Entering the Vortex

Curated on Posted on by Stefaan Verhulst

Essay by Nils Gilman: “A strange and unsettling weather pattern is forming over the landscape of scholarly research. For decades, the climate of academic inquiry was shaped by a prevailing high-pressure system, a consensus grounded in the vision articulated by Vannevar Bush in “Science: The Endless Frontier” (1945). That era was characterized by robust federal investment, a faith in the university as the engine of basic research, and a compact that traded public funding for scientific autonomy and the promise of long-term societal benefit. It was a climate conducive to the slow, deliberate, and often unpredictable growth of knowledge, nurtured by a diverse ecosystem of human researchers — the vital “seed stock” of intellectual discovery.

But that high-pressure system is collapsing. A brutal, unyielding cold front of academic defunding has swept across the nation, a consequence of shifting political priorities, populist resentment, and a calculated assault on the university as an institution perceived as hostile to certain political agendas. This is not merely a belt-tightening exercise; it is, for all intents and purposes, the dismantling of Vannevar Bush’s Compact, the end of the era of “big government”-funded Wissenschaft. Funding streams for basic research are dwindling, grant applications face increasingly long odds, and the financial precarity of academic careers deters the brightest minds. The human capital necessary for sustained, fundamental inquiry is beginning to wither.

Simultaneously, a warm, moisture-laden airmass is rapidly advancing: the astonishing rise of AI-based research tools. Powered by vast datasets and sophisticated algorithms, these tools promise to revolutionize every stage of the research process – from literature review and data analysis to hypothesis generation and the drafting of scholarly texts. As a recent New Yorker piece on AI and the humanities suggests, these AI engines can already generate deep research and coherent texts on virtually any subject, seemingly within moments. They offer the prospect of unprecedented efficiency, speed, and scale in the production of scholarly output.

The collision of these two epochal weather systems — the brutal cold front of academic defunding and the warm, expansive airmass of AI-based research tools — is creating an atmospheric instability unlike anything the world of scholarship has ever witnessed. Along the front where these forces meet, a series of powerful and unpredictable tornados are beginning to touch down, reshaping the terrain of knowledge production in real-time…(More)”.

Real-time prices, real results: comparing crowdsourcing, AI, and traditional data collection

Curated on Posted on by Stefaan Verhulst

Article by Julius Adewopo, Bo Andree, Zacharey Carmichael, Steve Penson, Kamwoo Lee: “Timely, high-quality food price data is essential for shock responsive decision-making. However, in many low- and middle-income countries, such data is often delayed, limited in geographic coverage, or unavailable due to operational constraints. Traditional price monitoring, which relies on structured surveys conducted by trained enumerators, is often constrained by challenges related to cost, frequency, and reach.

To help overcome these limitations, the World Bank launched the Real-Time Prices (RTP) data platform. This effort provides monthly price data using a machine learning framework. The models combine survey results with predictions derived from observations in nearby markets and related commodities. This approach helps fill gaps in local price data across a basket of goods, enabling real-time monitoring of inflation dynamics even when survey data is incomplete or irregular.

In parallel, new approaches—such as citizen-submitted (crowdsourced) data—are being explored to complement conventional data collection methods. These crowdsourced data were recently published in a Nature Scientific Data paper. While the adoption of these innovations is accelerating, maintaining trust requires rigorous validation.

A newly published study in PLOS compares the two emerging methods with the traditional, enumerator-led gold standard, providing  new evidence that both crowdsourced and AI-imputed prices can serve as credible, timely alternatives to traditional ground-truth data collection—especially in contexts where conventional methods face limitations…(More)”.

These Startups Are Building Advanced AI Models Without Data Centers

Curated on Posted on by Stefaan Verhulst

Article by Will Knight: “Researchers have trained a new kind of large language model (LLM) using GPUs dotted across the world and fed private as well as public data—a move that suggests that the dominant way of building artificial intelligence could be disrupted.

Article by Will Knight: “Flower AI and Vana, two startups pursuing unconventional approaches to building AI, worked together to create the new model, called Collective-1.

Flower created techniques that allow training to be spread across hundreds of computers connected over the internet. The company’s technology is already used by some firms to train AI models without needing to pool compute resources or data. Vana provided sources of data including private messages from X, Reddit, and Telegram.

Collective-1 is small by modern standards, with 7 billion parameters—values that combine to give the model its abilities—compared to hundreds of billions for today’s most advanced models, such as those that power programs like ChatGPT, Claude, and Gemini.

