- nexa - server-nexa.polito.it

Court orders maker of Pegasus spyware to hand over code to WhatsApp
by Antonio March 1, 2024

March 1, 2024

NSO Group, the maker of one the world's most sophisticated cyber weapons, has been ordered by a US court to hand its code for Pegasus and other spyware products to WhatsApp as part of the company's ongoing litigation. The decision by Judge Phyllis Hamilton is a major legal victory for WhatsApp, the Meta-owned communication app which has been embroiled in a lawsuit against NSO since 2019, when it alleged that the Israeli company's spyware had been used against 1,400 WhatsApp users over a two-week period. NSO's Pegasus code, and code for other surveillance products it sells, is seen as a closely and highly sought state secret. NSO is closely regulated by the Israeli ministry of defense, which must review and approve the sale of all licences to foreign governments. In reaching her decision, Hamilton considered a plea by NSO to excuse it of all its discovery obligations in the case due to "various US and Israeli restrictions." Ultimately, however, she sided with WhatsApp in ordering the company to produce"all relevant spyware" for a period of one year before and after the two weeks in which WhatsApp users were allegedly attacked: from 29 April 2018 to 10 May 2020. NSO must also give WhatsApp information "concerning the full functionality of the relevant spyware." Hamilton did, however, decide in NSO's favor on a different matter: the company will not be forced at this time to divulge the names of its clients or information regarding its server architecture. https://www.theguardian.com/technology/2024/feb/29/pegasus-surveillance-cod… Qualche vecchio link per rinfrescare la memoria su NSO: https://www.amnesty.org/en/documents/doc10/4516/2021/en/ https://globalfreedomofexpression.columbia.edu/wp-content/uploads/2020/07/4… https://citizenlab.ca/2018/09/hide-and-seek-tracking-nso-groups-pegasus-spy… A.

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La raccolta dati su Facebook e Instagram è "massiva" e "illegale"
by Antonio March 1, 2024

March 1, 2024

Otto gruppi europei [1] di consumatori hanno depositato un reclamo per la violazione delle norme sulla privacy da parte di Meta. Comunicato stampa: https://www.beuc.eu/press-releases/consumer-groups-launch-complaints-agains… Riassunto del reclamo: https://www.beuc.eu/sites/default/files/publications/BEUC-X-2024-020_How_Me… A. [1] dTest (Czech Republic), Forbrugerrådet Tænk (Denmark), EKPIZO (Greece), UFC-Que Choisir (France), Forbrukerrådet (Norway), Spoločnosť ochrany spotrebiteľov (S.O.S.) Poprad (Slovakia), Zveza Potrošnikov Slovenije – ZPS (Slovenia) and CECU (Spain)

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Re: [nexa] Microsoft, Mistral AI e l'AI Act
by Giuseppe Attardi Feb. 29, 2024

