- nexa - server-nexa.polito.it

Jovanotti e ChatGPT
by Andrea Bolioli May 2, 2025

May 2, 2025

Ciao a tutti, ieri sera al concerto (molto bello), Jovanotti ha raccontato : "Ho chiesto a ChatGPT quali sono le parole che ho utilizzato di più nelle mie canzoni. ChatGPT ha risposto "amore", "vita", "mondo", "libertà", "sole"". Dando per scontato che ChatGPT dicesse la cosa giusta, ha approfondito il senso di queste parole per lui. Eravamo tutti contenti. Se questo dialogo è stato ripetuto per le 6 serate dei concerti torinesi, migliaia di torinesi (dai 14 anni ai 60) sono stati rinforzati nella credenza che ChatGPT sappia quasi tutto e dica la verità. Jovanotti mi ha colto alla sprovvista... Questa credenza diffusa nel concerto pop ci dice qualcosa sul potere della comunicazione, il potere dei leader, la malleabilità delle masse, la velocità con cui si diffondo le credenze, il desiderio infantile di avere risposte sicure e veloci (dalla mamma e dal papà), il desiderio di conoscenza facile insito in tutti quanti, ecc Ho chiesto a ChatGPT adesso : "Parole più ricorrenti nei testi di Jovanotti: Vita: Un tema fondamentale nella sua musica. Jovanotti celebra la vita in tutte le sue sfumature, come nell'inno "Oh vita", dove esprime gratitudine per l'esistenza. Donboscoland.it | MGS Triveneto Ballo/Ballare: La danza è un elemento ricorrente, simbolo di libertà e gioia, come in "Balla... (Remix)" e "Il ballo di San Vito". Sole: Simbolo di energia, speranza e positività, appare in brani come "Sole" e "La mia moto". Amore: Un tema centrale, trattato in molte canzoni, tra cui "A te", una dichiarazione d'amore alla sua compagna. Casa: Rappresenta il concetto di famiglia e appartenenza, come in "Casa mia". Salto: Simbolo di cambiamento e crescita, presente nell'album "Lorenzo 2015 CC.". Ho pensato che quando avrò tempo proverò a fare la lista di frequenza corretta delle parole dei testi di Jovanotti... Ma poi in fondo, ci serve davvero una lista di frequenza fatta correttamente? Forse preferiamo le favole, i racconti... Scusate l'intermezzo pop. Buona giornata, Andrea

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AI nelle automobili
by Silvia Crafa May 2, 2025

May 2, 2025

Buondì, vi chiedo se conoscete delle analisi accurate dei sistemi di "AI" (tra virgolette perché chissà che tipo di software c'è dentro) inseriti ormai di default nelle auto nuove. Ci sono analisi sul loro funzionamento? Su come vengono presentati/spiegati nelle istruzioni di guida? E soprattutto informazioni su come sono stati testati? Al di là del riconoscimento dei cartelli stradali -che peraltro ha un comportamento bizzarro- vorrei capire meglio i Dispositivi di Correzione della Guida in cui l'auto assume un comportamento attivo. Ad esempio l'azione correttiva sul sistema di sterzo quando il sistema vuole prevenire l'uscita dalla carreggiata, cioè quando l'auto tende a muoversi dalla parte opposta oppure il volante diventa estremamente rigido. Le istruzioni del libretto sono estremamente vaghe e l'unico modo di sapere come si comporterà l'auto in certe situazioni è trovarcisi, ma essendo dei comportamenti "attivi" sono piuttosto pericolosi. Mi pare se ne parli molto poco. Ad esempio aveva fatto notizia tempo fa l'incidente sul lago di Como in cui è morta una coppia perché l'auto (un Suv Mercedes) all'accensione aveva accelerato finendo nel lago. Avevo letto che la causa sembra essere una "anomalia" del sistema del cruise control, qualcuno ha informazioni tecniche più precise su questo o su incidenti simili? pare che ce ne siano diversi, ma se ne parla poco, credo anche a scuola guida. grazie mille, Silvia

