Trust in artificial intelligence makes Trump/Vance a transhumanist ticket

Curated on Posted on by Stefaan Verhulst

Article by Filip Bialy: “AI plays a central role in the 2024 US presidential election, as a tool for disinformation and as a key policy issue. But its significance extends beyond these, connecting to an emerging ideology known as TESCREAL, which envisages AI as a catalyst for unprecedented progress, including space colonisation. After this election, TESCREALism may well have more than one representative in the White House, writes Filip Bialy

In June 2024, the essay Situational Awareness by former OpenAI employee Leopold Aschenbrenner sparked intense debate in the AI community. The author predicted that by 2027, AI would surpass human intelligence. Such claims are common among AI researchers. They often assert that only a small elite – mainly those working at companies like OpenAI – possesses inside knowledge of the technology. Many in this group hold a quasi-religious belief in the imminent arrival of artificial general intelligence (AGI) or artificial superintelligence (ASI)…

These hopes and fears, however, are not only religious-like but also ideological. A decade ago, Silicon Valley leaders were still associated with the so-called Californian ideology, a blend of hippie counterculture and entrepreneurial yuppie values. Today, figures like Elon Musk, Mark Zuckerberg, and Sam Altman are under the influence of a new ideological cocktail: TESCREAL. Coined in 2023 by Timnit Gebru and Émile P. Torres, TESCREAL stands for Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, and Longtermism.

While these may sound like obscure terms, they represent ideas developed over decades, with roots in eugenics. Early 20th-century eugenicists such as Francis Galton promoted selective breeding to enhance future generations. Later, with advances in genetic engineering, the focus shifted from eugenics’ racist origins to its potential to eliminate genetic defects. TESCREAL represents a third wave of eugenics. It aims to digitise human consciousness and then propagate digital humans into the universe…(More)”

Open-Access AI: Lessons From Open-Source Software

Curated on Posted on by Stefaan Verhulst

Article by Parth Nobel, Alan Z. Rozenshtein, Chinmayi Sharma: “Before analyzing how the lessons of open-source software might (or might not) apply to open-access AI, we need to define our terms and explain why we use the term “open-access AI” to describe models like Llama rather than the more commonly used “open-source AI.” We join many others in arguing that “open-source AI” is a misnomer for such models. It’s misleading to fully import the definitional elements and assumptions that apply to open-source software when talking about AI. Rhetoric matters, and the distinction isn’t just semantic; it’s about acknowledging the meaningful differences in access, control, and development. 

The software industry definition of “open source” grew out of the free software movement, which makes the point that “users have the freedom to run, copy, distribute, study, change and improve” software. As the movement emphasizes, one should “think of ‘free’ as in ‘free speech,’ not as in ‘free beer.’” What’s “free” about open-source software is that users can do what they want with it, not that they initially get it for free (though much open-source software is indeed distributed free of charge). This concept is codified by the Open Source Initiative as the Open Source Definition (OSD), many aspects of which directly apply to Llama 3.2. Llama 3.2’s license makes it freely redistributable by license holders (Clause 1 of the OSD) and allows the distribution of the original models, their parts, and derived works (Clauses 3, 7, and 8). ..(More)”.

Make it make sense: the challenge of data analysis in global deliberation

Curated on Posted on by Stefaan Verhulst

Blog by Iñaki Goñi: “From climate change to emerging technologies to economic justice to space, global and transnational deliberation is on the rise. Global deliberative processes aim to bring citizen-centred governance to issues that no single nation can resolve alone. Running deliberative processes at this scale poses a unique set of challenges. How to select participants, make the forums accountable, impactful, fairly designed, and aware of power imbalances, are all crucial and open questions….

Massifying participation will be key to invigorating global deliberation. Assemblies will have a better chance of being seen as legitimate, fair, and publicly supported if they involve thousands or even millions of diverse participants. This raises an operational challenge: how to systematise political ideas from many people across the globe.

In a centralised global assembly, anything from 50 to 500 citizens from various countries engage in a single deliberation and produce recommendations or political actions by crossing languages and cultures. In a distributed assembly, multiple gatherings are convened locally that share a common but flexible methodology, allowing participants to discuss a common issue applied both to local and global contexts. Either way, a global deliberation process demands the organisation and synthesis of possibly thousands of ideas from diverse languages and cultures around the world.

How could we ever make sense of all that data to systematise citizens’ ideas and recommendations? Most people turn their heads to computational methods to help reduce complexity and identify patterns. First up, one technique for analysing text amounts to little more than simple counting, through which we can produce something like a frequency table or a wordcloud…(More)”.

