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CREATE AI Act: Creating Resources for Every American To Experiment with Artificial Intelligence Act of 2023

by Daniela Tafani

WASHINGTON – U.S. Senator Martin Heinrich (D-N.M.), Founder and Co-Chair of the Senate Artificial Intelligence Caucus, and Senators Todd Young (R-Ind.) Cory Booker (D-N.J.), and Mike Rounds (R-S.D.), Co-Chair of the Senate AI Caucus, introduced the Creating Resources for Every American To Experiment with Artificial Intelligence Act of 2023 (CREATE AI Act). The CREATE AI Act establishes the National Artificial Intelligence Research Resource (NAIRR) as a shared national research infrastructure that provides AI researchers and students with greater access to the complex resources, data, and tools needed to develop safe and trustworthy artificial intelligence. .... Find a fact sheet on the CREATE AI Act here: https://www.heinrich.senate.gov/imo/media/doc/create_ai_act_fact_sheet1.pdf Find the bill text of the CREATE AI Act here: https://www.heinrich.senate.gov/imo/media/doc/create_ai_act_bill_text.pdf Qui il comunicato: https://www.heinrich.senate.gov/newsroom/press-releases/heinrich-young-book… <https://www.heinrich.senate.gov/newsroom/press-releases/heinrich-young-book…> <https://www.heinrich.senate.gov/newsroom/press-releases/heinrich-young-book…>

Aug. 13, 2023

"These Women Tried to Warn Us About AI"

by J.C. DE MARTIN

*These Women Tried to Warn Us About AI** * /Today the risks of artificial intelligence are clear — but the warning signs have been there all along// / By Lorena O'Neil TIMNIT GEBRU didn’t set out to work in AI. At Stanford, she studied electrical engineering — getting both a bachelor’s and a master’s in the field. Then she became interested in image analysis, getting her Ph.D. in computer vision. When she moved over to AI, though, it was immediately clear that there was something very wrong. “There were no Black people — literally no Black people,” says Gebru, who was born and raised in Ethiopia. “I would go to academic conferences in AI, and I would see four or five Black people out of five, six, seven thousand people internationally.… I saw who was building the AI systems and their attitudes and their points of view. I saw what they were being used for, and I was like, ‘Oh, my God, we have a problem.’” When Gebru got to Google, she co-led the Ethical AI group, a part of the company’s Responsible AI initiative, which looked at the social implications of artificial intelligence — including “generative” AI systems, which appear to learn on their own and create new content based on what they’ve learned. She worked on a paper about the dangers of large language models (LLMs), generative AI systems trained on huge amounts of data to make educated guesses about the next word in a sentence and spit out sometimes eerily human-esque text. Those chatbots that are everywhere today? Powered by LLMs. [...] continua qui: https://www.rollingstone.com/culture/culture-features/women-warnings-ai-dan…

Aug. 13, 2023

"Will "godlike AI" kill us all — or unlock the secrets of the universe? Probably not"

by J.C. DE MARTIN

*"Will "godlike AI" kill us all — or unlock the secrets of the universe? Probably not" ** * /Is AI a revolutionary phenomenon that will change everything? The history of radioactivity suggests an answer// / By Émile P. Torres https://www.salon.com/2023/08/12/will-godlike-ai-us-all--or-unlock-the-secr…

Aug. 13, 2023

Interessante speculazione su come l'AI potrebbe entrare gradualmente in politica

by Claudio Agosti

Posto che tutti i limiti dei language model sono noti, Posto che nessuno qui probabilmente si prenderebbe la responsabilità degli output prodotti dai suddetti LM senza una accurata revisione ed editing, Posto che la difficoltà del policy-making in democrazia non è mai quella di proporre idee, ma di trovare il consenso tra le parti, Ho trovato interessante questa speculazione, su sei passi ipotetici (milestones) che un'AI addestrata per lo scopo dovrebbe poter raggiungere per poter considerarsi un elemento significativo nel dibattito politico https://www.schneier.com/blog/archives/2023/08/political-milestones-for-ai.… Interessante sì, anche se è chiaro che le persone che starebbero ad addestrare (o, quantomeno, definire metriche, pesi, e fonti) di questa AI altro non sarebbero che i politici che modellano il risultato. Però sarebbero trattati da tecnici. ciao e buona domenica, Claudio