Nic Lane, a computer scientist at the University of Cambridge and cofounder of Flower AI, says that the distributed approach promises to scale far beyond the size of Collective-1. Lane adds that Flower AI is partway through training a model with 30 billion parameters using conventional data, and plans to train another model with 100 billion parameters—close to the size offered by industry leaders—later this year. “It could really change the way everyone thinks about AI, so we’re chasing this pretty hard,” Lane says. He says the startup is also incorporating images and audio into training to create multimodal models.

Distributed model-building could also unsettle the power dynamics that have shaped the AI industry…(More)”

Digital Public Infrastructure Could Make a Better Internet

Curated on Posted on by Stefaan Verhulst

Essay by Akash Kapur: “…The advent of AI has intensified geopolitical rivalries, and with them the risks of fragmentation, exclusion, and hyper-concentration that are already so prevalent. The prospects of a “Splinternet” have never appeared more real. The old dream of a global digital commons seems increasingly quaint; we are living amid what Yanis Varoufakis, the former Greek finance minister, calls “technofeudalism.”

DPI suggests it doesn’t have to be this way. The approach’s emphasis on loosening chokeholds, fostering collaboration, and reclaiming space from monopolies represents an effort to recuperate some of the internet’s original promise. At its most aspirational, DPI offers the potential for a new digital social contract: a rebalancing of public and private interests, a reorientation of the network so that it advances broad social goals even while fostering entrepreneurship and innovation. How fitting it would be if this new model were to emerge not from the entrenched powers that have so long guided the network, but from a handful of nations long confined to the periphery—now determined to take their seats at the table of global technology…(More)”.

Bad Public Policy: Malignity, Volatility and the Inherent Vices of Policymaking

Curated on Posted on by Stefaan Verhulst

Book by Policy studies assume the existence of baseline parameters – such as honest governments doing their best to create public value, publics responding in good faith, and both parties relying on a policy-making process which aligns with the public interest. In such circumstances, policy goals are expected to be produced through mechanisms in which the public can articulate its preferences and policy-makers are expected to listen to what has been said in determining their governments’ courses of action. While these conditions are found in some governments, there is evidence from around the world that much policy-making occurs without these pre-conditions and processes. Unlike situations which produce what can be thought of as ‘good’ public policy, ‘bad’ public policy is a more common outcome. How this happens and what makes for bad public policy are the subjects of this Element…(More)”.

Updating purpose limitation for AI: a normative approach from law and philosophy 

Curated on Posted on by Stefaan Verhulst

Paper by Rainer Mühlhoff and Hannah Ruschemeier: “The purpose limitation principle goes beyond the protection of the individual data subjects: it aims to ensure transparency, fairness and its exception for privileged purposes. However, in the current reality of powerful AI models, purpose limitation is often impossible to enforce and is thus structurally undermined. This paper addresses a critical regulatory gap in EU digital legislation: the risk of secondary use of trained models and anonymised training datasets. Anonymised training data, as well as AI models trained from this data, pose the threat of being freely reused in potentially harmful contexts such as insurance risk scoring and automated job applicant screening. We propose shifting the focus of purpose limitation from data processing to AI model regulation. This approach mandates that those training AI models define the intended purpose and restrict the use of the model solely to this stated purpose…(More)”.

Rebooting the global consensus: Norm entrepreneurship, data governance and the inalienability of digital bodies

Curated on Posted on by Stefaan Verhulst

Paper by Siddharth Peter de Souza and Linnet Taylor: “The establishment of norms among states is a common way of governing international actions. This article analyses the potential of norm-building for governing data and artificial intelligence technologies’ collective effects. Rather than focusing on state actors’s ability to establish and enforce norms, however, we identify a contrasting process taking place among civil society organisations in response to the international neoliberal consensus on the commodification of data. The norm we identify – ‘nothing about us without us’ – asserts civil society’s agency, and specifically the right of those represented in datasets to give or refuse permission through structures of democratic representation. We argue that this represents a form of norm-building that should be taken as seriously as that of states, and analyse how it is constructing the political power, relations, and resources to engage in governing technology at scale. We first outline how this counter-norming is anchored in data’s connections to bodies, land, community, and labour. We explore the history of formal international norm-making and the current norm-making work being done by civil society organisations internationally, and argue that these, although very different in their configurations and strategies, are comparable in scale and scope. Based on this, we make two assertions: first, that a norm-making lens is a useful way for both civil society and research to frame challenges to the primacy of market logics in law and governance, and second, that the conceptual exclusion of civil society actors as norm-makers is an obstacle to the recognition of counter-power in those spheres…(More)”.

