March 2023 - nexa - server-nexa.polito.it

Rapporto: "When Finance Meets Big Data" (Fondazione Rosa Luxemburg)

by J.C. DE MARTIN

*When Finance Meets Big Data** * /As FinTech scrambles to capture the African market, more regulation and public ownership is needed// / Fabio De Masi (per la Rosa Luxemburg Stiftung) /PDF scaricabile da questa pagina: https://www.rosalux.de/en/publication/id/50039/when-finance-meets-big-data/ <https://www.rosalux.de/en/publication/id/50039/when-finance-meets-big-data> Big Tech corporations like Amazon, Apple, Meta/Facebook, Alphabet/Google, or Alibaba have their sights set on financial markets and so-called “underbanked people”. Big Tech companies work with large datasets, also known as Big Data. Should “Big Data meet Big Finance”, megacorporations with unprecedented market and data power may emerge. Money is key to so-called “economic development”, i.e. the financing of investment as well as participation in the economy. Large parts of the global population — especially on the African continent and further in the informal sector — are excluded from the traditional banking system because they have no regular income or can hardly build up savings. Financial technology (FinTech) corporations, in turn, promise financial inclusion for “underbanked people”. When Finance Meets Big Data: Financial Technology and the Scramble for Africa reviews the economics of Big Data in the finance sector and subsequently analyses the role of money for investment and the effects of FinTech companies on the monetary system. The publication then discusses the special role of the FinTech sector in Africa. The FinTech business landscapes of two of the biggest Sub-Saharan FinTech Economies — Kenya and South Africa — is briefly assessed and the role of FinTech in economic betterment and poverty reduction critically reviewed. Finally, the study discusses risks to political and economic sovereignty. The study concludes with some preliminary recommendations of political strategies to counter the overconcentration of data and financial power. In Sub-Saharan Africa, FinTech platforms such as M-Pesa capitalize on millions of unbanked people in the informal sector. While many multilateral organizations embrace development through corporate-led financial inclusion, some economists criticize value extraction from poor communities by FinTech corporations through high interest rates and service fees (“digital colonialism”). The study finds that basic phone banking has facilitated financial access for the unbanked population in Kenya. However, M-Pesa is still highly concentrated on urban areas and extracts exorbitant fees from poor people. Mobile money finances consumer debt rather than sustaining investment into the productive capacity of previously unbanked people. In South Africa, digital banking has advanced but likewise with little impact on investment. African countries should strengthen data protection and public ownership in the telecommunications sector, as well as enforce antitrust legislation and regulate FinTech to at least the same extent as the banks. Another option could be to tax local data mining. Further, cash payments for smaller amounts should be protected and Central Bank Digital Currencies (CBDC) considered as a means of offering financial technology as a public good.

