For corporate America, the biggest trend to latch onto at the moment is artificial intelligence, stoked by the popularity of ChatGPT. But worries about the dangers of widespread A.I. use are growing as well. There's one big hitch: Governments -- notably Washington -- haven't kept pace with regulations for the technology. That could lead to dire consequences: "By failing to establish such guardrails, policymakers are creating the conditions for a race to the bottom in irresponsible A.I.," Carly Kind, the director of the Ada Lovelace Institute, a policy research group, told The Times. Washington has been largely hands off on A.I. rules, even as several lawmakers have pushed to tighten oversight.

From Cornwall and Wales to Essex, Blackpool and the Norfolk coast, Britain has experienced a flurry of earthquakes in the past month. The biggest – a 3.8 magnitude tremor that struck Wales on February 24 – sparked panic as locals reported their beds started to move and walls shook. One resident in the small Welsh town of Abertillery not far from the epicentre said the quake was so noticeable'it felt like the roof was falling off'. The Welsh quake was preceded by several more including a 1.5 magnitude quake in Cornwall and a 3.8 magnitude event off the coast of Great Yarmouth. Here's all you need to know about the British tremors – including whether recent tectonic activity suggests a'big one' is soon to hit parts of the country.

The upcoming coronation of King Charles III is the most hotly anticipated event of this year - so MailOnline asked an AI bot to provide a sneak peak... and the results of a mix of suprising and scary. These strange visuals give a glimpse of how scenes may play out at Westminster Abbey on Saturday, May 6 and how the nation might rally to watch the historic event unfold - Britain's first coronation since 1953. They stare through the gloom and grey hair, gawping at pub screens showing the King being ferried to Westminster Abbey in a golden articulated lorry. The coronation is the event of the year. The world is watching, though perhaps wondering, what on earth is going on?

Singapore's public service has never been afraid of innovation. Therefore, the move to integrate OpenAI's Chat Generative Pre-Trained Transformer -- ChatGPT -- into Microsoft Word for Government users, with the aim being to speed up work and free officers for higher level tasks is unsurprising. Those involved (not least GovTech's Open Government Products team) should be commended for their vision. The potential gains are evident. Enterprises of all sizes increasingly rely on tools that can automate mundane tasks, improve communications, and speed up customer support.

The head of Russia's Wagner Group said the eastern Ukrainian city of Bakhmut has been almost completely surrounded with only one road still open for Ukraine's soldiers. The commander of a Ukrainian drone unit active in Bakhmut said in a video that his unit had been ordered to withdraw immediately from the city. The United States announced a new military aid package for Ukraine worth $400m. It is primarily comprised of ammunition, but for the first time will include tactical bridges to move tanks and armoured vehicles. Russia said it would take measures to prevent new border incursions and President Vladimir Putin told his Security Council to discuss additional "anti-terrorism measures" after what he called a "terrorist attack" in the Bryansk region bordering Ukraine.

Exceptionally proud to be involved in this exciting programme. Ibex Medical Analytics have won our 2nd Artificial Intelligence in Health and Care Award. In collaboration with University of Nottingham & 5 NHS Trusts our Breast AI will be deployed to measure improvements in the quality of diagnosis, cost-effectiveness & turnaround times for patients.

Achieving and maintaining cooperation between agents to accomplish a common objective is one of the central goals of Multi-Agent Reinforcement Learning (MARL). Nevertheless in many real-world scenarios, separately trained and specialized agents are deployed into a shared environment, or the environment requires multiple objectives to be achieved by different coexisting parties. These variations among specialties and objectives are likely to cause mixed motives that eventually result in a social dilemma where all the parties are at a loss. In order to resolve this issue, we propose the Incentive Q-Flow (IQ-Flow) algorithm, which modifies the system's reward setup with an incentive regulator agent such that the cooperative policy also corresponds to the self-interested policy for the agents. Unlike the existing methods that learn to incentivize self-interested agents, IQ-Flow does not make any assumptions about agents' policies or learning algorithms, which enables the generalization of the developed framework to a wider array of applications. IQ-Flow performs an offline evaluation of the optimality of the learned policies using the data provided by other agents to determine cooperative and self-interested policies. Next, IQ-Flow uses meta-gradient learning to estimate how policy evaluation changes according to given incentives and modifies the incentive such that the greedy policy for cooperative objective and self-interested objective yield the same actions. We present the operational characteristics of IQ-Flow in Iterated Matrix Games. We demonstrate that IQ-Flow outperforms the state-of-the-art incentive design algorithm in Escape Room and 2-Player Cleanup environments. We further demonstrate that the pretrained IQ-Flow mechanism significantly outperforms the performance of the shared reward setup in the 2-Player Cleanup environment.

Shape change enables new capabilities for robots. One class of robots capable of dramatic shape change is soft growing "vine" robots. These robots usually feature global actuation methods for bending that limit them to simple, constant-curvature shapes. Achieving more complex "multi-bend" configurations has also been explored but requires choosing the desired configuration ahead of time, exploiting contact with the environment to maintain previous bends, or using pneumatic actuation for shape locking. In this paper, we present a novel design that enables passive, on-demand shape locking. Our design leverages a passive tip mount to apply hook-and-loop fasteners that hold bends without any pneumatic or electrical input. We characterize the robot's kinematics and ability to hold locked bends. We also experimentally evaluate the effect of hook-and-loop fasteners on beam and joint stiffness. Finally, we demonstrate our proof-of-concept prototype in 2D. Our passive shape locking design is a step towards easily reconfigurable robots that are lightweight, low-cost, and low-power.

With recent trends indicating cyber crimes increasing in both frequency and cost, it is imperative to develop new methods that leverage data-rich hacker forums to assist in combating ever evolving cyber threats. Defining interactions within these forums is critical as it facilitates identifying highly skilled users, which can improve prediction of novel threats and future cyber attacks. We propose a method called Next Paragraph Prediction with Instructional Prompting (NPP-IP) to predict thread structures while grounded on the context around posts. This is the first time to apply an instructional prompting approach to the cybersecurity domain. We evaluate our NPP-IP with the Reddit dataset and Hacker Forums dataset that has posts and thread structures of real hacker forums' threads, and compare our method's performance with existing methods. The experimental evaluation shows that our proposed method can predict the thread structure significantly better than existing methods allowing for better social network prediction based on forum interactions.

A novel method, named Curvature-Augmented Manifold Embedding and Learning (CAMEL), is proposed for high dimensional data classification, dimension reduction, and visualization. CAMEL utilizes a topology metric defined on the Riemannian manifold, and a unique Riemannian metric for both distance and curvature to enhance its expressibility. The method also employs a smooth partition of unity operator on the Riemannian manifold to convert localized orthogonal projection to global embedding, which captures both the overall topological structure and local similarity simultaneously. The local orthogonal vectors provide a physical interpretation of the significant characteristics of clusters. Therefore, CAMEL not only provides a low-dimensional embedding but also interprets the physics behind this embedding. CAMEL has been evaluated on various benchmark datasets and has shown to outperform state-of-the-art methods, especially for high-dimensional datasets. The method's distinct benefits are its high expressibility, interpretability, and scalability. The paper provides a detailed discussion on Riemannian distance and curvature metrics, physical interpretability, hyperparameter effect, manifold stability, and computational efficiency for a holistic understanding of CAMEL. Finally, the paper presents the limitations and future work of CAMEL along with key conclusions.