January 2023 - nexa - server-nexa.polito.it

ChatGpt e la scuola
by Alessandro Brolpito Jan. 20, 2023

Jan. 20, 2023

Buongiorno, Come potrebbe essere usato ChatGpt a scuola? Se la "*scuola dovrebbe insegnare ai suoi allievi a fare le domande per formare innovatori,"* János Sándor Petőfi, l'articolo offre alcuni spunti su come ChatGpt possa essere utilizzato: Chi ha paura di ChatGpt a Scuola? Impariamo piuttosto a collaborarci - Agenda Digitale <https://www.agendadigitale.eu/scuola-digitale/chi-ha-paura-di-chatgpt-a-scu…> Alessandro

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The End of Programming
by Alberto Cammozzo Jan. 20, 2023

Jan. 20, 2023

Non credo che questo accadrà, ma credo che sia uno dei desideri dell'industria ICT quello di rimpiazzare il processo di programmazione, che presenta diversi problemi di scala, con qualcosa di più gestibile industrialmente. <https://cacm.acm.org/magazines/2023/1/267976-the-end-of-programming/fulltext> Viewpoint The End of Programming By Matt Welsh Communications of the ACM, January 2023, Vol. 66 No. 1, Pages 34-35 10.1145/3570220 [...] *Programming will be obsolete.* I believe the conventional idea of "writing a program" is headed for extinction, and indeed, for all but very specialized applications, most software, as we know it, will be replaced by AI systems that are /trained/ rather than /programmed./ In situations where one needs a "simple" program (after all, not everything should require a model of hundreds of billions of parameters running on a cluster of GPUs), those programs will, themselves, be generated by an AI rather than coded by hand. I do not think this idea is crazy. No doubt the earliest pioneers of computer science, emerging from the (relatively) primitive cave of electrical engineering, stridently believed that all future computer scientists would need to command a deep understanding of semiconductors, binary arithmetic, and microprocessor design to understand software. Fast-forward to today, and I am willing to bet good money that 99% of people who are writing software have almost no clue how a CPU actually works, let alone the physics underlying transistor design. By extension, I believe the computer scientists of the future will be so far removed from the classic definitions of "software" that they would be hard-pressed to reverse a linked list or implement Quicksort. (I am not sure I remember how to implement Quicksort myself.) AI coding assistants such as CoPilot are only scratching the surface of what I am describing. It seems totally obvious to me that /of course/ all programs in the future will ultimately be written by AIs, with humans relegated to, at best, a supervisory role. Anyone who doubts this prediction need only look at the very rapid progress being made in other aspects of AI content generation, such as image generation. The difference in quality and complexity between DALL-E v1 and DALL-E v2—announced only 15 months later—is staggering. If I have learned anything over the last few years working in AI, it is that it is /very/ easy to underestimate the power of increasingly large AI models. Things that seemed like science fiction only a few months ago are rapidly becoming reality. So I am not just talking about things like Github's CoPilot replacing programmers.^1 <https://cacm.acm.org/magazines/2023/1/267976-the-end-of-programming/fulltex…> I am talking about /replacing the entire concept of writing programs with training models./ In the future, CS students are not going to need to learn such mundane skills as how to add a node to a binary tree or code in C++. That kind of education will be antiquated, like teaching engineering students how to use a slide rule. The engineers of the future will, in a few keystrokes, fire up an instance of a four-quintillion-parameter model that already encodes the full extent of human knowledge (and then some), ready to be given any task required of the machine. The bulk of the intellectual work of getting the machine to do what one wants will be about coming up with the right examples, the right training data, and the right ways to evaluate the training process. Suitably powerful models capable of generalizing via few-shot learning will require only a few good examples of the task to be performed. Massive, human-curated datasets will no longer be necessary in most cases, and most people "training" an AI model will not be running gradient descent loops in PyTorch, or anything like it. They will be teaching by example, and the machine will do the rest. [...]