Feb. 29, 2024

Secondo Creative Commons, l’utilizzo di pagine web per l’addestramento di modelli, costituisce “fair use”: https://creativecommons.org/2023/02/17/fair-use-training-generative-ai/ — > On 27 Feb 2024, at 12:00, nexa-request(a)server-nexa.polito.it wrote: > > From: Stefano Zacchiroli <zack(a)upsilon.cc> > To: nexa <nexa(a)server-nexa.polito.it> > Subject: Re: [nexa] Microsoft, Mistral AI e l'AI Act > Message-ID: <20240227093929.hnchfamvlyh2lo75(a)upsilon.cc> > Content-Type: text/plain; charset=utf-8 > >> On Tue, Feb 27, 2024 at 09:17:10AM +0100, Giuseppe Attardi wrote: >> Facciamolo con fondi pubblici un modello davvero completamente Open, >> dai dati di apprendimento, al codice, ai pesi del modello, ai test di >> valutazione. > > Concordo con l'obiettivo e sul fatto che una AI che possa dirsi "open" > (o meglio: "libera") dovrebbe esserlo in tutto: dataset di training, > codice di training, codice di inferenza, pesi del modello. > > Ma attenzione al fatto che, a leggi vigenti, tale obiettivo non è > raggiungibile per modelli a-la ChatGPT. Il motivo è che includono nei > loro dataset di training grandi parti del Web (solitamente ottenute via > crawling fatto in casa), che nessuna parte terza può legittimamente > redistribuire, dato che solo una piccolissima parte del Web è > disponibile sotto licenze libere. > > Una AI "libera", secondo i criteri accennati sopra, ha quindi oggi uno > svantaggio competitivo enorme rispetto a quelle chiuse --- il che è > molto deprimente. L'evoluzione tecnologica e scientifica sta andando nel > verso giusto, con modelli sempre più aperti che riescono sempre meglio a > rivalizzare con quelli chiusi (o ibridi) ma la strada è ancora lunga e > non è detto che il gap sia completamente colmabile. > > Nel mentre dovremmo guardare criticamente alle regolamentazioni che > hanno permesso tutto questo "yolo training", che dà un vantaggio > competitivo enorme a chi libero non vuole essere. > > Ciao > -- > Stefano Zacchiroli . zack(a)upsilon.cc . https://upsilon.cc/zack _. ^ ._ > Full professor of Computer Science o o o \/|V|\/ > Télécom Paris, Polytechnic Institute of Paris o o o </> <\> > Co-founder & CTO Software Heritage o o o o /\|^|/\ > https://twitter.com/zacchiro . https://mastodon.xyz/@zacchiro '" V "'

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funzione di ricerca nelle mail della lista?
by maurizio lana Feb. 29, 2024

Feb. 29, 2024

vorrei recuperare i messaggi scambiati in lista in cui si parla della scuola, ma sono un po' in difficoltà con https://www.mail-archive.com/search?a=1&l=all&haswords=&x=12&y=7&from=nexa posto che gli archivi sono online, ci sono altri strumenti che si possono usare, al posto di mail-archive? Maurizio ------------------------------------------------------------------------ one of the things I really believed in is the idea of simplicity, that life should always be moving toward more simplicity rather than more complexity yvon chouinard ------------------------------------------------------------------------ Maurizio Lana Università del Piemonte Orientale Dipartimento di Studi Umanistici Piazza Roma 36 - 13100 Vercelli

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LMs and AI make software development harder
by Daniela Tafani Feb. 29, 2024

