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Stefaan Verhulst

Book by David Weinberger: “Amid the clamor about AI, Beautiful Particulars poses a unique question: Not how does our use of AI change what we do, but how is the idea of AI already subtly yet fundamentally reshaping how we think about ourselves and our world?

In clear, engaging, and even humorous prose, David Weinberger offers a new perspective on how machine learning works and how it may already be changing our basic and deepest ideas. Through lively examples, he shows that AI’s success—with all its risks and dangers—is taking us past our millennia of reliance on laws and principles by revealing what morality, free will, creativity, causality knowledge, and even reality itself look like now that we’re able to let the particulars speak their tiny truths.

The author poses questions not usually asked: What does a coin look like to AI? When AI gives you evidence that the earth is round, why are its own reasons for saying that so different? Why does an AI image generator create a “selfie” of ancient Aztec warriors with broad smiles that it “believes” are historically unlikely? If AI becomes conscious but bodiless, could it still make good moral decisions? Why is the world so much better at being causal than AI could ever “understand”? How does AI show us that genuine creativity is not about a “spark” but about moments that feel right? And how can you take the temperature of a joke?

Beautiful Particulars offers a new—and hopeful—perspective on how machine learning works, and how that’s already changing how we understand what it means to be human in the age of AI…(More)”

Beautiful Particulars

Book by Catarina Zuzarte Tully: “This book is different.It is written for people who are actively trying to transform the systems around them in a time of uncertainty and change. It is written for people who are experiencing the present as a period of frustration, anxiety and retrenchment—but who also know that it is a time of extraordinary possibility to shape a positive future. It is written for people who understand that transformation is ultimately about power: speaking truth to it, challenging it and redistributing it. It is written for people who feel a sense of stewardship: a responsibility to connect people working across past, present and future in service of a more prosperous, secure and sustainable world.

Future generations are not strangers who happen to live later. They are extensions of ourselves, just as we are extensions of those who came before us. Playing with Time is rooted in this simple idea. It invites us to loosen our grip on the present, reconnect with longer-term horizons, and allow possible futures to reshape how we act today.

This moment calls for our creative, collective response. It asks us to articulate and defend the values we care about, work across difference wherever possible, and act for the wellbeing of current and future generations. These are things that all of us can do. This book shows how...(More)”.

Playing with Time

Report by the Wellcome Trust: “The Wellcome Global Monitor is the world’s largest study of how people think and feel about science and health. The 2026 edition draws on surveys of more than 140,000 people in 140 countries, making it a uniquely rich and globally comparable data set on public trust in science. 

This is the third wave of the Wellcome Global Monitor, following surveys in 2018 and 2020. Over the course of these surveys, we saw trust rise during the pandemic, when science was more visible in people’s lives, but that rise has not been sustained. Today, trust in science is below 2018 levels globally. 

Global averages only tell part of the story. Trust levels vary widely between and within countries, and it is closely linked to people’s everyday experiences of healthcare, of public institutions and of whether they feel they are getting by. 

The full report, the dataset, country and regional factsheets and an interactive dashboard are all freely available…(More)”.

Wellcome Global Monitor 2026: Trust in science under pressure

Book by Gary Wolf: “Science has given us tools and methods that allow us to observe things that were once imperceptible—and this book shows what we can learn when we use them to look at ourselves.

Written by science journalist Gary Wolf, co-founder of the Quantified Self movement, this book teaches readers how to use proven scientific methods to answer their own health questions. At a time when trust in expertise is fractured and people are divided not just politically but over how to know what’s true, The Quantified Self makes the case that science belongs to everyone—and proves it by putting the method in the reader’s hands.

Wolf teaches ten reasoning patterns adapted from the history of scientific practice and illustrates them with dozens of personal narratives. Across chapters on pain, mood, cognition, sleep, digestion, heart health, and hormones, readers learn a practical method for answering the health questions that matter most to them…(More)”.

The Quantified Self: Learning to Observe

Article by Stefaan Verhulst and Andrew Zahuranec: “What would it take for Indigenous communities not simply to be represented in the emerging AI ecosystem but to shape, govern, and benefit from it?

That question is what animated our event during UNGA High-Level Week on Advancing Public Interest AI Through Data Commons for Indigenous Languages and Cultures. 

The discussion brought together Indigenous leaders and technologists, funders, researchers, international organizations, civil society, and industry around a growing challenge: As AI becomes embedded in education, public services, cultural production, and everyday life, many Indigenous communities are being drawn into AI systems that they did not design, using data they may not control, and reflecting assumptions and values that may not be their own.

The stakes are particularly high for Indigenous languages and cultural knowledge. AI could become a powerful instrument for language revitalization, education, cultural transmission, and economic opportunity. But without different approaches to data, infrastructure, governance, and investment, it could just as easily reproduce longstanding patterns of extraction while amplifying misinformation. This leads to weaker foundations of reliable information for AI and other systems that subsequently harm communities…Five themes emerged from the discussion…(More)”.

Data Commons as Infrastructure for Indigenous-Led AI

Paper by Geoff Mulgan and Alex Fischer: “Work in Progress argues that democracy has not failed as an idea, but that many of the ways we practise it remain rooted in the 19th century and have not kept pace with a rapidly changing world. Falling trust, political disengagement and declining participation point to a system that is too often failing to listen, learn and adapt.

Drawing on more than 50 examples of democratic innovation from over 25 countries, Mulgan and Fischer set out a framework for understanding democracy as an interconnected system of functions – from elections and decision-making to public services, scrutiny, knowledge and citizen participation. They explore how these functions can be strengthened, alongside the relational foundations that allow democracy to work effectively.