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Re: [nexa] Could a robot ever recreate the aura of a Leonardo da Vinci masterpiece? It’s already happening
by maurizio lana Jan. 20, 2023

Jan. 20, 2023

> riferimenti più precisi in merito alla produzione delle immagini per quella campagna? in questo articolo "The San Francisco Ballet Is Catching Heat for Promoting Its ‘Nutcracker’ Performances With A.I.-Generated Art " del 23 dicembre 2022 (https://sfwrunway.com/the-san-francisco-ballet-is-catching-heat-for-promoti…) c'è una dichiarazione del portavoce del SF Ballet in merito all'utilizzo delle 30 persone : > “Almost 30 designers, producers, and creatives from native promoting > company contributed to our Nutcracker marketing campaign, which > presents us an opportunity to experiment with in the present day’s > technological instruments,” a SF Ballet spokesperson informed Artnet > Information through e mail. “We make use of a whole lot of San > Francisco artists on our phases, in our orchestra pit, and behind the > scenes each season, together with within the creation of our > advertising campaigns, and we glance ahead persevering with to take > action sooner or later.” il sito di BSSP (l'agenzia di comunicazione) comprensibilmente, credo, non dettaglia come ha operato con l'IA per il il SF Ballet (https://bssp.com/work/work-san-francisco-ballet-nutcracker/) perché questo è 'segreto industriale' in un certo senso Maurizio Il 20/01/23 10:25, 380° <g380(a)biscuolo.net> ha scritto: > Alberto Cammozzo via nexa<nexa(a)server-nexa.polito.it> writes: >> Anyone inclined to dismiss the idea that AI could take over creative >> jobs as scaremongering should know: it is already happening. This >> winter, San Francisco Ballet used the independent research lab >> Midjourney to create the visual campaign for its production of The >> Nutcracker (although a representative for the ballet said that, >> despite using AI, nearly 30 human designers, producers, and creatives >> were also employed in the campaign’s making). > per favore Alberto o qualcun altro ha dei riferimenti più precisi in > merito alla produzione delle immagini per quella campagna? > > Io non trovo da nessuna parte la dichiarazione del rappresentante del > BSSP in merito all'utilizzo delle 30 persone impiegate nella campagna e > in che modo hanno interagito con gli strumenti basati sul machine > learning > > qui un articolo: > https://www.lbbonline.com/news/bssp-taps-ai-to-reimagine-the-nutcracker-for… > > ...ma non dice molto > > [...] > > saluti, 380° > ------------------------------------------------------------------------ lo straniero non parla e non capisce la nostra lingua, che non è più nostra, perché la nostra vera lingua diventa la traduzione, lo scambio l. ferrieri, dalla public library all’open library ------------------------------------------------------------------------ Maurizio Lana Università del Piemonte Orientale Dipartimento di Studi Umanistici Piazza Roma 36 - 13100 Vercelli

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IFRI, "Sources d’influence. Enjeux économiques et géopolitiques des logiciels open source"
by J.C. DE MARTIN Jan. 20, 2023

