Abstract:Automated dialogue systems are important applications of artificial intelligence, and traditional systems struggle to understand user emotions and provide empathetic feedback. This study integrates emotional intelligence technology into automated dialogue systems and creates a dialogue generation model with emotional intelligence through deep learning and natural language processing techniques. The model can detect and understand a wide range of emotions and specific pain signals in real time, enabling the system to provide empathetic interaction. By integrating the results of the study "Can artificial intelligence detect pain and express pain empathy?", the model's ability to understand the subtle elements of pain empathy has been enhanced, setting higher standards for emotional intelligence dialogue systems. The project aims to provide theoretical understanding and practical suggestions to integrate advanced emotional intelligence capabilities into dialogue systems, thereby improving user experience and interaction quality.

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning. This task is very complex, as the behaviour of road agents depends on many factors and the number of possible future trajectories can be considerable (multi-modal). Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpretability. Moreover, the metrics used in current benchmarks do not evaluate all aspects of the problem, such as the diversity and admissibility of the output. In this work, we aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. We focus on evaluation criteria, robustness, and interpretability of outputs. First, we comprehensively analyse the evaluation metrics, identify the main gaps of current benchmarks, and propose a new holistic evaluation framework. We then introduce a method for the assessment of spatial and temporal robustness by simulating noise in the perception system. To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework, we propose an intent prediction layer that can be attached to multi-modal motion prediction models. The effectiveness of this approach is assessed through a survey that explores different elements in the visualization of the multi-modal trajectories and intentions. The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autonomous vehicles, advancing the field towards greater safety and reliability.

Artificial intelligence technology has been widely used in astronomy, and new artificial intelligence technologies and application scenarios are constantly emerging. There have been a large number of papers reviewing the application of artificial intelligence technology in astronomy. However, relevant articles seldom mention telescope intelligence separately, and it is difficult to understand the current development status and research hotspots of telescope intelligence from these papers. This paper combines the development history of artificial intelligence technology and the difficulties of critical technologies of telescopes, comprehensively introduces the development and research hotspots of telescope intelligence, then conducts statistical analysis on various research directions of telescope intelligence and defines the research directions' merits. All kinds of research directions are evaluated, and the research trend of each telescope's intelligence is pointed out. Finally, according to the advantages of artificial intelligence technology and the development trend of telescopes, future research hotspots of telescope intelligence are given.

This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles. As the automotive industry progressively integrates AI, generative artificial intelligence technologies hold the potential to revolutionize user interactions, delivering more immersive, intuitive, and personalised in-car experiences. We provide an overview of current applications of generative artificial intelligence in the automotive domain, emphasizing speech, audio, vision, and multimodal interactions. We subsequently outline critical future research areas, including domain adaptability, alignment, multimodal integration and others, as well as, address the challenges and risks associated with ethics. By fostering collaboration and addressing these research areas, generative artificial intelligence can unlock its full potential, transforming the driving experience and shaping the future of intelligent vehicles.

An Arizona mother detailed a terrifying phone scam that faked her 15-year-old daughter’s voice using artificial intelligence to claim she was kidnapped.

Artificial intelligence allows machines to complete tasks in a very efficient manner. AI is becoming more popular in the world of digital marketing due to its advanced and sophisticated algorithms. Different organizations and marketing teams prefer AI technologies to improve efficiency and the customer experience. In this article, I have collected data about how AI technology can help you optimize your marketing. Artificial intelligence technology helps marketers by automating many time-consuming and repetitive tasks.

Introduction: If you're new to AI, then this might be the perfect guide for you. In this guide, we will be looking at the different types of AI and how they can be used in your business. We will also look at some tips on how to get started with AI in your business. Artificial intelligence is the process of making a computer system that can think and learn like a human. Artificial intelligence technology is used to create programs that can solve problems and act on behalf of humans.

With the increasing awareness of human beings in the pursuit of human health, running in sports has become a fashionable and healthy first choice. The research uses artificial intelligence technology to conduct intelligent analysis when running training, and aims at the use of artificial intelligence technology. Artificial intelligence technology can accurately analyze and predict the application requirements of sports training postures. We proposed a set of sports posture analysis and predict system to design in this paper. It uses the running training record data in the watch heart rate and GPS smart sports watch, and uses Recurrent Neural Network (RNN), Long and short-term memory (LSTM) and Gate recursive unit (GRU) that three types of neural network models to predict whether the road race can be in the conference. And confirm it will be completed within the scheduled closing time, and it will also perform intelligent analysis of physical fitness (heart rate, pace) and running technology (cadence, pace).

More technologically knowledgeable stakeholders and committed leadership within the federal government are critical to successfully implementing scalable artificial intelligence technology in public offices. Speaking during a virtual panel discussion, officials including Jack Shanahan, the inaugural director of the Department of Defense's Joint Artificial Intelligence Center, discussed the need for modernization advocates in federal agencies, specifically needing a "disrupter" helming the implementation of new software. "If this is really the first big AI project designed to go fast and to go to scale, you need almost a classic glass breaker type of person that's just going to plow over all those bureaucratic obstacles," he explained. He added that agency leaders and deputies need to be completely on board with new technology rollouts to help ensure that adequate oversight and accountability is present in artificial intelligence acquisitions. Shanahan also noted that more employees with a technical aptitude are vital.

With the pace artificial intelligence is intertwining within our lives, there is no doubt that it will not end anytime soon. Rather, the future looks like a society that would breathe and thrive through artificial intelligence only. Experts believe that specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life. On the other hand, metaverse already has us wrapped in its not so little fingers. From Facebook to Instagram, virtual reality, Whatsapp, and many more, it is quite predictable that by 2030, its empire would only grow further.