Paper by Francesca Di Giuseppe, Joe McNorton, Anna Lombardi & Fredrik Wetterhall: “Recent advancements in machine learning (ML) have expanded the potential use across scientific applications, including weather and hazard forecasting. The ability of these methods to extract information from diverse and novel data types enables the transition from forecasting fire weather, to predicting actual fire activity. In this study we demonstrate that this shift is feasible also within an operational context. Traditional methods of fire forecasts tend to over predict high fire danger, particularly in fuel limited biomes, often resulting in false alarms. By using data on fuel characteristics, ignitions and observed fire activity, data-driven predictions reduce the false-alarm rate of high-danger forecasts, enhancing their accuracy. This is made possible by high quality global datasets of fuel evolution and fire detection. We find that the quality of input data is more important when improving forecasts than the complexity of the ML architecture. While the focus on ML advancements is often justified, our findings highlight the importance of investing in high-quality data and, where necessary create it through physical models. Neglecting this aspect would undermine the potential gains from ML-based approaches, emphasizing that data quality is essential to achieve meaningful progress in fire activity forecasting…(More)”.
LLM Social Simulations Are a Promising Research Method
Paper by Jacy Reese Anthis et al: “Accurate and verifiable large language model (LLM) simulations of human research subjects promise an accessible data source for understanding human behavior and training new AI systems. However, results to date have been limited, and few social scientists have adopted these methods. In this position paper, we argue that the promise of LLM social simulations can be achieved by addressing five tractable challenges. We ground our argument in a literature survey of empirical comparisons between LLMs and human research subjects, commentaries on the topic, and related work. We identify promising directions with prompting, fine-tuning, and complementary methods. We believe that LLM social simulations can already be used for exploratory research, such as pilot experiments for psychology, economics, sociology, and marketing. More widespread use may soon be possible with rapidly advancing LLM capabilities, and researchers should prioritize developing conceptual models and evaluations that can be iteratively deployed and refined at pace with ongoing AI advances…(More)”.
AI Liability Along the Value Chain
Report by Beatriz Botero Arcila: “…explores how liability law can help solve the “problem of many hands” in AI: that is, determining who is responsible for harm that has been dealt in a value chain in which a variety of different companies and actors might be contributing to the development of any given AI system. This is aggravated by the fact that AI systems are both opaque and technically complex, making their behavior hard to predict.
Why AI Liability Matters
To find meaningful solutions to this problem, different kinds of experts have to come together. This resource is designed for a wide audience, but we indicate how specific audiences can best make use of different sections, overviews, and case studies.
Specifically, the report:
- Proposes a 3-step analysis to consider how liability should be allocated along the value chain: 1) The choice of liability regime, 2) how liability should be shared amongst actors along the value chain and 3) whether and how information asymmetries will be addressed.
- Argues that where ex-ante AI regulation is already in place, policymakers should consider how liability rules will interact with these rules.
- Proposes a baseline liability regime where actors along the AI value chain share responsibility if fault can be demonstrated, paired with measures to alleviate or shift the burden of proof and to enable better access to evidence — which would incentivize companies to act with sufficient care and address information asymmetries between claimants and companies.
- Argues that in some cases, courts and regulators should extend a stricter regime, such as product liability or strict liability.
- Analyzes liability rules in the EU based on this framework…(More)”.
How crawlers impact the operations of the Wikimedia projects
Article by the Wikimedia Foundation: “Since the beginning of 2024, the demand for the content created by the Wikimedia volunteer community – especially for the 144 million images, videos, and other files on Wikimedia Commons – has grown significantly. In this post, we’ll discuss the reasons for this trend and its impact.
