Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversational agents, making them applicable to various fields (e.g., education). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as real-time interactions, multi-party dialogues, and extended contextual dependencies. To bridge this gap, we introduce DialSim, a real-time dialogue simulator. In this simulator, an agent is assigned the role of a character from popular TV shows, requiring it to respond to spontaneous questions using past dialogue information and to distinguish between known and unknown information. Key features of DialSim include evaluating the agent's ability to respond within a reasonable time limit, handling long-term multi-party dialogues, and managing adversarial settings (e.g., swap character names) to challenge the agent's reliance on pre-trained knowledge. We utilized this simulator to evaluate the latest conversational agents and analyze their limitations. Our experiments highlight both the strengths and weaknesses of these agents, providing valuable insights for future improvements in the field of conversational AI.

Never mind that it can pass the bar exam, ace medical tests and read bedtime stories with emotion, artificial intelligence will never match the marvel of the human mind without first mastering the art of sarcasm. But that art, it seems, may be next on the list of the technology's dizzying capabilities. Researchers in the Netherlands have built an AI-driven sarcasm detector that can spot when the lowest form of wit, and the highest form of intelligence, is being deployed. "We are able to recognise sarcasm in a reliable way, and we're eager to grow that," said Matt Coler at the University of Groningen's speech technology lab. "We want to see how far we can push it."

Magnetic Soft Catheters (MSCs) are capable of miniaturization due to the use of an external magnetic field for actuation. Through careful design of the magnetic elements within the MSC and the external magnetic field, the shape along the full length of the catheter can be precisely controlled. However, modeling of the magnetic-soft material is challenging due to the complex relationship between magnetic and elastic stresses within the material. Approaches based on traditional Finite Element Methods (FEM) lead to high computation time and rely on proprietary implementations. In this work, we showcase the use of our recently presented open-source simulation framework based on the Material Point Method (MPM) for the computational design of magnetic soft catheters to realize arbitrary shapes in 3D, and to facilitate follow-the-leader shape-forming insertion.

'The Big Sunday Show' panelists discuss how artificial intelligence could turn your pet's thoughts into reality. An estimated 55 dogs were rescued from an Arizona woman's home for special needs dogs after they were discovered to be living in filthy conditions, as well as those reportedly found dead in a freezer. Police in Chandler responded to April Mclaughlin's home on Friday and found dozens of dogs living in squalor with no water. Mclaughlin had been running a shelter for special needs dogs, but the reality had spiraled into such filthy conditions that firefighters had to wear special equipment to stand breathing in the home, according to AZ Family. Officials began investigating on Sept. 8 after a vet reached out to police that some of Mclaughlin's dogs were not in healthy conditions.

Last month, Sam Altman, the CEO of OpenAI and face of the artificial intelligence boom, sat in front of members of Congress urging them to regulate artificial intelligence (AI). As lawmakers on the Senate judiciary subcommittee asked the 38-year-old tech mogul about the nature of his business, Altman argued that the AI industry could be dangerous and that the government needs to step in. "I think if this technology goes wrong, it can go quite wrong," Altman said. "We want to be vocal about that." How governments should regulate artificial intelligence is a topic of increasing urgency in countries around the world, as advancements reach the general public and threaten to upend entire industries.

Can you guess the parents? A digital artist has used Artificial Intelligence to merge the faces of two TV celebrities to create lifelike images of their possible children. The images are a result of the imagination of Brazilian digital artist Hidreley Leli Dião. The artist created these super-realistic creations imagining what the love children of two TV stars would have looked like if their romance in the TV shows or films they starred in progressed. So, MailOnline asks, can you work out the parents that these children belong to? Some are harder than others - but we've started you off pretty soft.

Waymo, the robotaxi provider of Google's parent company Alphabet, said Wednesday that it will expand its ridehail service to Los Angeles. Waymo declined to say when fully autonomous car rides will be available to the public in the country's second largest metropolitan area. Waymo will begin with approximately a dozen vehicles in the coming months to lay the groundwork for operating a ridehail service by mapping the neighborhoods of Miracle Mile, Koreatown, Santa Monica, Westwood and West Hollywood. Mapping an area is a critical early step to operating Waymo's robotaxis, which rely on detailed maps, in addition to sensors, to help them navigate their surroundings. It offers robotaxi rides to the public in Chandler, Arizona and to its employees in San Francisco.

The rides are free and currently only available to Waymo employees. The Alphabet Inc. unit since 2017 has been operating autonomous cars in suburban Phoenix, which is a much less challenging traffic environment. In 2020, it opened rides up to the public in Chandler, Ariz.--which has a population of less than 300,000--and took away drivers. Waymo's latest move comes nearly two months after its biggest competitor, Cruise, a subsidiary of General Motors Co., started offering driverless rides to the public in San Francisco. The rides are also free and only available at night, from 10:30 p.m. to 5 a.m.

Video question answering (VideoQA) is designed to answer a given question based on a relevant video clip. The current available large-scale datasets have made it possible to formulate VideoQA as the joint understanding of visual and language information. However, this training procedure is costly and still less competent with human performance. In this paper, we investigate a transfer learning method by the introduction of domain-agnostic knowledge and domain-specific knowledge. First, we develop a novel transfer learning framework, which finetunes the pre-trained model by applying domain-agnostic knowledge as the medium. Second, we construct a new VideoQA dataset with 21,412 human-generated question-answer samples for comparable transfer of knowledge. Our experiments show that: (i) domain-agnostic knowledge is transferable and (ii) our proposed transfer learning framework can boost VideoQA performance effectively.

Mental health is a lot like a junk drawer to me,