Stefaan Verhulst
Paper by Neil D Lawrence and Jessica K Montgomery: “Public dialogues have produced clear demand signals for AI. These dialogues imagine innovations that improve our shared wellbeing and prosperity while developing under democratic control. AI development has largely advanced along another trajectory. We argue that this gap is a structural feature of an innovation system whose incentives are captured by the attention economy. Closing it requires a different driver of the innovation cycle. We propose an attention reinvestment cycle, in which efficiency gains accrue as freed time that can be invested in community innovation, with frontline professionals adapting and sharing tools that meet priority needs. Operating the cycle at scale requires institutional infrastructure—dialogic, absorptive, and distributive capacities—that the existing science-policy system has yet to develop…(More)”.
Release by University College London: “A major new resource that provides one of the most comprehensive pictures yet of what people are eating around the world has been introduced in a new study by a UCL and University of Oxford researcher.
From obesity and heart disease to climate change and food affordability, many of today’s biggest challenges are shaped by what we eat. But there is a surprisingly basic problem facing researchers and policymakers: We often don’t know with enough accuracy what people are actually consuming.
The new study, published in Nature Food and authored by Professor Marco Springmann (UCL Institute for Global Health as well as the Environmental Change Institute at the University of Oxford), introduces the Global Dietary Database for Impact Assessments (GDD-IA), which is freely available through an interactive online explorer, allowing users to investigate dietary patterns across countries and over time.
The GDD-IA combines information on food production, food waste, dietary surveys and human energy requirements to estimate what people eat from 1990 to 2020. It includes detail by age, sex and whether people live in urban or rural areas.
The resource has been designed to support research into some of the world’s most pressing questions: How do diets affect human health? What impact do they have on climate change and the environment? How affordable are healthy and sustainable diets for different populations?..(More)”.
Book by Andrew Guthrie Ferguson: “For consumers living in a digitally-connected world, smart technologies have built an inescapable trap of digital self-surveillance. Smart cars, smart homes, smart watches, and smart medical devices track our most private activities and intimate patterns. While these devices allow users to receive personal insights by monitoring their every move, that data can be accessed by police and prosecutors looking to find incriminating clues. Digital technology exposes everyone, everywhere, all at once, and we have few laws to regulate it.
In Your Data Will Be Used Against You, Andrew Guthrie Ferguson warns us of how the rise of sensor-driven technology, social media monitoring, and artificial intelligence can be weaponized against democratic values and personal freedoms. At the same time, that data will solve crimes, radically transforming how criminal cases are prosecuted. Ferguson explores how this proliferation of private data in combination with public surveillance networks promises new ways to solve previously unsolvable crimes, but also leaves us vulnerable to governmental overreach and abuse. He argues for legal interventions that address the threat of digital self-surveillance and provides concrete suggestions about how legislators, judges, and communities should respond…(More)”.
Article by Ioannis Chrysakis et al: “The 2020 European Strategy for Data aims at developing Common European Data Spaces as a means to build a pan-European single market for data, thereby supporting economic growth and maximizing citizens’ use of data. It demands data spaces in strategic sectors, with capabilities for effective data management and sharing. Although several initiatives support their adoption, data spaces are still in the early stages of development and face several data management and sharing challenges. To identify the requirements needed to address these challenges, we review the literature in developing a conceptual framework for applying data management and sharing in the context of data spaces. We then evaluate the practical implementation of the proposed solutions by analysing six representative European-funded projects. Focusing on requirements from trust and business models to interoperability, workflow orchestration, energy efficiency, and data quality, the work highlights prominent issues and explains how each project addresses them through technical means. Our evaluation outlines each project’s aim and contribution, along with a representative use case from different domains, e.g., water, agriculture, and energy. We recognize widely accepted strategies such as the use of semantic standards, data catalogues, distributed ledger technologies for trust enhancement, and federated identity management. This work highlights recurring patterns, common practices, and key differences in implementation and identifies open research gaps. Thus, it aims to inform future initiatives and provide concrete recommendations to researchers and practitioners on achieving data spaces with best practices. As the field matures, the work hopes to help achieve scalable, stable, and cross-domain data spaces that support sustainable innovation and long-term collaboration throughout Europe…(More)”.