Feb. 29, 2024

LLMs and AI make software development harder LLMs and AI make software development harder. Wait, what? Isn’t the whole point of AI to make writing code easier? Well, yes. But writing code is the easy part of software development. The hard part is understanding the problem, designing business logic and debugging tough bugs. And that’s where AI code assistants like copilot or chatgpt make our job harder, as they strip a way the easy parts of our job and only leave us with the hard parts and make it harder for new developers to master the craft of software development. Coding is the easy part? Is coding really that easy? No, not exactly easy - mastering a programming language still takes years of practice. But when looking at software development as a whole, writing code is one of the easier part and it is no wonder that chatgpt and copilot can write decent code. First, they have trained on millions of lines of code and second, code is by its nature very easy to understand for a machine as programming languages are very structured languages with limited vocabularies. For a LLM it is probably much easier to learn than natural language. Programming languages are just very powerful tools that we use to solve problems In the end, programming languages are just very powerful tools that we use to solve problems. And the hard part is not the learning tool, but understanding the problem and designing a solution for it. This is instantly obvious as most software engineering problems could be solved by a lot of different programming languages, which one to pick is a matter of context or even personal preference. Another indicator that programming is that easy part is, that the more senior a software developer gets, the less time they usually spend writing code. Instead seniors spend more time understanding the problem, designing the solution, jumping in to debug tough bugs or doing design decisions and of course mentoring junior team members. While this might not be true for every senior developer, when looking at my software development bubble this is a clear trend. The hard parts of software development Copilot and other AI assistants are a great help for developers, but they are not flawless. A part of it is natural, as they are trained on existing code without any context and there are also some bad habits from the training data that code assistants might have picked up. And while this might get optimized over time, at the moment it means that developers still have to review the code that is generated by the AI code assistants. And reviews are hard - especially if one cannot query the author of the code for their intent. And even if the code is good enough, it might still introduce flaws into the control logic of a program, might be missing edge cases or introduce a regression bug when integrated into an existing code base. This means that developers have to debug the code that is generated by the AI code assistants in case of an error. And debugging is hard - especially for these kind of problems where it might be hard to recreate the circumstances that cause the bug in the first place. As the generated code heavily depends on the context we give the AI code assistants, this means that we have to be very precise in our descriptions which means that we have to understand the problem very well, which requires domain knowledge and context awareness on the side of the developer. Even if we just focus on the technical part, being aware of the surrounding architecture and the existing code is crucial to get good results. Granted we could ask LLMs like chatgpt for help with integration into the codebase or we could just pass it the whole codebase and let it redesign everything. But apart from requiring lot of input to give enough context debugging in an unfamiliar codebase is even tougher than debugging stuff that you wrote yourself. And then there is the whole thing about figuring out what exactly our product should do, how it should behave and how it should look like. At the moment this still requires a lot of human smarts and while AI tools might allow us to iterate faster on figuring out what we want to build in the end it is still a human that has to make the decision. AI generated software development is exhausting It seems a given that AI assistants will change our job by automating away writing code and even helping us with some design decisions. It is very convenient that we can ask chatgpt questions regarding system design and get reasonable answers. What is still left to us is making the decision on which answer to pick and which prompt to give to the LLM to get the results that we need. And this is very exhausting - decision fatigue is a thing and it is very real. Already before AI code assistants the limiting factor in the speed of delivering software was not the often the decision making process of an organization or a team - not writing the code. The limiting factor in delivery speed is decision making, not writing code On top of that is that current company structures will most likely still hold software developers accountable for the code that is running in a product, not the AI code assistants that wrote them in the first place. This will add another layer of stress on it, not just do we need to make more decisions faster, we are also to blame if the AI code assistants make a mistake. And if there is a mistake then the debugging needs to be done, which often needs a lot of context and background knowledge to be efficient. AI tools are of less help there, because they cannot figure out context changes by themselves. They might help us with the easy part of debugging like running tests with different variations, to narrow down the cause but finding the prompt for an LLM to generate the fix will still be on us. Are AI tools replacing developers? AI assistants might lower the initial hurdle to get into software development, but they will not make it easier to become a good, experienced software developer. Most of the senior developers I know gained the background knowledge and context needed to formulate complex solutions from years of slogging through (bad) code and learning from their mistakes. This might be an inefficient way of learning but it is very effective in building up the domain knowledge that is needed for software development. This knowledge is also something that is very hard to teach in a formal way, as books or online tutorials by nature are somewhat generic and and adaption to real life situations still needs hands-on experience. As I see it, broad usage of AI tools will change the the skill distribution of software developers. We might end up with a lot more junior developers that are able to write code - or at least prompt the LLMs write the code - but lack the deep understanding of software development to be efficient in decision making. On the other hands senior developers that have acquired the context and domain knowledge will be fewer and fewer as the effort to acquire this knowledge will be higher as AI tools will hide away the parts that would enable us to learn unless the generated code is reviewed in-depth, which then raises the question if we gain that much efficiency through the tools at all. So are AI tools replacing developers? Currently no, they will transform the job of a developer but they will not replace them. The question is how we as an industry will make sure that we retain the knowledge and experience that we have gained over the years. It will also raise the question how we handle the human side of software development, as the job will either become more boring because we just feed machines with prompts, yet more stressful because we have to make more hard decisions faster. Or maybe AI tools are really just a hype and a fad and nothing will change at all. Written on November 3, 2023 https://dominikberner.ch/ai-tools-make-our-job-harder/

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Microsoft, Mistral AI e l'AI Act
by Daniela Tafani Feb. 28, 2024