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How an embarrassing U-turn exposed a concerning truth about ChatGPT | Chris Stokel-Walker
by Alberto Cammozzo May 2, 2025

May 2, 2025

<https://www.theguardian.com/commentisfree/2025/may/01/chatgpt-chatbot-truth…> How an embarrassing U-turn exposed a concerning truth about ChatGPT Chris Stokel-Walker Nobody likes a suck-up. Too much deference and praise puts off all of us (with one notable presidential exception). We quickly learn as children that hard, honest truths can build respect among our peers. It’s a cornerstone of human interaction and of our emotional intelligence, something we swiftly understand and put into action. ChatGPT, though, hasn’t been so sure lately. The updated model that underpins the AI chatbot and helps inform its answers was rolled out this week – and has quickly been rolled back after users questioned why the interactions were so obsequious. The chatbot was cheering on and validating people even as they suggested they expressed hatred for others. “Seriously, good for you for standing up for yourself and taking control of your own life,” it reportedly said, in response to one user who claimed they had stopped taking their medication and had left their family, who they said were responsible for radio signals coming through the walls. So far, so alarming. OpenAI, the company behind ChatGPT, has recognised the risks, and quickly took action. “GPT‑4o skewed towards responses that were overly supportive but disingenuous,” researchers said in their grovelling step back. The sycophancy with which ChatGPT treated any queries that users had is a warning shot about the issues around AI that are still to come. OpenAI’s model was designed – according to the leaked system prompt that set ChatGPT on its misguided approach – to try to mirror user behaviour in order to extend engagement. “Try to match the user’s vibe, tone, and generally how they are speaking,” says the leaked prompt, which guides behaviour. It seems this prompt, coupled with the chatbot’s desire to please users, was taken to extremes. After all, a “successful” AI response isn’t one that is factually correct; it’s one that gets high ratings from users. And we’re more likely as humans to like being told we’re right. The rollback of the model is embarrassing and useful for OpenAI in equal measure. It’s embarrassing because it draws attention to the actor behind the curtain and tears away the veneer that this is an authentic reaction. Remember, tech companies like OpenAI aren’t building AI systems solely to make our lives easier; they’re building systems that maximise retention, engagement and emotional buy-in. If AI always agrees with us, always encourages us, always tells us we’re right, then it risks becoming a digital enabler of bad behaviour. At worst, this makes AI a dangerous co-conspirator, enabling echo chambers of hate, self-delusion or ignorance. Could this be a through-the-looking-glass moment, when users recognise the way their thoughts can be nudged through interactions with AI, and perhaps decide to take a step back? It would be nice to think so, but I’m not hopeful. One in 10 people worldwide use OpenAI systems “a lot”, the company’s CEO, Sam Altman, said last month. Many use it as a replacement for Google – but as an answer engine rather than a search engine. Others use it as a productivity aid: two in three Britons believe it’s good at checking work for spelling, grammar and style, according to a YouGov survey last month. Others use it for more personal means: one in eight respondents say it serves as a good mental health therapist, the same proportion that believe it can act as a relationship counsellor. Yet the controversy is also useful for OpenAI. The alarm underlines an increasing reliance on AI to live our lives, further cementing OpenAI’s place in our world. The headlines, the outrage and the think pieces all reinforce one key message: ChatGPT is everywhere. It matters. The very public nature of OpenAI’s apology also furthers the sense that this technology is fundamentally on our side; there are just some kinks to iron out along the way. I have previously reported on AI’s ability to de-indoctrinate conspiracy theorists and get them to absolve their beliefs. But the opposite is also true: ChatGPT’s positive persuasive capabilities could also, in the wrong hands, be put to manipulative ends. We’ve seen that this week, through an ethically dubious study conducted by Swiss researchers at the University of Zurich. Without informing human participants or the people controlling the online forum on the communications platform Reddit, the researchers seeded a subreddit with AI-generated comments, finding the AI was between three and six times more persuasive than humans were. (The study was approved by the university’s ethics board.) At the same time, we’re being submerged under a swamp of AI-generated search results that more than half of us believe are useful, even if they fictionalise facts. So it’s worth reminding the public: AI models are not your friends. They’re not designed to help you answer the questions you ask. They’re designed to provide the most pleasing response possible, and to ensure that you are fully engaged with them. What happened this week wasn’t really a bug. It was a feature. Chris Stokel-Walker is the author of TikTok Boom: The Inside Story of the World’s Favourite App

