Stefaan Verhulst

Paper by Adnan Firoze, et al: “Historically, only resource-rich U.S. cities have collected data about where their public trees are, usually through labor-intensive manual surveys or via coarse canopy-cover estimation. However, a significant portion of city trees are on private property, making them difficult to quantify with surveys, yet they contribute uniquely to species diversity and ecosystem service distribution. Further, canopy-cover estimation cannot provide information about tree density, locations of trees across different land types, or changes in tree counts. Cities are under continual change, and the mean mortality rate of urban trees is twice that of rural trees.Thus, frequent updating of tree analytics is critical for sustainable, habitable cities.

Method.  Recent advances in computing—in particular, generative artificial intelligence (AI)—have enabled our multidisciplinary team, spanning computer science, engineering, and forestry, to develop a first-of-its-kind computational method that can individually locate and maintain an inventory of trees in at least 330 U.S. cities (Figure  1). Using satellite data, this approach can complete the inventory process in less than a day of automated computing. Individual trees are challenging to discern in satellite images due to occlusion and resolution limitations, which in turn limits traditional segmentation-based approaches. Our approach leverages several key insights to enable a scalable generative AI solution. First, a frequent capture rate of satellite imagery (e.g., daily, monthly, etc.) provides spatiotemporal vegetation footprints, yielding richer information than single images. Our method includes a deep spatiotemporal vegetation cover classification using satellite images that classifies a city into tree, grass, and background, followed by a cluster-creation process and then individual tree localization using a set of conditional generative adversarial networks (cGANs). Further, our method can be applied to current or archived satellite imagery, allowing for change detection and historical analysis…(More)”.

Article by Sébastien Bourdon and Antoine Schirer: “On the Strava fitness app, there are individual cases that raise concerns, some of which Le Monde has already documented. For example, the naval officer who went jogging in the middle of the Mediterranean Sea, thereby revealing, at the moment of recording his run, the exact position of the Charles de Gaulle aircraft carrier strike group. Or the military personnel who exercised on the docks of the Ile Longue base, where France’s nuclear ballistic missile submarines (SNLE) operate, and whose activities provide information about patrol schedules. Or even the bodyguards of the French, American and Russian presidents, whose sporting activities make it possible to track these heads of state and, in some cases, to anticipate their movements.

And most significantly, Le Monde has uncovered several thousand servicemembers, of all ranks and specialties, who, by exercising and running in far-flung corners of the world and sharing their performances on their public Strava profiles, reveal the activities of the French armed forces as a whole, from the most banal to the most sensitive…(More)”.

Article by Ridhi Purohit, and Judah Axelrod: “Before building a new house or apartment building, residents and developers must first ensure their project adheres to local zoning rules. 

But zoning documents are often difficult for both applicants and local government staff to navigate. This can lead to extended back and forth between staff and applicants, slowing down the permit process at a time when most communities don’t have enough housing.

Some local leaders have expressed interest to Urban researchers in the promise of using generative AI to make zoning codes easier to understand, whether through screener tools that scan permitting applications to ensure they are complete, or chatbots that can answer development questions. However, leaders are hesitant to adopt tools that haven’t been properly vetted for the quality of the information they produce.

To test how well generative AI tools could interpret zoning codes, we ran a benchmarking exercise that evaluated the capabilities of various large language models (LLMs), building on previous work that explored whether machine learning could help automate the collection of standardized zoning data. To do this, we developed a set of zoning and permitting queries for Minneapolis—a city with a complex, 467-page zoning code and with zoning processes familiar to our team…(More)”.

Article by Karine Perset and Anna Pietikäinen: “…Six insights about AI regulatory sandboxes from around the globe

1. AI sandboxes are not uniform

• According to the Datasphere Initiative, three primary types of sandboxes are emerging worldwide, especially within the context of AI. Regulatory sandboxes: Collaborative processes where regulators work with innovators to test innovations under regulatory supervision.
• Operational sandboxes: Testing environments and infrastructure where data can be hosted and accessed in controlled conditions.
• Hybrid models: Combining regulatory oversight with operational capabilities, sometimes offering infrastructure and operational spaces for testing and experimentation (e.g., “supercharged sandbox” in the UK).

These models intervene at different phases of the policy and regulatory lifecycle. Some are employed before formal regulation to identify gaps and suggest necessary updates. Others operate during the development process, supporting iterative regulatory design. Some focus on helping understand legal obligations and ensure regulatory compliance, such as under the EU AI Act. Sector-specific sandboxes are also common, with countries adopting different approaches depending on regulatory priorities and institutional settings. Across these models, regulatory waivers are frequently used to enable experimentation under regulatory supervision. 

Several experimentation-related initiatives, such as regulatory testbeds, living labs, or policy prototyping, share certain features and objectives with regulatory sandboxes. What truly distinguishes sandboxes is that they are the most institutionalised form of regulatory experimentation, usually led by regulators and integrated with regulatory supervision..(More)”.

