Stefaan Verhulst

Book edited by Sheila L. Macrine, Jennifer M. B. Fugate, Arsen Abdulali and Josie Hughes: “Intelligence research is undergoing a radical transformation, moving beyond the traditional focus on brains and code to increasingly recognize the role of embodiment, as well as our understanding of goal-directed behavior across different scales and substrates. In this edited collection, experts across fields including philosophy, phenomenology, neuroscience, cognitive psychology, robotics, AI, bio-inspired design, biology, and bioengineering initiate transdisciplinary dialogues and facilitate the sharing of insights on embodiment, enabling a new understanding of embodied intelligence and how intelligence manifests across diverse substrates.

By embracing a broad definition of embodied intelligence, the contributors transcend the traditional divide between the biological and the artificial, recognizing the potential for intelligence to emerge in unexpected forms. This perspective challenges us to reconsider our assumptions about the nature of intelligence and to appreciate the remarkable diversity of intelligent behavior in the world around us. This broadened concept holds immense promise to profoundly reshape our understanding of ourselves, the technologies we create, and the very nature of intelligence itself…(More)”.

Tool developed by the Center for Collective Learning (CCL): “Explore academic impact – without flattening it into a rank.

Rankless visualizes the global flow of ideas across universities, journals, scholars, and countries – revealing who influences whom, where knowledge travels, and which topics bind the world together…

We need to understand more and rank less.

Rankings flatten complexity into a single number. Rankless restores the context: domain, geography, collaboration, and time. Use it to make better-informed decisions – whether you’re a student, researcher, policymaker, or funder.

Rankless is an experimental data visualization project that allows users to explore the impact of thousands of universities. It is built on the idea that universities generate impact that is specific to a geography and to certain topics, and that rankings obscure that impact by reducing it to a single dimension. By transcending rankings, we highlight a university’s multidimensional impact by showing you who they work with and who cites them. To understand more, sometimes, we need to rank less…(More)”.

Essay by Akash Wadhwani: “On the morning of February 6, 2023, an earthquake killed more than fifty thousand people in Türkiye and Syria. I spent a week inside the edit history of the world’s free map, reading what the internet did that morning. I haven’t stopped thinking about it…

Why would a city of half a million people be missing from the map? That sounded impossible to me in 2023, so I went and looked it up, and the answer turns out to be money, twice.

Commercial maps are built where the money is. Google and Apple map roads because cars navigate them, and they map shops because businesses pay to be found. That works beautifully in London or Los Angeles. In a working-class Turkish city, and in the Syrian towns across the border, there is no ad revenue in knowing where each house stands, so no company ever paid to find out. The satellites photograph everything, but a photograph is not a map. Someone still has to look at the pixels and say: this shape is a building, this line is the road that reaches it.

The second reason surprised me more. Even where a commercial map looks complete, rescue teams mostly cannot use it. They can’t download it onto a GPS unit and carry it into a zone where the internet is down. They can’t count its buildings to estimate how many people might be trapped in a district. The data belongs to the company, and the license says no. OpenStreetMap is the exception, and it is the exception on purpose: it is the Wikipedia of maps, free for anyone to copy, carry, and analyse. When things go wrong, it is the map that gets used. It just has to be drawn first, by someone.

That morning, rescue teams were already in the air. They were flying toward a city the free map could not yet describe.

You cannot search rubble you don’t know exists…(More)”.

Essay anthology by the Bennett School of Public Policy : “…brings together visionary thinkers, policymakers, and experts to challenge the narrative of AI as a zero-sum competition.

The idea that AI is an arms race between the US and China has taken hold of contemporary geopolitics. But is it the right framing? 

Drawing on diverse perspectives from diplomacy, philanthropy, civil rights, national security and economics, the essays in this anthology explore the limitations of the arms race metaphor and ask key questions about its origins and influence. While each author offers a unique viewpoint from their own expertise, taken together their collective insights reveal the shortcomings of this framework as a lens for interpreting the complex geopolitical realities of AI and reveal alternative ways of understanding AI’s geopolitical influence and potential…(More)”.

Article by Heather Pearson: “A UK survey published in January found that only 40% of people think that science information they hear is “generally true”. Another global poll showed that 70% of people believe at least one false or unproven claim, such as that the risks of childhood vaccines outweigh the benefits.

In the United States, President Donald Trump and his administration are using the idea that science is not trustworthy as one reason to cut research budgets, reject evidence-based medical advice and exert unprecedented political control over research. “Over the last 5 years, confidence that scientists act in the best interests of the public has fallen significantly,” said Trump in an executive order last year.

Even the Vatican is voicing concern. This September, a meeting at the Pontifical Academy of Sciences will examine how “the crisis of trust in science has become a pressing issue”.