March 16, 2023

The stupidity of AI | Artificial intelligence (AI) | The Guardian

by Alberto Cammozzo

<https://www.theguardian.com/technology/2023/mar/16/the-stupidity-of-ai-arti…> In January 2021, the artificial intelligence research laboratory OpenAI gave a limited release to a piece of software called Dall-E. The software allowed users to enter a simple description of an image they had in their mind and, after a brief pause, the software would produce an almost uncannily good interpretation of their suggestion, worthy of a jobbing illustrator or Adobe-proficient designer – but much faster, and for free. Typing in, for example, “a pig with wings flying over the moon, illustrated by Antoine de Saint-Exupéry” resulted, after a minute or two of processing, in something reminiscent of the patchy but recognisable watercolour brushes of the creator of The Little Prince. A year or so later, when the software got a wider release, the internet went wild. Social media was flooded with all sorts of bizarre and wondrous creations, an exuberant hodgepodge of fantasies and artistic styles. And a few months later it happened again, this time with language, and a product called ChatGPT, also produced by OpenAI. Ask ChatGPT to produce a summary of the Book of Job in the style of the poet Allen Ginsberg and it would come up with a reasonable attempt in a few seconds. Ask it to render Ginsberg’s poem Howl in the form of a management consultant’s slide deck presentation and it would do that too. The abilities of these programs to conjure up strange new worlds in words and pictures alike entranced the public, and the desire to have a go oneself produced a growing literature on the ins and outs of making the best use of these tools, and particularly how to structure inputs to get the most interesting outcomes. The latter skill has become known as “prompt engineering”: the technique of framing one’s instructions in terms most clearly understood by the system, so it returns the results that most closely match expectations – or perhaps exceed them. Tech commentators were quick to predict that prompt engineering would become a sought-after and well remunerated job description in a “no code” future, where the most powerful way of interacting with intelligent systems would be through the medium of human language. No longer would we need to know how to draw, or how to write computer code: we would simply whisper our desires to the machine and it would do the rest. The limits on AI’s creations would be the limits of our own imaginations. Imitators of and advances on Dall-E followed quickly. Dall-E mini (later renamed Craiyon) gave those not invited to OpenAI’s private services a chance to play around with a similar, less powerful, but still highly impressive tool. Meanwhile, the independent commercial effort Midjourney and the open-source Stable Diffusion used a different approach to classifying and generating images, to much the same ends. Within a few months, the field had rapidly advanced to the generation of short videos and 3D models, with new tools appearing daily from academic departments and hobbyist programmers, as well as the established giants of social media and now AI: Facebook (aka Meta), Google, Microsoft and others. A new field of research, software and contestation had opened up. The name Dall-E combines the robot protagonist of Disney’s Wall-E with the Spanish surrealist artist Salvador Dalí. On the one hand, you have the figure of a plucky, autonomous and adorable little machine sweeping up the debris of a collapsed human civilisation, and on the other a man whose most repeated bon mots include, “Those who do not want to imitate anything, produce nothing,” and “What is important is to spread confusion, not eliminate it.” Both make admirable namesakes for the broad swathe of tools that have come to be known as AI image generators. In the past year, this new wave of consumer AI, which includes both image generation and tools such as ChatGPT, has captured the popular imagination. It has also provided a boost to the fortunes of major technology companies who have, despite much effort, failed to convince most of us that either blockchain or virtual reality (“the metaverse”) are the future that any of us want. At least this one feels fun, for five minutes or so; and “AI” still has that sparkly, science-fiction quality, redolent of giant robots and superhuman brains, which provides that little contact high with the genuinely novel. What’s going on under the hood, of course, is far from new. There have been no major breakthroughs in the academic discipline of artificial intelligence for a couple of decades. The underlying technology of neural networks – a method of machine learning based on the way physical brains function – was theorised and even put into practice back in the 1990s. You could use them to generate images then, too, but they were mostly formless abstractions, blobs of colour with little emotional or aesthetic resonance. The first convincing AI chatbots date back even further. In 1964, Joseph Weizenbaum, a computer scientist at the Massachusetts Institute of Technology, developed a chatbot called Eliza. Eliza was modelled on a “person-centred” psychotherapist: whatever you said, it would mirror back to you. If you said “I feel sad”, Eliza would respond with “Why do you feel sad?”, and so on. (Weizenbaum actually wanted his project to demonstrate the superficiality of human communication, not to be a blueprint for future products.) Early AIs didn’t know much about the world, and academic departments lacked the computing power to exploit them at scale. The difference today is not intelligence, but data and power. The big tech companies have spent 20 years harvesting vast amounts of data from culture and everyday life, and building vast, energy-hungry data centres filled with ever more powerful computers to churn through it. What were once creaky old neural networks have become super-powered, and the gush of AI we’re seeing is the result. AI image generation relies on the assembly and analysis of millions upon millions of tagged images; that is, images that come with some kind of description of their content already attached. These images and descriptions are then processed through neural networks that learn to associate particular and deeply nuanced qualities of the image – shapes, colours, compositions – with certain words and phrases. These qualities are then layered on top of one another to produce new arrangements of shape, colour and composition, based on the billions of differently weighted associations produced by a simple prompt. But where did all those original images come from? The datasets released by LAION, a German non-profit, are a good example of the kind of image-text collections used to train large AI models (they provided the basis for both Stable Diffusion and Google’s Imagen, among others). For more than a decade, another non-profit web organisation, Common Crawl, has been indexing and storing as much of the public world wide web as it can access, filing away as many as 3bn pages every month. Researchers at LAION took a chunk of the Common Crawl data and pulled out every image with an “alt” tag, a line or so of text meant to be used to describe images on web pages. After some trimming, links to the original images and the text describing them are released in vast collections: LAION-5B, released in March 2022, contains more than five billion text-image pairs. These images are “public” images in the broadest sense: any image ever published on the internet may be gathered up into them, with exactly the kind of strange effects one may expect. In September 2022, a San Francisco–based digital artist named Lapine was using a tool called Have I Been Trained, which allows artists to see if their work is being used to train AI image generation models. Have I Been Trained was created by the artists Mat Dryhurst and Holly Herndon, whose own work led them to explore the ways in which artists’ labour is coopted by AI. When Lapine used it to scan the LAION database, she found an image of her own face. She was able to trace this image back to photographs taken by a doctor when she was undergoing treatment for a rare genetic condition. The photographs were taken as part of her clinical documentation, and she signed documents that restricted their use to her medical file alone. The doctor involved died in 2018. Somehow, these private medical images ended up online, then in Common Crawl’s archive and LAION’s dataset, and were finally ingested into the neural networks as they learned about the meaning of images, and how to make new ones. For all we know, the mottled pink texture of our Saint-Exupéry-style piggy could have been blended, however subtly, from the raw flesh of a cancer patient. “It’s the digital equivalent of receiving stolen property. Someone stole the image from my deceased doctor’s files and it ended up somewhere online, and then it was scraped into this dataset,” Lapine told the website Ars Technica. “It’s bad enough to have a photo leaked, but now it’s part of a product. And this goes for anyone’s photos, medical