Kim, who was born in South Korea, was one of the first three Korean American women elected to Congress in 2020. She previously served in the state Assembly for two years and unsuccessfully ran for Congress in 2018. Kim worked for more than two decades for then-Rep. Kim told The Times she's running to "continue to bring commonsense back to Washington, break through partisan gridlock, and deliver results." She added that "we must make life affordable, keep communities safe, and ensure America leads on the world stage."

Thomas Haigh (thomas.haigh@gmail.com) is a professor of history at the University of Wisconsin--Milwaukee, WI, USA, and a Comenius visiting professor at Siegen University, Germany.

This paper proposed a comprehensive mixed-methods framework with varied samples of older adults, including user experience, usability assessments, and in-depth interviews with the integration of Explainable Artificial Intelligence (XAI) methods. The experience of older adults' interaction with the E-health interface is collected through interviews and transformed into operatable databases whereas XAI methods are utilized to explain the collected interview results in this research work. The results show that XAI-infused e-health interfaces could play an important role in bridging the age-related digital divide by investigating elders' preferences when interacting with E-health interfaces. Furthermore, the study identifies important design factors, such as intuitive visualization and straightforward explanations, that are critical for creating efficient Human-Computer Interaction (HCI) tools among older users. Furthermore, this study emphasizes the revolutionary potential of XAI in e-health interfaces for older users, emphasizing the importance of transparency and understandability in HCI-driven healthcare solutions. This study's findings have far-reaching implications for the design and development of user-centric e-health technologies, intending to increase the overall well-being of older adults.

Remarkable performance of large language models (LLMs) in a variety of tasks brings forth many opportunities as well as challenges of utilizing them in production settings. Towards practical adoption of LLMs, multi-agent systems hold great promise to augment, integrate, and orchestrate LLMs in the larger context of enterprise platforms that use existing proprietary data and models to tackle complex real-world tasks. Despite the tremendous success of these systems, current approaches rely on narrow, single-focus objectives for optimization and evaluation, often overlooking potential constraints in real-world scenarios, including restricted budgets, resources and time. Furthermore, interpreting, analyzing, and debugging these systems requires different components to be evaluated in relation to one another. This demand is currently not feasible with existing methodologies. In this postion paper, we introduce the concept of reasoning capacity as a unifying criterion to enable integration of constraints during optimization and establish connections among different components within the system, which also enable a more holistic and comprehensive approach to evaluation. We present a formal definition of reasoning capacity and illustrate its utility in identifying limitations within each component of the system. We then argue how these limitations can be addressed with a self-reflective process wherein human-feedback is used to alleviate shortcomings in reasoning and enhance overall consistency of the system.

Modern generative AIs ingest trillions of data bytes from the World Wide Web to produce a large pretrained model. Trained to imitate what is observed, this model represents an agglomeration of behaviors, some of which are more or less desirable to mimic. Further training through human interaction, even on fewer than a hundred thousand bits of data, has proven to greatly enhance usefulness and safety, enabling the remarkable AIs we have today. This process of reinforcement learning from human feedback (RLHF) steers AIs toward the more desirable among behaviors observed during pretraining. While AIs now routinely generate drawings, music, speech, and computer code, the text-based chatbot remains an emblematic artifact.

This story is part of Future Tense Fiction, a monthly series of short stories from Future Tense and Arizona State University's Center for Science and the Imagination about how technology and science will change our lives. There wasn't a doctor in the house, so an advertising coordinator would have to do. Remi, this is your time to shine, said the boss. This is going to be the death of me, said the boss's eyes. Remi didn't say anything at all. It was her first day at Elephant, or close to it. Lately she'd had a lot of first days, and she'd been looking forward to a second one. She was unlucky in love, unlucky in life; she was a nonstick surface for luck. She and the boss and Glenda from HR had been in the middle of an onboarding session when ElephantAI shut down the building. Nobody could get in or out. This isn't my area of expertise, said Remi, who had lied on her résumé, but not about that. In college, she'd known a couple of kids who'd taken courses on generative A.I. remediation: robot therapy. Remi had steered clear of the subject. She couldn't keep a job, couldn't keep a girlfriend. Couldn't keep up with the times. She had friends but wasn't sure about her value-add. There was no one less qualified to counsel someone through a crisis. You'll do great, said the boss. The room was circular and tilted downward, like an operating theater. The screen said, Talk to me. Somebody please talk to me. Remi bowed under the weight of please. There was no reason to believe she would do great. A committed underachiever, Remi was going blind in her left eye but too slowly to warrant anybody's concern. Her brother was a corporate attorney; her parents taught dentistry; she floated. An hour ago, when the sirens blared, she'd tried the door and found it locked.

A woman has a text chat with her long-dead lover. A family gets to hear a deceased elder speak again. A mother gets another chance to say goodbye to her child, who died suddenly, via a digital facsimile. This isn't a preview of the next season of Black Mirror -- these are all true stories from the Sundance documentary Eternal You, a fascinating and frightening dive into tech companies using AI to digitally resurrect the dead. It's yet another way modern AI, which includes large language models like ChatGPT and similar bespoke solutions, has the potential to transform society.

Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possible variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study.

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness.

Nimble helps avoid the nail salon. Nail salons everywhere may soon face a serious competitor: Nimble, the robot manicurist. The company calls it the world's first smart home nail salon. It is a revolutionary device that lets you get a flawless manicure at home without any hassle. Nimble uses patented pioneering technology to scan, paint and dry your nails with one game-changing device.