Business and technology are intricately connected through logic and design. They are equally sensitive to societal changes and may be devastated by scandal. Cooperative multi-robot systems (MRSs) are on the rise, allowing robots of different types and brands to work together in diverse contexts. Generative artificial intelligence has been a dominant topic in recent artificial intelligence (AI) discussions due to its capacity to mimic humans through the use of natural language and the production of media, including deep fakes. In this article, we focus specifically on the conversational aspects of generative AI, and hence use the term Conversational Generative artificial intelligence (CGI). Like MRSs, CGIs have enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business. From a business perspective, cooperative MRSs alone, with potential conflicts of interest, privacy practices, and safety concerns, require ethical examination. MRSs empowered by CGIs demand multi-dimensional and sophisticated methods to uncover imminent ethical pitfalls. This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model.

And because a LLM doesn't "know" when it's quoting from training data, there's no obvious way to prevent the behavior. I spoke with Florian Tramèr, a prominent AI-security researcher and co-author of some of the above studies. It's "an extremely tricky problem to study," he told me. "It's very, very hard to pin down a good definition of memorization." One way to understand the concept is to think of an LLM as an enormous decision tree in which each node is an English word. From a given starting word, an LLM chooses the next word from the entire English vocabulary.

The AAAI/SIGAI Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. This year, 30 students have been selected for this programme, and we'll be hearing from them over the course of the next few months. Our first interviewee is Changhoon Kim. I am pursuing my Ph.D. at Arizona State University, a vibrant hub of innovation located in one of the U.S.'s sunniest cities. This unique setting provides a conducive atmosphere for focused research, particularly during summer when the intense heat encourages more indoor lab work.

ChatGPT has been evidenced to enhance human performance in creative tasks. Yet, it is still unclear if this boosting effect sustains with and without ChatGPT. In a pre-registered seven-day lab experiment and a follow-up survey after 30 days of experiment completion, we examined the impacts of ChatGPT presence and absence on sustained creativity using a text dataset of 3302 creative ideas and 427 creative solutions from 61 college students. Participants in the treatment group used ChatGPT in creative tasks, while those in the control group completed the tasks by themselves. The findings show that although the boosting effect of ChatGPT was consistently observed over a five-day creative journey, human creative performance reverted to baseline when ChatGPT was down on the 7th and the 30th day. More critically, the use of ChatGPT in creative tasks resulted in increasingly homogenized contents, and this homogenization effect persisted even when ChatGPT was absence. These findings pose a challenge to the prevailing argument that ChatGPT can enhance human creativity. In fact, generative AI like ChatGPT lends to human with a temporary rise in creative performance but boxes human creative capability in the long run, highlighting the imperative for cautious generative AI integration in creative endeavors.

Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the major obstacle to their widespread application. Many studies have extensively investigated risks in LLM systems and developed the corresponding mitigation strategies. Leading-edge enterprises such as OpenAI, Google, Meta, and Anthropic have also made lots of efforts on responsible LLMs. Therefore, there is a growing need to organize the existing studies and establish comprehensive taxonomies for the community. In this paper, we delve into four essential modules of an LLM system, including an input module for receiving prompts, a language model trained on extensive corpora, a toolchain module for development and deployment, and an output module for exporting LLM-generated content. Based on this, we propose a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system and discusses the corresponding mitigation strategies. Furthermore, we review prevalent benchmarks, aiming to facilitate the risk assessment of LLM systems. We hope that this paper can help LLM participants embrace a systematic perspective to build their responsible LLM systems.

Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment. An important aspect of MCQs is the distractors, i.e., incorrect options that are designed to target specific misconceptions or insufficient knowledge among students. To date, the task of crafting high-quality distractors has largely remained a labor-intensive process for teachers and learning content designers, which has limited scalability. In this work, we explore the task of automated distractor and corresponding feedback message generation in math MCQs using large language models. We establish a formulation of these two tasks and propose a simple, in-context learning-based solution. Moreover, we propose generative AI-based metrics for evaluating the quality of the feedback messages. We conduct extensive experiments on these tasks using a real-world MCQ dataset. Our findings suggest that there is a lot of room for improvement in automated distractor and feedback generation; based on these findings, we outline several directions for future work.

Do AI companies need to pay for the training data that powers their generative AI systems? The question is hotly contested in Silicon Valley and in a wave of lawsuits levied against tech behemoths like Meta, Google, and OpenAI. In Washington, DC, though, there seems to be a growing consensus that the tech giants need to cough up. Today, at a Senate hearing on AI's impact on journalism, lawmakers from both sides of the aisle agreed that OpenAI and others should pay media outlets for using their work in AI projects. "It's not only morally right," said Richard Blumenthal, the Democrat who chairs the Judiciary Subcommittee on Privacy, Technology, and the Law that held the hearing.

AI poses a grave threat to journalism, experts warned Congress at a hearing on Wednesday. Media executives and academic experts testified before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law about how AI is contributing to the big tech-fueled decline of journalism. They also talked about intellectual property issues arising from AI models being trained on the work of journalists, and raised alarms about the increasing dangers of AI-powered misinformation. "The rise of big tech has been directly responsible for the decline in local news," said Senator Richard Blumenthal, a Connecticut Democrat and chair of the subcommittee. "First, Meta, Google and OpenAI are using the hard work of newspapers and authors to train their AI models without compensation or credit. Adding insult to injury, those models are then used to compete with newspapers and broadcasters, cannibalizing readership and revenue from the journalistic institutions that generate the content in the first place."

If you squint and tilt your head, you can see some similarities in the blurry shapes that are Harvard and OpenAI. Each is a leading institution for building minds, whether real or artificial--Harvard educates smart humans, while OpenAI engineers smart machines--and each has been forced in recent days to stare down a common allegation. Namely, that they are represented by intellectual thieves. Last month, the conservative activist Christopher Rufo and the journalist Christopher Brunet accused then–Harvard President Claudine Gay of having copied short passages without attribution in her dissertation. Gay later admitted to "instances in my academic writings where some material duplicated other scholars' language, without proper attribution," for which she requested corrections. The two cases share common ground, yet many of the responses to them could not be more different.

OpenAI on Wednesday launched its GPT Store, a marketplace where paid ChatGPT users can buy and sell specialized chatbot agents based on the company's language models. The company, whose wildly popular product ChatGPT helped kickstart the boom in AI, already offers customized bots through its paid ChatGPT Plus service. The new store will allow users to offer and monetize a broader range of tools. Through the new models, chatbot agents could be developed with their own personalities or themes, including models for salary negotiating, creating lesson plans and developing recipes. The store has been compared with Apple's App store, fostering new development in the AI space from a wider range of users. Meta offers chatbots with differing personalities in a similar offering.