana

qui è utile richiamare Giraudo, Marco. «On Legal Bubbles: Some Thoughts on Legal Shockwaves at the Core of the Digital Economy». /Journal of Institutional Economics/ 18, fasc. 4 (agosto 2022): 587–604. https://doi.org/10.1017/S1744137421000473. Maurizio Il 06/09/24 00:02, Daniela Tafani ha scritto: > Press Release: A computer scientist and a legal scholar shed light on the black box of processing steps in AI training - for the first time on this scale. > > The presentation of the interdisciplinary study “Copyright & Training of Generative AI - Technological and Legal Foundations” took place today in the European Parliament. > > In spring, the Copyright Initiative commissioned Prof. Dr. Tim W. Dornis (University of Hannover) in collaboration with Prof. Dr. Sebastian Stober (University of Magdeburg) with a tandem expert opinion on the technological and legal aspects of training generative AI models. Their interdisciplinary research provides urgently needed new insights into the technically necessary intermediate steps in the training of generative artificial intelligence. For the first time on this scale, a computer scientist and a legal scholar are jointly creating evidence regarding the processing steps in AI training. During the event, many open questions about protected materials were answered in a well-founded, reliable manner and in line with the current state of the art. > > The work of Prof. Dornis and Prof. Stober focuses on the copyright assessment of the processing of protected material in AI training: > > “As a closer look at the technology of generative AI models reveals, the training of such models is not a case of text and data mining. It is a case of copyright infringement – no exception applies under German and European copyright law,” says Prof. Dornis. Prof. Stober explains that “parts of the training data can be memorized in whole or in part by current generative models - LLMs and (latent) diffusion models - and can therefore be generated again with suitable prompts by end users and thus reproduced.” > Axel Voss, MEP and host of today's event in the European Parliament, expressly thanks the scientists Dornis and Stober and is pleased that > > “the study not only proves that the training of Generative AI models is not covered by text and data mining, but that it also provides further important indications and suggestions for a better balance between the protection of human creativity and the promotion of AI innovation.” > “This study is explosive because it proves that we are dealing with large-scale theft of intellectual property. The ball is now in the politicians' court to draw the necessary conclusions and finally put an end to this theft at the expense of journalists and other authors,” > commented Hanna Möllers, legal advisor to the DJV and representative of the European Federation of Journalists (EFJ). > > Katharina Uppenbrink, Managing Director of the Initiative Urheberrecht, emphasizes: > > “It is a groundbreaking result if we now have proof that the reproduction of works by an AI model constitutes a copyright-relevant reproduction and, in addition, that making them available on the European Union market may infringe the right of making available to the public.” > The composer and spokesperson for the Copyright Initiative, Matthias Hornschuh, comments: > > “There would be a new, profitable licensing market on the horizon, but no remuneration is flowing, while generative AI is preparing to replace those whose content it lives from in its own market. This jeopardizes professional knowledge work and cannot be in the interests of society, culture or the economy. All the better that the authors of our tandem study provide the technological and copyright basis for finally turning the legal consideration of generative artificial intelligence from its head to its feet.” > Dornis, Tim W. and Stober, Sebastian, Copyright and training of generative AI models - technological and legal foundations > > (September 4, 2024). > > https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4946214 > > (in German) > > Please find below the downloads: > > The abstract can be found below and here (in English and German language): > <https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringem…> > The executive summary:<https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringem…> > The presentation in Berlin will take place at the end of September. > > <https://urheber.info/diskurs/ai-training-is-copyright-infringement> ------------------------------------------------------------------------ felicità del poco edith bruck ------------------------------------------------------------------------ Maurizio Lana Università del Piemonte Orientale Dipartimento di Studi Umanistici Piazza Roma 36 - 13100 Vercelli