The Wikimedia projects are the largest collection of open knowledge in the world. Our sites are an invaluable destination for humans searching for information, and for all kinds of businesses that access our content automatically as a core input to their products. Most notably, the content has been a critical component of search engine results, which in turn has brought users back to our sites. But with the rise of AI, the dynamic is changing: We are observing a significant increase in request volume, with most of this traffic being driven by scraping bots collecting training data for large language models (LLMs) and other use cases. Automated requests for our content have grown exponentially, alongside the broader technology economy, via mechanisms including scraping, APIs, and bulk downloads. This expansion happened largely without sufficient attribution, which is key to drive new users to participate in the movement, and is causing a significant load on the underlying infrastructure that keeps our sites available for everyone.
When Jimmy Carter died in December 2024, his page on English Wikipedia saw more than 2.8 million views over the course of a day. This was relatively high, but manageable. At the same time, quite a few users played a 1.5 hour long video of Carter’s 1980 presidential debate with Ronald Reagan. This caused a surge in the network traffic, doubling its normal rate. As a consequence, for about one hour a small number of Wikimedia’s connections to the Internet filled up entirely, causing slow page load times for some users. The sudden traffic surge alerted our Site Reliability team, who were swiftly able to address this by changing the paths our internet connections go through to reduce the congestion. But still, this should not have caused any issues, as the Foundation is well equipped to handle high traffic spikes during exceptional events. So what happened?…
Since January 2024, we have seen the bandwidth used for downloading multimedia content grow by 50%. This increase is not coming from human readers, but largely from automated programs that scrape the Wikimedia Commons image catalog of openly licensed images to feed images to AI models. Our infrastructure is built to sustain sudden traffic spikes from humans during high-interest events, but the amount of traffic generated by scraper bots is unprecedented and presents growing risks and costs…(More)”.
AI, Innovation and the Public Good: A New Policy Playbook
Paper by Burcu Kilic: “When Chinese start-up DeepSeek released R1 in January 2025, the groundbreaking open-source artificial intelligence (AI) model rocked the tech industry as a more cost-effective alternative to models running on more advanced chips. The launch coincided with industrial policy gaining popularity as a strategic tool for governments aiming to build AI capacity and competitiveness. Once dismissed under neoliberal economic frameworks, industrial policy is making a strong comeback with more governments worldwide embracing it to build digital public infrastructure and foster local AI ecosystems. This paper examines how the national innovation system framework can guide AI industrial policy to foster innovation and reduce reliance on dominant tech companies…(More)”.
Oxford Intersections: AI in Society
Series edited by Philipp Hacker: “…provides an interdisciplinary corpus for understanding artificial intelligence (AI) as a global phenomenon that transcends geographical and disciplinary boundaries. Edited by a consortium of experts hailing from diverse academic traditions and regions, the 11 edited and curated sections provide a holistic view of AI’s societal impact. Critically, the work goes beyond the often Eurocentric or U.S.-centric perspectives that dominate the discourse, offering nuanced analyses that encompass the implications of AI for a range of regions of the world. Taken together, the sections of this work seek to move beyond the state of the art in three specific respects. First, they venture decisively beyond existing research efforts to develop a comprehensive account and framework for the rapidly growing importance of AI in virtually all sectors of society. Going beyond a mere mapping exercise, the curated sections assess opportunities, critically discuss risks, and offer solutions to the manifold challenges AI harbors in various societal contexts, from individual labor to global business, law and governance, and interpersonal relationships. Second, the work tackles specific societal and regulatory challenges triggered by the advent of AI and, more specifically, large generative AI models and foundation models, such as ChatGPT or GPT-4, which have so far received limited attention in the literature, particularly in monographs or edited volumes. Third, the novelty of the project is underscored by its decidedly interdisciplinary perspective: each section, whether covering Conflict; Culture, Art, and Knowledge Work; Relationships; or Personhood—among others—will draw on various strands of knowledge and research, crossing disciplinary boundaries and uniting perspectives most appropriate for the context at hand…(More)”.
Robotics for Global development
Report by the Frontier Tech Hub: “Robotics could enable progress on 46% of SDG targets yet this potential remains largely untapped in low and middle-income countries.