Article by Jack Hardinges, and Irina Bejan: “Attribution has always been a cornerstone of working with other people’s creativity and knowledge.
As Creative Commons reminds us, practicing attribution serves many important functions, from providing verifiable evidence of a claim, to paying respect to the work of others, and creating pathways for traffic and financial value to flow.
Despite the importance of attribution, many of today’s AI systems fail to acknowledge the sources of knowledge and creativity that make them possible.
A recent study led by the AI Disclosures Project found that more than 30% of responses by leading search-enabled Large Language Models (LLMs) provided no attribution whatsoever. Even where attribution is provided, it can be limited in ways that are hidden to users, and deep technical challenges persist in connecting outputs from AI to the sources they are derived from.
Despite this, we believe there are good reasons to be optimistic that AI systems can attribute the sources they use. Not perfectly, and not always, but enough and improving, such that we should reject the argument that a lack of attribution is an inevitable side effect of the way the technology works. The commons needn’t become “hidden substrate.”..(More)”.
Article by Alexandra Bruell: “One of the richest sources of online information is re-evaluating its relationship with Google.
Reddit, the online message board that powers a swath of Google search results, has discussed shutting off the technology giant’s access to its content for AI use, according to people familiar with the matter.
It is part of a growing chorus of online media companies expressing frustration with the tech giant as AI changes the way people ask questions, siphons off search traffic and upends publishers’ revenue models. They say the search engine is no longer a reliable source of visitors, especially after Alphabet’s GOOGL Google expanded its AI search features in recent months. USA Today, Politico, the Economist, People Inc. and Reuters are all evaluating how, or even if, they will continue to work with Google.
Reddit struck a $60 million-a-year deal in 2024 that allowed Google to use its material to train AI models. But with AI-generated answers to queries reducing clicks to outside websites, Reddit executives are assessing what the upside is of continuing to feed its content to Google, said the people familiar with the matter. The companies are in talks about potentially renewing their deal, which is ending soon.
“This is existential for some categories of publishers,” said David Buttle, CEO of media consulting firm DJB Strategies. “They are looking at more radical things.”..(More)”.
Paper by Zeynep Engin et al: “The digital substrate of states — data, algorithms, infrastructure, platforms, applications — is being governed without adequate conceptual foundations. The ability and legitimacy required to govern this substrate, and to govern with it, are simultaneously misaligned, contested, and structurally absent. We introduce digital statecraft as the organising concept for this emerging field, arguing that ‘digital’ reconstitutes the statecraft question rather than merely extending its domain. The concept operates on two dimensions – statecraft over digital systems, concerning the authority and capacity of the state in relation to the digital substrate itself, and statecraft with digital systems, concerning the deployment of algorithmic tools as instruments of governing authority. And it rests on two foundational requirements, technical coherence and legitimate authority, that are genuinely in tension. We derive ten principles of digital statecraft from these foundations, each naming a condition whose absence produces an identifiable and structural governance failure: public interest first, human-machine complementarity, governability by design, systemic coherence, hybrid institutions, adaptive governance, human centricity and civic agency, accountable and traceable authority, judgment across time, and the non-delegable core. This article takes the state as the starting point, the institutional form that developed historically in response to the problem of effective and legitimate public governance, and the only current candidate for which the full set of legitimacy conditions is institutionally available. But the digital statecraft programme holds open a deeper question than just whether states can reform themselves: governing well in the algorithmic age may require rethinking the boundaries, scale, and affiliative basis of statehood itself…(More)”.