Jan. 20, 2023

*Sources d’influence. Enjeux économiques et géopolitiques des logiciels open source ** *Etudes de l'Ifri, décembre 2022 Alice PANNIER L’open source est au cœur de l’infrastructure internet, des solutions logicielles utilisées par les individus comme par les gouvernements, et des processus d’innovation des entreprises technologiques. Face aux menaces sur la sécurité et la pérennité du modèle open source, les États cherchent à se saisir de plus en plus du sujet, qui devient un enjeu géopolitique. L’open source tient une place centrale dans les logiciels : il est au fondement de briques logicielles critiques, et est devenu un élément déterminant dans les processus d’innovation des entreprises du numérique. Il est aussi une alternative attrayante aux solutions propriétaires. L’open source peut toutefois être victime de son succès et souffre d’un manque de moyens dédiés à sa maintenance. Or, les vulnérabilités dans les codes sources ouverts peuvent avoir de graves conséquences, comme l’a illustré la faille « Log4Shell » révélée en décembre 2021. Les entreprises privées investissent financièrement et humainement au développement et au maintien de l’écosystème open source. Cependant, cette implication n’est pas sans danger pour l’écosystème open source, qui est de plus en plus modelé par les intérêts privés des Big Tech. Parallèlement, les gouvernements sont de plus en plus préoccupés par les risques de l’open source en matière de cybersécurité, du fait de vulnérabilités accidentelles, mais aussi de la manipulation des codes par des criminels et des agents étrangers. L’analyse des cas américain, chinois et européen montre que l’implication des gouvernements dans l’open source n’est pas seulement pragmatique ; elle est de plus en plus politisée et sert à soutenir les ambitions des gouvernements en matière de sécurité nationale, d’influence internationale ou de souveraineté numérique. L’étude met en évidence les dilemmes qui émergent, pour les autorités publiques, des tensions entre le désir de sécuriser des composants open source critiques universels, le désir de développer des technologies « souveraines », et le risque d’empiéter sur le fonctionnement horizontal et décentralisé de l’open source. Cette publication est aussi disponible en anglais : *Software Power: The Economic and Geopolitical Implications of Open Source Software*

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Could a robot ever recreate the aura of a Leonardo da Vinci masterpiece? It’s already happening
by Alberto Cammozzo Jan. 20, 2023