Article by Ian Leavitt: “Connecticut is one of many U.S. states increasingly using cross-sector data-sharing to improve public health outcomes, with the Prevention Data Portal a prime example of the state’s efforts. The portal demonstrates how state leadership buy-in, paired with expertise from nongovernmental partners, can overcome common barriers to cross-sector data-sharing and produce clear and meaningful information to help shape state policy—without requiring large new investments in data infrastructure or centralized control. Insights gained from Connecticut’s experience can inform the work of other state public health agencies as they look to expand cross-sector data-sharing through creative partnerships, simple use cases that show value early, and other avenues within their own states.

The Prevention Data Portal, launched in 2018 by Connecticut’s State Epidemiological Outcomes Workgroup (SEOW), houses data from local, state, and federal sources to advance health promotion and substance use prevention in the state. It provides free access to a wide range of data-driven products about populations in Connecticut, including epidemiological profiles, data stories using multiple streams of information, and infographics on mental health, substance use, suicide, gambling, and other public health topics. The portal is supported through federal block grant funding and partnerships between the SEOW and the Connecticut State Department of Mental Health and Addiction Services (DMHAS), the Center for Prevention Evaluation and Statistics (CPES) at UConn Health, and the Connecticut Data Collaborative (CTData).

In an ongoing collaboration with states to improve public health data, The Pew Charitable Trusts commissioned interviews with Connecticut officials and other participants in the conceptualization, creation, and use of the SEOW Prevention Data Portal. Those interviews produced several key takeaways…(More)”.

European Commission: “Open Research Europe (ORE) is the European Commission’s open access publishing platform for research funded by all EU programmes. Launched in 2021 to promote innovative, no-fee open access publishing, the platform is now preparing to enter a new phase.

Backed by a nearly €17 million budget for the period 2026-2031 and co-funded by the European Commission by up to €10 million, the new phase of ORE is set to begin operations as a collectively supported publishing service in the autumn of 2026, with CERN operating the platform. Leveraging on its success, the publishing service will be driven by national research organisations from 11 countries (Austria, France, Germany, Italy, the Netherlands, Norway, Portugal, Slovenia, Spain, Sweden, and Switzerland). In addition to researchers benefiting from EU grants, collective funding will also enable researchers from participating countries to publish without fees, thus expanding significantly author eligibility. ORE was conceived within the European Research Area (ERA)’s policy framework with the objective of ensuring open access to high-quality EU-funded research results, strengthening the free circulation of knowledge and maximising the impact of publicly funded research. In the five years since its launch, the platform has seen a steady growth and uptake among the research community, with more than 1,200 articles published and over 6,300 authors from more than 3,000 institutions worldwide.

The platform began its journey with a vision to redefine scholarly publishing, not only by offering researchers a no-fee open access platform, but also by enriching public knowledge and fostering trust in science through an innovative publishing model marked by rigorous and open post-publication peer review…(More)”.

Paper by Stefaan Verhulst: “As data sharing and reuse become central to scientific discovery, artificial intelligence, and public decision-making, the challenge of data governance has shifted from a primarily technical problem to one of institutional design. Over the past decade, a wide range of governance models for data collaboration have emerged-including data trusts, data commons, data cooperatives, data intermediaries, data unions, data sandboxes, and data spaces. These models are often presented as competing institutional solutions to the problem of responsible data sharing. This paper argues instead that such models represent distinct governance responses to different structural challenges within data ecosystems. 

Building on the concept of data collaboratives-which I introduced in 2017 as cross-sectoral arrangements for responsible data reuse in the public interest-I propose a purpose-driven typology that identifies seven governance archetypes and the specific coordination problems they address, including transaction costs, power asymmetries, legitimacy deficits, collective governance needs, ownership inequality, systemic uncertainty, and scaling complexity. 

I argue that the question is not which governance model is normatively superior, but rather which model is fit for purpose within particular institutional contexts. The paper concludes by introducing a functional theory of data collaboration centered on institutional orchestration, whereby multiple governance arrangements coexist and evolve within polycentric data ecosystems. In this framework, strategic data stewardship becomes essential for diagnosing governance needs and sequencing institutional responses that enable responsible and sustainable data reuse…(More)”.

Article by Geoff Mulgan: “…The roots of the word intelligence (drawing on the Latin words ‘inter’ and ‘legere’. to choose between) are a reminder that intelligence is not processing speed but rather the ability to choose between options. As such intelligence only makes sense in relation to purposes and environments. It doesn’t float freely, even if some of the tools it uses may be generic. This is why real-life intelligence is so varied, and so often an assembly or hybrid of multiple elements, fitted to ecological niches.

The autonomous vehicle is a good example. It uses multiple tools to sense, navigate and decide rather than a single AI, and it often requires adjustments for different environments. Everyday uses of AI in teams also illustrate the point. These usually mobilise multiple agents or tools to help them think, sometimes relying heavily on the technologies, sometimes putting them to one side.