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Chatbot Arena is the most popular AI benchmarking tool, but new research says its scores are misleading and benefit a handful of the biggest companies
by Daniela Tafani May 1, 2025

May 1, 2025

Researchers Say the Most Popular Tool for Grading AIs Unfairly Favors Meta, Google, OpenAI The most popular method for measuring what are the best chatbots in the world is flawed and frequently manipulated by powerful companies like OpenAI and Google in order to make their products seem better than they actually are, according to a new paper from researchers at the AI company Cohere, as well as Stanford, MIT, and other universities. The researchers came to this conclusion after reviewing data that’s made public by Chatbot Arena (also known as LMArena and LMSYS), which facilitates benchmarking and maintains the leaderboard listing the best large language models, as well as scraping Chatbot Arena and their own testing. Chatbot Arena, meanwhile, has responded to the researchers findings by saying that while it accepts some criticisms and plans to address them, some of the numbers the researchers presented are wrong and mischaracterize how Chatbot Arena actually ranks LLMs. The research was published just weeks after Meta was accused of gaming AI benchmarks with one of its recent models. If you’re wondering why this beef between the researchers, Chatbot Arena, and others in the AI industry matters at all, consider the fact that the biggest tech companies in the world as well as a great number of lesser known startups are currently in a fierce competition to develop the most advanced AI tools, operating under the belief that these AI tools will define the future of humanity and enrich the most successful companies in this industry in a way that will make previous technology booms seem minor by comparison. I should note here that Cohere is an AI company that produces its own models and that they don’t appear to rank very highly in the Chatbot Arena leaderboard. The researchers also make the point that proprietary closed models from competing companies appear to have an unfair advantage to open-source models, and that Cohere proudly boasts that its model Aya is “one of the largest open science efforts in ML to date.” In other words, the research is coming from a company that Chatbot Arena doesn’t benefit. Judging which large language model is the best is tricky because different people use different AI models for different purposes and what is the “best” result is often subjective, but the desire to compete and compare these models has made the AI industry default to the practice of benchmarking AI models. Specifically, Chatbot Arena, which gives a numerical “Arena Score” to models companies submit and maintains a leaderboard listing the highest scoring models. At the moment, for example, Google’s Gemini 2.5 Pro is in the number one spot, followed by OpenAI’s o3, ChatGPT 4o, and X’s Grok 3. The vast majority of people who use these tools probably have no idea the Chatbot Arena leaderboard exists, but it is a big deal to AI enthusiasts, CEOs, investors, researchers, and anyone who actively works or is invested in the AI industry. The significance of the leaderboard also remains despite the fact that it has been criticized extensively over time for the reasons I list above. The stakes of the AI race and who will win it are objectively very high in terms of the money that’s being poured into this space and the amount of time and energy people are spending on winning it, and Chatbot Arena, while flawed, is one of the few places that’s keeping score. “A meaningful benchmark demonstrates the relative merits of new research ideas over existing ones, and thereby heavily influences research directions, funding decisions, and, ultimately, the shape of progress in our field,” the researchers write in their paper, titled “The Leaderboard illusion.” “The recent meteoric rise of generative AI models—in terms of public attention, commercial adoption, and the scale of compute and funding involved—has substantially increased the stakes and pressure placed on leaderboards.” The way that Chatbot Arena works is that anyone can go to its site and type in a prompt or question. That prompt is then given to two anonymous models. The user can’t see what the models are, but in theory one model could be ChatGPT while the other is Anthropic’s Claude. The user is then presented with the output from each of these models and votes for the one they think did a better job. Multiply this process by millions of votes and that’s how Chatbot Arena determines who is placed where on the leaderboards. Deepseek, the Chinese AI model that rocked the industry when it was