Jan. 20, 2023

[...] Anyone inclined to dismiss the idea that AI could take over creative jobs as scaremongering should know: it is already happening. [....] <https://www.theguardian.com/commentisfree/2023/jan/02/robot-leonardo-da-vin…> This month, the internet was flooded with stunningly ethereal digital art portraits, thanks to the work of the latest artificial intelligence-assisted application to go viral: Lensa. Users uploaded their photographs to the app and then – for a small fee – it used AI to transform their profile pictures into, say, a magical elfin warrior princess version of themselves, in no time at all. This year has seen a breakthrough for AI-driven image generators, which are now better than ever in quality, speed and affordability. The AI models are “trained” on millions of pieces of image and text data scraped from publicly available content online, and as in the case of Microsoft-backed DALL-E, can turn short text prompts such as “Ronald McDonald performing open heart surgery” into unique images. Anyone can now produce professional-looking images tailored to their desires, without having any training in art or design themselves. If that sounds great to you, you might not be one of the millions of humans whose livelihoods depend on being able to exchange those skills for money. Those working in the more cognitive creative industries have long felt that they had nothing to fear from automation. After all, how could a computer ever recreate the aura of a masterpiece by Leonardo da Vinci, or possess the unique skill set required to devise a compelling visual marketing campaign for a luxury brand? Early images generated with these tools were full of glitches that marked them out as machine-made. But as the results have become more convincing, creatives have grown more concerned. On the frontlines of this debate are gig workers such as graphic artists and commercial illustrators, who take art commissions based on client specifications. Anyone inclined to dismiss the idea that AI could take over creative jobs as scaremongering should know: it is already happening. This winter, San Francisco Ballet used the independent research lab Midjourney to create the visual campaign for its production of The Nutcracker (although a representative for the ballet said that, despite using AI, nearly 30 human designers, producers, and creatives were also employed in the campaign’s making). Another threat to artist livelihoods comes from these tools’ ability to create imagery “in the style of” specific artists. This functionality is fun when used to conjure up quirky visions of how Van Gogh might have painted Rishi Sunak riding into No 10 on a unicorn, but when it comes to living artists who have spent years developing their own distinctive style, the AI’s uncanny ability to mimic, without credit or compensation, becomes problematic. Earlier this year, fantasy art illustrator Greg Rutkowski found out that his name was one of the most popular prompts on the AI platform Stable Diffusion – more popular than Picasso or Leonardo. “The only thing that could at least stop feeding the algorithm is to stop posting your work on the internet, which is impossible in our industry,” says Rutkowski. The legal recourse for artists who feel these tools are infringing on their copyright is knotty and unclear. In the EU, lawyers are contesting the legality of using images under copyright for training AI models but as the UK bids to become an industry leader, it has already proposed a bill to allow carte blanche AI training for commercial purposes. Meanwhile it remains unclear if traditional copyright even applies here, as it is difficult to copyright a visual style. Open AI’s representation of ‘A sea otter in the style of Girl With a Pearl Earing by Vermeer’. Open AI’s representation of ‘A sea otter in the style of Girl With a Pearl Earing by Vermeer’. Photograph: OpenAI/AFP/Getty Images While these issues have only recently garnered mainstream attention, there are factions of artists who predicted this when the field was still in its infancy, and have been working to develop solutions. Among them are Berlin-based artists Mat Dryhurst and Holly Herndon, who have created a search function that anyone can use to see whether their work has been scraped for a 150-terabyte dataset called LAION, which is used to train most AI image generators. Their organisation, Spawning, is also developing another tool that would allow artists to set permissions on how their style and likeness can be used by the algorithms, including the option to opt out entirely. Both Stability AI – the organisation behind Stable Diffusion – and LAION have committed to partner with Spawning to honour consent requests made in advance of the next training of Stable Diffusion, and a recent update to the tool removed the ability to write prompts that specify an artist by name. There are other flaws in the vast open datasets on which the AI models are trained, which limit its potential. Deficiencies in the diversity of the data, as well as biases held by the humans who originally labelled the images it learns from, have unwittingly coded the models with harmful stereotypes and representations. Some users are finding that Lensa creates overly sexualised female avatars, exaggerates racial phenotypes in its outputs, and has difficulty reading mixed-race features. Such issues might give pause to anyone thinking of using the technology for commercial purposes – at least until the training datasets are improved. The Guardian view on ChatGPT: an eerily good human impersonator | Editorial Read more Many artists remain unfazed, and in fact believe the technology could open up possibilities for them to make better work, or at least to work more efficiently. Though she has not used it yet, the UK-based illustrator Michelle Thompson sees potential in the idea of using AI both to develop concepts and to refine artistic outputs. “I see it less as a threat and more of an opportunity,” she said, adding: “Like everything else, there will always be artists who can use the tools better.” These tools are only as good as the datasets they are trained on. Human imagination, on the other hand, has no limit. For Dryhurst, AI models “could attempt to make a pale version of something we did years ago,” but that “doesn’t account for what we might do next”. For those watching closely, the visual outputs of these widely available AI tools are already getting repetitive, and even untrained eyes will learn soon to recognise the hand of the machine. Some of the most interesting and conceptually rich work being made with AI is still coming from artists such as Mario Klingemann and Anna Ridler, who are customising their own training datasets, and curating the machine outputs in imaginative ways. The kind of artificial intelligence we might imagine replacing artists – an entirely autonomous creative robot capable of human-like imagination and expression – does not yet exist, but it is coming. And as AI becomes more ubiquitous, artists, illustrators and designers will ultimately be set apart not by if, but by how, they use the technology. Naomi Rea is European market editor at Artnet News, an online art industry newswire

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Il progetto "GAFAM Empire"
by J.C. DE MARTIN Jan. 19, 2023

