One of only four teams remaining from more than 130 competitors worldwide, our team AURA Foresight is developing autonomous technology to stop wildfires before they grow out of control. AURA Foresight has been selected as a finalist in the prestigious XPRIZE Wildfire Autonomous Wildfire Response competition, emerging as one of just four teams remaining from more than 130 teams from around the world. XPRIZE Wildfire is a four-year, US$11 million global competition designed to accelerate breakthrough technologies capable of ending destructive wildfires. The Autonomous Wildfire Response track, worth US$5 million, challenges teams to autonomously detect, verify and respond to wildfire ignitions across a 1,000 km landscape within just ten minutes. The finals will take place in Nenana, Alaska, where teams will demonstrate their technologies in realistic wildfire response scenarios.

Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications across fields of study, and the geographical spread of scientific research on generative AI. We also investigate team size and international collaborations to explore whether GenAI, as an emerging scientific research area, shows different collaboration patterns compared to other AI technologies. The results indicate that generative AI has experienced rapid growth and increasing presence in scientific publications. The use of GenAI now extends beyond computer science to other scientific research domains. Over the study period, U.S. researchers contributed nearly two-fifths of global GenAI publications. The U.S. is followed by China, with several small and medium-sized advanced economies demonstrating relatively high levels of GenAI deployment in their research publications. Although scientific research overall is becoming increasingly specialized and collaborative, our results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields. Furthermore, notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.

AI is a technology like no other in human history,

On the supercomputers at the National Supercomputer Center at Linköping University, researchers simulate how atoms in different materials behave. Data from such simulations is made available worldwide via the OPTIMADE standard to train future AI models for materials research. From left: Oskar Andersson, doctoral student, and Rickard Armiento, associate professor. Artificial intelligence (AI) is accelerating the development of new materials. A prerequisite for AI in materials research is large-scale use and exchange of data on materials, which is facilitated by a broad international standard.

On an ordinary weekday in November, Bletchley Park plays host to a mixture of elderly pensioners and bands of unruly schoolchildren, visiting to learn about the codebreakers--including computing pioneer Alan Turing--who were based here during World War II, and helped the Allied Forces defeat the Nazis. But this is no ordinary week, and these are no ordinary visitors. On Wednesday and Thursday, delegates from 27 governments around the world, as well as the heads of top artificial intelligence companies, gathered for the world's first AI Safety Summit at this former stately home near London, now a museum. The high-profile event, hosted by the Rishi Sunak-led U.K. government, caps a year of intense escalation in global discussions about AI safety, following the launch of ChatGPT nearly a year ago. The chatbot displayed for the first time--to many users at least--the powerful general capabilities of the latest generation of AI systems.

In the spring of 2023, the UK government set out its plans to address the rapidly evolving AI landscape. In a white paper titled "A pro-innovation approach to AI regulation" the secretary of state for science, innovation and technology described the many benefits and opportunities she believed the technology to hold and explained the government's decision to take a "principles-based approach" to regulating it. In short: the UK didn't plan to create new legislation, instead opting to clarify existing laws that could apply to AI. "New rigid and onerous legislative requirements on businesses could hold back AI innovation and reduce our ability to respond quickly and in a proportionate way to future technological advances," the white paper reads. Between the lines of the government's leaflet, experts say, is a coded message: we want AI companies' business; we're not going to regulate AI right now. In the lead-up to the global AI summit the UK is convening in early November, Rishi Sunak has echoed the desire to strengthen the UK's position as an AI leader, both in terms of innovation and safety oversight.

The spread of the Sars-COV-2 pandemic in 2020 had a huge impact on the life course of all of us. This rapid spread has also caused an increase in the research production in topics related to COVID-19 with regard to different aspects. Italy has, unfortunately, been one of the first countries to be massively involved in the outbreak of the disease. In this paper we present an extensive scientometric analysis of the research production both at global (entire literature produced in the first 2 years after the beginning of the pandemic) and local level (COVID-19 literature produced by authors with an Italian affiliation). Our results showed that US and China are the most active countries in terms of number of publications and that the number of collaborations between institutions varies according to geographical distance. Moreover, we identified the medical-biological as the fields with the greatest growth in terms of literature production. Furthermore, we also better explored the relationship between the number of citations and variables obtained from the data set (e.g. number of authors per article). Using multiple correspondence analysis and quantile regression we shed light on the role of journal topics and impact factor, the type of article, the field of study and how these elements affect citations.

An updated roadmap to focus federal investments in AI research and development (R&D). The National AI R&D Strategic Plan has been updated (for the first time since 2019), and outlines priorities and goals for federal investments in AI R&D. The executive summary of the document notes that: "The federal government must place people and communities at the center by investing in responsible R&D that serves the public good, protects people's rights and safety, and advances democratic values. This update to the National AI R&D Strategic Plan is a roadmap for driving progress toward that goal." The plan reaffirms the eight strategies from the 2019 plan, and adds a ninth.

This week NVIDIA, LXAI, and Tecnológico de Monterrey announced the upcoming launch of the AI Supercomputer Network in collaboration with Hub de IA del Tec de Monterrey. Many countries in Latin America including Mexico, Brazil, Perú, Chile, and Colombia, are developing their national AI strategies but many LATAM universities and research centers struggle to access high-performance computing due to high prices, low government investment in research, and limited international collaborations. This initiative aims to address these challenges by providing an international network of state of the art GPUs for LATAM access. The official launch will be in Guadalajara on August 9th. The objective of this initiative is to strengthen the capacities of the AI ecosystem in Latin America including developing and attracting AI experts, building technological infrastructure, boosting international collaboration, and providing easy access to public data.

New technologies and data sources have tremendous potential to improve statistical production. They offer a way to generate statistics in a more timely, accurate and cost-efficient manner. Yet, keeping up with the pace of change is challenging, especially for National Statistical Organisations (NSOs) that must innovate with care to maintain a "gold standard" in their outputs. International cooperation between NSOs and other official statistical bodies is one way to help accelerate change in a responsible way. In 2021, the Office for National Statistics (ONS) and the United Nations Economic Commission for Europe (UNECE) Machine Learning Group (ML 2021) demonstrated the benefits of international cooperation for technological advance.