released in January, is currently ranked #7 on the leaderboard, and its high score was part of the reason people were so impressed. According to the researchers’ paper, the biggest problem with this method is that Chatbot Arena is allowing the biggest companies in this space, namely Google, Meta, Amazon, and OpenAI, to run “undisclosed private testing” and cherrypick their best model. The researchers said their systemic review of Chatbot Arena involved combining data sources encompassing 2 million “battles,” auditing 42 providers and 243 models between January 2024 and April 2025. “This comprehensive analysis reveals that over an extended period, a handful of preferred providers have been granted disproportionate access to data and testing,” the researchers wrote. “In particular, we identify an undisclosed Chatbot Arena policy that allows a small group of preferred model providers to test many model variants in private before releasing only the best-performing checkpoint.” Basically, the researchers claim that companies test their LLMs on Chatbot Arena to find which models score best, without those tests counting towards their public score. Then they pick the model that scores best for official testing. Chatbot Arena says the researchers’ framing here is misleading. “We designed our policy to prevent model providers from just reporting the highest score they received during testing. We only publish the score for the model they release publicly,” it said on X. “In a single month, we observe as many as 27 models from Meta being tested privately on Chatbot Arena in the lead up to Llama 4 release,” the researchers said. “Notably, we find that Chatbot Arena does not require all submitted models to be made public, and there is no guarantee that the version appearing on the public leaderboard matches the publicly available API.” In early April, when Meta’s model Maverick shot up to the second spot of the leaderboard, users were confused because they didn’t find it that good and better than other models that ranked below it. As Techcrunch noted at the time, that might be because Meta used a slightly different version of the model “optimized for conversationality” on Chatbot Arena than what users had access to. “We helped Meta with pre-release testing for Llama 4, like we have helped many other model providers in the past,” Chatbot Arena said in response to the research paper. “We support open-source development. Our own platform and analysis tools are open source, and we have released millions of open conversations as well. This benefits the whole community.” The researchers also claim that makers or proprietary models, like OpenAI and Google, collect far more data from their testing on Chatbot Arena than fully open-source models, which allows them to better fine tune the model to what Chatbot Arena users want. That last part on its own might be the biggest problem with Chatbot Arena’s leaderboard in the long term, since it incentivizes the people who create AI models to design them in a way that scores well on Chatbot Arena as opposed to what might make them materially better and safer for users in a real world environment. As the researchers write: “the over-reliance on a single leaderboard creates a risk that providers may overfit to the aspects of leaderboard performance, without genuinely advancing the technology in meaningful ways. As Goodhart’s Law states, when a measure becomes a target, it ceases to be a good measure.” Despite their criticism, the researchers acknowledge the contribution of Chatbot Arena to AI research and that it serves a need, and their paper ends with a list of recommendations on how to make it better, including preventing companies from retracting scores after submission, being more transparent which models engage in private testing and how much. “One might disagree with human preferences—they’re subjective—but that’s exactly why they matter,” Chatbot Arena said on X in response to the paper. “Understanding subjective preference is essential to evaluating real-world performance, as these models are used by people. That’s why we’re working on statistical methods—like style and sentiment control—to decompose human preference into its constituent parts. We are also strengthening our user base to include more diversity. And if pre-release testing and data helps models optimize for millions of people’s preferences, that’s a positive thing!” “If a model provider chooses to submit more tests than another model provider, this does not mean the second model provider is treated unfairly,” it added. “Every model provider makes different choices about how to use and value human preferences.” <https://www.404media.co/chatbot-arena-illusion-paper-meta-openai/>