record or not. And the future abuse potential is really high.” (According to her Twitter account, Lapine continues to use tools like Dall-E to make her own art.) The entirety of this kind of publicly available AI, whether it works with images or words, as well as the many data-driven applications like it, is based on this wholesale appropriation of existing culture, the scope of which we can barely comprehend. Public or private, legal or otherwise, most of the text and images scraped up by these systems exist in the nebulous domain of “fair use” (permitted in the US, but questionable if not outright illegal in the EU). Like most of what goes on inside advanced neural networks, it’s really impossible to understand how they work from the outside, rare encounters such as Lapine’s aside. But we can be certain of this: far from being the magical, novel creations of brilliant machines, the outputs of this kind of AI is entirely dependent on the uncredited and unremunerated work of generations of human artists. AI image and text generation is pure primitive accumulation: expropriation of labour from the many for the enrichment and advancement of a few Silicon Valley technology companies and their billionaire owners. These companies made their money by inserting themselves into every aspect of everyday life, including the most personal and creative areas of our lives: our secret passions, our private conversations, our likenesses and our dreams. They enclosed our imaginations in much the same manner as landlords and robber barons enclosed once-common lands. They promised that in doing so they would open up new realms of human experience, give us access to all human knowledge, and create new kinds of human connection. Instead, they are selling us back our dreams repackaged as the products of machines, with the only promise being that they’ll make even more money advertising on the back of them. The weirdness of AI image generation exists in the output as well as the input. One user tried typing in nonsense phrases and was confused and somewhat discomforted to discover that Dall-E mini seemed to have a very good idea of what a “Crungus” was: an otherwise unknown phrase that consistently produced images of a snarling, naked, ogre-like figure. Crungus was sufficiently clear within the program’s imagination that he could be manipulated easily: other users quickly offered up images of ancient Crungus tapestries, Roman-style Crungus mosaics, oil paintings of Crungus, photos of Crungus hugging various celebrities, and, this being the internet, “sexy” Crungus. So, who or what is Crungus? Twitter users were quick to describe him as “the first AI cryptid”, a creature like Bigfoot who exists, in this case, within the underexplored terrain of the AI’s imagination. And this is about as clear an answer as we can get at this point, due to our limited understanding of how the system works. We can’t peer inside its decision-making processes because the way these neural networks “think” is inherently inhuman. It is the product of an incredibly complex, mathematical ordering of the world, as opposed to the historical, emotional way in which humans order their thinking. The Crungus is a dream emerging from the AI’s model of the world, composited from billions of references that have escaped their origins and coalesced into a mythological figure untethered from human experience. Which is fine, even amazing – but it does make one ask, whose dreams are being drawn upon here? What composite of human culture, what perspective on it, produced this nightmare? A similar experience occurred to another digital artist experimenting with negative prompts, a technique for generating what the system considers to be the polar opposite of what is described. When the artist entered “Brando::-1”, the system returned something that looked a bit like a logo for a video game company called DIGITA PNTICS. That this may, across the multiple dimensions of the system’s vision of the world, be the opposite of Marlon Brando seems reasonable enough. But when they checked to see if it went the other way, by typing in “DIGITA PNTICS skyline logo::-1”, something much stranger happened: all of the images depicted a creepy-looking woman with sunken eyes and reddened cheeks, who the artist christened Loab. Once discovered, Loab seemed unusually and disturbingly persistent. Feeding the image back into the program, combined with ever more divergent text prompts, kept bringing Loab back, in increasingly nightmarish forms, in which blood, gore and violence predominated. Here’s one explanation for Loab, and possibly Crungus: although it’s very, very hard to imagine the way the machine’s imagination works, it is possible to imagine it having a shape. This shape is never going to be smooth or neatly rounded: rather, it is going to have troughs and peaks, mountains and valleys, areas full of information and areas lacking many features at all. Those areas of high information correspond to networks of associations that the system “knows” a lot about. One can imagine the regions related to human faces, cars and cats, for example, being pretty dense, given the distribution of images one finds on a survey of the whole internet. It is these regions that an AI image generator will draw on most heavily when creating its pictures. But there are other places, less visited, that come into play when negative prompting – or indeed, nonsense phrases – are deployed. In order to satisfy such queries, the machine must draw on more esoteric, less certain connections, and perhaps even infer from the totality of what it does know what its opposite may be. Here, in the hinterlands, Loab and Crungus are to be found. That’s a satisfying theory, but it does raise certain uncomfortable questions about why Crungus and Loab look like they do; why they tip towards horror and violence, why they hint at nightmares. AI image generators, in their attempt to understand and replicate the entirety of human visual culture, seem to have recreated our darkest fears as well. Perhaps this is just a sign that these systems are very good indeed at aping human consciousness, all the way down to the horror that lurks in the depths of existence: our fears of filth, death and corruption. And if so, we need to acknowledge that these will be persistent components of the machines we build in our own image. There is no escaping such obsessions and dangers, no moderating or engineering away the reality of the human condition. The dirt and disgust of living and dying will stay with us and need addressing, just as the hope, love, joy and discovery will. This matters, because AI image generators will do what all previous technologies have done, but they will also go further. They will reproduce the biases and prejudices of those who create them, like the webcams that only recognise white faces, or the predictive policing systems that lay siege to low-income neighbourhoods. And they will also up the game: the benchmark of AI performance is shifting from the narrow domain of puzzles and challenges – playing chess or Go, or obeying traffic laws – to the much broader territory of imagination and creativity. While claims about AI’s “creativity” might be overblown – there is no true originality in image generation, only very skilled imitation and pastiche – that doesn’t mean it isn’t capable of taking over many common “artistic” tasks long considered the preserve of skilled workers, from illustrators and graphic designers to musicians, videographers and, indeed, writers. This is a huge shift. AI is now engaging with the underlying experience of feeling, emotion and mood, and this will allow it to shape and influence the world at ever deeper and persuasive levels. ChatGPT was introduced in November 2022 by OpenAI, and further shifted our understanding of how AI and human creativity might interact. Structured as a chatbot – a program that mimics human conversation – ChatGPT is capable of a lot more than conversation. When properly entreated, it is capable of writing working computer code, solving mathematical problems and mimicking common writing tasks, from book reviews to academic papers, wedding speeches and legal contracts. It was immediately obvious how the program could be a boon to those who find, say, writing emails or essays difficult, but also how, as with image generators, it could be used to replace those who make a living from those tasks. Many schools and universities have already implemented policies that ban the use of ChatGPT amid fears that students will use it to write their essays, while the academic journal Nature has had to publish policies explaining why the program cannot be listed as an author of research papers (it can’t give consent, and it can’t be held accountable). But institutions themselves are not immune from inappropriate uses of the tool: in February, Peabody College, a private university in Tennessee, shocked students when it sent out a letter of condolence and advice following a school shooting in Michigan. While the letter spoke of the value of community, mutual respect and togetherness, a note at the bottom stated that it