Jan. 19, 2023

https://gafam.theglassroom.org/ *GAFAM Empire* is a project in which we look at all the known acquisitions conducted by five big tech companies: Google, Amazon, Facebook, Apple, and Microsoft, from the moment they made their first one to the end of summer of 2022, when we stopped collecting data. Why do we call it an empire? Our focus is on trying to understand how these companies purchase their power and ability to grow their respective market positions based on their information infrastructure through the simple operation of acquiring other businesses and their products. Our understanding of empire here is very basic: empire controls three basic layers of knowledge: 1) collection of data/intelligence, 2) storage of that intelligence as well as 3) capacity of processing it. These three unique characteristics give them powerful and unprecedented insights into how our societies operate. Why are we not calling them monopolies? Because this framing based on the idea of limited resources and competition for consumers frankly does not apply - the resources are unlimited - and that is /our data and attention/. When it comes to competition we are free to chose the ones we like - we can use products of them all in fact. In short we call them empires in a collonial sense, as in /digital colonialism/ and not monopolies in a market competition sense. All of these five companies dominate the space of collecting data (number of users), managing and processing that data (cloud and other infrastructures they own, including hardware) as well as processing and analysing that data (machine learning, AI, algorithms). They all monetize this dominant position, making them the richest tech companies out there. How big are they, which sectors do they dominate and in which do they compete, what futures are they trying to prepare for? It is obvious that for them the ideal future is the one where we do everything online, everything is digital and goes though their channels. These were the questions driving our research and this visualization. You can learn more about the data used here in the Methodology section, however, we have to say that the data we acquired has significant limits. One would hope that there is a clear way of learning how much each of these acquisitions was worth; sadly that is not the case. This specific data is sporadic and often unreliable. It would be great to know the exact reasons for each of the acquisitions - this is also very arbitrary and ephemeral - as the descriptions of purchased companies and products often do not reflect the actual reasons of purchases: was it buying a product, buying out a competitor, absorbing talent, skills, or expertise, or to exploit the content (which is why GitHub users filed a class-action lawsuit against Microsoft for training an AI tool with their code)? *So what is it that you can actually learn here?* We can learn how they grew - which sectors they expanded to, what types of know-how they absorbed, and in fact who they actually are besides who we think they are. We, the users of their services, often associate who they are with the most popular services we use, but looking at their acquisitions we are looking at a very different picture - a picture dominated by strategic purchases and political visions. In 2021 alone it is estimated that the technology acquisition market exceeded over three trillion US dollars. It is not insignificant what they acquire and we should know more about why.

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[supply chain attack] Compromised PyTorch-nightly dependency chain
by 380° Jan. 19, 2023