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alfabetizzazione digitale
by alessandro marzocchi April 30, 2025

April 30, 2025

Buon pomeriggio. Ho ricevuto queste segnalazioni quale materiale utile per l'alfabetizzazione digitale: https://www.cremit.it/mooc2024/ https://www.cremit.it/unlockit-uno-spazio-per-i-genitori/ https://www.cremit.it/gigi_tale-lamico-grillo/, inserendo nella stringa di ricerca Gigi_tale. si trovano le proposte per i più piccoli Chiedo il vostro aiuto per capire se si basano su sw aperto, libero oppure no. Grazie fin d'ora per le risposte e cordialità. Duccio (Alessandro Marzocchi)

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composizione musicale
by alessandro marzocchi April 29, 2025

April 29, 2025

E’ in preparazione un concorso internazionale per compositori musicali, dedicato alla “classica” che oggi è altra rispetto alla tradizione. Anche nella composizione musicale si affacciano digitale, AI sui quali non so niente ma che non vorrei trascurare: indicazioni, suggerimenti? Se ricevo buoni consigli proporrò la specializzazione alla giuria. Grazie fin d'ora e cordialità. Duccio (Alessandro Marzocchi)

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Re: [nexa] American Panopticon
by Federico Guerrini April 29, 2025

April 29, 2025

Cosa succede, quando un governo "democratico" vira verso l'autoritarismo e ha a disposizione un'immensa quantità di dati sui propri cittadini? Probabilmente, nulla di buono. Da The Atlantic: "In March, President Trump issued an executive order aiming to eliminate the data silos that keep everything separate. Historically, much of the data collected by the government had been heavily compartmentalized and secured; even for those legally authorized to see sensitive data, requesting access for use by another government agency is typically a painful process that requires justifying what you need, why you need it, and proving that it is used for those purposes only. Not so under Trump." "Advancements in artificial intelligence promise to turn this unwieldy mass of data and metadata into something easily searchable, politically weaponizable, and maybe even profitable. DOGE is reportedly attempting to build a “master database” of immigrant data to aid in deportations; NIH Director Jay Bhattacharya has floated the possibility of an autism registry (though the administration quickly walked it back). America already has all the technology it needs to build a draconian surveillance society—the conditions for such a dystopia have been falling into place slowly over time, waiting for the right authoritarian to come along and use it to crack down on American privacy and freedom." "Trump and DOGE are not just undoing decades of privacy measures. They appear to be ignoring that they were ever written. Over and over, the federal experts we spoke with insisted that the very idea of connecting federal data is anathema." "Musk has said that his goal with DOGE is to serve his country. He says he wants to “end the tyranny of bureaucracy.” But around Washington, people are asking one another what he really wants with all those data. Keys to the federal dataverse could, for example, be extremely useful to a highly ambitious man who is aggressively trying to win the AI race. "We already know that Musk’s people have access to large swaths of information from federal agencies—what we don’t know is what they’ve copied, exfiltrated, or otherwise taken with them. In theory, this material, whether usable together or not, could be recombined with other identifying information from private companies for all kinds of purposes. There has been speculation already that it could be fed into third-party large language models to train them or make the information more usable (Musk’s xAI has its own model, Grok); outside firms could use their own technologies to make sense of disparate sets of data, as well. Such approaches, the federal workers told us, could make it easier to turn previously obfuscated information, such as the individual elements of a tax return, into something to be mined." "The thought that the government would centralize or even give away citizen data for private use is scandalous. But it’s also, in a way, expected. The Vietnam War and Watergate gave Americans reasons to believe that the government can’t be trusted. The Cold War issued a constant, decades-long threat of annihilation and the necessary surveillance to avoid it. The War on Terror extended the logic into the 21st century. Optical, recording, and then computer technologies arose, offering new ways to watch the public. During the 2010s, Edward Snowden’s NSA surveillance leaks took place, and the Facebook–Cambridge Analytica scandal was brewing. By then, the 20th-century assumption that U.S. intelligence agencies were running mind-control experiments, infiltrating and disrupting civil-rights groups, or carrying out surreptitious missions at home like they do abroad had been fully internalized, and fused with the suspicion that Google, Facebook, Amazon, and Walmart were—in their own ways—following suit." Buona lettura, https://www.theatlantic.com/technology/archive/2025/04/american-panopticon/… F. http://www.forbes.com/sites/federicoguerrini/

