- nexa - server-nexa.polito.it

170° Mercoledì di Nexa | 13 marzo 2024, ore 17.00
by Nexa - Media March 4, 2024

March 4, 2024

Gentilissime, gentilissimi, Vi invitiamo a partecipare al 170° Mercoledì di Nexa, che si terrà mercoledì 13 marzo, alle ore 17.00, con un incontro dal titolo "A fin di bene: il nuovo potere della ragione artificiale". Ospite dell'incontro: Stefano Isola (Università di Camerino). 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-170 Cordiali saluti, -- Valeria Bergantino Communication Officer 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

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"Posta Elettronica" e Atenei: la situazione è peggiore di quanto sembrasse...
by Damiano Verzulli March 4, 2024

March 4, 2024

Lo scorso dicembre segnalavo in lista [1] un aggiornamento di un mio vecchio censimento dal quale emergeva che negli ultimi tre anni 6 atenei avessero abbandonato il servizio di posta on-prem, favorendo la soluzione cloud. Dovendo discutere dell'argomento in un contesto per me importante, ho voluto verificare meglio lo stato di quegli Atenei che, ai miei occhi, risultavano ancora "on-prem". *IL RISULTATO È STATO AGGHIACCIANTE* Oltre ad UniMilano (della quale gia' sapevamo che la migrazione era in corso) ed ad un mio errore/refuso sulla Scuola Universitaria Superiore Sant'Anna, di Pisa (per la quale avevo erroneamente verificato il dominio "sssup.it" anziché l'ufficiale "santannapisa.it", chiaramente gestito da Microsoft), ho riscontrato che altri 7 Atenei, inclusi POLITO e UNIPI, sono passati a Microsoft. Di fatto, dalla mia analisi, attualmente emerge che soltanto 4 Atenei gestiscono la posta internamente: * *uniba.it* - Università degli Studi di Bari Aldo Moro * *unich.it* - Università degli Studi Gabriele d’Annunzio Chieti Pescara * *unina.it* - Università di Napoli Federico II * *uniroma2.it* - Università di Roma Tor Vergata La situazione dettagliata è sempre disponibile al vecchio indirizzo. Mi chiedo (...e vi chiedo) se questo scenario è ormai da considerare irrimediabilmente compromesso (e quindi è il caso di supportare lo spegnimento di queste ultime 4 infrastrutture), oppure se ci possano essere le condizioni per considerare questi ultimi 4 atenei una sorta di "avanguardia" da cui ripartire.... La domanda nasce dal fatto che... potrei avere un qualche ruolo nel determinare il futuro di quella relativa ad "unich.it"... Un saluto, DV [1] https://server-nexa.polito.it/pipermail/nexa/2023-December/052038.html [2] https://dvblog.soabit.com/la-posta-elettronica-negli-atenei-italiani/ -- Damiano Verzulli e-mail:damiano@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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Re: [nexa] LMs and AI make software development harder
by Giuseppe Attardi March 4, 2024

March 4, 2024

Questo signore si dichiara Agilist, una delle metodologie moderne di sviluppo software, ma sembra non aderire ad alcuni suoi principi fondamentali, quali: 1. Pairwise programming: qui un copilot funge benissimo da secondo nella coppia 2. Deliver working software frequently: il che significa dedicare gran parte del tempo a scrivere codice — Beppe > On 29 Feb 2024, at 12:32, nexa-request(a)server-nexa.polito.it wrote: > > From: Daniela Tafani <daniela.tafani(a)unipi.it <mailto:daniela.tafani@unipi.it>> > To: "nexa(a)server-nexa.polito.it <mailto:nexa@server-nexa.polito.it>" <nexa(a)server-nexa.polito.it <mailto:nexa@server-nexa.polito.it>> > Subject: [nexa] LMs and AI make software development harder > Message-ID: <1ff9c3d20cd643eabb1bbe07a054d2ba(a)unipi.it <mailto:1ff9c3d20cd643eabb1bbe07a054d2ba@unipi.it>> > Content-Type: text/plain; charset="Windows-1252" > > 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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White Paper - Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World
by Daniela Tafani March 3, 2024

March 3, 2024

White Paper February 22, 2024 Rethinking Privacy in the AI Era: Policy Provocations for a Data-Centric World Jennifer King, Caroline Meinhardt Executive Summary ➜ In this paper, we present a series of arguments and predictions about how existing and future privacy and data protection regulation will impact the development and deployment of AI systems. ➜ Data is the foundation of all AI systems. Going forward, AI development will continue to increase developers’ hunger for training data, fueling an even greater race for data acquisition than we have already seen in past decades. ➜ Largely unrestrained data collection poses unique risks to privacy that extend beyond the individual level—they aggregate to pose societal-level harms that cannot be addressed through the exercise of individual data rights alone. ➜ While existing and proposed privacy legislation, grounded in the globally accepted Fair Information Practices (FIPs), implicitly regulate AI development, they are not sufficient to address the data acquisition race as well as the resulting individual and systemic privacy harms. ➜ Even legislation that contains explicit provisions on algorithmic decision-making and other forms of AI does not provide the data governance measures needed to meaningfully regulate the data used in AI systems. ➜ We present three suggestions for how to mitigate the risks to data privacy posed by the development and adoption of AI: 1. Denormalize data collection by default by shifting away from opt-out to opt-in data collection. Data collectors must facilitate true data minimization through “privacy by default” strategies and adopt technical standards and infrastructure for meaningful consent mechanisms. 2. Focus on the AI data supply chain to improve privacy and data protection. Ensuring dataset transparency and accountability across the entire life cycle must be a focus of any regulatory system that addresses data privacy. 3. Flip the script on the creation and management of personal data. Policymakers should support the development of new governance mechanisms and technical infrastructure (e.g., data intermediaries and data permissioning infrastructure) to support and automate the exercise of individual data rights and preferences. https://hai.stanford.edu/white-paper-rethinking-privacy-ai-era-policy-provo…

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Vi racconto com'è “vendere” la propria voce per addestrare un'intelligenza artificiale
by don Luca Peyron March 2, 2024

March 2, 2024

Tra costume e futuro https://www.wired.it/article/intelligenza-artificiale-addestramento-voce-fr… Buona lettura dl _________________________ 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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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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