Stefaan Verhulst

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)”.

Essay by Audrey Tang: “When people ask whether AI threatens humanism, I often feel the question arrives a little late. The deeper threat came earlier, when digital systems learned to sort attention at industrial scale. Long before generative models could write essays, compose songs or simulate conversation, many of us had already been trained to behave like components inside ranking systems: always visible, always reactive, always measurable. By the time synthetic fluency arrived, a quieter transformation was underway. We were becoming easier to score.

This is why I do not think the seminal question of this century is whether machines will become more human. The question is whether humans will still be allowed to remain more than something that machines can easily evaluate.

I have seen both possibilities. On one hand, a language model can help me enter a conversation I otherwise could not have had. Before meeting a Japanese thinker, whose newest work I could not read in the original, I used AI to build a working vocabulary across our different philosophical traditions. The system did not replace the encounter. It made the encounter possible. Once the conversation began, the model’s importance began to fade. It had done its work well precisely because it no longer needed to be at the center.

On the other hand, we have all experienced systems that do the opposite. They do not deepen understanding. They train people to live at the tempo of the feed, to compress themselves into whatever is most legible to the platform, and to mistake constant reaction for participation. The issue is not that AI will talk like us. The issue is that institutions will reward us for talking like machines.

Our scarcity is not content. It is mutual comprehension. A humane technological future will not be secured by sentimentality, nor by a nostalgic defense of every task that software can now accelerate. It will depend on whether we can design tools, institutions and norms that protect what is distinctively human in public life: the capacity to interpret one another across differences, to make commitments that bind us over time, to revise ourselves without humiliation, to hold power answerable and to care for consequences that no benchmark can fully capture…(More)”.

Editorial by Rachel A. Ankeny: “When we hear talk of ‘open science’, we often focus on the idea of what it means to make scientific knowledge and associated infrastructures more accessible, transparent, and collaborative, wondering for whom scientific results or methods are now more readily available (and whom not), and paying particular attention to problems associated with equity, justice, and fairness in open science and its practices. But to fully explore the ‘opening up’ of the processes and outputs of scientific research, it is equally important to ask how open science-related practices have changed contemporary science in perhaps unanticipated or unexpected ways. In short, how has open science affected what we consider to be or to count as ‘science’ and the practices associated with it?

Practices in scientific fields have always varied and evolved over time due to the interactions of diverse factors that go far beyond the epistemic and methodological content of science, as previously argued in our repertoires framework (Ankeny and Leonelli 2016) and as explored by many philosophers of science in practice. Thomas Gieryn’s use of ‘boundary work’ (Gieryn 1983, 1999) to capture the discursive strategies as well as the material and procedural tools which scientists use to separate legitimate science from other practices, and the fluidities of these boundaries, is more aligned with the questions that I wish to explore than are the factors highlighted in traditional, analytic debates over demarcation. If, as Gieryn describes it, the qualities ascribed to science change depending on the activities with which it is compared, it is important to pose the question of what expectations have come to be associated with ‘good’ scientific practices in the early twenty-first century and how these have been shaped by the ‘opening up’ of science. To put it another way: how can explorations of open science shed light on evolving understandings of what science is, who practices it, and what makes activities ‘scientific’? And as a result, have these understandings changed in ways that rely on fewer unwarranted presuppositions about what counts as ‘science’ and fostered greater equity? Note that I favour discourse related to ‘equity’ here rather than ‘democratisation’: the latter, with its focus on making scientific research, data, results, and so on widely accessible via open access to publications and data, is arguably critical and perhaps even necessary, but not sufficient, for opening up science on a deeper, epistemic level. However, openness is always context dependent and its instantiation complex in any real-life field or case (see e.g. Levin and Leonelli 2017). Furthermore, there is no ‘one-size-fits-all’ solution for achieving equity in these domains (Bezuidenhout 2025) as well illustrated by the papers in this special issue…(More)”.