Sept. 5, 2024

AI Training is Copyright Infringement

by Daniela Tafani

Press Release: A computer scientist and a legal scholar shed light on the black box of processing steps in AI training - for the first time on this scale. The presentation of the interdisciplinary study “Copyright & Training of Generative AI - Technological and Legal Foundations” took place today in the European Parliament. In spring, the Copyright Initiative commissioned Prof. Dr. Tim W. Dornis (University of Hannover) in collaboration with Prof. Dr. Sebastian Stober (University of Magdeburg) with a tandem expert opinion on the technological and legal aspects of training generative AI models. Their interdisciplinary research provides urgently needed new insights into the technically necessary intermediate steps in the training of generative artificial intelligence. For the first time on this scale, a computer scientist and a legal scholar are jointly creating evidence regarding the processing steps in AI training. During the event, many open questions about protected materials were answered in a well-founded, reliable manner and in line with the current state of the art. The work of Prof. Dornis and Prof. Stober focuses on the copyright assessment of the processing of protected material in AI training: “As a closer look at the technology of generative AI models reveals, the training of such models is not a case of text and data mining. It is a case of copyright infringement – no exception applies under German and European copyright law,” says Prof. Dornis. Prof. Stober explains that “parts of the training data can be memorized in whole or in part by current generative models - LLMs and (latent) diffusion models - and can therefore be generated again with suitable prompts by end users and thus reproduced.” Axel Voss, MEP and host of today's event in the European Parliament, expressly thanks the scientists Dornis and Stober and is pleased that “the study not only proves that the training of Generative AI models is not covered by text and data mining, but that it also provides further important indications and suggestions for a better balance between the protection of human creativity and the promotion of AI innovation.” “This study is explosive because it proves that we are dealing with large-scale theft of intellectual property. The ball is now in the politicians' court to draw the necessary conclusions and finally put an end to this theft at the expense of journalists and other authors,” commented Hanna Möllers, legal advisor to the DJV and representative of the European Federation of Journalists (EFJ). Katharina Uppenbrink, Managing Director of the Initiative Urheberrecht, emphasizes: “It is a groundbreaking result if we now have proof that the reproduction of works by an AI model constitutes a copyright-relevant reproduction and, in addition, that making them available on the European Union market may infringe the right of making available to the public.” The composer and spokesperson for the Copyright Initiative, Matthias Hornschuh, comments: “There would be a new, profitable licensing market on the horizon, but no remuneration is flowing, while generative AI is preparing to replace those whose content it lives from in its own market. This jeopardizes professional knowledge work and cannot be in the interests of society, culture or the economy. All the better that the authors of our tandem study provide the technological and copyright basis for finally turning the legal consideration of generative artificial intelligence from its head to its feet.” Dornis, Tim W. and Stober, Sebastian, Copyright and training of generative AI models - technological and legal foundations (September 4, 2024). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4946214 (in German) Please find below the downloads: The abstract can be found below and here (in English and German language): <https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringem…> The executive summary: <https://urheber.info/media/pages/diskurs/ai-training-is-copyright-infringem…> The presentation in Berlin will take place at the end of September. <https://urheber.info/diskurs/ai-training-is-copyright-infringement>

Sept. 5, 2024

draft 0.0.9 di Open Source AI Definition (da: large language model e open washing)

by Antonio

> Per questo OSI sta lavorando a una Open Source AI Definition: > https://opensource.org/deepdive/drafts/the-open-source-ai-definition-draft-… Il 22 agosto u.s. è stata pubblicata la versione 0.0.9: https://opensource.org/deepdive/drafts/open-source-ai-definition-draft-v-0-… - Data information: Sufficiently detailed information about the data used to train the system, so that a skilled person can recreate a substantially equivalent system using the same or similar data. [Data information shall be made available with licenses that comply with the Open Source Definition.] (aggiunto) - Code: The source code used to train and run the system [, made available with OSI-approved licenses]. (aggiunto) - Model: The model parameters. (eliminato) - Weights: The model weights and parameters, made available under OSI-approved terms (aggiunto) A.

Sept. 5, 2024

Re: [nexa] Quando il "cloud" viene adottato, senza "controllo" o, peggio, senza saperlo...

by B. J.

In effetti, già in un passato recente ci sono stati attacchi informatici alle varie Grid: - https://en.wikipedia.org/wiki/2015_Ukraine_power_grid_hack - https://en.wikipedia.org/wiki/2016_Kyiv_cyberattack - https://www.zdnet.com/article/how-hackers-attacked-ukraines-power-grid-impl… - https://subscriber.politicopro.com/article/eenews/1061111289 Da quanto è noto, la digitalizzazione della Grid italiana è in stato molto avanzato, anche grazie all'installazione dei contatori di nuova generazione: https://www.eai.enea.it/archivio/energia-e-green-new-deal-sommario/le-smart… B. On Tuesday, 20 August 2024 at 4:33 PM, Damiano Verzulli <damiano(a)verzulli.it> wrote: > Ho appena notato questo articolo: > > => "The gigantic and unregulated power plants in the cloud" > https://berthub.eu/articles/posts/the-gigantic-unregulated-power-plants-in-… > > che credo sia uno dei primi che affronta seriamente il gigantesco tema dei dispositivi (diversi dai PC e dagil smartphone) che hanno bisogno dell'accesso ad Internet (...senza il quale, è molto probabile che NON funzionino), per "dialogare" con un servizio cloud ad-hoc previsto dal produttore. > > Per dirla in altri termini, piu' comprensibili: > > - Ogni Alexa ha bisogno di Internet per "parlare" con AWS (almeno credo; non ho Alexa); > - Ogni dispositivo Alexa ha bisogno di Internet, per parlare con AWS (almeno credo; non ho dispositivi Alexa-enabled); > - Ogni smart-meter commerciale, ha bisogno del sito del produttore per inviargli i dati, che poi l'utente visualizza via APP; > - Moltissimissime webcam, soprattutto quelle che possono essere consultate, via APP, da mobile, anche dal di fuori della propria casa e senza una VPN, mandano il flusso video (e spesso audio) al sito del produttore (che poi, appunto, lo rende fruibile all'APP); > - etc. etc. > > e.... > > - molti impianti fotovoltaici sono gestiti da apparecchiature che "parlano" con il relativo fornitore, sia per questioni di telecontrollo che per questioni di "configurazione". > > E se questi impianti, complessivamente, producono *TANTA* energia... e il dialogo fra apparato e fornitore NON è stato organizzato "adeguatamente"... ecco che qualcuno puo'... fare delle brutte cose. > > Buona lettura :-) > > Bye, > DV > > > > -- > Damiano Verzulli > e-mail: damiano(a)verzulli.it > --- > possible?ok:while(!possible){open_mindedness++} > --- > "...I realized that free software would not generate the kind of > income that was needed. Maybe in USA or Europe, you may be able > to get a well paying job as a free software developer, but not > here [in Africa]..." -- Guido Sohne - 1973-2008 > http://ole.kenic.or.ke/pipermail/skunkworks/2008-April/005989.html