Feb. 28, 2024

Microsoft and Mistral AI announce new partnership to accelerate AI innovation and introduce Mistral Large first on Azure By Eric Boyd, Corporate Vice President, Azure AI Platform, Microsoft https://azure.microsoft.com/en-us/blog/microsoft-and-mistral-ai-announce-ne… ----- Il commento di Luca Bertuzzi: This is a mind-blowing announcement. Mistral AI, the French company that has been fighting tooth and nail to water down the #AIAct's foundation model rules, is partnering up with Microsoft. So much for 'give us a fighting chance against Big Tech'. The first question that comes to mind is: was this deal in the making while the AI Act was being negotiated? That would mean Mistral discussed selling a minority stake to Microsoft while playing the 'European champion' card with the EU and French institutions. If so, this whole thing might be a masterclass in astroturfing, and it seems unrealistic for a partnership like this to be finalised in less than a month. Many people involved in the AI Act noted how Big Tech's lobbying on GPAI suddenly went quiet toward the end. That is because they did not need to intervene since Mistral was doing the 'dirty work' for them. Remarkably, Mistral's talking points were extremely similar to those of Big Tech rather than those of a small AI start-up, based on their ambition to reach that scale. The other question is how much the French government knew about this upcoming partnership with Microsoft. It seems unlikely Paris was kept completely in the dark, but cosying up with Big Tech does not really sit well with France's strive for 'strategic autonomy'. Especially since the agreement includes making Mistral's large language model available on Microsoft's Azure AI platform, while France has been pushing for an EU cybersecurity scheme to exclude American hyperscalers from the European market. Still today, and I doubt it is a coincidence, Mistral has announced the launch of Large, a new language model intended to compete directly with OpenAI's GPT-4. However, unlike previous models, Large will not be open source. In other words, Mistral is no longer (just) a European leader and is backtracking on its much-celebrated open source approach. Where does this leave the start-up vis-à-vis EU policymakers as the AI Act's enforcement approaches? My guess is that someone will inevitably feel played. https://twitter.com/BertuzLuca/status/1762197130616209765 oppure https://www.linkedin.com/posts/luca-bertuzzi-186729130_introducing-mistral-…

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Re: [nexa] nexa Digest, Vol 178, Issue 52
by maurizio lana Feb. 28, 2024

Feb. 28, 2024

Il 28/02/24 14:44, maurizio lana <maurizio.lana(a)uniupo.it> ha scritto: > vorrei recuperare i messaggi scambiati in lista in cui si parla della scuola, ma sono un po' in difficoltà con > https://www.mail-archive.com/search?a=1&l=all&haswords=&x=12&y=7&from=nexa > posto che gli archivi sono online, ci sono altri strumenti che si possono usare, al posto di mail-archive? PS: eventualmente scaricare il corpus dei messaggi per poter poi cercare in locale? M ------------------------------------------------------------------------ citazioni come briganti ai bordi della strada che balzano fuori armati e strappano l’assenso all’ozioso viandante walter benjamin ------------------------------------------------------------------------ Maurizio Lana Università del Piemonte Orientale Dipartimento di Studi Umanistici Piazza Roma 36 - 13100 Vercelli

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Google Is Paying Publishers to Test an Unreleased Gen AI Platform
by Daniela Tafani Feb. 28, 2024