A shared destiny for public sector data

Curated on Posted on by Stefaan Verhulst

Blog post by Shona Nicol: “As a data professional, it can sometime feel hard to get others interested in data. Perhaps like many in this profession, I can often express the importance and value of data for good in an overly technical way. However when our biggest challenges in Scotland include eradicating child poverty, growing the economy and tackling the climate emergency, I would argue that we should all take an interest in data because it’s going to be foundational in helping us solve these problems.

Data is already intrinsic to shaping our society and how services are delivered. And public sector data is a vital component in making sure that services for the people of Scotland are being delivered efficiently and effectively. Despite an ever growing awareness of the transformative power of data to improve the design and delivery of services, feedback from public sector staff shows that they can face difficulties when trying to influence colleagues and senior leaders around the need to invest in data.

A vision gap

In the Scottish Government’s data maturity programme and more widely, we regularly hear about the challenges data professionals encounter when trying to enact change. This community tell us that a long-term vision for public sector data for Scotland could help them by providing the context for what they are trying to achieve locally.

Earlier this year we started to scope how we might do this. We recognised that organisations are already working to deliver local and national strategies and policies that relate to data, so any vision had to be able to sit alongside those, be meaningful in different settings, agnostic of technology and relevant to any public sector organisation. We wanted to offer opportunities for alignment, not enforce an instruction manual…(More)”.

Statistical Significance—and Why It Matters for Parenting

Curated on Posted on by Stefaan Verhulst

Blog by Emily Oster: “…When we say an effect is “statistically significant at the 5% level,” what this means is that there is less than a 5% chance that we’d see an effect of this size if the true effect were zero. (The “5% level” is a common cutoff, but things can be significant at the 1% or 10% level also.) 

The natural follow-up question is: Why would any effect we see occur by chance? The answer lies in the fact that data is “noisy”: it comes with error. To see this a bit more, we can think about what would happen if we studied a setting where we know our true effect is zero. 

My fake study 

Imagine the following (fake) study. Participants are randomly assigned to eat a package of either blue or green M&Ms, and then they flip a (fair) coin and you see if it is heads. Your analysis will compare the number of heads that people flip after eating blue versus green M&Ms and report whether this is “statistically significant at the 5% level.”…(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)”.

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)”.

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)”.

We are Developing AI at the Detriment of the Global South — How a Focus on Responsible Data Re-use Can Make a Difference

Curated on Posted on by Stefaan Verhulst

Article by Stefaan Verhulst and Peter Addo: “…At the root of this debate runs a frequent concern with how data is collected, stored, used — and responsibly reused for other purposes that initially collected for…

In this article, we propose that promoting responsible reuse of data requires addressing the power imbalances inherent in the data ecology. These imbalances disempower key stakeholders, thereby undermining trust in data management practices. As we recently argued in a report on “responsible data reuse in developing countries,” prepared for Agence Française de Development (AFD), power imbalences may be particularly pernicious when considering the use of data in the Global South. Addressing these requires broadening notions of consent, beyond current highly individualized approaches, in favor of what we instead term a social license for reuse.

In what follows, we explain what a social license means, and propose three steps to help achieve that goal. We conclude by calling for a new research agenda — one that would stretch existing disciplinary and conceptual boundaries — to reimagine what social licenses might mean, and how they could be operationalized…(More)”.

Buried Academic Treasures

Curated on Posted on by Stefaan Verhulst

Barrett and Greene: “…one of the presenters who said: “We have lots of research that leads to no results.”

As some of you know, we’ve written a book with Don Kettl to help academically trained researchers write in a way that would be understandable by decision makers who could make use of their findings. But the keys to writing well are only a small part of the picture. Elected and appointed officials have the capacity to ignore nearly anything, no matter how well written it is.

This is more than just a frustration to researchers, it’s a gigantic loss to the world of public administration. We spend lots of time reading through reports and frequently come across nuggets of insights that we believe could help make improvements in nearly every public sector endeavor from human resources to budgeting to performance management to procurement and on and on. We, and others, can do our best to get attention for this kind of information, but that doesn’t mean that the decision makers have the time or the inclination to take steps toward taking advantage of great ideas.

We don’t want to place the blame for the disconnect between academia and practitioners on either party. To one degree or the other they’re both at fault, with taxpayers and the people who rely on government services – and that’s pretty much everybody except for people who have gone off the grid – as the losers.

Following, from our experience, are six reasons we believe that it’s difficult to close the gap between the world of research and the realm of utility. The first three are aimed at government leaders, the last three have academics in mind…(More)”