External Researcher Access to Closed Foundation Models

Curated on Posted on by Stefaan Verhulst

Report by Esme Harrington and Dr. Mathias Vermeulen: “…addresses a pressing issue: independent researchers need better conditions for accessing and studying the AI models that big companies have developed. Foundation models — the core technology behind many AI applications — are controlled mainly by a few major players who decide who can study or use them.

What’s the problem with access?

  • Limited access: Companies like OpenAI, Google and others are the gatekeepers. They often restrict access to researchers whose work aligns with their priorities, which means independent, public-interest research can be left out in the cold.
  • High-end costs: Even when access is granted, it often comes with a hefty price tag that smaller or less-funded teams can’t afford.
  • Lack of transparency: These companies don’t always share how their models are updated or moderated, making it nearly impossible for researchers to replicate studies or fully understand the technology.
  • Legal risks: When researchers try to scrutinize these models, they sometimes face legal threats if their work uncovers flaws or vulnerabilities in the AI systems.

The research suggests that companies need to offer more affordable and transparent access to improve AI research. Additionally, governments should provide legal protections for researchers, especially when they are acting in the public interest by investigating potential risks…(More)”.

Tech Agnostic

Curated on Posted on by Stefaan Verhulst

Book by Greg Epstein: “…Today’s technology has overtaken religion as the chief influence on twenty-first century life and community. In Tech Agnostic, Harvard and MIT’s influential humanist chaplain Greg Epstein explores what it means to be a critical thinker with respect to this new faith. Encouraging readers to reassert their common humanity beyond the seductive sheen of “tech,” this book argues for tech agnosticism—not worship—as a way of life. Without suggesting we return to a mythical pre-tech past, Epstein shows why we must maintain a freethinking critical perspective toward innovation until it proves itself worthy of our faith or not.

Epstein asks probing questions that center humanity at the heart of engineering: Who profits from an uncritical faith in technology? How can we remedy technology’s problems while retaining its benefits? Showing how unbelief has always served humanity, Epstein revisits the historical apostates, skeptics, mystics, Cassandras, heretics, and whistleblowers who embody the tech reformation we desperately need. He argues that we must learn how to collectively demand that technology serve our pursuit of human lives that are deeply worth living…(More)”.

The Number

Curated on Posted on by Stefaan Verhulst

Article by John Lanchester: “…The other pieces published in this series have human protagonists. This one doesn’t: The main character of this piece is not a person but a number. Like all the facts and numbers cited above, it comes from the federal government. It’s a very important number, which has for a century described economic reality, shaped political debate and determined the fate of presidents: the consumer price index.

The CPI is crucial for multiple reasons, and one of them is not because of what it is but what it represents. The gathering of data exemplifies our ambition for a stable, coherent society. The United States is an Enlightenment project based on the supremacy of reason; on the idea that things can be empirically tested; that there are self-evident truths; that liberty, progress and constitutional government walk arm in arm and together form the recipe for the ideal state. Statistics — numbers created by the state to help it understand itself and ultimately to govern itself — are not some side effect of that project but a central part of what government is and does…(More)”.

Key lesson of this year’s Nobel Prize: The importance of unlocking data responsibly to advance science and improve people’s lives

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst, Anna Colom, and Marta Poblet: “This year’s Nobel Prize for Chemistry owes a lot to available, standardised, high quality data that can be reused to improve people’s lives. The winners, Prof David Baker from the University of Washington, and Demis Hassabis and John M. Jumper from Google DeepMind, were awarded respectively for the development and prediction of new proteins that can have important medical applications. These developments build on AI models that can predict protein structures in unprecedented ways. However, key to these models and their potential to unlock health discoveries is an open curated dataset with high quality and standardised data, something still rare despite the pace and scale of AI-driven development.

We live in a paradoxical time of both data abundance and data scarcity: a lot of data is being created and stored, but it tends to be inaccessible due to private interests and weak regulations. The challenge, then, is to prevent the misuse of data whilst avoiding its missed use.

The reuse of data remains limited in Europe, but a new set of regulations seeks to increase the possibilities of responsible data reuse. When the European Commission made the case for its European Data Strategy in 2020, it envisaged the European Union “a role model for a society empowered by data to make better decisions — in business and the public sector,” and acknowledged the need to improve “governance structures for handling data and to increase its pools of quality data available for use and reuse”…(More)”.

It is about time! Exploring the clashing timeframes of politics and public policy experiments

Curated on Posted on by Stefaan Verhulst

Paper by Ringa Raudla, Külli Sarapuu, Johanna Vallistu, and Nastassia Harbuzova: “Although existing studies on experimental policymaking have acknowledged the importance of the political setting in which policy experiments take place, we lack systematic knowledge on how various political dimensions affect experimental policymaking. In this article, we address a specific gap in the existing understanding of the politics of experimentation: how political timeframes influence experimental policymaking. Drawing on theoretical discussions on experimental policymaking, public policy, electoral politics, and mediatization of politics, we outline expectations about how electoral and problem cycles may influence the timing, design, and learning from policy experiments. We argue electoral timeframes are likely to discourage politicians from undertaking large-scale policy experiments and if politicians decide to launch experiments, they prefer shorter designs. The electoral cycle may lead politicians to draw too hasty conclusions or ignore the experiment’s results altogether. We expect problem cycles to shorten politicians’ time horizons further as there is pressure to solve problems quickly. We probe the plausibility of our theoretical expectations using interview data from two different country contexts: Estonia and Finland…(More)“.