Jan. 19, 2023

Buongiorno, notizia un po' tecnica ma spero abbastanza comprensibile a tutti. https://pytorch.org/blog/compromised-nightly-dependency/ «Compromised PyTorch-nightly dependency chain» Si tratta dell'ennesimo attacco /supply chain/ causato da uno dei tanti (troppi) package manager e relativi repository progettati e implementati talmente male che davvero peggio non si può. Il succo del problema è questo: --8<---------------cut here---------------start------------->8--- At around 4:40pm GMT on December 30 (Friday), we learned about a malicious dependency package (torchtriton) that was uploaded to the Python Package Index (PyPI) code repository with the same package name as the one we ship on the PyTorch nightly package index. Since the PyPI index takes precedence, this malicious package was being installed instead of the version from our official repository. This design enables somebody to register a package by the same name as one that exists in a third party index, and pip will install their version by default. This malicious package has the same name torchtriton but added in code that uploads sensitive data from the machine. --8<---------------cut here---------------end--------------->8--- Sostanzialmente su PyPI (et al.) chiunque può caricare un pacchetto col nome che vuole e questo viene **ufficialmente** distribuito a tutti gli utenti "pip". La vulnerabilità [1] è nota da prima del Luglio 2020, ma praticamente "sconosciuta" a chi crea pacchetti PyPI; nella issue [1] ci sono anche alcuni link a casi concreti osservati, in particolare "la madre di tutte le prove": https://www.bleepingcomputer.com/news/security/researcher-hacks-over-35-tec… «Researcher hacks over 35 tech firms in novel supply chain attack» (Febbraio 2021) --8<---------------cut here---------------start------------->8--- Using this technique, Birsan executed a successful supply chain attack against Microsoft, Apple, PayPal, Shopify, Netflix, Tesla, Yelp, and Uber simply by publishing public packages using the same name as the company's internal ones. [...] Knowing that his scripts would be making connections from corporate networks, Birsan decided to use DNS to exfiltrate the data to bypass detection. --8<---------------cut here---------------end--------------->8--- Quindi il fenomeno **non** è limitato a PyPI ed è talmente diffuso (e **pericoloso**) che ha un suo nome: **dependency confusion** (aka depencency hell). Quello che mi rattrista tantissimo è che distribuzioni come Debian et al [2] è da **molti** anni che lavorano per creare un ambiente di distribuzione del software (package manager e repository del software) sicuro [3] e /contemporaneamente/ aperto all'ingresso di svariati contributori... poi sono arrivati 'sti fenomeni e hanno agito come se quegli "ecosistemi software" ben progettati fossero una palla al piede, perché nella distribuzione del software bisogna essere agili e scattanti, /futuristi/ mi verrebbe da dire: che menata la sicurezza, eh?!? :-O Morale della favola: non usate PyPI, npm, RubyGems o simili per installare il vostro software, usare una distribuzione **seria**, fatevi un favore ;-) Saluti, 380° [1] https://github.com/pypa/pip/issues/8606 [2] e più di recente Nix e Guix [3] e "magari" pure riproducibile e boostrappable, sto infierendo? -- 380° (Giovanni Biscuolo public alter ego) «Noi, incompetenti come siamo, non abbiamo alcun titolo per suggerire alcunché» Disinformation flourishes because many people care deeply about injustice but very few check the facts. Ask me about <https://stallmansupport.org>.

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Survival of the Richest
by Antonio Jan. 19, 2023

Jan. 19, 2023

The richest 1 percent grabbed nearly two-thirds of all new wealth worth $42 trillion created since 2020 ... While ordinary people are making daily sacrifices on essentials like food, the super-rich have outdone even their wildest dreams. Just two years in, this decade is shaping up to be the best yet for billionaires a roaring ‘20s boom for the world’s richest ... Taxing the super-rich and big corporations is the door out of today’s overlapping crises. It’s time we demolish the convenient myth that tax cuts for the richest result in their wealth somehow ‘trickling down’ to everyone else. Forty years of tax cuts for the super-rich have shown that a rising tide doesn’t lift all ships —just the superyachts ... Elon Musk paid a “true tax rate” of about 3 percent between 2014 and 2018. Aber Christine, a flour vendor in Uganda, makes $80 a month and pays a tax rate of 40 percent ... "Richest 1% bag nearly twice as much wealth as the rest of the world put together over the past two years" Published: 16th January 2023 https://www.oxfam.org/en/press-releases/richest-1-bag-nearly-twice-much-wea…

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Tesla video promoting self-driving was staged, senior engineer testifies
by Alberto Cammozzo Jan. 17, 2023