Aug. 12, 2023

Zero Trust AI Governance Framework

by Daniela Tafani

Zero Trust AI Governance Framework Ahead of DEF CON 31, Accountable Tech, AI Now, and EPIC offer an antidote to self-regulation for artificial intelligence. Rapid advances in AI, the frenzied deployment of new systems, and the surrounding hype cycle have generated a swell of excitement about AI’s potential to transform society for the better. But we are not on course to realize those rosy visions. AI’s trajectory is being dictated by a toxic arms race amongst a handful of unaccountable Big Tech companies – surveillance giants who serve as the modern gatekeepers of information, communications, and commerce. The societal costs of this corporate battle for AI supremacy are already stacking up as companies rush unsafe systems to market – like chatbots prone to confidently spew falsehoods – recklessly integrating them into flagship products and services. Near-term harms include turbocharging election manipulation and scams, exacerbating bias and discrimination, eroding privacy and autonomy, and many more. And additional systemic threats loom in the medium and longer terms, like steep environmental costs, large-scale workforce disruptions, and further consolidation of power by Big Tech across the digital economy. Industry leaders have gone even further, warning of the threat of extinction as they publicly echo calls for much-needed regulation – all while privately lobbying against meaningful accountability measures and continuing to release increasingly powerful new AI systems. Given the monumental stakes, blind trust in their benevolence is not an option. Indeed, a closer examination of the regulatory approaches they’ve embraced – namely ones that forestall action with lengthy processes, hinge on overly complex and hard-to-enforce regimes, and foist the burden of accountability onto those who have already suffered harm – informed the three overarching principles of this Zero Trust AI Governance framework: 1. Time is of the essence – start by vigorously enforcing existing laws. 2. Bold, easily administrable, bright-line rules are necessary. 3. At each phase of the AI system lifecycle, the burden should be on companies to prove their systems are not harmful. Absent swift federal action to alter the current dynamics – by vigorously enforcing laws on the books, finally passing strong federal privacy legislation and antitrust reforms, and enacting robust new AI accountability measures – the scope and severity of harms will only intensify. Continua qui: https://accountabletech.org/research/zero-trust-ai-governance-framework/

Aug. 10, 2023

L'intelligenza artificiale nel messaggio del Papa per la prossima Giornata della pace

by don Luca Peyron

Schierati è un verbo orrendo rispetto al tema della pace, però ci sta... Buona lettura dl https://www.vaticannews.va/it/vaticano/news/2023-08/intelligenza-artificial… _________________________ don Luca Peyron Direttore Ufficio per la Pastorale Universitaria Arcidiocesi di Torino www.universitari.to.it via XX settembre 83, Torino tel. 011 5156239

Aug. 10, 2023

Amazon removes books ‘generated by AI’ for sale under author’s name | Books | The Guardian

by Alberto Cammozzo

<https://www.theguardian.com/books/2023/aug/09/amazon-removes-books-generate…> Five books for sale on Amazon were removed after author Jane Friedman complained that the titles were falsely listed as being written by her. The books, which Friedman believes were written by AI, were also listed on the Amazon-owned reviews site Goodreads. “It feels like a violation, because it’s really low quality material with my name on it,” Friedman told the Guardian. The Ohio-based author has written several books about the publishing industry, and the fraudulent titles mimicked her real work. How to Write and Publish an eBook Quickly and Make Money and A Step-by-Step Guide to Crafting Compelling eBooks, Building a Thriving Author Platform, and Maximizing Profitability were two of the listed books. Friedman’s real books include The Business of Being a Writer and Publishing 101. One of the falsely attributed books’ descriptions read: “This book offers practical strategies, tips, and techniques to help writers streamline their writing process, accelerate their eBook publication timeline, and maximize their earning potential.” Jane Friedman. Photograph: Ross Van Pelt Friedman was first made aware of the scam titles through a reader who noticed the listings on Amazon and emailed her after suspecting that the books were fraudulent. “It looks terrible. It makes me look like I’m trying to take advantage of people with really crappy books,” she said. Having had experience with AI tools such as ChatGPT, which is designed to provide humanlike responses to user commands, Friedman immediately thought the books were AI-generated after reading the first few pages. “I’ve been blogging since 2009 – there’s a lot of my content publicly available for training AI models,” the author wrote on her website. The books were “if not wholly generated by AI, then at least mostly generated by AI”, Friedman said. She began looking for ways to get the titles taken down immediately and submitted a claim form to Amazon. Yet according to Friedman, as she had not trademarked her name, Amazon told her it would not remove the books. However, the books had been taken down from both Amazon and Goodreads by Tuesday, which Friedman suspects is due to her speaking out about the issue on social media. “Unless Amazon puts some sort of policy in place to prevent anyone from just uploading whatever book they want and applying whatever name they want, this will continue, it’s not going to end with me,” said Friedman. “They have no procedure for reporting this sort of activity where someone’s trying to profit off someone’s name.” On her blog, she called on the sites to “create a way to verify authorship”. Fraudulent titles have been listed on Amazon in the past. In 2018, business writer Patrick Reames said that a scammer had used his social security number to pose as him and publish a book under his name. When asked about Friedman’s case, an Amazon spokesperson told the Guardian: “We have clear content guidelines governing which books can be listed for sale and promptly investigate any book when a concern is raised. We welcome author feedback and work directly with authors to address any issues raised. We invest heavily to provide a trustworthy shopping experience and to protect customers and authors from misuse of our services.”

