AI Is Evolving — And Changing Our Understanding Of Intelligence

Curated on Posted on by Stefaan Verhulst

Essay by Blaise Agüera y Arcas and James Manyika: “Dramatic advances in artificial intelligence today are compelling us to rethink our understanding of what intelligence truly is. Our new insights will enable us to build better AI and understand ourselves better.

In short, we are in paradigm-shifting territory.

Paradigm shifts are often fraught because it’s easier to adopt new ideas when they are compatible with one’s existing worldview but harder when they’re not. A classic example is the collapse of the geocentric paradigm, which dominated cosmological thought for roughly two millennia. In the geocentric model, the Earth stood still while the Sun, Moon, planets and stars revolved around us. The belief that we were at the center of the universe — bolstered by Ptolemy’s theory of epicycles, a major scientific achievement in its day — was both intuitive and compatible with religious traditions. Hence, Copernicus’s heliocentric paradigm wasn’t just a scientific advance but a hotly contested heresy and perhaps even, for some, as Benjamin Bratton notes, an existential trauma. So, today, artificial intelligence.

In this essay, we will describe five interrelated paradigm shifts informing our development of AI:

  1. Natural Computing — Computing existed in nature long before we built the first “artificial computers.” Understanding computing as a natural phenomenon will enable fundamental advances not only in computer science and AI but also in physics and biology.
  2. Neural Computing — Our brains are an exquisite instance of natural computing. Redesigning the computers that power AI so they work more like a brain will greatly increase AI’s energy efficiency — and its capabilities too.
  3. Predictive Intelligence — The success of large language models (LLMs) shows us something fundamental about the nature of intelligence: it involves statistical modeling of the future (including one’s own future actions) given evolving knowledge, observations and feedback from the past. This insight suggests that current distinctions between designing, training and running AI models are transitory; more sophisticated AI will evolve, grow and learn continuously and interactively, as we do.
  4. General Intelligence — Intelligence does not necessarily require biologically based computation. Although AI models will continue to improve, they are already broadly capable, tackling an increasing range of cognitive tasks with a skill level approaching and, in some cases, exceeding individual human capability. In this sense, “Artificial General Intelligence” (AGI) may already be here — we just keep shifting the goalposts.
  5. Collective Intelligence — Brains, AI agents and societies can all become more capable through increased scale. However, size alone is not enough. Intelligence is fundamentally social, powered by cooperation and the division of labor among many agents. In addition to causing us to rethink the nature of human (or “more than human”) intelligence, this insight suggests social aggregations of intelligences and multi-agent approaches to AI development that could reduce computational costs, increase AI heterogeneity and reframe AI safety debates.

But to understand our own “intelligence geocentrism,” we must begin by reassessing our assumptions about the nature of computing, since it is the foundation of both AI and, we will argue, intelligence in any form…(More)”.

‘We are flying blind’: RFK Jr.’s cuts halt data collection on abortion, cancer, HIV and more

Curated on Posted on by Stefaan Verhulst

Article by Alice Miranda Ollstein: “The federal teams that count public health problems are disappearing — putting efforts to solve those problems in jeopardy.

Health Secretary Robert F. Kennedy Jr.’s purge of tens of thousands of federal workers has halted efforts to collect data on everything from cancer rates in firefighters to mother-to-baby transmission of HIV and syphilis to outbreaks of drug-resistant gonorrhea to cases of carbon monoxide poisoning.

The cuts threaten to obscure the severity of pressing health threats and whether they’re getting better or worse, leaving officials clueless on how to respond. They could also make it difficult, if not impossible, to assess the impact of the administration’s spending and policies. Both outside experts and impacted employees argue the layoffs will cost the government more money in the long run by eliminating information on whether programs are effective or wasteful, and by allowing preventable problems to fester.

“Surveillance capabilities are crucial for identifying emerging health issues, directing resources efficiently, and evaluating the effectiveness of existing policies,” said Jerome Adams, who served as surgeon general in the first Trump’s administration. “Without robust data and surveillance systems, we cannot accurately assess whether we are truly making America healthier.”..(More)”.

Statistical methods in public policy research

Curated on Posted on by Stefaan Verhulst

Chapter by Andrew Heiss: “This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century.