Technical Tiers: A New Classification Framework for Global AI Workforce Analysis

Curated on Posted on by Stefaan Verhulst

Report by Siddhi Pal, Catherine Schneider and Ruggero Marino Lazzaroni: “… introduces a novel three-tiered classification system for global AI talent that addresses significant methodological limitations in existing workforce analyses, by distinguishing between different skill categories within the existing AI talent pool. By distinguishing between non-technical roles (Category 0), technical software development (Category 1), and advanced deep learning specialization (Category 2), our framework enables precise examination of AI workforce dynamics at a pivotal moment in global AI policy.

Through our analysis of a sample of 1.6 million individuals in the AI talent pool across 31 countries, we’ve uncovered clear patterns in technical talent distribution that significantly impact Europe’s AI ambitions. Asian nations hold an advantage in specialized AI expertise, with South Korea (27%), Israel (23%), and Japan (20%) maintaining the highest proportions of Category 2 talent. Within Europe, Poland and Germany stand out as leaders in specialized AI talent. This may be connected to their initiatives to attract tech companies and investments in elite research institutions, though further research is needed to confirm these relationships.

Our data also reveals a shifting landscape of global talent flows. Research shows that countries employing points-based immigration systems attract 1.5 times more high-skilled migrants than those using demand-led approaches. This finding takes on new significance in light of recent geopolitical developments affecting scientific research globally. As restrictive policies and funding cuts create uncertainty for researchers in the United States, one of the big destinations for European AI talent, the way nations position their regulatory environments, scientific freedoms, and research infrastructure will increasingly determine their ability to attract and retain specialized AI talent.

The gender analysis in our study illuminates another dimension of competitive advantage. Contrary to the overall AI talent pool, EU countries lead in female representation in highly technical roles (Category 2), occupying seven of the top ten global rankings. Finland, Czechia, and Italy have the highest proportion of female representation in Category 2 roles globally (39%, 31%, and 28%, respectively). This gender diversity represents not merely a social achievement but a potential strategic asset in AI innovation, particularly as global coalitions increasingly emphasize the importance of diverse perspectives in AI development…(More)”

In Uncertain Times, Get Curious

Curated on Posted on by Stefaan Verhulst

Chapter (and book) by Elizabeth Weingarten: “Questions flow from curiosity. If we want to live and love the questions of our lives—How to live a life of purpose? Who am I in the aftermath of a big change or transition? What kind of person do I want to become as I grow older?—we must first ask them into conscious existence.

Many people have written entire books defining and redefining curiosity. But for me, the most helpful definition comes from a philosophy professor, Perry Zurn, and a systems neuroscientist, Dani Bassett: “For too long—and still too often—curiosity has been oversimplified,” they write, typically “reduced to the simple act of raising a hand or voicing a question, especially from behind a desk or a podium. . . . Scholars generally boil it down to ‘information-seeking’ behavior or a ‘desire to know.’ But curiosity is more than a feeling and certainly more than an act. And curiosity is always more than a single move or a single question.”Curiosity works, they write, by “linking ideas, facts, perceptions, sensations and data points together.”It is complex, mutating, unpredictable, and transformational. It is, fundamentally, an act of connection, an act of creating relationships between ideas and people. Asking questions then, becoming curious, is not just about wanting to find the answer—it is also about our need to connect, with ourselves, with others, with the world.

And this, perhaps, is why our deeper questions are hardly ever satisfied by Google or by fast, easy answers from the people I refer to as the Charlatans of Certainty—the gurus, influencers, and “experts” peddling simple solutions to all the complex problems you face. This is also the reason there is no one-size-fits-all formula for cultivating curiosity—particularly the kind that allows us to live and love our questions, especially the questions that are hard to love, like “How can I live with chronic pain?” or “How do I extricate myself from a challenging relationship?” This kind of curiosity is a special flavor…(More)”. See also: Inquiry as Infrastructure: Defining Good Questions in the Age of Data and AI.

Make privacy policies longer and appoint LLM readers

Curated on Posted on by Stefaan Verhulst

Paper by Przemysław Pałka et al: “In a world of human-only readers, a trade-off persists between comprehensiveness and comprehensibility: only privacy policies too long to be humanly readable can precisely describe the intended data processing. We argue that this trade-off no longer exists where LLMs are able to extract tailored information from clearly-drafted fully-comprehensive privacy policies. To substantiate this claim, we provide a methodology for drafting comprehensive non-ambiguous privacy policies and for querying them using LLMs prompts. Our methodology is tested with an experiment aimed at determining to what extent GPT-4 and Llama2 are able to answer questions regarding the content of privacy policies designed in the format we propose. We further support this claim by analyzing real privacy policies in the chosen market sectors through two experiments (one with legal experts, and another by using LLMs). Based on the success of our experiments, we submit that data protection law should change: it must require controllers to provide clearly drafted, fully comprehensive privacy policies from which data subjects and other actors can extract the needed information, with the help of LLMs…(More)”.