AGI vs. AAI: Grassroots Ingenuity and Frugal Innovation Will Shape the Future

Curated on Posted on by Stefaan Verhulst

Article by Akash Kapur: “Step back from the day-to-day flurry surrounding AI, and a global divergence in narratives is becoming increasingly clear. In Silicon Valley, New York, and London, the conversation centers on the long-range pursuit of artificial general intelligence (AGI)—systems that might one day equal or surpass humans at almost everything. This is the moon-shot paradigm, fueled by multi-billion-dollar capital expenditure and almost metaphysical ambition.

In contrast, much of the Global South is converging on something more grounded: the search for near-term, proven use cases that can be deployed with today’s hardware, and limited budgets and bandwidth. Call it Applied AI, or AAI. This quest for applicability—and relevance—is more humble than AGI. Its yardstick for success is more measured, and certainly less existential. Rather than pose profound questions about the nature of consciousness and humanity, Applied AI asks questions like: Does the model fix a real-world problem? Can it run on patchy 4G, a mid-range GPU, or a refurbished phone? What new yield can it bring to farmers or fishermen, or which bureaucratic bottleneck can it cut?

One way to think of AAI is as intelligence that ships. Vernacular chatbots, offline crop-disease detectors, speech-to-text tools for courtrooms: examples of similar applications and products, tailored and designed for specific sectors, are growing fast. In Africa, PlantVillage Nuru helps Kenyan farmers diagnose crop diseases entirely offline; South-Africa-based Lelapa AI is training “small language models” for at least 13 African languages; and Nigeria’s EqualyzAI runs chatbots that are trained to provide Hausa and Yoruba translations for customers…(More)”.

Tech: When Silicon Valley Remakes the World

Curated on Posted on by Stefaan Verhulst

Book by Olivier Alexandre: “Sometimes only an outsider can show how an industry works—and how that industry works upon the world. In Tech, sociologist Olivier Alexandre takes us on a revealing tour of Silicon Valley’s prominent personalities and vibrant networks to capture the way its denizens live, think, relate, and innovate, and how they shape the very code and conduct of business itself.
 
Even seasoned observers will gain insight into the industry’s singular milieu from Alexandre’s piercing eye. He spends as much time with Silicon Valley’s major players as with those who fight daily to survive within a system engineered for disruption. Embedded deep within the community, Alexandre accesses rooms shut tight to the public and reports back on the motivations, ambitions, and radical vision guiding tech companies. From the conquest of space to quantum computing, engineers have recast the infinitely large and small. Some scientists predict the end of death and the replacement of human beings with machines. But at what cost? Alexandre sees a shadow hanging over the Valley, jeopardizing its future and the economy made in its image. Critical yet fair, Tech illuminates anew a world of perpetual revolution…(More)”.

Intended, afforded, and experienced serendipity: overcoming the paradox of artificial serendipity

Curated on Posted on by Stefaan Verhulst

Paper by Annelien Smets: “Designing for serendipity in information technologies presents significant challenges for both scholars and practitioners. This paper presents a theoretical model of serendipity that aims to address this challenge by providing a structured framework for understanding and designing for serendipity. The model delineates between intended, afforded, and experienced serendipity, recognizing the role of design intents and the subjective nature of experiencing serendipity. Central to the model is the recognition that there is no single definition nor a unique operationalization of serendipity, emphasizing the need for a nuanced approach to its conceptualization and design. By delineating between the intentions of designers, the characteristics of the system, and the experiences of end-users, the model offers a pathway to resolve the paradox of artificial serendipity and provides actionable guidelines to design for serendipity in information technologies. However, it also emphasizes the importance of establishing ‘guardrails’ to guide the design process and mitigate potential negative unintended consequences. The model aims to lay ground to advance both research and the practice of designing for serendipity, leading to more ethical and effective design practices…(More)”.

Companies Are Missing The Chance To Improve The World With Their Data

Curated on Posted on by Stefaan Verhulst

Article by Nino Letteriello: “This September will mark two years since the Data Governance Act officially became applicable across the European Union. This regulation, part of the broader European data strategy, focuses primarily on data sharing between public and private entities and the overall development of a data-driven economy.

Although less known than its high-profile counterparts—the Data Act and especially the Artificial Intelligence Act—the Data Governance Act introduces a particularly compelling concept: data altruism.

Data altruism refers to the voluntary sharing of data—by individuals or companies—without expecting any reward for purposes of general interest. Such data has immense potential to advance research and drive innovation in areas like healthcare, environmental sustainability and mobility…The absence of structured research into corporate resistance to data donation suggests that the topic remains niche—mostly embraced by tech giants with strong data capabilities and CSR programs, like Meta for Good and Google AI for Good—but still virtually unknown to most companies.

Before we talk about resistance to data donation, perhaps we should explore the level of awareness companies have about the impact such donations could have.