I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore what exists and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask why does it exist and how can it be influenced. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as what will happen next and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations.

I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part “data imperative” where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field…(More)”.

Energy and AI

Curated on Posted on by Stefaan Verhulst

Report by the International Energy Agency (IEA): “The development and uptake of artificial intelligence (AI) has accelerated in recent years – elevating the question of what widespread deployment of the technology will mean for the energy sector. There is no AI without energy – specifically electricity for data centres. At the same time, AI could transform how the energy industry operates if it is adopted at scale. However, until now, policy makers and other stakeholders have often lacked the tools to analyse both sides of this issue due to a lack of comprehensive data. 

This report from the International Energy Agency (IEA) aims to fill this gap based on new global and regional modelling and datasets, as well as extensive consultation with governments and regulators, the tech sector, the energy industry and international experts. It includes projections for how much electricity AI could consume over the next decade, as well as which energy sources are set to help meet it. It also analyses what the uptake of AI could mean for energy security, emissions, innovation and affordability…(More)”.

Data Sharing: A Case-Study of Luxury Surveillance by Tesla

Curated on Posted on by Stefaan Verhulst

Paper by Marc Schuilenburg and Yarin Eski: “Why do people voluntarily give away their personal data to private companies? In this paper, we show how data sharing is experienced at the level of Tesla car owners. We regard Tesla cars as luxury surveillance goods for which the drivers voluntarily choose to share their personal data with the US company. Based on an analysis of semi-structured interviews and observations of Tesla owners’ posts on Facebook groups, we discern three elements of luxury surveillance: socializing, enjoying and enduring. We conclude that luxury surveillance can be traced back to the social bonds created by a gift economy…(More)”.

The Future of Health Is Preventive — If We Get Data Governance Right

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst: “After a long gestation period of three years, the European Health Data Space (EHDS) is now coming into effect across the European Union, potentially ushering in a new era of health data access, interoperability, and innovation. As this ambitious initiative enters the implementation phase, it brings with it the opportunity to fundamentally reshape how health systems across Europe operate. More generally, the EHDS contains important lessons (and some cautions) for the rest of the world, suggesting how a fragmented, reactive model of healthcare may transition to one that is more integrated, proactive, and prevention-oriented.

For too long, health systems–in the EU and around the world–have been built around treating diseases rather than preventing them. Now, we have an opportunity to change that paradigm. Data, and especially the advent of AI, give us the tools to predict and intervene before illness takes hold. Data offers the potential for a system that prioritizes prevention–one where individuals receive personalized guidance to stay healthy, policymakers access real-time evidence to address risks before they escalate, and epidemics are predicted weeks in advance, enabling proactive, rapid, and highly effective responses.

But to make AI-powered preventive health care a reality, and to make the EHDS a success, we need a new data governance approach, one that would include two key components:

  • The ability to reuse data collected for other purposes (e.g., mobility, retail sales, workplace trends) to improve health outcomes.
  • The ability to integrate different data sources–clinical records and electronic health records (EHRS), but also environmental, social, and economic data — to build a complete picture of health risks.

In what follows, we outline some critical aspects of this new governance framework, including responsible data access and reuse (so-called secondary use), moving beyond traditional consent models to a social license for reuse, data stewardship, and the need to prioritize high-impact applications. We conclude with some specific recommendations for the EHDS, built from the preceding general discussion about the role of AI and data in preventive health…(More)”.

Unlocking Public Value with Non-Traditional Data: Recent Use Cases and Emerging Trends

Curated on Posted on by Stefaan Verhulst

Article by Adam Zable and Stefaan Verhulst: “Non-Traditional Data (NTD)—digitally captured, mediated, or observed data such as mobile phone records, online transactions, or satellite imagery—is reshaping how we identify, understand, and respond to public interest challenges. As part of the Third Wave of Open Data, these often privately held datasets are being responsibly re-used through new governance models and cross-sector collaboration to generate public value at scale.

In our previous post, we shared emerging case studies across health, urban planning, the environment, and more. Several months later, the momentum has not only continued but diversified. New projects reaffirm NTD’s potential—especially when linked with traditional data, embedded in interdisciplinary research, and deployed in ways that are privacy-aware and impact-focused.