Article by Hetan Shah: “…Given AI’s exponentially growing ability to solve well-defined, data-rich problems, it is plausible that employers will place more of a premium on the skills the humanities offer, such as critical and creative thinking. Anthropic’s co-founder Daniela Amodei recently said: “I actually think studying the humanities is going to be more important than ever . . . this idea that there are things that make us uniquely human — understanding ourselves, understanding history, understanding what makes us tick — I think that will always be really, really important.”

It would be ironic if universities cut these subjects just as demand turned a corner — switching from literature to AI-prompt engineering may turn out to have been a poor trade. And it would be bad for society if we made these subjects the preserve of the wealthy. Our research at the British Academy shows emerging regional “cold spots” where it is no longer possible to study particular humanities courses at a reasonable distance from home. This is increasingly important as nearly a third of prospective students — often the poorest — intend to stay at home while they study.

Cutbacks in universities are also affecting humanities research, where the UK is world-leading, which contributes in a broad way to building a thoughtful and prosperous society. Historians can guide us on why our policymaking is the way it is, so we can try to learn from the past. Philosophers are thinking about responsible use of AI in multiple contexts, including the creation and preservation of artworks. Academics feed into the development of video games such as the globally successful Hellblade, which had a reader in Scandinavian history as its historical adviser to provide an accurate account of Viking society and beliefs.

You might think these research cutbacks in universities are the exercise of market forces, with the strongest departments surviving. But it is more complicated than that because of the complex cross-subsidies within higher education. Financial instability is largely driven by the drop in high-paying international students and the erosion in the value of tuition fees by inflation over the past decade. Caught up in the crossfire of this instability and through no fault of their own, some of the top-ranked humanities research departments are closing, while our analysis shows the number of early-career academics in the humanities has fallen by 20 per cent over the past decade…(More)”.

Article by James Evans, Casey Petroff, and Gary King: “…Information theory, pioneered by mathematician Claude Shannon, offers a different perspective: only surprises carry new information. If people in an American town have conservative political preferences, and someone demonstrates that they are more likely to vote Republican, little is learned. A paper that confirms what readers already believe tells them little they did not already know and perhaps nothing that changes their worldview. Yet, the incentives of modern science reward this type of uninformative communication. Peer reviewers are drawn from one’s own field and publication norms favor deference to established paradigms. Scientists cite sources in their immediate field roughly 500% more than those in distant fields. This is performance art designed for reviewers rather than a search for the most informative surprise designed to advance knowledge. Thus, most papers are written to be unsurprising, and consequently, have minimal impact. These consequences are linked. The ultimate value that a paper adds to collective knowledge depends not only on what it shows, but on whose worldview it updates, and by how much.

To understand why surprise is the currency of impact requires understanding what science is. Science is not merely a method. It is an emergent, socially organized system that has produced more knowledge over the last 400 years than at any time in human history, by any means. The architecture deserves credit, and within it, the papers that contribute the most are those that update knowledge the most. This structure is governed by rules of cooperation (sharing methods, data, and goals) and decorum (validating empirical claims solely with empirical evidence, and justifying the importance of these claims based solely on whether other scientists will go along). But its primary driver is competition. Progress requires persuading the scientific community to accept new empirical claims. What is less appreciated is that the farther a scientist can move the predominant position of a community, the more people will acknowledge the finding, and the more importance will be attributed to the work. The predominant view is often supported by considerable evidence, which is why moving it demands extraordinary evidence and why the system offers its largest rewards for successes and largest penalties for failures, the farther a claim sits from that view. A researcher who convinces the scientific community, with robust evidence, that objects can travel faster than the speed of light would become famous, and the researcher who presses such claims without convincing evidence would be ignored or sanctioned…(More)”

Report by the OECD: “..explores the opportunities and challenges of the adoption of artificial intelligence (AI) tools to improve citizen participation. To do so, it builds on desk research and analysis of 50 AI use cases in citizen participation processes from 22 OECD Member and partner countries.