And so, in trying to answer the question I posed at the beginning of this article, perhaps the most appropriate response is yet another question: Do companies even realize that the data they collect, generate and manage could be a vital resource for building a better world?

And if they were more aware of the different ways they could do good with data—would they be more inclined to act?

Despite the existence of the Data Governance Act and the Data Act, these questions remain largely unanswered. But the hope is that, as data becomes more democratized within organizations and as social responsibility and sustainability take center stage, “Data for Good” will become a standard theme in corporate agendas.

After all, private companies are the most valuable and essential data providers and partners for this kind of transformation—and it is often we, the people, who provide them with the very data that could help change our world…(More)”.

What Counts as Discovery?

Curated on Posted on by Stefaan Verhulst

Essay by Nisheeth Vishnoi: “Long before there were “scientists,” there was science. Across every continent, humans developed knowledge systems grounded in experience, abstraction, and prediction—driven not merely by curiosity, but by a desire to transform patterns into principles, and observation into discovery. Farmers tracked solstices, sailors read stars, artisans perfected metallurgy, and physicians documented plant remedies. They built calendars, mapped cycles, and tested interventions—turning empirical insight into reliable knowledge.

From the oral sciences of Africa, which encoded botanical, medical, and ecological knowledge across generations, to the astronomical observatories of Mesoamerica, where priests tracked solstices, eclipses, and planetary motion with remarkable accuracy, early human civilizations sought more than survival. In Babylon, scribes logged celestial movements and built predictive models; in India, the architects of Vedic altars designed ritual structures whose proportions mirrored cosmic rhythms, embedding arithmetic and geometry into sacred form. Across these diverse cultures, discovery was not a separate enterprise—it was entwined with ritual, survival, and meaning. Yet the tools were recognizably scientific: systematic observation, abstraction, and the search for hidden order.

This was science before the name. And it reminds us that discovery has never belonged to any one civilization or era. Discovery is not intelligence itself, but one of its sharpest expressions—an act that turns perception into principle through a conceptual leap. While intelligence is broader and encompasses adaptation, inference, and learning in various forms (biological, cultural, and even mechanical), discovery marks those moments when something new is framed, not just found. 

Life forms learn, adapt, and even innovate. But it is humans who turned observation into explanation, explanation into abstraction, and abstraction into method. The rise of formal science brought mathematical structure and experiment, but it did not invent the impulse to understand—it gave it form, language, and reach.

And today, we stand at the edge of something unfamiliar: the possibility of lifeless discoveries. Artificial Intelligence machines, built without awareness or curiosity, are beginning to surface patterns and propose explanations, sometimes without our full understanding. If science has long been a dialogue between the world and living minds, we are now entering a strange new phase: abstraction without awareness, discovery without a discoverer.

AI systems now assist in everything from understanding black holes to predicting protein folds and even symbolic equation discovery. They parse vast datasets, detect regularities, and generate increasingly sophisticated outputs. Some claim they’re not just accelerating research, but beginning to reshape science itself—perhaps even to discover.

But what truly counts as a scientific discovery? This essay examines that question…(More)”

A.I. Is Starting to Wear Down Democracy

Curated on Posted on by Stefaan Verhulst

Article by Steven Lee Myers and Stuart A. Thompson: “Since the explosion of generative artificial intelligence over the last two years, the technology has demeaned or defamed opponents and, for the first time, officials and experts said, begun to have an impact on election results.

Free and easy to use, A.I. tools have generated a flood of fake photos and videos of candidates or supporters saying things they did not or appearing in places they were not — all spread with the relative impunity of anonymity online.

The technology has amplified social and partisan divisions and bolstered antigovernment sentiment, especially on the far right, which has surged in recent elections in Germany, Poland and Portugal.

In Romania, a Russian influence operation using A.I. tainted the first round of last year’s presidential election, according to government officials. A court there nullified that result, forcing a new vote last month and bringing a new wave of fabrications. It was the first major election in which A.I. played a decisive role in the outcome. It is unlikely to be the last.

As the technology improves, officials and experts warn, it is undermining faith in electoral integrity and eroding the political consensus necessary for democratic societies to function.

Madalina Botan, a professor at the National University of Political Studies and Public Administration in Romania’s capital, Bucharest, said there was no question that the technology was already “being used for obviously malevolent purposes” to manipulate voters.

“These mechanics are so sophisticated that they truly managed to get a piece of content to go very viral in a very limited amount of time,” she said. “What can compete with this?”

In the unusually concentrated wave of elections that took place in 2024, A.I. was used in more than 80 percent, according to the International Panel on the Information Environment, an independent organization of scientists based in Switzerland.

It documented 215 instances of A.I. in elections that year, based on government statements, research and news reports. Already this year, A.I. has played a role in at least nine more major elections, from Canada to Australia…(More)”.