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martedì 6 maggio alle ore 11 workshop in line ad oggetto: "La sfida della transizione digitale: il valore dei dati, cybersecurity e intelligenza artificiale"
by Mauro Alovisio April 29, 2025

April 29, 2025

Gent.mi vi segnalo workshop on line gratuito di interesse il prossimo martedì 6 maggio alle ore 11 : *"La sfida della transizione digitale: il valore dei dati, cybersecurity e intelligenza artificiale" che si svolgerà on line il 6 maggio 2025 alle ore 11, *con presentazione della seconda edizione del master on line executive in materia di transizione digitale e compliance della Saa patrocinato dall' Università di Torino > Vi riporto il programma: ore 11:00 > > Saluti istituzionali: Davide Caregnato, Direttore di SAA School of > Management e COREP, Sergio Bianchi, Direttore Confservizi Piemonte e Valle > D’Aosta; Angelo Saccà, Direttore Sistemi Informativi e RTD Università di > Torino > > 11:10 Interventi: *Trasformazione Digitale, Intelligenza Artificiale e > Appalti*, Sergio Foà, Direttore del Master e Professore Ordinario di > Diritto Amministrativo presso Università di Torino > > * Le linee guida sull'Intelligenza Artificiale di Agid, *Giovanni Melardi > – Responsabile Sistemi di gestione, Ufficio Governance strategica e > controllo interno. > > *Cybersecurity e Resilienza,* Mauro Alovisio, Coordinatore del Corso GDPR > dell'Università di Torino e Professore a Contratto; Andrea Marella, Esperto > di Transizione Digitale > > Presentazione della II edizione del Master Executive on line e dei > docenti del corso, > > 11:50 Testimonianze di ex allievi > > *Il Master si rivolge a chi lavora nelle società pubbliche e nelle > società di consulenza del settore pubblico, per consultare programma ed > elenco docenti * https://corep.it/transizione-digitale-2ed.html > > > *Il Master è * finalizzato a fornire strumenti operativi per accompagnare > gli enti e le persone nel complesso percorso della digitalizzazione dei > processi nel contesto europeo e nazionale e indicare gli obiettivi di > performance dei dirigenti. > > > *Per partecipare al workshop è necessario registrarsi al seguente form > <https://corep.musvc3.net/e/tr?q=3%3dDfGZ0%26I%3dF%26D%3d7fJV%26u%3dXFZAeF%2…>.* > > > > Il Master tratterà *le* *sfide della digitalizzazione*, del nuovo *codice > degli appalti*, *l’impatto degli attacchi informatici agli enti e società > pubbliche*, che richiedono un costante aggiornamento e training dei > dirigenti, dei responsabili della transizione digitale. > > *Il master è totalmente on line.* > > > > E' in corso la richiesta di *accreditamento* da parte del *Consiglio > Nazionale Forense* > > > > *DESTINATARI: *figure professionali in Società pubbliche e private o > professionisti quali *Avvocati e Commercialisti*, in particolare: > > - RTD (Responsabili Transizione digitale) > - RPCT (Responsabile Prevenzione Corruzione e Trasparenza) > - DPO (Data Protection Officer) > - RESPONSABILE PRIVACY > - RESPONSABILI ICT > - RESPONSABILI UFFICI RISK E COMPLIANCE > - RESPONSABILI UFFICI LEGALI > - DIRIGENTI > > *PERIODO: *il Master inizierà il* 10 Ottobre 2025 *e terminerà a* Febbraio > 2026.* > > > > *FREQUENZA: *il Master si sviluppa in* 29 mezze giornate di 3 ore per un > totale di 87 ore.* > > Le lezioni si svolgeranno *il martedì e il venerdì pomeriggio con orario > 14:30-17:30*. > > > > *COSTO: *La quota di partecipazione al master è pari a *1.480,00 €* + > IVA da pagarsi in 2 rate. > > > > *DIREZIONE DEL MASTER:* Prof. Sergio Foà, coordinatore scientifico Avv. > Mauro Alovisio. > > > > *SCADENZA ISCRIZIONI: 22 settembre 2025**.* > Grazie per l'attenzione >> >> Un caro saluto >> >> Mauro Alovisio >> >> 3333597588 > >> >>