Sept. 5, 2024

The Environmental Impacts of AI -- Policy Primer

by J.C. DE MARTIN

https://huggingface.co/blog/sasha/ai-environment-primer (messaggio spedito in movimento - scusate brevità ed eventuali refusi)

Sept. 4, 2024

Re: [nexa] Why A.I. Isn’t Going to Make Art

by Guido Vetere

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.depetra(a)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.… > > > > > > > > > Il giorno dom 1 set 2024 alle 22:26 Guido Vetere <vetere.guido(a)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.lana(a)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-ma… >>> >>> >>> >>> ------------------------------ >>> >>> 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=gm…> >>> >>

Sept. 4, 2024

Re: [nexa] Why A.I. Isn’t Going to Make Art

by de petra giulio

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.… Il giorno dom 1 set 2024 alle 22:26 Guido Vetere <vetere.guido(a)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.lana(a)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-ma… >> >> >> >> ------------------------------ >> >> 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=gm…> >> >

Sept. 3, 2024

Incontro (online) con Marco Calamari il 25 settembre 2024 ore 21

by Andrea Trentini

Alla fine siamo riusciti a organizzare, grazie pubblicamente a Marco Calamari per la disponibilità Allego locandina https://www.youtube.com/@comitatomilano-liberistiita -- Andrea Trentini ⠠⠵ http://atrent.it public key ID: 0xA7A91E3B Dip.to di Informatica Università degli Studi di Milano

Sept. 2, 2024

altro governo che vuole impedire comunicazioni crittografate... brutta tendenza?

by Andrea Trentini

https://cybernews.com/privacy/denmark-ban-telegram-signal-whatsapp/ -- Andrea Trentini ⠠⠵ http://atrent.it public key ID: 0xA7A91E3B Dip.to di Informatica Università degli Studi di Milano

Sept. 2, 2024

165° Mercoledì di Nexa | 11 settembre 2024, ore 17.00

by Nexa - Media

Gentilissime, gentilissimi, Vi invitiamo a partecipare al 175° Mercoledì di Nexa, che si terrà mercoledì 11 settembre, alle ore 17.00, con un incontro dal titolo "Le emissioni segrete. L'impatto ambientale dell'universo digitale". Ospite dell'incontro: Giovanna Sissa (Università di Genova). L'incontro si terrà IN PRESENZA e ONLINE. SEDE FISICA dell'incontro: Centro Nexa su Internet e Società, Politecnico di Torino, Via Boggio 65/a, Torino (1° piano). Per accedere alla sala si raccomanda di suonare al citofono Portineria e di seguire le indicazioni segnalate lungo il percorso. QUI<https://nexa.polito.it/contatti> maggiori informazioni su come raggiungerci. STANZA VIRTUALE dell'incontro: https://didattica.polito.it/VClass/NexaEvent Di seguito maggiori dettagli: https://nexa.polito.it/mercoledi-175 Cordiali saluti, -- Valeria Bergantino Communication Manager Nexa Center for Internet & Society Politecnico di Torino - DAUIN Via Pier Carlo Boggio, 65/A - 10138 Torino web: https://nexa.polito.it/ mail: valeria.bergantino(a)polito.it<mailto:valeria.bergantino@polito.it> tel: 3473443585

Sept. 2, 2024

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