Further Reflections on the Journey Towards an International Framework for Data Governance

Curated on Posted on by Stefaan Verhulst

Paper by Steve MacFeely, Angela Me, Rachael Beaven, Joseph Costanzo, David Passarelli, Carolina Rossini, Friederike Schueuer, Malarvizhi Veerappan, and Stefaan Verhulst: “The use of data is paramount both to inform individual decisions and to address major global challenges. Data are the lifeblood of the digital economy, feeding algorithms, currencies, artificial intelligence, and driving international services trade, improving the way we respond to crises, informing logistics, shaping markets, communications and politics. But data are not just an economic commodity, to be traded and harvested, they are a personal and social artifact. They contain our most personal and sensitive information – our financial and health records, our networks, our memories, and our most intimate secrets and aspirations. With the advent of digitalization and the internet, our data are ubiquitous – we are the sum of our data. Consequently, this powerful treasure trove needs to be protected carefully. This paper presents arguments for an international data governance framework, the barriers to achieving such a framework and some of the costs of failure. It also articulates why the United Nations is uniquely positioned to host such a framework, and learning from history, the opportunity available to solve a global problem…(More)”.

AI Scraping Bots Are Breaking Open Libraries, Archives, and Museums

Curated on Posted on by Stefaan Verhulst

Article by Emanuel Maiberg: “The report, titled “Are AI Bots Knocking Cultural Heritage Offline?” was written by Weinberg of the GLAM-E Lab, a joint initiative between the Centre for Science, Culture and the Law at the University of Exeter and the Engelberg Center on Innovation Law & Policy at NYU Law, which works with smaller cultural institutions and community organizations to build open access capacity and expertise. GLAM is an acronym for galleries, libraries, archives, and museums. The report is based on a survey of 43 institutions with open online resources and collections in Europe, North America, and Oceania. Respondents also shared data and analytics, and some followed up with individual interviews. The data is anonymized so institutions could share information more freely, and to prevent AI bot operators from undermining their counter measures.  

Of the 43 respondents, 39 said they had experienced a recent increase in traffic. Twenty-seven of those 39 attributed the increase in traffic to AI training data bots, with an additional seven saying the AI bots could be contributing to the increase. 

“Multiple respondents compared the behavior of the swarming bots to more traditional online behavior such as Distributed Denial of Service (DDoS) attacks designed to maliciously drive unsustainable levels of traffic to a server, effectively taking it offline,” the report said. “Like a DDoS incident, the swarms quickly overwhelm the collections, knocking servers offline and forcing administrators to scramble to implement countermeasures. As one respondent noted, ‘If they wanted us dead, we’d be dead.’”…(More)”

AI and Social Media: A Political Economy Perspective

Curated on Posted on by Stefaan Verhulst

Paper by Daron Acemoglu, Asuman Ozdaglar & James Siderius: “We consider the political consequences of the use of artificial intelligence (AI) by online platforms engaged in social media content dissemination, entertainment, or electronic commerce. We identify two distinct but complementary mechanisms, the social media channel and the digital ads channel, which together and separately contribute to the polarization of voters and consequently the polarization of parties. First, AI-driven recommendations aimed at maximizing user engagement on platforms create echo chambers (or “filter bubbles”) that increase the likelihood that individuals are not confronted with counter-attitudinal content. Consequently, social media engagement makes voters more polarized, and then parties respond by becoming more polarized themselves. Second, we show that party competition can encourage platforms to rely more on targeted digital ads for monetization (as opposed to a subscription-based business model), and such ads in turn make the electorate more polarized, further contributing to the polarization of parties. These effects do not arise when one party is dominant, in which case the profit-maximizing business model of the platform is subscription-based. We discuss the impact regulations can have on the polarizing effects of AI-powered online platforms…(More)”.

The Data-Informed City: A Conceptual Framework for Advancing Research and Practice

Curated on Posted on by Stefaan Verhulst

Paper by Jorrit de Jong, Fernando Fernandez-Monge et al: “Over the last decades, scholars and practitioners have focused their attention on the use of data for improving public action, with a renewed interest in the emergence of big data and artificial intelligence. The potential of data is particularly salient in cities, where vast amounts of data are being generated from traditional and novel sources. Despite this growing interest, there is a need for a conceptual and operational understanding of the beneficial uses of data. This article presents a comprehensive and precise account of how cities can use data to address problems more effectively, efficiently, equitably, and in a more accountable manner. It does so by synthesizing and augmenting current research with empirical evidence derived from original research and learnings from a program designed to strengthen city governments’ data capacity. The framework can be used to support longitudinal and comparative analyses as well as explore questions such as how different uses of data employed at various levels of maturity can yield disparate outcomes. Practitioners can use the framework to identify and prioritize areas in which building data capacity might further the goals of their teams and organizations…(More)“