But is trust in science really that weak? Researchers studying this have reached some surprising conclusions. From a global perspective, public trust in science and scientists is high, they say. One of the largest studies, which surveyed nearly 72,000 people across 68 countries in 2022–23, reported a “moderately high” average trust score of 3.6 out of 5. “The idea that there’s a generalized, pervasive lack of trust in science and experts is just completely unfounded in my mind,” says David Bersoff, head of research at the Edelman Trust Institute, a think tank in New York City…(More)”

Press Release by Google Research: “Approximately 500,000 deaths every year are attributed to extreme heat, a crisis intensified by the urban heat island effect, which causes metropolitan areas to warm at double the worldwide average. Earlier this month, record-breaking heat waves across Western Europe pushed temperatures past 40°C (104°F). The prevalence of heat-trapping materials, like dark pavements and roofs, combined with a lack of vegetation, largely drives this localized warming. Heat mitigation measures are critical to reducing this toll, and cool roofs offer a highly cost-effective solution. By increasing rooftop reflectivity (albedo), we can significantly reduce the amount of solar energy absorbed by buildings, ultimately lowering local surface temperatures and protecting vulnerable communities.

To address this, Google Research is building AI-driven tools to help lower city temperatures and keep communities safe. By applying AI to high-resolution satellite and aerial imagery, our Heat Resilience tools help cities quantify the impact of targeted cooling interventions. In 2024, we piloted this approach with 14 cities, providing them with rooftop reflectivity data to identify highly vulnerable neighborhoods and determine where cool roofs would yield the greatest temperature reductions. This data guided critical decisions across several cities, resulting in initiatives such as cool roof ordinances and adaptation plans.

Now, we are scaling this impact. In “Estimating high-resolution albedo for urban applications“, published in Nature Communications, we detail our methodology for mapping building-level reflectivity across diverse urban environments. This research bridges the gap between general climate observations and actionable, building-level data. We are also releasing an expanded albedo dataset covering over 50 global cities to empower urban planners worldwide to prioritize cool-roof interventions. This dataset is open and accessible through our new, high-resolution Heat Resilience Earth Engine App…(More)”.

Book by Michael Clarke, Manuel Garcia-Garcia, and Michael Joffe: “When a chatbot lies about an airline’s bereavement policy, who is to blame? When an AI-generated painting wins a state art competition, what does it mean to be a creator? Our relationship with artificial intelligence is not just technical; it’s profoundly human. Smarter Together is your essential guide to the hidden psychology behind the AI revolution. Drawing on insights from neuroscience, behavioral science, and their popular NYU courses, the authors reveal how intelligent systems are designed to mirror our thinking, feeling, and decision-making. Through unforgettable case studies, this book unpacks the new equations of trust, the cognitive biases that shape our choices, and the cultural forces defining AI’s promise and challenge. Moving from theory to practice, it provides a vital toolkit for designing and marketing AI products that augment, rather than replace, human intelligence…(More)”.

Report by Nathan Goldschlag: “How many firms are using Artificial Intelligence? What are they using it for? How many workers are using AI, and how are they using it? 

To track and understand the effects of AI on the economy, researchers will need accurate, detailed, comprehensive answers to these fundamental questions. 

Partial answers won’t do — not at a time when policymakers are struggling to catch up with the sweeping consequences of AI for workers and businesses. Without improved measurement, they risk getting the policy response wrong. 

Fortunately, the statistical infrastructure is already in place to help get it right. But that infrastructure needs an upgrade, fast, to match the scale of the challenge. 

In this essay, EIG’s Nathan Goldschlag outlines the necessary investments in the U.S. statistical agencies that will give them the ability to answer the most pressing and vital questions about the impact of AI on the American economy…(More)”.

The  Preliminary Report of the Independent International Scientific Panel on AI: “…a first-of-its-kind independent scientific assessment of the capabilities, emerging opportunities and risks of artificial intelligence. The Panel, composed of independent scientists and experts from all 5 UN regions, outlines trends in AI. It’s central warning: current safeguards cannot keep pace with the growth of AI’s capabilities.

It identifies a crucial evidence challenge for decision-makers around the world: policymakers need scientific evidence to effectively govern AI, but by the time the evidence is clear, it may be too late to act on it. In the report, the Panel outlines its findings across seven key domains:

• AI science, advances & trajectories
• Societal applications: science, health, education & agriculture
• Economic implications
• Security, systems & environmental implications
• Human rights, information & democracy
• Cultural & individual flourishing, autonomy and child safety
• Management, governance & reliability..(More)”.

Map by Current AI: “By nature, all AI systems are opaque and multidimensional. They become even more complex when thousands of developers from all over the world contribute unique projects across different layers. This is the current state of the open source AI stack: seriously robust, but fragmented, duplicative, and hard to see as a coherent whole.

The Gap Map is a living, actionable visualization of AI’s open source landscape.

Building on work from leading open source AI experts at the Columbia Convening, the Model Openness Framework, Hugging Face, and others, it comes out of cumulative work to identify the points of highest leverage in the stack: where to build new, where to invest in capability, where to open up the tools. By creating an up-to-date visualization of the ecosystem where we can all see both the progress and the gaps, we can rally the community around a collective roadmap.

We intentionally don’t compare closed versus open AI ecosystems, or point to where open source AI leads or lags. Instead, the Gap Map illustrates what’s needed to build the system we want.

For details on how products are scored and categories assessed, see our methodology…(More)”.