Stefano Bianchini’s research while affiliated with Bureau for Economic Theory and Applications and other places

The migration of artificial intelligence (AI) researchers from academia to industry has recently sparked concerns about its implications for scientific progress. Can academia retain enough talent to shape AI advancements and counterbalance the growing influence of corporate AI labs? Analyzing OpenAlex data, we find a significant transition of premier talent to industry roles over the past decade, particularly to major tech firms. Young, highly cited scholars from leading institutions are the most likely to make this move. Following the transition, their research tends to show reduced novelty and impact. This industry-dominant shift in AI research highlights worries of an “AI brain drain”, the sidelining of exploratory science for commercial interests, and the potential misalignment with societal goals.

Artificial intelligence (AI) is widely regarded as one of the most promising technologies for advancing science, fostering innovation, and solving global challenges. Recent years have seen a push for teamwork between experts from different fields and AI specialists, but the outcomes of these collaborations have yet to be studied. We focus on approximately 15,000 papers at the intersection of AI and COVID-19 – arguably one of the major challenges of recent decades – and show that interdisciplinary collaborations between medical professionals and AI specialists have largely resulted in publications with low visibility and impact. Our findings suggest that impactful research depends less on the overall interdisciplinary of author teams and more on the diversity of knowledge they actually harness in their research. We conclude that team composition significantly influences the successful integration of new computational technologies into science and that obstacles still exist to effective interdisciplinary collaborations in the realm of AI. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00329

This study reveals both the spatial and the combinatory patterns of the digital and green scientific knowledge bases for the creation of new knowledge in the domain of the so-called ‘twin transition’. The recent and rapid diffusion of this twin scientific knowledge in the European regions has not adhered to clearly defined spatial patterns and has been characterised by a dynamic process of actor reconfiguration. However, regions with a strong green and digital science base have a greater propensity to produce more, better quality and more visible twin knowledge. Among the most prevalent twin knowledge fields emerging today are artificial intelligence (AI)- and Internet of Things (IoT)-powered applications for energy storage, distribution and consumption; environmental monitoring and modelling; and urban planning.

New technologies have the power to revolutionize science. It has happened in the past and is happening again with the emergence of new computational tools, such as Artificial Intelligence (AI) and Machine Learning (ML). Despite the documented impact of these technologies, there remains a significant gap in understanding the process of their adoption within the scientific community. In this paper, we draw on theories of scientific and technical human capital (STHC) to study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions. We validate our hypotheses on a large sample of publications from OpenAlex, covering all sciences from 1980 to 2020. We find that the diffusion of AI is strongly driven by social mechanisms that organize the deployment and creation of human capital that complements the technology. Our results suggest that AI is pioneered by domain scientists with a `taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers; they also come from institutions with high citation impact and a relatively strong publication history on AI. The pattern is similar across scientific disciplines, the exception being access to high-performance computing (HPC), which is important in chemistry and the medical sciences but less so in other fields. Once AI is integrated into research, most adoption factors continue to influence its subsequent reuse. Implications for the organization and management of science in the evolving era of AI-driven discovery are discussed.

In 2015, the UN General Assembly adopted the “2030 Agenda for Sustainable Development” containing 17 Sustainable Development Goals (SDGs) to achieve a sustainable, fair, and inclusive future for people worldwide by 2030. With less than 10 years left, Science, technology, and innovation (STI) are among the key enablers for the achievement of the 2030 Agenda’s ambitions. This report provides first evidence of the emergence of scientific research that jointly studies SDGs and Digital Technologies (DTs). The combination of SDGs and DTs in scientific research is a recent development that has surfaced over the past ten years and is rapidly expanding. The growth is driven by scientific advancements in research jointly addressing SDG7 (Affordable and clean energy) and Internet of Things, and SDG13 (Climate change) and artificial intelligence. While China and the United States are the major players in scientific research in these domains, the European Union as a whole produces more scientific publications than any single country, including China and the United States, in the SDGs and SDGS-DTs domains, while China leads with regards to DTs. Yet, fully realizing the potential of EU scientific research for sustainable development requires improvements in the integration of national research systems enabling the exploitation of scale and scope economies that Member States cannot achieve in isolation.

The Sustainable Development Goals (SDGs) set ambitious targets to be met by 2030. With less than10 years left, technology might be crucial to close the sustainability gap. - This brief provides evidence on the scientific knowledge related to SDGs, Digital Technologies (DTs), and their intersection. - China and the United States are major players in scientific research in the selected domains. Yet, EU countries together produce more scientific publications than any single country. - Unlike other DTs, Artificial Intelligence (AI) finds applications in almost all SDGs. - NLP analyses show that AI is positively associated with most SDGs, although this does not apply to SDG5 (Gender Equality), SDG10 (Reduced Inequality) and SDG16 (Peace, Justice, and Strong Institutions). The latter is the most negatively affected by digital technologies, particularly blockchain and computing infrastructure.

Artificial intelligence (AI) has emerged as one of the most promising technologies to support COVID-19 research, with interdisciplinary collaborations between medical professionals and AI specialists being actively encouraged since the early stages of the pandemic. Yet, our analysis of more than 10,000 papers at the intersection of COVID-19 and AI suggest that these collaborations have largely resulted in science of low visibility and impact. We show that scientific impact was not determined by the overall interdisciplinarity of author teams, but rather by the diversity of knowledge they actually harnessed in their research. Our results provide insights into the ways in which team and knowledge structure may influence the successful integration of new computational technologies in the sciences.

This study explores the nexus between digital and green transformations—the so-called “twin” transition—in European regions in an effort to identify the impact of digital and environmental technologies on the greenhouse gas (GHG) emissions originating from industrial production. We conduct an empirical analysis based on an original dataset that combines information on environmental and digital patent applications with information on GHG emissions from highly polluting plants for the period 2007–2016 at the metropolitan region level in the European Union and the UK. Results show that the local development of environmental technologies reduces GHG emissions, while the local development of digital technologies increases them, albeit in the latter case different technologies seem to have different impacts on the environment, with big data and computing infrastructures being the most detrimental. We also find differential impacts across regions depending on local endowment levels of the respective technologies: the beneficial effect of environmental technologies is stronger in regions with large digital technology endowments and, conversely, the detrimental effect of digital technologies is weaker in regions with large green technology endowments. Policy actions promoting the “twin” transition should take this evidence into account, in light of the potential downside of the digital transformation when not combined with the green transformation.

This paper offers insights into the diffusion and impact of artificial intelligence in science. More specifically, we show that neural network-based technology meets the essential properties of emerging technologies in the scientific realm. It is novel, because it shows discontinuous innovations in the originating domain and is put to new uses in many application domains; it is quick growing, its dimensions being subject to rapid change; it is coherent, because it detaches from its technological parents, and integrates and is accepted in different scientific communities; and it has a prominent impact on scientific discovery, but a high degree of uncertainty and ambiguity associated with this impact. Our findings suggest that intelligent machines diffuse in the sciences, reshape the nature of the discovery process and affect the organization of science. We propose a new conceptual framework that considers artificial intelligence as an emerging general method of invention and, on this basis, derive its policy implications.