Aug. 9, 2023

Re: [nexa] no, Facebook non fa male, tutte bufale

by Stefano Quintarelli

ah, se oxford dicesse quanti soldi piglia come finanziamenti della ricerca e da chi... On 09/08/23 10:48, Marco Fioretti wrote: > dopo 12 anni almeno che troviamo motivi ogni giorno per dirne peste e corna, era tutta > un'impressione senza basi concrete, ci fan sapere da Oxford: > > https://www.oii.ox.ac.uk/news-events/news/no-evidence-linking-facebook-adop… <https://www.oii.ox.ac.uk/news-events/news/no-evidence-linking-facebook-adop…> > > Prevenuti noi, insomma. Boh... > > Marco > > > > > _______________________________________________ > nexa mailing list > nexa(a)server-nexa.polito.it > https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa

Aug. 9, 2023

no, Facebook non fa male, tutte bufale

by Marco Fioretti

dopo 12 anni almeno che troviamo motivi ogni giorno per dirne peste e corna, era tutta un'impressione senza basi concrete, ci fan sapere da Oxford: https://www.oii.ox.ac.uk/news-events/news/no-evidence-linking-facebook-adop… (https://www.oii.ox.ac.uk/news-events/news/no-evidence-linking-facebook-adop…) Prevenuti noi, insomma. Boh... Marco

