Cloudflare Introduces Default Blocking of A.I. Data Scrapers

Curated on Posted on by Stefaan Verhulst

Article by Natallie Rocha: “Data for A.I. systems has become an increasingly contentious issue. OpenAI, Anthropic, Google and other companies building A.I. systems have amassed reams of information from across the internet to train their A.I. models. High-quality data is particularly prized because it helps A.I. models become more proficient in generating accurate answers, videos and images.

But website publishers, authors, news organizations and other content creators have accused A.I. companies of using their material without permission and payment. Last month, Reddit sued Anthropic, saying the start-up had unlawfully used the data of its more than 100 million daily users to train its A.I. systems. In 2023, The New York Times sued OpenAI and its partner, Microsoft, accusing them of copyright infringement of news content related to A.I. systems. OpenAI and Microsoft have denied those claims.

Some publishers have struck licensing deals with A.I. companies to receive compensation for their content. In May, The Times agreed to license its editorial content to Amazon for use in the tech giant’s A.I. platforms. Axel Springer, Condé Nast and News Corp have also entered into agreements with A.I. companies to receive revenue for the use of their material.

Mark Howard, the chief operating officer of Time, said he welcomed Cloudflare’s move. Data scraping by A.I. companies threatens anyone who creates content, he said, adding that news publishers like Time deserved fair compensation for what they published…(More)”.

AI and Assembly: Coming Together and Apart in a Datafied World

Curated on Posted on by Stefaan Verhulst

Book edited by Toussaint Nothias and Lucy Bernholz: “Artificial intelligence has moved from the lab into everyday life and is now seemingly everywhere. As AI creeps into every aspect of our lives, the data grab required to power AI also expands. People worldwide are tracked, analyzed, and influenced, whether on or off their screens, inside their homes or outside in public, still or in transit, alone or together. What does this mean for our ability to assemble with others for collective action, including protesting, holding community meetings and organizing rallies ? In this context, where and how does assembly take place, and who participates by choice and who by coercion? AI and Assembly explores these questions and offers global perspectives on the present and future of assembly in a world taken over by AI.

The contributors analyze how AI threatens free assembly by clustering people without consent, amplifying social biases, and empowering authoritarian surveillance. But they also explore new forms of associational life that emerge in response to these harms, from communities in the US conducting algorithmic audits to human rights activists in East Africa calling for biometric data protection and rideshare drivers in London advocating for fair pay. Ultimately, AI and Assembly is a rallying cry for those committed to a digital future beyond the narrow horizon of corporate extraction and state surveillance…(More)”.

Beyond AI and Copyright

Curated on Posted on by Stefaan Verhulst

White Paper by Paul Keller: “…argues for interventions to ensure the sustainability of the information ecosystem in the age of generative AI. Authored by Paul Keller, the paper builds on Open Future’s ongoing work on Public AI and on AI and creative labour, and proposes measures aimed at ensuring a healthy and equitable digital knowledge commons.

Rather than focusing on the rights of individual creators or the infringement debates that dominate current policy discourse, the paper frames generative AI as a new cultural and social technology—one that is rapidly reshaping how societies access, produce, and value information. It identifies two major structural risks: the growing concentration of control over knowledge, and the hollowing out of the institutions and economies that sustain human information production.

To counter these risks, the paper calls for the development of public AI infrastructures and a redistributive mechanism based on a levy on commercial AI systems trained on publicly available information. The proceeds would support not only creators and rightholders, but also public service media, cultural heritage institutions, open content platforms, and the development of Public AI systems…(More)”.

Community Engagement Is Crucial for Successful State Data Efforts

Curated on Posted on by Stefaan Verhulst

Resource by the Data Quality Campaign: “Engaging communities is a critical step toward ensuring that data efforts work for their intended audiences. People, including state policymakers, school leaders, families, college administrators, employers, and the public, should have a say in how their state provides access to education and workforce data. And as state leaders build robust statewide longitudinal data systems (SLDSs) or move other data efforts forward, they must deliberately create consistent opportunities for communities to weigh in. This resource explores how states can meaningfully engage with communities to build trust and improve data efforts by ensuring that systems, tools, and resources are valuable to the people who use them…(More)”.

