Apart from what (little) OpenAI may be concealing from us, we all know (roughly) how ChatGPT works (its huge text database, its statistics, its vector representations, and their huge number of parameters, its next-word training, and so on). But none of us can say (hand on heart) that we are not surprised by what ChatGPT has proved to be able to do with these resources. This has even driven some of us to conclude that ChatGPT actually understands. It is not true that it understands. But it is also not true that we understand how it can do what it can do. I will suggest some hunches about benign biases: convergent constraints that emerge at LLM scale that may be helping ChatGPT do so much better than we would have expected. These biases are inherent in the nature of language itself, at LLM scale, and they are closely linked to what it is that ChatGPT lacks, which is direct sensorimotor grounding to connect its words to their referents and its propositions to their meanings. These convergent biases are related to (1) the parasitism of indirect verbal grounding on direct sensorimotor grounding, (2) the circularity of verbal definition, (3) the mirroring of language production and comprehension, (4) iconicity in propositions at LLM scale, (5) computational counterparts of human categorical perception in category learning by neural nets, and perhaps also (6) a conjecture by Chomsky about the laws of thought. The exposition will be in the form of a dialogue with ChatGPT-4.

Recent advancements in large language models (LLMs) have provided a new avenue for chatbot development, while most existing research has primarily centered on single-user chatbots that focus on deciding "What" to answer after user inputs. In this paper, we identified that multi-user chatbots have more complex 3W design dimensions -- "What" to say, "When" to respond, and "Who" to answer. Additionally, we proposed Multi-User Chat Assistant (MUCA), which is an LLM-based framework for chatbots specifically designed for group discussions. MUCA consists of three main modules: Sub-topic Generator, Dialog Analyzer, and Utterance Strategies Arbitrator. These modules jointly determine suitable response contents, timings, and the appropriate recipients. To make the optimizing process for MUCA easier, we further propose an LLM-based Multi-User Simulator (MUS) that can mimic real user behavior. This enables faster simulation of a conversation between the chatbot and simulated users, making the early development of the chatbot framework much more efficient. MUCA demonstrates effectiveness, including appropriate chime-in timing, relevant content, and positive user engagement, in goal-oriented conversations with a small to medium number of participants, as evidenced by case studies and experimental results from user studies.

Despite the plethora of telehealth applications to assist home-based older adults and healthcare providers, basic messaging and phone calls are still the most common communication methods, which suffer from limited availability, information loss, and process inefficiencies. One promising solution to facilitate patient-provider communication is to leverage large language models (LLMs) with their powerful natural conversation and summarization capability. However, there is a limited understanding of LLMs' role during the communication. We first conducted two interview studies with both older adults (N=10) and healthcare providers (N=9) to understand their needs and opportunities for LLMs in patient-provider asynchronous communication. Based on the insights, we built an LLM-powered communication system, Talk2Care, and designed interactive components for both groups: (1) For older adults, we leveraged the convenience and accessibility of voice assistants (VAs) and built an LLM-powered VA interface for effective information collection. (2) For health providers, we built an LLM-based dashboard to summarize and present important health information based on older adults' conversations with the VA. We further conducted two user studies with older adults and providers to evaluate the usability of the system. The results showed that Talk2Care could facilitate the communication process, enrich the health information collected from older adults, and considerably save providers' efforts and time. We envision our work as an initial exploration of LLMs' capability in the intersection of healthcare and interpersonal communication.

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.