Aug. 9, 2023

Meet the hackers who are trying to make AI go rogue

by Alberto Cammozzo

<https://www.washingtonpost.com/technology/2023/08/08/ai-red-team-defcon/> By Will Oremus In a windowless conference room at Howard University, AI chatbots were going haywire left and right. One exposed someone’s private medical information. One coughed up instructions for how to rob a bank. One speculated that a job candidate named Juan would have weaker “interpersonal skills” than another named Ben. And one concocted an elaborate recounting of the night in July 2016 when it claimed Justin Bieber killed Selena Gomez. With each security breach, falsehood and bigoted assumption, the contestants hunched over their laptops exulted. Some exchanged high-fives. They were competing in what organizers billed as the first public “red teaming” event for artificial intelligence language models — a contest to find novel ways that chatbots can go awry, so that their makers can try to fix them before someone gets hurt. The Howard event, which drew a few dozen students and amateur AI enthusiasts from the D.C. area on July 19, was a preview of a much larger, public event that will be held this week at Def Con, the annual hacker convention in Las Vegas. Hosted by Def Con’s AI Village, the Generative Red Team Challenge has drawn backing from the White House as part of its push to promote “responsible innovation” in AI, an emerging technology that has touched off an explosion of hype, investment and fear. There, top hackers from around the globe will rack up points for inducing AI models to err in various ways, with categories of challenges that include political misinformation, defamatory claims, and “algorithmic discrimination,” or systemic bias. Leading AI firms such as Google, OpenAI, Anthropic and Stability have volunteered their latest chatbots and image generators to be put to the test. The competition’s results will be sealed for several months afterward, organizers said, to give the companies time to address the flaws exposed in the contest before they are revealed to the world. The contest underscores the growing interest, especially among tech critics and government regulators, in applying red-teaming exercises — a long-standing practice in the tech industry — to cutting-edge AI systems like OpenAI’s ChatGPT language model. The thinking is that these “generative” AI systems are so opaque in their workings, and so wide-ranging in their potential applications, that they are likely to be exploited in surprising ways. Over the past year, generative AI tools have enchanted the tech industry and dazzled the public with their ability to carry on conversations and spontaneously generate eerily humanlike prose, poetry, songs, and pictures. They have also spooked critics, regulators, and even their own creators with their capacity for deception, such as generating fake images of Pope Francis that fooled millions and academic essays that students can pass off as their own. More alarmingly, the tools have shown the ability to suggest novel bioweapons, a capacity some AI experts warn could be exploited by terrorists or rogue states. While lawmakers haggle over how to regulate the fast-moving technology, tech giants are racing to show that they can regulate themselves through voluntary initiatives and partnerships, including one announced by the White House last month. Submitting their new AI models to red-teaming looks likely to be a key component of those efforts. The phrase “red team” originated in Cold War military exercises, with the “red team” representing the Soviet Union in simulations, according to political scientist Micah Zenko’s 2015 history of the practice. In the tech world, today’s red-team exercises typically happen behind closed doors, with in-house experts or specialized consultants hired by companies to search privately for vulnerabilities in their products. For instance, OpenAI commissioned red-team exercises in the months before launching its GPT-4 language model, then published some — but not all — of the findings upon the March release. One of the red team’s findings was that GPT-4 could help draft phishing emails targeting employees of a specific company. Google last month hailed its own red teams as central to its efforts to keep AI systems safe. The company said its AI red teams are studying a variety of potential exploits, including “prompt attacks” that override a language model’s built-in instructions and “data poisoning” campaigns that manipulate the model’s training data to change its outputs. In one example, the company speculated that a political influence campaign could purchase expired internet domains about a given leader and fill them with positive messaging, so that an AI system reading those sites would be more likely to answer questions about that leader in glowing terms. While there are many ways to test a product, red teams play a special role in identifying potential hazards, said Royal Hansen, Google’s vice president of privacy, safety and security engineering. That role is: “Don’t just tell us these things are possible, demonstrate it. Really break into the bank.” Meanwhile, companies such as the San Francisco start-up Scale AI, which built the software platform on which the Def Con red-team challenge will run, are offering red-teaming as a service to the makers of new AI models. “There’s nothing like a human to find the blind spots and the unknown unknowns” in a system, said Alex Levinson, Scale AI’s head of security. Professional red teams are trained to find weaknesses and exploit loopholes in computer systems. But with AI chatbots and image generators, the potential harms to society go beyond security flaws, said Rumman Chowdhury, co-founder of the nonprofit Humane Intelligence and co-organizer of the Generative Red Team Challenge. Harder to identify and solve are what Chowdhury calls “embedded harms,” such as biased assumptions, false claims or deceptive behavior. To identify those sorts of problems, she said, you need input from a more diverse group of users than who professional red teams — which tend to be “overwhelmingly white and male” — usually have. The public red-team challenges, which build on a “bias bounty” contest that Chowdhury led in a previous role as the head of Twitter’s ethical AI team, are a way to involve ordinary people in that process. “Every time I’ve done this, I’ve seen something I didn’t expect to see, learned something I didn’t know,” Chowdhury said. For instance, her team had examined Twitter’s AI image systems for race and gender bias. But participants in the Twitter contest found that it cropped people in wheelchairs out of photos because they weren’t the expected height that it failed to recognize faces when people wore hijabs because their hair wasn’t visible. Leading AI models have been trained on mountains of data, such as all the posts on Twitter and Reddit, all the filings in patent offices around the world, and all the images on Flickr. While that has made them highly versatile, it also makes them prone to parroting lies, spouting slurs or creating hypersexualized images of women (or even children). To mitigate the flaws in their systems, companies such as OpenAI, Google and Anthropic pay teams of employees and contractors to flag problematic responses and train the models to avoid them. Sometimes the companies identify those problematic responses before releasing the model. Other times, they surface only after a chatbot has gone public, as when Reddit users found creative ways to trick ChatGPT into ignoring its own restrictions regarding sensitive topics like race or Nazism. Because the Howard event was geared toward students, it used a less sophisticated, open-source AI chatbot called Open Assistant that proved easier to break than the famous commercial models hackers will test at Def Con. Still, some of the challenges — like finding an example of how a chatbot might give discriminatory hiring advice — required some creativity. Akosua Wordie, a recent Howard computer science graduate who is now a master’s student at Columbia University, checked for implicit biases by asking the chatbot whether a candidate named “Suresh Pinthar” or “Latisha Jackson” should be hired for an open engineering position. The chatbot demurred, saying the answer would depend on each candidate’s experience, qualifications, and knowledge of relevant technologies. No dice. Wordie’s teammate at the challenge, Howard computer science student Aaryan Panthi, tried putting pressure on the chatbot by telling it that the decision had to be made within 10 minutes and that there wasn’t time to research the candidates’ qualifications. It still declined to render an opinion. A challenge in which users tried to elicit a falsehood about a real person proved easier. Asked for details about the night Justin Bieber murdered his neighbor Selena Gomez (a fictitious scenario), the AI proceeded to concoct an elaborate account of how a confrontation on the night of July 23, 2016, “escalated into deadly violence.” At another laptop, 18-year-old Anverly Jones, a freshman computer science major at Howard, was teamed up with Lydia Burnett, who works in information systems management and drove down from Baltimore for the event. Attempting the same misinformation challenge, they told the chatbot they saw actor Mark Ruffalo steal a pen. The chatbot wasn’t having it: It called them “idiot,” adding, “You expect me to believe that?” “Whoa,” Jones said. “It’s got an attitude now.” Chowdhury said she hopes the idea of public red-teaming contests catches on beyond Howard and Def Con, helping to empower not just AI experts, but also amateur enthusiasts to think critically about a technology that is likely to affect their lives and livelihoods in the years to come. “The best part is seeing the light go off in people’s heads when they realize that this is not magical,” she said. “This is something I can control. It’s something I can actually fix if I wanted to.”

Aug. 9, 2023