Data integration and synthesis for pandemic and epidemic intelligence

Curated on Posted on by Stefaan Verhulst

Paper by Barbara Tornimbene et al: “The COVID-19 pandemic highlighted substantial obstacles in real-time data generation and management needed for clinical research and epidemiological analysis. Three years after the pandemic, reflection on the difficulties of data integration offers potential to improve emergency preparedness. The fourth session of the WHO Pandemic and Epidemic Intelligence Forum sought to report the experiences of key global institutions in data integration and synthesis, with the aim of identifying solutions for effective integration. Data integration, defined as the combination of heterogeneous sources into a cohesive system, allows for combining epidemiological data with contextual elements such as socioeconomic determinants to create a more complete picture of disease patterns. The approach is critical for predicting outbreaks, determining disease burden, and evaluating interventions. The use of contextual information improves real-time intelligence and risk assessments, allowing for faster outbreak responses. This report captures the growing acknowledgment of data integration importance in boosting public health intelligence and readiness and show examples of how global institutions are strengthening initiatives to respond to this need. However, obstacles persist, including interoperability, data standardization, and ethical considerations. The success of future data integration efforts will be determined by the development of a common technical and legal framework, the promotion of global collaboration, and the protection of sensitive data. Ultimately, effective data integration can potentially transform public health intelligence and our way to successfully respond to future pandemics…(More)”.

Why AI hardware needs to be open

Curated on Posted on by Stefaan Verhulst

Article by Ayah Bdeir: “Once again, the future of technology is being engineered in secret by a handful of people and delivered to the rest of us as a sealed, seamless, perfect device. When technology is designed in secrecy and sold to us as a black box, we are reduced to consumers. We wait for updates. We adapt to features. We don’t shape the tools; they shape us. 

This is a problem. And not just for tinkerers and technologists, but for all of us.

We are living through a crisis of disempowerment. Children are more anxious than ever; the former US surgeon general described a loneliness epidemic; people are increasingly worried about AI eroding education. The beautiful devices we use have been correlated with many of these trends. Now AI—arguably the most powerful technology of our era—is moving off the screen and into physical space. 

The timing is not a coincidence. Hardware is having a renaissance. Every major tech company is investing in physical interfaces for AI. Startups are raising capital to build robots, glasses, wearables that are going to track our every move. The form factor of AI is the next battlefield. Do we really want our future mediated entirely through interfaces we can’t open, code we can’t see, and decisions we can’t influence? 

This moment creates an existential opening, a chance to do things differently. Because away from the self-centeredness of Silicon Valley, a quiet, grounded sense of resistance is reactivating. I’m calling it the revenge of the makers. 

In 2007, as the iPhone emerged, the maker movement was taking shape. This subculture advocates for learning-through-making in social environments like hackerspaces and libraries. DIY and open hardware enthusiasts gathered in person at Maker Faires—large events where people of all ages tinkered and shared their inventions in 3D printing, robotics, electronics, and more. Motivated by fun, self-fulfillment, and shared learning, the movement birthed companies like MakerBot, Raspberry Pi, Arduino, and (my own education startup) littleBits from garages and kitchen tables. I myself wanted to challenge the notion that technology had to be intimidating or inaccessible, creating modular electronic building blocks designed to put the power of invention in the hands of everyone…(More)”

5 Ways Cooperatives Can Shape the Future of AI

Curated on Posted on by Stefaan Verhulst

Article by Trebor Scholz and Stefano Tortorici: “Today, AI development is controlled by a small cadre of firms. Companies like OpenAI, Alphabet, Amazon, Meta, and Microsoft dominate through vast computational resources, massive proprietary datasets, deep pools of technical talent, extractive data practices, low-cost labor, and capital that enables continuous experimentation and rapid deployment. Even open-source challengers like DeepSeek run on vast computational muscle and industrial training pipelines.