Humans of the future may be equally hybrid. Many will have implants and prosthetics, living as part of networks of varying degrees of humanness, cyborgs for whom the boundaries of the human may be ever harder to define.

The key point is that we need to think in a biological way rather than a mechanistic way: how AI shapes humans and how humans shape AI will be more like an ecosystem or a jungle than a super machine, more like a forest in which there are predators, diseases and periodic fires as well as harmony, rather than a factory production line.

Too often we try to squeeze AI into anachronistic categories, asking: is it conscious, self-aware or creative, or should it have rights… ?

I suspect that few of these categories will turn out to be adequate to the task of making sense of life with super-intelligent machines. Nor will AI solve the problems of running a society in a neat new maths. Precisely because intelligence is so inseparable from life, and the life-wishes of entities as varied as wasps and bears, humans and machines, there is no conceivable calculus that could capture them let alone make them commensurate. Money is commensurable. Value is not. Machines can be aligned. Sentient beings cannot be…(More)”.

Article by Martin Wolf: “The creation, communication and exploitation of knowledge are the defining abilities of human beings as social animals. These capacities, more than anything else, made them masters of the planet. This makes our tools of communication — from language to writing, printing, telecommunications, radio, television and now the internet — the defining technologies of their eras. Their invention and use have shaped not just what we can do at any time, but who we are.

New technologies of communication transform society. As the late Jürgen Habermas argued, liberal democracy, now in peril, was the child of the book, the pamphlet and the newspaper. The digital technologies of our time are similarly transformative. Unfortunately, along with many gains, they bring huge potential harms that today threaten the health of our societies. These harms are not theoretical; they are all too visible.

Knowledge is in the language of economics, a “public good”. This means that, if it is publicly available, it will be potentially available to everybody and also that everybody can have it without anybody being deprived of it: technically, it is “non-excludable” and “non-rival”. Knowledge, as is sometimes said, “wants to be free”. Indeed, given today’s technology the marginal cost of disseminating information is essentially zero.

Yet creating true information is not free at all. This creates a huge market failure: the creation and dissemination of reliable information is at an economic disadvantage relative to the creation and dissemination of fabrications. The public good of knowledge can readily turn into the public bad of confident ignorance or, worse, raging prejudice.

Thus, just like rivers or the air, the knowledge society shares and uses can become polluted. Worse, this can be a very profitable business. It is not hard to think of contemporary examples. Above all, if there is a market failure, competition alone will not cure it. Free speech is an important attribute of a free society. But, on its own, it does not guarantee reliable truth. Rivers of cheap lies can all too easily drown the costly truth…(More)”.

Article by Ezra Klein: “…Researchers have drawn a distinction between “cognitive offloading” and “cognitive surrender.” Cognitive offloading comes when you shift a discrete task over to a tool like a calculator; cognitive surrender comes when, as Steven Shaw and Gideon Mave of the University of Pennsylvania put it, “the user relinquishes cognitive control and adopts the A.I.’s judgment as their own.” In practice, I wonder whether this distinction is so clean: My use of calculators has surely atrophied my math skills, as my use of mapping services has allowed my (already poor) sense of direction to diminish further.

But cognitive surrender is clearly real, and with it will come the atrophy of certain skills and capacities, or the absence of their development in the first place. The work I am doing now, struggling through yet another draft of this essay, is the work that deepens my thinking for later.

In a thoughtful piece, the technology writer Azeem Azhar describes his efforts to safeguard “the space where ideas arrive before they’re shaped.” But how many of us will put in such careful, reflective effort to protect our most generative spaces of thought? How many people even know which spaces should be protected? For me, the arrival of an idea is less generative than the work that goes into chiseling that idea into something publishable. This whole essay began as a vague thought about A.I. and McLuhan. If I have gained anything in this process, it has been in the toil that followed inspiration.

The other thing I notice the A.I. doing is constantly referring back to other things it knows, or thinks it knows, about me. Sycophancy, in my experience, has given way to an occasionally unsettling attentiveness; a constant drawing of connections between my current concerns and my past queries, like a therapist desperate to prove he’s been paying close attention.

The result is a strange amalgam of feeling seen and feeling caricatured. Ideas I might otherwise have dropped keep getting reanimated; personal struggles I might otherwise move on from keep returning unexpectedly to my screen. I am occasionally startled by the recognition of a pattern I hadn’t noticed; I am often irked by the recitation of a thought I’m no longer interested in. The effect is to constantly reinforce a certain version of myself. My self is quite settled, but what if it wasn’t?

The A.I. knows me imperfectly, and so it overtorques on what it knows and ignores what it doesn’t. But there is much it can never know about me, and there is much I won’t share, or don’t even know about myself. I wonder whether deeper reliance on A.I. would desiccate those less legible aspects of myself, and it’s one reason I hold myself back. But I am in my 40s, and I still feel the shock of something new and strange when I reveal myself to these systems. I think the young will allow themselves to be known to their A.I.s in ways that will make their elders shudder…(More)”.