As AI advances, could it replace your travel agent? To investigate just how effective a holiday planner AI can be, MailOnline Travel asked two chatbots - ChatGPT, created by California AI firm OpenAI, and Google's Bard - to plan a variety of trips. Scroll down to see the answers the chatbots provided, from hotel recommendations in Iraq to advice on planning budget sun holidays, honeymoons and stag weekends away. For a budget break in the sun, Bard recommended jetting off to Bulgaria, where it says that you can find a week-long all-inclusive holiday'for as little as £200'. MailOnline Travel asked ChatGPT and Google's Bard to plan a variety of holidays.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The French government on Tuesday approved a key budget bill presented as the country's biggest military spending spree in more than 50 years, underscoring the impact of Russia's war in Ukraine. The bill foresees $450 billion in military spending or the period covering 2024-2030 - up by more than a third relative to the previous timeframe. Defense Minister Sébastien Lecornu said bill's political, budgetary, military and technological drive is comparable to the huge push in the 1960s that saw France develop nuclear weapons, making the country one of the world's major military powers.

Transformer networks have revolutionized NLP representation learning since they were introduced. Though a great effort has been made to explain the representation in transformers, it is widely recognized that our understanding is not sufficient. One important reason is that there lack enough visualization tools for detailed analysis. In this paper, we propose to use dictionary learning to open up these "black boxes" as linear superpositions of transformer factors. Through visualization, we demonstrate the hierarchical semantic structures captured by the transformer factors, e.g., word-level polysemy disambiguation, sentence-level pattern formation, and long-range dependency. While some of these patterns confirm the conventional prior linguistic knowledge, the rest are relatively unexpected, which may provide new insights. We hope this visualization tool can bring further knowledge and a better understanding of how transformer networks work. The code is available at https://github.com/zeyuyun1/TransformerVis

Efficient and intuitive Human-Robot interfaces are crucial for expanding the user base of operators and enabling new applications in critical areas such as precision agriculture, automated construction, rehabilitation, and environmental monitoring. In this paper, we investigate the design of human-robot interfaces for the teleoperation of dynamical systems. The proposed framework seeks to find an optimal interface that complies with key concepts such as user comfort, efficiency, continuity, and consistency. As a proof-of-concept, we introduce an innovative approach to teleoperating underwater vehicles, allowing the translation between human body movements into vehicle control commands. This method eliminates the need for divers to work in harsh underwater environments while taking into account comfort and communication constraints. We conducted a study with human subjects using a head-mounted display attached to a smartphone to control a simulated ROV. Also, numerical experiments have demonstrated that the optimal translation is often the most intuitive and natural one, aligning with users' expectations.

Communication and computation are often viewed as separate tasks. This approach is very effective from the perspective of engineering as isolated optimizations can be performed. However, for many computation-oriented applications, the main interest is a function of the local information at the devices, rather than the local information itself. In such scenarios, information theoretical results show that harnessing the interference in a multiple access channel for computation, i.e., over-the-air computation (OAC), can provide a significantly higher achievable computation rate than separating communication and computation tasks. Moreover, the gap between OAC and separation in terms of computation rate increases with more participating nodes. Given this motivation, in this study, we provide a comprehensive survey on practical OAC methods. After outlining fundamentals related to OAC, we discuss the available OAC schemes with their pros and cons. We provide an overview of the enabling mechanisms for achieving reliable computation in the wireless channel. Finally, we summarize the potential applications of OAC and point out some future directions.

Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless cloud's performance is cold-start, which is when the time of provisioning the needed cloud resource to serve customers' requests incurs unacceptable costs to the service providers and/or the customers. This paper proposes a novel low-coupling, high-cohesion ensemble policy that addresses the cold-start problem at infrastructure- and function-levels of the serverless cloud stack, while the state of the art policies have a more narrowed focus. This ensemble policy anchors on the prediction of function instance arrivals, 10 to 15 minutes into the future. It is achievable by using the temporal convolutional network (TCN) deep-learning method. Bench-marking results on a real-world dataset from a large-scale serverless cloud provider show that TCN out-performs other popular machine learning algorithms for time series. Going beyond cold-start management, the proposed policy and publicly available codes can be adopted in solving other cloud problems such as optimizing the provisioning of virtual software-defined network assets.

We consider the problem of dynamic platoon leader selection, user association, channel assignment, and power allocation on a cellular vehicle-to-everything (C-V2X) based highway, where multiple vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) links share the frequency resources. There are multiple roadside units (RSUs) on a highway, and vehicles can form platoons, which has been identified as an advanced use case to increase road efficiency. The traditional optimization methods, requiring global channel information at a central controller, are not viable for high-mobility vehicular networks. To deal with this challenge, we propose a distributed multi-agent reinforcement learning (MARL) for resource allocation (RA). Each platoon leader, acting as an agent, can collaborate with other agents for joint sub-band selection and power allocation for its V2V links, and joint user association and power control for its V2I links. Moreover, each platoon can dynamically select the vehicle most suitable to be the platoon leader. We aim to maximize the V2V and V2I packet delivery probability in the desired latency using the deep Q-learning algorithm. Simulation results indicate that our proposed MARL outperforms the centralized hill-climbing algorithm, and platoon leader selection helps to improve both V2V and V2I performance.

It started in the Caribbean Sea. Jaime Ascencio, then a business development engineer working across Latin America, was eager to find sustainable ways to combat the coastal erosion that was eating away at the region's treasured beaches--and threatening the tourism dollars brought in by its seaside resorts. "If there is no sand, there are no guests," he says. But Ascensio, who knew that artificial reefs could make for natural breakwaters, could only find solutions that were neither sustainable nor stable enough to resist the force of the waves. So he went on to get a master's in coastal engineering at the celebrated Delft University of Technology in the Netherlands--and developed one himself.

Recent image generation models such as Stable Diffusion have exhibited an impressive ability to generate fairly realistic images starting from a simple text prompt. Could such models render real images obsolete for training image prediction models? In this paper, we answer part of this provocative question by investigating the need for real images when training models for ImageNet classification. Provided only with the class names that have been used to build the dataset, we explore the ability of Stable Diffusion to generate synthetic clones of ImageNet and measure how useful these are for training classification models from scratch. We show that with minimal and class-agnostic prompt engineering, ImageNet clones are able to close a large part of the gap between models produced by synthetic images and models trained with real images, for the several standard classification benchmarks that we consider in this study. More importantly, we show that models trained on synthetic images exhibit strong generalization properties and perform on par with models trained on real data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd/

Nikki Haley, presidential candidate and former U.S. ambassador to the U.N., weighs in after President Biden authorized an air strike in response to an Iranian drone that killed an American. Republican presidential candidate Nikki Haley called on the Biden administration to get tough on a slew of foreign adversaries or risk war after an American citizen was killed in an Iranian drone strike in Syria. "It shows what happens when there's American weakness," Haley said Friday of the attack on "America's Newsroom." "Whether it's in Afghanistan, whether you see it in Ukraine, whether you see it on the southern border, you're going to continue to see more of these things happen." "There is no deterrence," she continued.