Jan. 17, 2023

<https://www.theguardian.com/technology/2023/jan/17/tesla-self-driving-video…> A 2016 video that Tesla used to promote its self-driving technology was staged to show capabilities like stopping at a red light and accelerating at a green light that the system did not have, according to testimony by a senior engineer. The video, which remains archived on Tesla’s website, was released in October 2016 and promoted on Twitter by Elon Musk as evidence that “Tesla drives itself”. But the Model X was not driving itself with technology Tesla had deployed, Ashok Elluswamy, director of Autopilot software at Tesla, said in the transcript of a July deposition taken as evidence in a lawsuit against Tesla for a 2018 fatal crash involving a former Apple engineer. The previously unreported testimony by Elluswamy represents the first time a Tesla employee has confirmed and detailed how the video was produced. The video carries a tagline saying: “The person in the driver’s seat is only there for legal reasons. He is not doing anything. The car is driving itself.” Elluswamy said Tesla’s Autopilot team set out to engineer and record a “demonstration of the system’s capabilities” at the request of Musk. Elluswamy, Musk and Tesla did not respond to a request for comment. However, the company has warned drivers that they must keep their hands on the wheel and maintain control of their vehicles while using Autopilot. The Tesla technology is designed to assist with steering, braking, speed and lane changes but its features “do not make the vehicle autonomous”, the company says on its website. To create the video, the Tesla used 3D mapping on a predetermined route from a house in Menlo Park, California, to Tesla’s then-headquarters in Palo Alto, he said. Drivers intervened to take control in test runs, he said. When trying to show the Model X could park itself with no driver, a test car crashed into a fence in Tesla’s parking lot, he said. “The intent of the video was not to accurately portray what was available for customers in 2016. It was to portray what was possible to build into the system,” Elluswamy said, according to a transcript of his testimony seen by Reuters. When Tesla released the video, Musk tweeted: “Tesla drives itself (no human input at all) thru urban streets to highway to streets, then finds a parking spot.” Tesla faces lawsuits and regulatory scrutiny over its driver assistance systems. The US Department of Justice began a criminal investigation into Tesla’s claims that its electric vehicles can drive themselves in 2021, after a number of crashes, some of them fatal, involving Autopilot, Reuters has reported. The New York Times reported in 2021 that Tesla engineers had created the 2016 video to promote Autopilot without disclosing that the route had been mapped in advance or that a car had crashed in trying to complete the shoot, citing anonymous sources. When asked if the 2016 video showed the performance of the Tesla Autopilot system available in a production car at the time, Elluswamy said: “It does not.” Elluswamy was deposed in a lawsuit against Tesla over a 2018 crash in Mountain View, California, that killed Walter Huang, an Apple engineer. Andrew McDevitt, the lawyer who represents Huang’s wife and who questioned Elluswamy’s in July, told Reuters it was “obviously misleading to feature that video without any disclaimer or asterisk”. The National Transportation Safety Board concluded in 2020 that Huang’s fatal crash was probably caused by his distraction and the limitations of Autopilot. It said Tesla’s “ineffective monitoring of driver engagement” had contributed to the crash. Elluswamy said drivers could “fool the system”, making a Tesla system believe that they were paying attention based on feedback from the steering wheel when they were not. But he said he saw no safety issue with Autopilot if drivers were paying attention.

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"Primo progetto nazionale di Educazione Digitale" (Evento Luiss Guido Carli - 25/01/2023)
by Damiano Verzulli Jan. 17, 2023

Jan. 17, 2023

Mi è appena passata sotto agli occhi la notizia seguente: => "Primo progetto nazionale di Educazione Digitale rivolto agli Istituti di Istruzione Superiore nazionale. L’iniziativa è in partnership con Google e promossa dal Laboratorio Luiss sul diritto del digitale e dal Garante privacy, di cui alleghiamo la scheda dei lavori." https://lawlab.luiss.it/homepage/primo-progetto-nazionale-di-educazione-dig… --- L'unica cosa che mi sento di aggiungere è che... da quell'altro lato, ci si da un gran da fare (perché è evidente che il "mercato" fa da traino). Da "questo" lato (quello di questa ML e dei principi che la guidano...) c'è grande qualità nelle discussioni (e lo dico senza ironia ne sarcasmo), ma in termini di "action"... i risultati presentano ampi spazi di miglioramento :-) Qualcuno andra' a Roma? Qualcun'altro/a organizzera' un evento "simile", ma "opposto", altrove? Qualcun'altro ancora capira' come mai il Garante multa Regione Lazio sull'uso della Posta Elettronica [1] e, subito dopo, sembra sedersi di fianco a Google, "...a scuola di 'Educazione Digitale'"? Un saluto, DV [1] https://www.garanteprivacy.it/home/docweb/-/docweb-display/docweb/9833616 -- 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

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