was written by ChatGPT – which felt both morally wrong and somehow false or uncanny to many. It seems there are many areas of life where the intercession of machines requires some deeper thought. If it would be inappropriate to replace our communications wholesale with ChatGPT, then one clear trend is for it to become a kind of wise assistant, guiding us through the morass of available knowledge towards the information we seek. Microsoft has been an early mover in this direction, reconfiguring its often disparaged search engine Bing as a ChatGPT-powered chatbot, and massively boosting its popularity by doing so. But despite the online (and journalistic) rush to consult ChatGPT on almost every conceivable problem, its relationship to knowledge itself is somewhat shaky. Q&A AI explained: why do chatbots make errors? Show One recent personal interaction with ChatGPT went like this. I asked it to suggest some books to read based on a new area of interest: multi-species democracy, the idea of including non-human creatures in political decision-making processes. It’s pretty much the most useful application of the tool: “Hey, here’s a thing I’m thinking about, can you tell me some more?” And ChatGPT obliged. It gave me a list of several books that explored this novel area of interest in depth, and described in persuasive human language why I should read them. This was brilliant! Except, it turned out that only one of the four books listed actually existed, and several of the concepts ChatGPT thought I should explore further were lifted wholesale from rightwing propaganda: it explained, for example, that the “wise use” movement promoted animal rights, when in fact it is a libertarian, anti-environment concept promoting the expansion of property rights. Now, this didn’t happen because ChatGPT is inherently rightwing. It’s because it’s inherently stupid. It has read most of the internet, and it knows what human language is supposed to sound like, but it has no relation to reality whatsoever. It is dreaming sentences that sound about right, and listening to it talk is frankly about as interesting as listening to someone’s dreams. It is very good at producing what sounds like sense, and best of all at producing cliche and banality, which has composed the majority of its diet, but it remains incapable of relating meaningfully to the world as it actually is. Distrust anyone who pretends that this is an echo, even an approximation, of consciousness. (As this piece was going to publication, OpenAI released a new version of the system that powers ChatGPT, and said it was “less likely to make up facts”.) The belief in this kind of AI as actually knowledgeable or meaningful is actively dangerous. It risks poisoning the well of collective thought, and of our ability to think at all. If, as is being proposed by technology companies, the results of ChatGPT queries will be provided as answers to those seeking knowledge online, and if, as has been proposed by some commentators, ChatGPT is used in the classroom as a teaching aide, then its hallucinations will enter the permanent record, effectively coming between us and more legitimate, testable sources of information, until the line between the two is so blurred as to be invisible. Moreover, there has never been a time when our ability as individuals to research and critically evaluate knowledge on our own behalf has been more necessary, not least because of the damage that technology companies have already done to the ways in which information is disseminated. To place all of our trust in the dreams of badly programmed machines would be to abandon such critical thinking altogether. AI technologies are bad for the planet too. Training a single AI model – according to research published in 2019 – might emit the equivalent of more than 284 tonnes of carbon dioxide, which is nearly five times as much as the entire lifetime of the average American car, including its manufacture. These emissions are expected to grow by nearly 50% over the next five years, all while the planet continues to heat up, acidifying the oceans, igniting wildfires, throwing up superstorms and driving species to extinction. It’s hard to think of anything more utterly stupid than artificial intelligence, as it is practised in the current era. So, let’s take a step back. If these current incarnations of “artificial” “intelligence” are so dreary, what are the alternatives? Can we imagine powerful information sorting and communicating technologies that don’t exploit, misuse, mislead and supplant us? Yes, we can – once we step outside the corporate power networks that have come to define the current wave of AI. In fact, there are already examples of AI being used to benefit specific communities by bypassing the entrenched power of corporations. Indigenous languages are under threat around the world. The UN estimates that one disappears every two weeks, and with that disappearance goes generations of knowledge and experience. This problem, the result of colonialism and racist assimilation policies over centuries, is compounded by the rising dominance of machine-learning language models, which ensure that popular languages increase their power, while lesser-known ones are drained of exposure and expertise. In Aotearoa New Zealand, a small non-profit radio station called Te Hiku Media, which broadcasts in the Māori language, decided to address this disparity between the representation of different languages in technology. Its massive archive of more than 20 years of broadcasts, representing a vast range of idioms, colloquialisms and unique phrases, many of them no longer spoken by anyone living, was being digitised, but needed to be transcribed to be of use to language researchers and the Māori community. In response, the radio station decided to train its own speech recognition model, so that it would be able to “listen” to its archive and produce transcriptions. Over the next few years, Te Hiku Media, using open-source technologies as well as systems it developed in house, achieved the almost impossible: a highly accurate speech recognition system for the Māori language, which was built and owned by its own language community. This was more than a software effort. The station contacted every Māori community group it could and asked them to record themselves speaking pre-written statements in order to provide a corpus of annotated speech, a prerequisite for training its model. There was a cash prize for whoever submitted the most sentences – one activist, Te Mihinga Komene, recorded 4,000 phrases alone – but the organisers found that the greatest motivation for contributors was the shared vision of revitalising the language while keeping it in the community’s ownership. Within a few weeks, it created a model that recognised recorded speech with 86% accuracy – more than enough to get it started transcribing its full archive. Te Hiku Media’s achievement cleared a path for other indigenous groups to follow, with similar projects now being undertaken by Mohawk peoples in south-eastern Canada and Native Hawaiians. It also established the principle of data sovereignty around indigenous languages, and by extension, other forms of indigenous knowledge. When international for-profit companies started approaching Māori speakers to help build their own models, Te Hiku Media campaigned against these efforts, arguing, “They suppressed our languages and physically beat it out of our grandparents, and now they want to sell our language back to us as a service.” “Data is the last frontier of colonisation,” wrote Keoni Mahelona, a Native Hawaiian and one of the co-founders of Te Hiku Media. All of Te Hiku’s work is released under what it named the Kaitiakitanga License, a legal guarantee of guardianship and custodianship that ensures that all the data that went into the language model and other projects remains the property of the community that created it – in this case, the Māori speakers who offered their help – and is theirs to license, or not, as they deem appropriate according to their tikanga (Māori customs and protocols). In this way, the Māori language is revitalised, while resisting and altering the systems of digital colonialism that continue to repeat centuries of oppression. The lesson of the current wave of “artificial” “intelligence”, I feel, is that intelligence is a poor thing when it is imagined by corporations. If your view of the world is one in which profit maximisation is the king of virtues, and all things shall be held to the standard of shareholder value, then of course your artistic, imaginative, aesthetic and emotional expressions will be woefully impoverished. We deserve better from the tools we use, the media we consume and the communities we live within, and we will only get what we deserve when we are capable of participating in them fully. And don’t be intimidated by them either – they’re really not that complicated. As the science-fiction legend Ursula K Le Guin wrote: “Technology is what we can learn to do.” This article was adapted from the new edition of New Dark Age: Technology and the End of the Future, published by Verso