The paper considers the opportunities and risks presented by digital technology and AI, arguing for deliberate investment in new forms of cognitive infrastructure that can help democracies harness collective intelligence, improve decision-making and rebuild trust.

The authors’ central message is clear: democracy should be treated as a work in progress – something to be upgraded, tested and renewed, rather than a finished achievement to simply defend…(More)”.

Work in Progress: How to Upgrade Democracy in the Age of AI

Article by Indrabati Lahiri: “Citizen science is rising globally, driven by technology and public participation in research. Volunteers help professional scientists gather critical data across vast geographic areas and timeframes. In tourism, guests at resorts participate in coral planting, wildlife tracking, and environmental DNA collection. Crowdsourced data also tracks climate change, monitors watersheds, and supports disaster preparedness. However, challenges remain regarding data quality, reliability, structural biases, and institutional hesitation. Ultimately, combining volunteer participation with professional expertise helps address data gaps in environmental research…(More)”.

Why Citizen Science Is On The Rise

Article by Justin Gest: “Last month, the Trump administration proposed new rules that would limit who is counted in the census. It’s an initiative that is ideologically consistent with President Trump’s voter-suppression efforts, his aggressive deportation policies and his executive order that seeks to purge national parks and museums of information about slavery and Native American history that, in his words, casts our country “in a negative light.”

The Constitution requires the census to count everyone in America, but Mr. Trump clearly wants to decide who counts.

Under current practice, the Census Bureau, which is part of the Department of Commerce, looks to count everyone living here, regardless of citizenship or immigration status. Census data is used for purposes ranging from congressional apportionment to government-funding formulas.

Under the administration’s proposal, the census would count only citizens and green card holders. Undocumented immigrants, international students, asylum seekers, refugees without permanent residency and others would be excluded. The administration argues that individuals in these groups lack the “sufficient tie and allegiance” required to establish them as “true inhabitants” of the United States — a rationale akin to one advanced by some critics of birthright citizenship…(More)”.

The Census Counts Everyone. Trump Doesn’t Want That.

Press release by Biohub: “…the U.S. Department of Energy, the National Institutes of Health, and new funding partners today announced a major expansion of an international effort to generate and make accessible the data enabling predictive AI models of biology. Together, the organizations are investing $1.8 billion in funding, data, computation, and new measurement technology, the largest coordinated commitment to generating AI-ready biological data to date. The result will be an open resource for the research community that provides the foundation for greater understanding and ultimately treatment of human diseases.

As part of this announcement, Biohub has partnered with the Department of Energy (DOE) Office of Science and the National Institutes of Health (NIH) to advance the frontier of artificial intelligence in biology. DOE will invest more than $500 million over five years in lab measurement, modeling and computation toward the international effort to build an AI-ready open data resource. NIH will coordinate the contribution of relevant datasets, repositories, and knowledge bases developed through more than $500 million in prior federal investment aligned to this initiative. Biohub will work with NIH to standardize these datasets for AI model training.

In addition, Google DeepMind, Isomorphic Labs, and Meta are collectively investing $300 million in the Virtual Biology Initiative to create the technologies and multi-modal datasets needed to build predictive models of life.

These datasets will enable the global scientific community to collectively build and use AI models that allow researchers to ask, predict, and answer biological questions digitally, accelerating the path to new ways of preventing and treating diseases. This initiative will deliver the foundational measurements to train these models, expanding cell response data to interventions across far more cell types and conditions than have yet been studied, and building and validating technologies for studying cells and cellular interactions at greater scale, speed, and accuracy.

The scale of the challenge explains why no single institution is attempting it alone. Modern AI models in biology, from protein structure prediction to whole-cell simulators, are ultimately limited by the quality and breadth of the experimental data used to train them. Today’s datasets capture cell responses to interventions across only a small fraction of the cell types and conditions that matter in human health. The Virtual Biology Initiative is designed to close that gap by coordinating data generation across institutions and disciplines, expanding cell response measurements to far more cell types and conditions than have yet been studied, and building and validating technologies capable of studying cells and their interactions at greater scale, speed, and accuracy. The result, organizers say, will be a foundational, openly accessible dataset that no laboratory, company, or government agency could produce on its own…(More)”.

Global coalition commits $1.8 billion to build open data for AI models of biology

Book by Matthew Botvinick: “American democracy is under pressure, as institutions that we once thought stable suddenly seem precarious. At the same time, artificial intelligence has taken over much of our online life, with enormous offline consequences. In AI and Political Freedom, Matthew Botvinick examines the interaction of AI and democracy, exploring how AI is likely to challenge democracy as we move further into the twenty-first century. Botvinick—an expert on both democratic theory and AI technology—finds that the prognosis is serious. AI is poised to destabilize our democratic institutions—to attack them at their most vulnerable points and provide a useful tool for leaders with authoritarian aspirations. Yet despite the potential threat posed by the collision of AI and democracy, Botvinick argues that there are ways that we can safeguard our political freedom.

Botvinick points out that concern about our democratic backsliding emerged just as public attention turned to AI safety. Worries over the proliferation of deepfakes and AI-generated misinformation as well as the expanding political power of technology companies circulated widely—but were seldom connected to concern over the decay of democratic institutions. Botvinick makes this connection, considering AI’s potential effects on our politics. Mass unemployment caused by technological advances creates a receptiveness to authoritarian appeals; security crises could enable governments to abuse emergency powers; and the use of AI in intelligence work creates the risk of politicized surveillance. To stop the retreat from democratic values, Botvinick argues, we must reinforce our democratic institutions and, ultimately, reimagine how democracy works, building richer and more engaging forms of citizen participation…(More)”.

AI and Political Freedom: The Risk to Democracy and How to Respond

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