Britons are concerned about AI's impact on the economy and jobs in particular. Britons are concerned about AI's impact on the economy and jobs in particular. TBI says poll data threatens Keir Starmer's ambition for UK to become artificial intelligence'superpower' The Tony Blair Institute warned that the poll findings threatened Keir Starmer's ambition for the UK to become an AI "superpower" and urged the government to convince the public of the technology's benefits. TBI commissioned a survey that found 38% of Britons see AI as an economic risk while 20% see it as an opportunity. The poll of more than 3,700 adults also showed that lack of trust was the biggest barrier to adoption.

In the current context of accelerated globalization and digitalization, the complexity and uncertainty of financial markets are increasing, and the identification and prevention of economic risks have become a key link in maintaining the stability of the financial system. Traditional risk identification methods often have limitations because they are difficult to cope with the multi-level and dynamically changing complex relationships in financial networks. With the rapid development of financial technology, graph neural network (GNN) technology, as an emerging deep learning method, has gradually shown great potential in the field of financial risk management. GNN can map transaction behaviors, financial institutions, individuals, and their interactive relationships in financial networks into graph structures, and effectively capture potential patterns and abnormal signals in financial data through embedded representation learning. Using this technology, financial institutions can extract valuable information from complex transaction networks, identify hidden dangers or abnormal behaviors that may cause systemic risks in a timely manner, optimize decision-making processes, and improve the accuracy of risk warnings. This paper explores the economic risk identification algorithm based on the GNN algorithm, aiming to provide financial institutions and regulators with more intelligent technical tools to help maintain the security and stability of the financial market. Improving the efficiency of economic risk identification through innovative technical means is expected to further enhance the risk resistance of the financial system and lay the foundation for building a robust global financial system.

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator's profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to $47\%$. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.

There is a large and growing environmental problem that is not on this Earth--it's trash orbiting our planet in space. Space debris is a not only an economic risk that can affect every day modern living, but also it is an existential risk that jeopardizes the ability for scientists to research weather and climate changes that impact all living things on Earth. In early February 2019, researchers from Stanford University's Space Rendezvous Laboratory (SLAB) announced a plan to crowdsource artificial intelligence (AI) to solve the massive problem of orbiting space debris. Simone D'Amico, founder and director of the Stanford's Space Rendezvous Lab, is partnering with the European Space Agency (ESA) to create an AI system that will provide navigational guidance to a space "tow truck" in order to identify, fix or remove defunct, orbiting satellites that are above the atmosphere, yet doomed to orbit due to the gravitational pull of the Earth. How can artificial intelligence solve the problem of space debris?