March 16, 2023

Re: [nexa] GTP-4 passa l'esame da avvocato

by Daniela Tafani

"The judges did not use ChatGPT in an informed or responsible manner" "The short answer is that they used ChatGPT as if it was an oracle: a trustworthy source of knowledge that did not require any sort of verification. While the judges were transparent about the fact that they used the tool and included quotation marks to distinguish the content produced by ChatGPT, their use was not informed nor responsible. There are three main reasons why the way ChatGPT was used by the judiciary in these cases is very concerning for Colombians and beyond." "First, the stakes in judicial rulings are too high – especially when human rights are involved – to justify the use of unreliable and insufficiently tested technologies. Due to the way that LLMs like ChatGPT are developed and operate, these tools tend to produce incorrect and imprecise answers and confuse reality with fiction<https://dl.acm.org/doi/10.1145/3442188.3445922>. Even the CEO of OpenAI acknowledged in December 2022<https://twitter.com/sama/status/1601731295792414720?s=20>, that “ChatGPT is incredibly limited (…) it’s a mistake to be relying on it for anything important right now”. Furthermore, due to structural reasons, it is unlikely that these problems of LLMs<https://www.theverge.com/2023/2/9/23592647/ai-search-bing-bard-chatgpt-micr…> will be solved soon." "Secondly, ChatGPT’s answers should not have been accepted and taken at face value, but rather contrasted with other more reliable sources" "The judgement states that the information offered by ChatGPT would be “corroborated”. However, there is no explicit trace in the text that allows us to conclude that judge Padilla or his clerk effectively checked whether ChatGPT’s responses were accurate. In fact, I replicated the four queries<https://twitter.com/JuanDGut/status/1620579558742429697?s=20> posed by judge Padilla and the chatbot answered slightly differently, a result that is not surprising given how the tool works. Furthermore, when I prompted ChatGPT to provide examples of case law from the Constitutional Court<https://twitter.com/JuanDGut/status/1620589338085171200?s=20> that justified its answers, the chatbot invented the facts and ratio decidendi of one ruling, and cited a judgement that did not exist (inventing the facts and the verdict" "If ChatGPT and other LLMs currently available are evidently unreliable, since their outputs tend to include incorrect and false information, then judges would require significant time to check the validity of the AI-generated content, thereby undoing any significant “time savings”. As it happens with AI in other areas, under the narrative of supposed “efficiencies”, fundamental rights can be put at risk. Finally, there is a risk that the judges and its clerks over-rely on the AI’s recommendations, incurring what is known as “automation bias”." https://verfassungsblog.de/colombian-chatgpt/ (grassetti miei) Buona giornata, Daniela ________________________________ Da: nexa <nexa-bounces(a)server-nexa.polito.it> per conto di Guido Noto La Diega <noto.la.diega(a)gmail.com> Inviato: mercoledì 15 marzo 2023 21:25 A: nexa(a)server-nexa.polito.it Oggetto: [nexa] GTP-4 passa l'esame da avvocato "In this paper, we experimentally evaluate the zero-shot performance of a preliminary version of GPT-4 against prior generations of GPT on the entire Uniform Bar Examination (UBE), including not only the multiple-choice Multistate Bar Examination (MBE), but also the open-ended Multistate Essay Exam (MEE) and Multistate Performance Test (MPT) components. On the MBE, GPT-4 significantly outperforms both human test-takers and prior models, demonstrating a 26% increase over ChatGPT and beating humans in five of seven subject areas. On the MEE and MPT, which have not previously been evaluated by scholars, GPT-4 scores an average of 4.2/6.0 as compared to much lower scores for ChatGPT. Graded across the UBE components, in the manner in which a human tast-taker would be, GPT-4 scores approximately 297 points, significantly in excess of the passing threshold for all UBE jurisdictions. These findings document not just the rapid and remarkable advance of large language model performance generally, but also the potential for such models to support the delivery of legal services in society." https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315202551060565… Sent from Outlook for Android<https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315202551060565…>

March 16, 2023

Re: [nexa] GTP-4 passa l'esame da avvocato

by Marco Fioretti

On Wed, Mar 15, 2023 at 21:25, Guido Noto La Diega wrote: "I...PT-4 scores approximately 297 points, significantly in excess of the passing threshold for all UBE jurisdictions." Grande, cosi' appena lo addestrano coi codici italiani risolviamo questo problema: "aspiranti magistrati bocciati in massa all’esame" https://www.orizzontescuola.it/ce-chi-ha-lacune-tecniche-ma-anche-chi-non-c… (https://www.orizzontescuola.it/ce-chi-ha-lacune-tecniche-ma-anche-chi-non-c…) mannaggia... Marco -- vi aspetto su Substack: https://mfioretti.substack.com