Discounting the Future: The Ascendency of a Political Technology

Curated on Posted on by Stefaan Verhulst

Book by Liliana Doganova: “Forest fires, droughts, and rising sea levels beg a nagging question: have we lost our capacity to act on the future? Liliana Doganova’s book sheds new light on this anxious query. It argues that our relationship to the future has been trapped in the gears of a device called discounting. While its incidence remains little known, discounting has long been entrenched in market and policy practices, shaping the ways firms and governments look to the future and make decisions accordingly. Thus, a sociological account of discounting formulas has become urgent.

Discounting means valuing things through the flows of costs and benefits that they are likely to generate in the future, with these future flows being literally dis-counted as they are translated in the present. How have we come to think of the future, and of valuation, in such terms? Building on original empirical research in the historical sociology of discounting, Doganova takes us to some of the sites and moments in which discounting took shape and gained momentum: valuation of European forests in the eighteenth and nineteenth centuries; economic theories devised in the early 1900s; debates over business strategies in the postwar era; investor-state disputes over the nationalization of natural resources; and drug development in the biopharmaceutical industry today. Weaving these threads together, the book pleads for an understanding of discounting as a political technology, and of the future as a contested domain…(More)”

How Artificial Intelligence Can Support Peace

Curated on Posted on by Stefaan Verhulst

Essay by Adam Zable, Marine Ragnet, Roshni Singh, Hannah Chafetz, Andrew J. Zahuranec, and Stefaan G. Verhulst: “In what follows we provide a series of case studies of how AI can be used to promote peace, leveraging what we learned at the Kluz Prize for PeaceTech and NYU Prep and Becera events. These case studies and applications of AI are limited to what was included in these initiatives and are not fully comprehensive. With these examples of the role of technology before, during, and after a conflict, we hope to broaden the discussion around the potential positive uses of AI in the context of today’s global challenges.

Ai for Peace Blog GraphicThe table above summarizes the how AI may be harnessed throughout the conflict cycle and the supporting examples from the Kluz Prize for PeaceTech and NYU PREP and Becera events

(1) The Use of AI Before a Conflict

AI can support conflict prevention by predicting emerging tensions and supporting mediation efforts. In recent years, AI-driven early warning systems have been used to identify patterns that precede violence, allowing for timely interventions. 

For instance, The Violence & Impacts Early-Warning System (VIEWS), developed by a research consortium at Uppsala University in Sweden and the Peace Research Institute Oslo (PRIO) in Norway, employs AI and machine learning algorithms to analyze large datasets, including conflict history, political events, and socio-economic indicators—supporting negative peace and peacebuilding efforts. These algorithms are trained to recognize patterns that precede violent conflict, using both supervised and unsupervised learning methods to make predictions about the likelihood and severity of conflicts up to three years in advance. The system also uses predictive analytics to identify potential hotspots, where specific factors—such as spikes in political unrest or economic instability—suggest a higher risk of conflict…(More)”.

WikiProject AI Cleanup

Curated on Posted on by Stefaan Verhulst

Article by Emanuel Maiberg: “A group of Wikipedia editors have formed WikiProject AI Cleanup, “a collaboration to combat the increasing problem of unsourced, poorly-written AI-generated content on Wikipedia.”

The group’s goal is to protect one of the world’s largest repositories of information from the same kind of misleading AI-generated information that has plagued Google search results, books sold on Amazon, and academic journals.

“A few of us had noticed the prevalence of unnatural writing that showed clear signs of being AI-generated, and we managed to replicate similar ‘styles’ using ChatGPT,” Ilyas Lebleu, a founding member of WikiProject AI Cleanup, told me in an email. “Discovering some common AI catchphrases allowed us to quickly spot some of the most egregious examples of generated articles, which we quickly wanted to formalize into an organized project to compile our findings and techniques.”…(More)”.

Machines of Loving Grace

Curated on Posted on by Stefaan Verhulst

Essay by Dario Amodei: “I think and talk a lot about the risks of powerful AI. The company I’m the CEO of, Anthropic, does a lot of research on how to reduce these risks. Because of this, people sometimes draw the conclusion that I’m a pessimist or “doomer” who thinks AI will be mostly bad or dangerous. I don’t think that at all. In fact, one of my main reasons for focusing on risks is that they’re the only thing standing between us and what I see as a fundamentally positive future. I think that most people are underestimating just how radical the upside of AI could be, just as I think most people are underestimating how bad the risks could be.

In this essay I try to sketch out what that upside might look like—what a world with powerful AI might look like if everything goes right. Of course no one can know the future with any certainty or precision, and the effects of powerful AI are likely to be even more unpredictable than past technological changes, so all of this is unavoidably going to consist of guesses. But I am aiming for at least educated and useful guesses, which capture the flavor of what will happen even if most details end up being wrong. I’m including lots of details mainly because I think a concrete vision does more to advance discussion than a highly hedged and abstract one…(More)”.

We’ve Got a Big Problem

Curated on Posted on by Stefaan Verhulst

Blog by Daro: “There is a problem related to how we effectively help people receiving social services and public benefit programs. It’s a problem that we have been thinking, talking, and writing about for years. It’s a problem that once you see it, you can’t unsee it. It’s also a problem that you’re likely familiar with, whether you have direct experience with the dynamics themselves, or you’ve been frustrated by how these dynamics impact your work. In February, we organized a convening at Georgetown University in collaboration with Georgetown’s Massive Data Institute to discuss how so many of us can be frustrated by the same problem but haven’t been able to really make any headway toward a solution. 

For as long as social services have existed, people have been trying to understand how to manage and evaluate those services. How do we determine what to scale and what to change? How do we replicate successes and how do we minimize unsuccessful interventions? To answer these questions we have tried to create, use, and share evidence about these programs to inform our decision-making. However – and this is a big however – despite our collective efforts, we have difficulty determining whether there’s been an increase in using evidence, or most importantly, whether there’s actually been an improvement in the quality and impact of social services and public benefit programs…(More)”.