In second language learning, scenario-based conversation practice is important for language learners to achieve fluency in speaking, but students often lack sufficient opportunities to practice their conversational skills with qualified instructors or native speakers. To bridge this gap, we propose situational dialogue models for students to engage in conversational practice. Our situational dialogue models are fine-tuned on large language models (LLMs), with the aim of combining the engaging nature of an open-ended conversation with the focused practice of scenario-based tasks. Leveraging the generalization capabilities of LLMs, we demonstrate that our situational dialogue models perform effectively not only on training topics but also on topics not encountered during training. This offers a promising solution to support a wide range of conversational topics without extensive manual work. Additionally, research in the field of dialogue systems still lacks reliable automatic evaluation metrics, leading to human evaluation as the gold standard (Smith et al., 2022), which is typically expensive. To address the limitations of existing evaluation methods, we present a novel automatic evaluation method that employs fine-tuned LLMs to efficiently and effectively assess the performance of situational dialogue models.

Conceptual reasoning, the ability to reason in abstract and high-level perspectives, is key to generalization in human cognition. However, limited study has been done on large language models' capability to perform conceptual reasoning. In this work, we bridge this gap and propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions and generate solutions in a verifiable symbolic space. Using this framework as an analytical tool, we show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks compared to direct inference methods. We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making. We propose two techniques to add trustworthy induction signals by generating familiar questions with similar underlying reasoning paths and asking models to perform self-refinement. Experiments show that our proposed techniques improve models' conceptual reasoning performance by 8% to 11%, achieving a more robust reasoning system that relies less on inductive biases.

In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in IoT and we analyze the potential for data breaches when using generative AI and the misuse of generative AI technologies in IoT ecosystems. These risks not only threaten the privacy and efficiency of IoT systems but also pose broader implications for trust and safety in AI-driven environments. The discussion in this article extends to strategic approaches for mitigating these risks, including the development of robust security protocols, the multi-layered security approaches, and the adoption of AI technological solutions. Through a comprehensive analysis, this article aims to shed light on the critical balance between embracing AI advancements and ensuring stringent security in IoT, providing insights into the future direction of these intertwined technologies.

This story about intelligence testing in schools was produced by the Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Even before her son started kindergarten, Ashley Meier Barlow realized that she might have to fight for his education. Her son has Down syndrome; when he was in prekindergarten, school officials in Fort Thomas, Kentucky, told Barlow that he wouldn't be going to the neighborhood school, with some special education accommodations, as she had assumed. Instead, the educators told Barlow that they wanted her son to attend a classroom across town meant for children who are profoundly impacted by their disabilities. Barlow immediately resisted because she knew the curriculum would likely focus on life skills, and her son might never be taught much reading beyond learning the shape of common, functional words like stop and exit.

At the International Joint Conference on Artificial Intelligence (IJCAI) I was lucky enough to catch up with Francesca Rossi, IBM fellow and AI Ethics Global Leader, and President of AAAI. There were so many questions I wanted to ask, and we covered some pressing topics in AI today. Andrea Rafai: My first question concerns the UN Sustainable Development Goals (SDGs). It seems that there is a lot of potential for using AI in helping to work towards the 17 goals. What is your view on these goals and the long-term outlook?

Afshin Mehin has become the go-to designer for companies working on devices that aim to tap into or modulate the brain. The creative agency he founded, San Francisco–based Card79, has worked with Elon Musk's Neuralink to design a surgical robot for installing a coin-sized implant into people's heads. The device, known as a brain-computer interface, records and transmits brain activity with the goal of enabling paralyzed people to control a computer. Mehin worked with Neuralink to design the external parts of this system--the installation robot and also a wearable that would sit behind the ear and transfer data and power to an implanted wireless receiver. This device, which looked like a sleek, white hearing aid, was an early prototype.

Mariza Ferro is a professor at the Federal Fluminense University and a visiting professor at Bordeaux University. She has been working in the field of AI since 2002. She works on AI for good, including human-centric AI, ethical and trustworthy AI, green and sustainable AI, and AI for sustainable development goals. She guides her research based on the principle that AI must benefit humankind. Furthermore, she is also working with public outreach by making science available for all.

Kennedy initially launched his presidential bid as a Democrat last April, but he later announced an independent run in October. Independent presidential candidate Robert F. Kennedy, Jr. announced Tuesday that attorney and tech entrepreneur Nicole Shanahan will be his vice presidential running mate heading into the November general election. A native of Oakland, California, the 38-year-old Shanahan is a philanthropist with a long history of donating to Democrat and left-leaning causes, including supporting President Biden in his 2020 election bid before switching to Kennedy when he launched his own run for the Democrat nomination last year. Kennedy announced Shanahan by praising her insight into "how Big Tech uses AI to manipulate the public," her athletic ability, and willingness to be a "partner" in a number of policy areas, including on securing the border. Independent presidential candidate Robert F. Kennedy, Jr., left, and entrepreneur Nicole Shanahan, right.

Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains. Given the fact that psychiatric interviews are goal-oriented and structured dialogues between the professional interviewer and the interviewee, it is one of the most underexplored areas where LLMs can contribute substantial value. Here, we explore the use of LLMs for enhancing psychiatric interviews, by analyzing counseling data from North Korean defectors with traumatic events and mental health issues. Specifically, we investigate whether LLMs can (1) delineate the part of the conversation that suggests psychiatric symptoms and name the symptoms, and (2) summarize stressors and symptoms, based on the interview dialogue transcript. Here, the transcript data was labeled by mental health experts for training and evaluation of LLMs. Our experimental results show that appropriately prompted LLMs can achieve high performance on both the symptom delineation task and the summarization task. This research contributes to the nascent field of applying LLMs to psychiatric interview and demonstrates their potential effectiveness in aiding mental health practitioners.

On Wednesday, author David Brin announced that Vernor Vinge, sci-fi author, former professor, and father of the technological singularity concept, died from Parkinson's disease at age 79 on March 20, 2024, in La Jolla, California. The announcement came in a Facebook tribute where Brin wrote about Vinge's deep love for science and writing. "A titan in the literary genre that explores a limitless range of potential destinies, Vernor enthralled millions with tales of plausible tomorrows, made all the more vivid by his polymath masteries of language, drama, characters, and the implications of science," wrote Brin in his post. As a sci-fi author, Vinge won Hugo Awards for his novels A Fire Upon the Deep (1993), A Deepness in the Sky (2000), and Rainbows End (2007). He also won Hugos for novellas Fast Times at Fairmont High (2002) and The Cookie Monster (2004).