Feb. 28, 2024

Google Is Paying Publishers to Test an Unreleased Gen AI Platform In exchange for a five-figure sum, publishers must use the tool to publish 3 stories per day By Mark Stenberg Google launched a private program for a handful of independent publishers last month, providing the news organizations with beta access to an unreleased generative artificial intelligence platform in exchange for receiving analytics and feedback, according to documents seen by ADWEEK. As part of the agreement, the publishers are expected to use the suite of tools to produce a fixed volume of content for 12 months. In return, the news outlets receive a monthly stipend amounting to a five-figure sum annually, as well as the means to produce content relevant to their readership at no cost. “In partnership with news publishers, especially smaller publishers, we’re in the early stages of exploring ideas to potentially provide AI-enabled tools to help journalists with their work,” a Google representative said in a statement. “These tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating and fact-checking their articles.” The AI-enabled results could throttle readership to an ‘apocalyptic’ degree. The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, like government agencies and neighboring news outlets, and then summarizing and publishing them as a new article. Other gen AI experiments Google has released over the past two years include the codenamed Genesis, which can reportedly produce whole news articles and was privately demonstrated to several publishers last summer, according to The New York Times. Others, including Search Generative Experience and Gemini, are available for public use and threaten to upend many of the commercial foundations of digital publishing. The program is part of the Google News Initiative, which launched in 2018 to provide publishers with technology and training. Although many of its programs indisputably benefit the publishers involved, the broader reception of GNI has been mixed. Google has used GNI to drum up positive press and industry goodwill during moments of reputational duress, and many of the commercial problems it aims to solve for publishers were created by Google in the first place, said Digital Content Next CEO Jason Kint. “The larger point here is that Google is in legislative activity and antitrust enforcement globally for extracting revenue from the publishing world,” Kint said. “Instead of giving up some of that revenue, it’s attacking the cost side for its long-tail members with the least bargaining power.” Details of the program Google first shared a call for news organizations to apply to test the emerging technologies in an October edition of the Local Independent Online News newsletter. GNI began onboarding publishers in January, and the yearlong program kicked off in February. According to the conditions of the agreement, participating publishers must use the platform to produce and publish three articles per day, one newsletter per week and one marketing campaign per month. To produce articles, publishers first compile a list of external websites that regularly produce news and reports relevant to their readership. These sources of original material are not asked for their consent to have their content scraped or notified of their participation in the process—a potentially troubling precedent, said Kint. When any of these indexed websites produce a new article, it appears on the platform dashboard. The publisher can then apply the gen AI tool to summarize the article, altering the language and style of the report to read like a news story. The resulting copy is underlined in different colors to indicate its potential accuracy: yellow, with language taken almost verbatim from the source material, is the most accurate, followed by blue and then red, with text that is least based on the original report. A human editor then scans the copy for accuracy before publishing three such stories per day. The program does not require that these AI-assisted articles be labeled. The platform cannot gather facts or information that have not already been produced elsewhere, limiting its utility for premium publishers. Articles produced by the platform could also draw traffic away from the original sources, negatively affecting their businesses. The process resembles the ripping technique newly in use at Reach plc, except that in this case, the text is pulled from external sources. “I think this calls into question the mission of GNI,” Kint said. “It’s hard to argue that stealing people’s work supports the mission of the news. This is not adding any new information to the mix.” https://www.adweek.com/media/google-paying-publishers-unreleased-gen-ai/

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The Stockholm Declaration on AI for Science
by Antonio Vetro' Feb. 28, 2024

Feb. 28, 2024

Dall’appello alla firma pubblicato su Nature qualche giorno fa: Stockholm declaration on AI ethics: why others should sign<https://www.nature.com/articles/d41586-024-00517-7> "The use of artificial intelligence (AI) in science has potential to do both harm and good. As a step towards preventing the harms we have prepared the Stockholm Declaration on AI for Science. Signatories commit to using AI responsibly and ethically. We encourage researchers involved in developing AI to sign (see go.nature.com/486azp7) […] " Qui la dichiarazione: https://sites.google.com/view/stockholm-declaration In questo passaggio della dichiarazione (grassetto mio), sostituendo Big Data con AI, sembra di rileggere la “End of theory”<https://www.wired.com/2008/06/pb-theory/> di Chris Anderson nel 2008: "We believe that by harnessing the power of AI we can propel humanity toward a future where groundbreaking achievements in science even achievements worthy of a Nobel Prize can be fully automated. We believe that this is achievable by the year 2050". Al di là del personale scetticismo che potrebbe essere sbagliato, ritengo utile condividerlo Antonio

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Per un’intelligenza artificiale bene comune.
by don Luca Peyron Feb. 27, 2024

Feb. 27, 2024

Considerazioni interessanti, buona lettura dl https://michelekettmaier.nova100.ilsole24ore.com/2024/02/26/per-unintellige… _________________________ don Luca Peyron Pastorale Universitaria - Apostolato Digitale Arcidiocesi di Torino www.universitari.to.it via XX settembre 83, Torino tel. 011 5156239

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