March 16, 2023

Re: [nexa] GPT-4

by Guido Vetere

D'altra parte, qualcuno ha mai smesso di fumare perché sul pacchetto di sigarette c'è scritto che il fumo uccide? La verità è che tutti finiremo per usare GPT e le tante cose del genere che stanno già venendo fuori. Vi sono moltissimi impieghi per i quali l'inconsapevolezza di questi sistemi non emerge, o non rileva. Nell'industria del software, ad esempio, GPT accelera moltissimo quello che gli sviluppatori fanno abitualmente quando sono alle prese con un puzzle (cioè sempre): ricerche su Google, che puntualmente conducono a siti specialistici dove tuttavia c'è da perder tempo. GPT, sulla base di informazione attinta da quelle stesse fonti, dà in pochi secondi la soluzione 'out of the box', quasi sempre soddisfacente. Nessuno sviluppatore ne farà a meno. L'effetto devastante per l'ecosistema dello sviluppo software è facilmente intuibile: verrà tutto risucchiato dentro i 'gazillion parameters' di GPT. Ma vi sono anche cose molto interessanti che riguardano le parole. La lessicografia classica, cioè basata fondamentalmente sull'introspezione, già da tempo incalzata dalle analisi quantitative (il lessico di frequenza di De Mauro risale agli anni '80) è finita. I Large Language Model non sono certamente un modello plausibile del linguaggio umano, tuttavia rendono possibile osservare, nei 'giochi linguistici', quelle 'somiglianze di famiglia' di cui parlava Wittgenstein, su una scala che non ha precedenti. Risolta la questione se useremo o meno questi oggetti, la domanda ora è: possiamo costruirli e gestirli in modo che non aumentino le disuguaglianze e i pericoli? Uno spunto può venire dal confronto tra i diversi ordini di grandezza che dividono GPT-3 da GPT-4 e le differenze non altrettanto grandi tra le prestazioni dei due sistemi, come se il rapporto tra dimensioni e prestazioni fosse logaritmico. Se così fosse, allora sapremmo qual è il limite del reale vantaggio dei GAFAM rispetto a 'the rest of us'. Viviamo in tempi interessanti. Guido On Wed, 15 Mar 2023 at 20:01, Daniela Tafani <daniela.tafani(a)unipi.it> wrote: > Già. O dei multimiliardari che chiedono di pagare più tasse. > > > Qui però c'è anche il convitato di pietra della responsabilità: > > assomigliano perciò al quel personaggio di Pirandello che girava sempre > con le mani avanti... > > > ------------------------------ > *Da:* Antonio Casilli <antonio.casilli(a)telecom-paris.fr> > *Inviato:* mercoledì 15 marzo 2023 19:45 > *A:* Daniela Tafani > *Cc:* Nexa > *Oggetto:* Re: [nexa] GPT-4 > > Possiamo chiamarlo "L'approccio dei Sam" (Altman e Bankman-Fried): non > rispettare le regole e poi dire "ma com'è che non mi regolate?" > > ----- Original Message ----- > From: "Daniela Tafani" <daniela.tafani(a)unipi.it> > To: "Nexa" <nexa(a)server-nexa.polito.it> > Sent: Wednesday, March 15, 2023 7:11:09 PM > Subject: [nexa] GPT-4 > > - GPT-4 has the potential to be used to attempt to identify private > individuals when augmented with outside data; > > - GPT-4 is capable of generating discriminatory content favorable to > autocratic governments across > multiple languages > > - it does continue the trend of potentially lowering the cost of certain > steps of a successful cyberattack; > > - GPT-4 has the tendency to “hallucinate,” i.e. “produce content that is > nonsensical or untruthful in > relation to certain sources.” This tendency can be particularly harmful as > models become > increasingly convincing and believable, leading to overreliance on them by > users. > Counterintuitively, hallucinations can become more dangerous as models > become more truthful, as users build trust in the model when it provides > truthful information in > areas where they have some familiarity. Additionally, as these models are > integrated into society > and used to help automate various systems, this tendency to hallucinate is > one of the factors that > can lead to the degradation of overall information quality and further > reduce veracity of and trust in > freely available information; > > Non lo scrive un luddista, lo scrive - in un lungo elenco di ulteriori > effetti nefasti - OpenAI, nella System Card > di un documento travestito da articolo arxiv, ma il cui autore è l'azienda > stessa: > > > > < [ > https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562… > | > https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562… > ] > > > > > > Nella consapevolezza della fragilità degli interventi mitigatori, OpenAI > rileva "a need for effective regulation". > > > > Buona serata, > > Daniela > > _______________________________________________ > nexa mailing list > nexa(a)server-nexa.polito.it > > https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562… > _______________________________________________ > nexa mailing list > nexa(a)server-nexa.polito.it > https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa >

March 16, 2023

GTP-4 passa l'esame da avvocato

by Guido Noto La Diega

"In this paper, we experimentally evaluate the zero-shot performance of a preliminary version of GPT-4 against prior generations of GPT on the entire Uniform Bar Examination (UBE), including not only the multiple-choice Multistate Bar Examination (MBE), but also the open-ended Multistate Essay Exam (MEE) and Multistate Performance Test (MPT) components. On the MBE, GPT-4 significantly outperforms both human test-takers and prior models, demonstrating a 26% increase over ChatGPT and beating humans in five of seven subject areas. On the MEE and MPT, which have not previously been evaluated by scholars, GPT-4 scores an average of 4.2/6.0 as compared to much lower scores for ChatGPT. Graded across the UBE components, in the manner in which a human tast-taker would be, GPT-4 scores approximately 297 points, significantly in excess of the passing threshold for all UBE jurisdictions. These findings document not just the rapid and remarkable advance of large language model performance generally, but also the potential for such models to support the delivery of legal services in society." https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4389233 Sent from Outlook for Android<https://aka.ms/AAb9ysg>

March 15, 2023

Re: [nexa] GPT-4

by Daniela Tafani

Già. O dei multimiliardari che chiedono di pagare più tasse. Qui però c'è anche il convitato di pietra della responsabilità: assomigliano perciò al quel personaggio di Pirandello che girava sempre con le mani avanti... ________________________________ Da: Antonio Casilli <antonio.casilli(a)telecom-paris.fr> Inviato: mercoledì 15 marzo 2023 19:45 A: Daniela Tafani Cc: Nexa Oggetto: Re: [nexa] GPT-4 Possiamo chiamarlo "L'approccio dei Sam" (Altman e Bankman-Fried): non rispettare le regole e poi dire "ma com'è che non mi regolate?" ----- Original Message ----- From: "Daniela Tafani" <daniela.tafani(a)unipi.it> To: "Nexa" <nexa(a)server-nexa.polito.it> Sent: Wednesday, March 15, 2023 7:11:09 PM Subject: [nexa] GPT-4 - GPT-4 has the potential to be used to attempt to identify private individuals when augmented with outside data; - GPT-4 is capable of generating discriminatory content favorable to autocratic governments across multiple languages - it does continue the trend of potentially lowering the cost of certain steps of a successful cyberattack; - GPT-4 has the tendency to “hallucinate,” i.e. “produce content that is nonsensical or untruthful in relation to certain sources.” This tendency can be particularly harmful as models become increasingly convincing and believable, leading to overreliance on them by users. Counterintuitively, hallucinations can become more dangerous as models become more truthful, as users build trust in the model when it provides truthful information in areas where they have some familiarity. Additionally, as these models are integrated into society and used to help automate various systems, this tendency to hallucinate is one of the factors that can lead to the degradation of overall information quality and further reduce veracity of and trust in freely available information; Non lo scrive un luddista, lo scrive - in un lungo elenco di ulteriori effetti nefasti - OpenAI, nella System Card di un documento travestito da articolo arxiv, ma il cui autore è l'azienda stessa: < [ https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562… | https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562… ] > Nella consapevolezza della fragilità degli interventi mitigatori, OpenAI rileva "a need for effective regulation". Buona serata, Daniela _______________________________________________ nexa mailing list nexa(a)server-nexa.polito.it https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=20230315184510010562…