This domination brings problems: privacy violation and cost-minimizing labor strategies, high environmental costs from data centers, and evident biases in models that can reinforce discrimination in hiring, healthcare, credit scoring, policing, and beyond. These problems tend to affect the people who are already too often left out. AI’s opaque algorithms don’t just sidestep democratic control and transparency—they shape who gets heard, who’s watched, and who’s quietly pushed aside.

Yet, as companies consider using this technology, it can seem that there are few other options. As such, it can seem that they are locked into these compromises.

A different model is taking shape, however, with little fanfare, but with real potential. AI cooperatives—organizations developing or governing AI technologies based on cooperative principles—offer a promising alternative. The cooperative movement, with its global footprint and diversity of models, has been successful from banking and agriculture to insurance and manufacturing. Cooperatives enterprises, which are owned and governed by their members, have long managed infrastructure for the public good.

A handful of AI cooperatives offer early examples of how democratic governance and shared ownership could shape more accountable and community-centered uses of the technology. Most are large agricultural cooperatives that are putting AI to use in their day-to-day operations, such as IFFCO’s DRONAI program (AI for fertilization), FrieslandCampina (dairy quality control), and Fonterra (milk production analytics). Cooperatives must urgently organize to challenge AI’s dominance or remain on the sidelines of critical political and technological developments.​

There is undeniably potential here, for both existing cooperatives and companies that might want to partner with them. The $589 billion drop in Nvidia’s market cap DeepSeek triggered shows how quickly open-source innovation can shift the landscape. But for cooperative AI labs to do more than signal intent, they need public infrastructure, civic partnerships, and serious backing…(More)”.

Trends in AI Supercomputers

Curated on Posted on by Stefaan Verhulst

Paper by Konstantin F. Pilz, James Sanders, Robi Rahman, and Lennart Heim: “Frontier AI development relies on powerful AI supercomputers, yet analysis of these systems is limited. We create a dataset of 500 AI supercomputers from 2019 to 2025 and analyze key trends in performance, power needs, hardware cost, ownership, and global distribution. We find that the computational performance of AI supercomputers has doubled every nine months, while hardware acquisition cost and power needs both doubled every year. The leading system in March 2025, xAI’s Colossus, used 200,000 AI chips, had a hardware cost of $7B, and required 300 MW of power, as much as 250,000 households. As AI supercomputers evolved from tools for science to industrial machines, companies rapidly expanded their share of total AI supercomputer performance, while the share of governments and academia diminished. Globally, the United States accounts for about 75% of total performance in our dataset, with China in second place at 15%. If the observed trends continue, the leading AI supercomputer in 2030 will achieve 2×1022 16-bit FLOP/s, use two million AI chips, have a hardware cost of $200 billion, and require 9 GW of power. Our analysis provides visibility into the AI supercomputer landscape, allowing policymakers to assess key AI trends like resource needs, ownership, and national competitiveness…(More)”.

Data Collection and Analysis for Policy Evaluation: Not for Duty, but for Knowledge

Curated on Posted on by Stefaan Verhulst

Paper by Valentina Battiloro: “This paper explores the challenges and methods involved in public policy evaluation, focusing on the role of data collection and use. The term “evaluation” encompasses a variety of analyses and approaches, all united by the intent to provide a judgment on a specific policy, but which, depending on the precise knowledge objective, can translate into entirely different activities. Regardless of the type of evaluation, a brief overview of which is provided, the collection of information represents a priority, often undervalued, under the assumption that it is sufficient to “have the data.“ Issues arise concerning the precise definition of the design, the planning of necessary information collection, and the appropriate management of timelines. With regard to administrative data, a potentially valuable source, a number of unresolved challenges remain due to a weak culture of data utilization. Among these are the transition from an administrative data culture to a statistical data culture, and the fundamental issue of microdata accessibility for research purposes, which is currently hindered by significant barriers…(More)”.

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)”.