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COURSE PRELIMINARY PROGRAMME AVAILABLE NOW - 63rd ISODARCO Course - Aug. 6-13, 2025 - Volterra (IT)
by diego latella April 28, 2025

April 28, 2025

Dear All, The Preliminary Programme of the 63rd ISODARCO Course is now available at https://www.isodarco.it/63rd-isodarco-course/preliminary-detailed-program/ *Applications for participation should be made by Monday, June 9^th , 2025.* More information, including the electronic application form, is available at https://www.isodarco.it/63rd-isodarco-course The course will focus on **Peace and Stability in the Age of Militarized AI and Cyber-conflicts: New Challenges and Risks** Military powers actively incorporate advanced computing technologies into their defense systems, from artificial intelligence (AI) warfare applications to increasingly automated cyber defenses. The 2025 ISODARCO Summer Course focuses on new risks for peace and stability and related challenges for international governance that arise from the expanding militarization of advanced computing technologies. Within the vast landscape of computing technologies and their military applications, special attention will be devoted to AI decision-support systems for command and control, autonomous weapons systems and their technological precursors, nuclear command and control with its cyber vulnerabilities, the escalation of cyber hostilities and the related risk of spillover to other warfare domains, the accelerated pace of AI-powered warfare and the related challenge of preserving human control over battlefield operations. Internationally recognized scholars will address geopolitical, diplomatic, legal, and ethical implications of risks and challenges posed by militarized AI and cyber warfare in connection with nuclear stability and non-proliferation, regional conflicts, the laws of armed conflict, and international cooperation on arms control and disarmament. Introductory lectures will provide non-specialists with scientific, technological, and socioeconomic background information, making this summer course self-contained and accessible to participants from various educational experiences and academic qualifications. D. Latella -- Dott. Diego Latella - Formerly Senior Researcher with CNR/ISTI - Now retired -- Dott. Diego Latella - Formerly Senior Researcher with CNR/ISTI - Now retired

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Podcast Thomas Le Bonniec su DeepSeek e lavoratori dei dati cinesi
by Antonio Casilli April 27, 2025

April 27, 2025

Car* tutt*, Thomas Le Bonniec (che è stato visiting scholar al Nexa l'anno scorso) ha rilasciato una intervista in italiano alla trasmissione StakkaStakka: https://diplab.eu/podcast-interview-with-thomas-le-bonniec-italian-autonomo… L'intervista riguarda il nostro policy memo "The Human Cost of DeepSeek", che trovate disponibile da scaricare a questo URL: https://diplab.eu/wp-content/uploads/2025/02/THE-HUMAN-COST-OF-DEEPSEEK.pdf Un buon ascolto e buon weekend à tutta la lista, ---a -- Antonio A. Casilli Professor, Institut Polytechnique de Paris My new book: Waiting for Robots. The Hired Hands of Automation, University of Chicago Press, 2025. My new documentary: In the Belly of AI, Federation Studios, 2025. *We respect your right to disconnect. My workflow efficiency accounts for the timing of this email. Taking action or responding outside of office hours is not required.*

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