March 15, 2023

Re: [nexa] GPT-4

by Antonio Casilli

Possiamo chiamarlo "L'approccio dei Sam" (Altman e Bankman-Fried): non rispettare le regole e poi dire "ma com'è che non mi regolate?" ----- Original Message ----- From: "Daniela Tafani" <daniela.tafani(a)unipi.it> To: "Nexa" <nexa(a)server-nexa.polito.it> Sent: Wednesday, March 15, 2023 7:11:09 PM Subject: [nexa] GPT-4 - GPT-4 has the potential to be used to attempt to identify private individuals when augmented with outside data; - GPT-4 is capable of generating discriminatory content favorable to autocratic governments across multiple languages - it does continue the trend of potentially lowering the cost of certain steps of a successful cyberattack; - GPT-4 has the tendency to “hallucinate,” i.e. “produce content that is nonsensical or untruthful in relation to certain sources.” This tendency can be particularly harmful as models become increasingly convincing and believable, leading to overreliance on them by users. Counterintuitively, hallucinations can become more dangerous as models become more truthful, as users build trust in the model when it provides truthful information in areas where they have some familiarity. Additionally, as these models are integrated into society and used to help automate various systems, this tendency to hallucinate is one of the factors that can lead to the degradation of overall information quality and further reduce veracity of and trust in freely available information; Non lo scrive un luddista, lo scrive - in un lungo elenco di ulteriori effetti nefasti - OpenAI, nella System Card di un documento travestito da articolo arxiv, ma il cui autore è l'azienda stessa: < [ https://cdn.openai.com/papers/gpt-4.pdf | https://cdn.openai.com/papers/gpt-4.pdf ] > Nella consapevolezza della fragilità degli interventi mitigatori, OpenAI rileva "a need for effective regulation". Buona serata, Daniela _______________________________________________ nexa mailing list nexa(a)server-nexa.polito.it https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa

March 15, 2023

GPT-4

by Daniela Tafani

- GPT-4 has the potential to be used to attempt to identify private individuals when augmented with outside data; - GPT-4 is capable of generating discriminatory content favorable to autocratic governments across multiple languages - it does continue the trend of potentially lowering the cost of certain steps of a successful cyberattack; - GPT-4 has the tendency to “hallucinate,” i.e. “produce content that is nonsensical or untruthful in relation to certain sources.” This tendency can be particularly harmful as models become increasingly convincing and believable, leading to overreliance on them by users. Counterintuitively, hallucinations can become more dangerous as models become more truthful, as users build trust in the model when it provides truthful information in areas where they have some familiarity. Additionally, as these models are integrated into society and used to help automate various systems, this tendency to hallucinate is one of the factors that can lead to the degradation of overall information quality and further reduce veracity of and trust in freely available information; Non lo scrive un luddista, lo scrive - in un lungo elenco di ulteriori effetti nefasti - OpenAI, nella System Card di un documento travestito da articolo arxiv, ma il cui autore è l'azienda stessa: <https://cdn.openai.com/papers/gpt-4.pdf> Nella consapevolezza della fragilità degli interventi mitigatori, OpenAI rileva "a need for effective regulation". Buona serata, Daniela