This update profiles recent initiatives that push the boundaries of what NTD can do. Together, they highlight the evolving domains where this type of data is helping to surface hidden inequities, improve decision-making, and build more responsive systems:

  • Financial Inclusion
  • Public Health and Well-Being
  • Socioeconomic Analysis
  • Transportation and Urban Mobility
  • Data Systems and Governance
  • Economic and Labor Dynamics
  • Digital Behavior and Communication…(More)”.

Fostering Open Data

Curated on Posted on by Stefaan Verhulst

Paper by Uri Y. Hacohen: “Data is often heralded as “the world’s most valuable resource,” yet its potential to benefit society remains unrealized due to systemic barriers in both public and private sectors. While open data-defined as data that is available, accessible, and usable-holds immense promise to advance open science, innovation, economic growth, and democratic values, its utilization is hindered by legal, technical, and organizational challenges. Public sector initiatives, such as U.S. and European Union open data regulations, face uneven enforcement and regulatory complexity, disproportionately affecting under-resourced stakeholders such as researchers. In the private sector, companies prioritize commercial interests and user privacy, often obstructing data openness through restrictive policies and technological barriers. This article proposes an innovative, four-layered policy framework to overcome these obstacles and foster data openness. The framework includes (1) improving open data infrastructures, (2) ensuring legal frameworks for open data, (3) incentivizing voluntary data sharing, and (4) imposing mandatory data sharing obligations. Each policy cluster is tailored to address sector-specific challenges and balance competing values such as privacy, property, and national security. Drawing from academic research and international case studies, the framework provides actionable solutions to transition from a siloed, proprietary data ecosystem to one that maximizes societal value. This comprehensive approach aims to reimagine data governance and unlock the transformative potential of open data…(More)”.

Trump Wants to Merge Government Data. Here Are 314 Things It Might Know About You.

Curated on Posted on by Stefaan Verhulst

Article by Emily Badger and Sheera Frenkel: “The federal government knows your mother’s maiden name and your bank account number. The student debt you hold. Your disability status. The company that employs you and the wages you earn there. And that’s just a start. It may also know your …and at least 263 more categories of data.These intimate details about the personal lives of people who live in the United States are held in disconnected data systems across the federal government — some at the Treasury, some at the Social Security Administration and some at the Department of Education, among other agencies.

The Trump administration is now trying to connect the dots of that disparate information. Last month, President Trump signed an executive order calling for the “consolidation” of these segregated records, raising the prospect of creating a kind of data trove about Americans that the government has never had before, and that members of the president’s own party have historically opposed.

The effort is being driven by Elon Musk, the world’s richest man, and his lieutenants with the Department of Government Efficiency, who have sought access to dozens of databases as they have swept through agencies across the federal government. Along the way, they have elbowed past the objections of career staff, data security protocols, national security experts and legal privacy protections…(More)”.

We Must Steward, Not Subjugate Nor Worship AI

Curated on Posted on by Stefaan Verhulst

Essay by Brian J. A. Boyd: “…How could stewardship of artificially living AI be pursued on a broader, even global, level? Here, the concept of “integral ecology” is helpful. Pope Francis uses the phrase to highlight the ways in which everything is connected, both through the web of life and in that social, political, and environmental challenges cannot be solved in isolation. The immediate need for stewardship over AI is to ensure that its demands for power and industrial production are addressed in a way that benefits those most in need, rather than de-prioritizing them further. For example, the energy requirements to develop tomorrow’s AI should spur research into small modular nuclear reactors and updated distribution systems, making energy abundant rather than causing regressive harms by driving up prices on an already overtaxed grid. More broadly, we will need to find the right institutional arrangements and incentive structures to make AI Amistics possible.

We are having a painfully overdue conversation about the nature and purpose of social media, and tech whistleblowers like Tristan Harris have offered grave warnings about how the “race to the bottom of the brain stem” is underway in AI as well. The AI equivalent of the addictive “infinite scroll” design feature of social media will likely be engagement with simulated friends — but we need not resign ourselves to it becoming part of our lives as did social media. And as there are proposals to switch from privately held Big Data to a public Data Commons, so perhaps could there be space for AI that is governed not for maximizing profit but for being sustainable as a common-pool resource, with applications and protocols ordered toward long-run benefit as defined by local communities…(More)”.