While technological developments and new-found applications of artificial intelligence (AI) keep captivating significant attention and investments, using robotics to advance the Sustainable Development Goals (SDGs) is consistently overlooked. This is especially true when the focus moves from aerial robotics (drones) to robotic arms, ground robotics, and aquatic robotics. How might these types of robots accelerate global development in the least developed countries?
We aim to answer this question and inform the UK Foreign, Commonwealth & Development Office’s (FCDO) investment and policy towards robotics in the least developed countries (LDCs). In an emergent space, the UK FCDO has a unique opportunity to position itself as a global leader in leveraging robotics technology to accelerate sustainable development outcomes…(More)”.
Cloze Encounters: The Impact of Pirated Data Access on LLM Performance
Paper by Stella Jia & Abhishek Nagaraj: “Large Language Models (LLMs) have demonstrated remarkable capabilities in text generation, but their performance may be influenced by the datasets on which they are trained, including potentially unauthorized or pirated content. We investigate the extent to which data access through pirated books influences LLM responses. We test the performance of leading foundation models (GPT, Claude, Llama, and Gemini) on a set of books that were and were not included in the Books3 dataset, which contains full-text pirated books and could be used for LLM training. We assess book-level performance using the “name cloze” word-prediction task. To examine the causal effect of Books3 inclusion we employ an instrumental variables strategy that exploits the pattern of book publication years in the Books3 dataset. In our sample of 12,916 books, we find significant improvements in LLM name cloze accuracy on books available within the Books3 dataset compared to those not present in these data. These effects are more pronounced for less popular books as compared to more popular books and vary across leading models. These findings have crucial implications for the economics of digitization, copyright policy, and the design and training of AI systems…(More)”.
Bubble Trouble
Article by Bryan McMahon: “…Venture capital (VC) funds, drunk on a decade of “growth at all costs,” have poured about $200 billion into generative AI. Making matters worse, the stock market’s bull run is deeply dependent on the growth of the Big Tech companies fueling the AI bubble. In 2023, 71 percent of the total gains in the S&P 500 were attributable to the “Magnificent Seven”—Apple, Nvidia, Tesla, Alphabet, Meta, Amazon, and Microsoft—all of which are among the biggest spenders on AI. Just four—Microsoft, Alphabet, Amazon, and Meta—combined for $246 billion of capital expenditure in 2024 to support the AI build-out. Goldman Sachs expects Big Tech to spend over $1 trillion on chips and data centers to power AI over the next five years. Yet OpenAI, the current market leader, expects to lose $5 billion this year, and its annual losses to swell to $11 billion by 2026. If the AI bubble bursts, it not only threatens to wipe out VC firms in the Valley but also blow a gaping hole in the public markets and cause an economy-wide meltdown…(More)”.
The Language Data Space (LDS)
European Commission: “… welcomes launch of the Alliance for Language Technologies European Digital Infrastructure Consortium (ALT-EDIC) and the Language Data Space (LDS).
Aimed at addressing the shortage of European language data needed for training large language models, these projects are set to revolutionise multilingual Artificial Intelligence (AI) systems across the EU.
By offering services in all EU languages, the initiatives are designed to break down language barriers, providing better, more accessible solutions for smaller businesses within the EU. This effort not only aims to preserve the EU’s rich cultural and linguistic heritage in the digital age but also strengthens Europe’s quest for tech sovereignty. Formed in February 2024, the ALT-EDIC includes 17 participating Member States and 9 observer Member States and regions, making it one of the pioneering European Digital Infrastructure Consortia.
The LDS, part of the Common European Data Spaces, is crucial for increasing data availability for AI development in Europe. Developed by the Commission and funded by the DIGITAL programme, this project aims to create a cohesive marketplace for language data. This will enhance the collection and sharing of multilingual data to support European large language models. Initially accessible to selected institutions and companies, the project aims to eventually involve all European public and private stakeholders.
Find more information about the Alliance for Language Technologies European Digital Infrastructure Consortium (ALT-EDIC) and the Language Data Space (LDS)…(More)”