March 15, 2023

Re: [nexa] a proposito dell'articolo di Chomsky sul NYT

by Enrico Nardelli

Grazie del commento Rob. Io non sono un cognitivista quindi mi esprimo un'opinione consapevole che possa essere estremamente ingenua. Trovo il confronto con una teoria della mente molto fuorviante, dal momento che le menti che conosco sono menti che posseggono una semantica mentre ChatGPT possiede solo capacità sintattiche approssimando quelle semantiche mediante la statistica. Per non parlare poi degli aspetti di consapevolezza... Ciao, Enrico Per non parlare poi Il 15/03/2023 14:26, Roberto Dolci ha scritto: > > Ciao Enrico, > > > le principali differenze tra il fanciullo e ChatGPT, di cui notiamo comunque essere il primo robot a raggiungere la performance di un 9nne sulla scala di teoria della mente (era 3enne solo a novembre scorso): > > > 1. il consumo energetico. Il nostro cervello non arriva ad una lampadina, mentre per ogni richiesta di ChatGP consumiamo una quantita' immonda di energia. > > 2. la creazione. Il bambino crea la spiegazione, e poi la testa immediatamente, validandola o creadone un altra. ChatGPT parte dallo scibile disponibile per inferire e quindi imparare, ovviamente non crea mai nulla di effettivamente nuovo. > > 3. la plasticita' degli organoidi cerebrali rispetto ai chip sotto ChatGPT e l'utilizzo della chimica nei processi cerebrali. La gestione della "potatura" delle memorie inutili nel nostro cervello e' ordini di grandezza piu' efficiente ed efficace di quanto disponibile con AI. Su Zafferano di sabato > > > Un abbraccio > Roberto > ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ > *Da:* nexa <nexa-bounces(a)server-nexa.polito.it> per conto di Enrico Nardelli <nardelli(a)mat.uniroma2.it> > *Inviato:* martedì 14 marzo 2023 17:30:08 > *A:* Nexa > *Oggetto:* [nexa] a proposito dell'articolo di Chomsky sul NYT > > Si tratta di questo articolo: https://www.nytimes.com/2023/03/08/opinion/noam-chomsky-chatgpt-ai.html > (mi scuso qualora se ne fosse già discusso in lista) > > Ne stavo discutendo con colleghi di vari paesi in un'altra mailing list e in risposta ad alcune critiche fatte all'articolo ho scritto quanto riporto sotto (scusatemi se lo lascio in inglese ma siccome coinvolge l'uso di ChatGPT nell'analisi di un testo in inglese traducendolo diventava un'altra cosa) > > ... > > I find your viewpoint highly interesting, even if I somehow disagree on some of your criticism to Chomsky's article. > > I find an analogy between the way ChatGPT learned to master the language in comparison to human beings and how Alpha Zero learned to master board games in comparison to human beings. > > The deep-learning based approach exploited in both cases (almost) all available data, either through their availability on the Internet (in the case of the language) or through sophisticated systematic search through the strategy space (in the case of board games). > > Human beings become master (in both fields) leveraging a much smaller information space: the number of examples a child is exposed to while learning language is, just by considering time and space constraints, much smaller than what ChatGPT used. I do not know of any estimation of this (which would be an interesting thing to do) but I would say the size is inferior of some orders of magnitude (which to me implies the "language learning algorithm" inside human brain is exponentially more efficient then those used in DL - but that's a different story). > > Moreover human beings know the "meaning" of their action and are aware of the fact that they know such meaning. > > (See the note at the end supporting my statements.) > > This appears to me to be an important difference between the two approaches and I think is the reason why Chomsky wrote that ChatGTP is not going to teach us anything useful about how human beings learn and use language. > > One more interesting discussion of the Chomsky viewpoint, this time defending it, I suggest (in case you've not already read it) is the piece of Gary Marcus https://garymarcus.substack.com/p/caricaturing-noam-chomsky > > Best, Enrico > > NOTE: To test my statements I asked ChatGPT to analyse a paragraph of my mail. You can read the prompt and the answer further below. > > The answer is at best confusing. > > ChatGPT fails to identify the text has 2 sentences and not just 1. > > It does a wrong analysis of the first sentence, which contain two main clauses and not one main and one subordinate. > > That's particularly interesting since right after the wrong analysis it "explains" that "a main clause is an independent clause that stands alone as a complete sentence". So, it writes that it "knows" what a main clause is right after having shown it doesn't know. > > And the rest contains a number of additional errors. > > So, even if the examples ChatGPT has been exposed to surely contain many descriptions of grammar rules, it has not been able to get any understanding of them. A secondary high school student, having received much less data and much less training is able to do a much better job. > > CVD :-) > > > PROMPT > Please perform the analysis of clause and sentence structure of the text enclosed below between «»: > «Human beings become master (in both fields) leveraging a much smaller information space: the number of examples a child is exposed to while learning language is, just by considering time and space constraints, much smaller than what ChatGPT used. I do not know of any estimation of this (which would be an interesting thing to do) but I would say the size is inferior of some orders of magnitude (which to me implies the "language learning algorithm" inside human brain is exponentially more efficient then those used in DL - but that's a different story).» > > > ANSWER (ChatGPT release of Feb.13 - tested on Mar.14th) > The text contains one sentence: > > "Human beings become master (in both fields) leveraging a much smaller information space: the number of examples a child is exposed to while learning language is, just by considering time and space constraints, much smaller than what ChatGPT used. I do not know of any estimation of this (which would be an interesting thing to do) but I would say the size is inferior of some orders of magnitude (which to me implies the "language learning algorithm" inside human brain is exponentially more efficient then those used in DL - but that's a different story)." > > The sentence contains two clauses: > > Main clause: "Human beings become master leveraging a much smaller information space" > Subordinate clause: "the number of examples a child is exposed to while learning language is, just by considering time and space constraints, much smaller than what ChatGPT used" > > The main clause is an independent clause that stands alone as a complete sentence. It contains a subject ("human beings") and a predicate ("become master leveraging a much smaller information space"). > > The subordinate clause is a dependent clause that cannot stand alone as a complete sentence. It provides additional information to the main clause by describing the size of the information space that humans use to become masters in both fields. The subordinate clause contains a subject ("the number of examples a child is exposed to while learning language") and a predicate ("is much smaller than what ChatGPT used"). The phrase "just by considering time and space constraints" acts as an adverbial modifier that further clarifies the subordinate clause. > > The text also contains two embedded clauses: > > "which would be an interesting thing to do" is an embedded clause that acts as an object of the verb "know" in the main clause. > "which to me implies the 'language learning algorithm' inside human brain is exponentially more efficient then those used in DL - but that's a different story" is an embedded clause that acts as the subject of the verb "is" in the subordinate clause. > > Additionally, the text contains a parenthetical remark: "but that's a different story," which is not syntactically connected to the rest of the sentence. > > END OF CHATGPT ANSWER > > > -- > > -- EN > > https://www.hoepli.it/libro/la-rivoluzione-informatica/9788896069516.html > ====================================================== > Prof. Enrico Nardelli > Presidente di "Informatics Europe" > Direttore del Laboratorio Nazionale "Informatica e Scuola" del CINI > Dipartimento di Matematica - Università di Roma "Tor Vergata" > Via della Ricerca Scientifica snc - 00133 Roma > home page: https://www.mat.uniroma2.it/~nardelli > blog: https://link-and-think.blogspot.it/ > tel: +39 06 7259.4204 fax: +39 06 7259.4699 > mobile: +39 335 590.2331 e-mail: nardelli(a)mat.uniroma2.it > online meeting: https://blue.meet.garr.it/b/enr-y7f-t0q-ont > ====================================================== > > > -- -- -- EN https://www.hoepli.it/libro/la-rivoluzione-informatica/9788896069516.html ====================================================== Prof. Enrico Nardelli Presidente di "Informatics Europe" Direttore del Laboratorio Nazionale "Informatica e Scuola" del CINI Dipartimento di Matematica - Università di Roma "Tor Vergata" Via della Ricerca Scientifica snc - 00133 Roma home page: https://www.mat.uniroma2.it/~nardelli blog: https://link-and-think.blogspot.it/ tel: +39 06 7259.4204 fax: +39 06 7259.4699 mobile: +39 335 590.2331 e-mail: nardelli(a)mat.uniroma2.it online meeting: https://blue.meet.garr.it/b/enr-y7f-t0q-ont ====================================================== --

March 15, 2023