Article by Emanuel Maiberg: “As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing. “The world’s best AI training data is sitting on a shelf,” ISBNdb, a company that produces what it claims is “the world’s largest book database,” and that offers high-volume book acquisition services for AI companies, says on its site. “Books represent curated, peer-reviewed, domain-specific human knowledge, structured in a way no web crawl can replicate. Dense, edited, authoritative.” In one article on its site, ISBNdb explains that printed books published before 2022 are ideal for AI training data because they don’t include AI generated text. As the article correctly notes, much of the data that AI companies can scrape from the internet today is likely to include AI generated text, which could result in “model collapse,” a process by which AI models that are trained on AI generated data results in worse models that are more prone to errors. The article also notes that book authors who object to their writing being scraped for training purposes can now easily poison AI models by producing writing designed to manipulate and sabotage the resulting AI models. As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing…(More)”
Article by Leya Mohsin: “…Successful moonshots involve both a clear what and a clear how. If you think about prior moonshot efforts, there are probably a select few that jump to mind: the Apollo Program, the Manhattan Project, and the Human Genome Project are the most notable efforts. What’s something these all have in common?: A “moon”, a goal that anyone can clearly define. This is a critical element of successful moonshots.
But other similar efforts have been tried before to less avail. As an example, consider the Human Brain Project. This project had a clear goal: to simulate the entire human brain on a computer. It concluded in 2023 after 10 years and €1 billion spent. The effort did lead to significant advances in neuroscience, but it failed to accomplish its stated goal. From the jump, scientists were highly critical of this initiative because they did not believe the requisite technology existed, or was even close to existing. In other words, the how was unclear.
This is common for many such initiatives. For example, when the Human Genome Project was launched, we did not have the technology capable of meeting the mission. The project was highly criticized by many in the research community because the traditional method of DNA sequencing could not possibly be scaled to meet the goal. They needed a new way to sequence DNA faster, with longer reads, and for less money. Therefore, the first goal was to drive investment in the development of technology that could answer the how question.
A successful moonshot is one where it’s clear that we are on the precipice of achieving a once-unimaginable goal, if only we make a significant investment toward that end. Think of the original moonshot: when JFK announced this initiative, we had seen a man orbit the Earth. And we knew that putting a man on the moon was challenging, but far from impossible – we just needed to devote the appropriate time, resources, and organization around that goal…(More)”.
Article by Anne-Laure Le Cunff: “More than 60 percent of Google searches in the United States now end without the user clicking on a link. We type a question, read an artificial-intelligence-generated summary of the results and leave with our answer.
Google is hardly alone. Claude, ChatGPT and upstart competitors like Perplexity do roughly the same thing: They take a question and swiftly return an answer, compressing what used to be a meandering journey through the internet into an immediate arrival at your destination. The explorative phase of searches — clicking through links, stumbling onto unexpected pages, following a reference that leads to somewhere unplanned — is disappearing.
For anyone who publishes on the internet, this is a troubling development, since it lowers website traffic and makes protecting and profiting from your intellectual property more difficult. But you might think it is good news for internet users. Could there be anything wrong with getting a reliable answer more quickly?
There is. By shortening the time between asking a question and getting an answer, these tools are actually undermining curiosity — and paradoxically threatening our ability to understand the world.
About a decade ago, I worked at Google. When I was there, we often measured the value of internet content based on factors that indicated user engagement, like clicks and scroll depth. The metric Google seemed to reward — people exploring — is precisely what its A.I. products are now designed to eliminate.
I left Google to study neuroscience, and what I found in the research literature helps explain why the A.I. summary poses a danger to learning. Curiosity, it turns out, is not just an individual’s desire to find out discrete facts; it’s also a feature of our biology designed to help us learn more broadly. And it requires a specific condition: a gap between what you want to know and what you find out.
Researchers have found that people in a state of curiosity, while waiting for an answer to an intriguing question, remember unrelated information they encounter during that time far better than they otherwise would. In that study, the researchers also placed those people in brain scanners. They found that waiting for an answer activates reward circuits in the brain and readies the hippocampus to help create memories. Similar findings have been reported by other researchers in studies involving infants, older children and adults.
In short, curiosity puts the entire brain into a mode of heightened receptivity — not just for the specific thing you want to know but also for everything around it. Curiosity opens a window, and while the window is open, learning deepens across the board…(More)”.