It proposes a typology of applications to help government officials and practitioners navigate the landscape of AI tools for participation based on their needs and the challenges they face. The report also provides insights on emerging trends in the adoption of AI tools for participation, analyses the related risks, and outlines relevant mitigation strategies that allow governments to steer the trustworthy adoption of the technology…(More)“

Article by Anthony Vargas: “For publishers, getting cited in AI chatbot responses isn’t enough. They also want those answers to reflect their editorial judgment and the culture of the communities they cover.

A new initiative called SAIL – which stands for Standardized Agentic Intelligence Ledger – aims to do both, compensating publishers when AI scrapes their content and guaranteeing the outputs adhere to the same cultural standards they apply to their own coverage.

The framework was designed by AI licensing platform Next Net in partnership with Sundial Media & Technology Group, the publisher of Essence, Refinery29 and Afropunk, among others.

SAIL is a digital record-keeping system that tracks how AI solutions use publisher content and mix it with other sources, said Sundial CEO Kirk McDonald. It’s meant to protect the value of high-quality publisher content when it’s cited by AI alongside less rigorous – but still culturally relevant – user-generated content.

Think of the framework as a more collaborative alternative to striking one-off licensing deals with AI vendors, McDonald said, or to the nuclear option of suing them over unauthorized content scraping…(More)”.

Paper by the Europeana Initiative: “…places Public AI within the realities of the cultural heritage sector, outlining four concrete ways for the sector supported by the data space to contribute to making it happen in practical operational terms.

The four contributions to Public AI from our sector are to:

• Provide high-quality data and the knowledge needed to keep it traceable, interpretable and reliable. In doing so, cultural heritage institutions help make AI outputs more robust, contextualised and open to scrutiny.
• Manage data access and reuse in fair and reciprocal ways. Cultural heritage institutions are reliable intermediaries and should influence the way cultural information is accessed and reused by AI developers, helping to prevent value extraction without reciprocity and the depletion of public infrastructure.
• Shape how smaller, domain-relevant AI systems are developed and used. This includes helping to shape the governance of their development and deployment, ensuring that AI remains transparent, contestable and aligned with democratic values and regulatory frameworks.
• Strengthen Public AI literacy across institutions and society. Institutions must upskill professionals while helping audiences understand how AI works and engage critically with AI systems they encounter…(More)”.

Article by Sarah O’Connor: “In the early 20th century, a gifted engineer called Frederick Winslow Taylor embarked on an ambitious task: to extract knowledge from inside the heads of workers on America’s factory floors.

In the eyes of Taylor, who would go on to become one of the world’s first management consultants, factory workers possessed a “mass of rule-of-thumb or traditional knowledge” which had been “handed down from man to man by word of mouth” or “almost unconsciously learnt through personal observation”. Taylor thought it was about time this knowledge was “codified or systematically analysed or described”. To that end, he sent managers with stopwatches and notebooks on to shop floors to observe, time and record every stage of every job.

More than a century later, employers of white-collar professionals are beginning to confront a similar challenge. It is becoming increasingly clear that the knowledge required to make AI models genuinely powerful in a swath of workplaces is currently locked inside employees’ heads.

This isn’t true in every workplace. AI models have transformed the software profession, for example, because the task of writing code is testable and rules-based and there were vast reams of training data publicly available, thanks to online forums like Stack Overflow.

But for many other jobs, that sort of data just does not exist on the web. Indeed, some subtle but important skills are very hard to codify at all, which is why they are often learnt through experience and osmosis. This sort of tacit knowledge was famously summed up by the scientist and philosopher Michael Polanyi with the phrase: “we can know more than we can tell”.

As a result, general-purpose AI models are simply not very good at many specific tasks which require both domain and institutional knowledge. Investment firm Bridgewater Associates recently experimented, for example, with using LLMs to do something their human professionals do all the time: parsing reams of news stories and financial documents for information that might be relevant to their investment decisions.

While this could be a useful timesaver, Bridgewater found that variants of Gemini, Claude and GPT only tended to get it right about 50 per cent of the time..(More)”.