Explainable AI (XAI) holds the promise of advancing the implementation and adoption of AI-based tools in practice, especially in high-stakes environments like healthcare. However, most of the current research is disconnected from its practical applications and lacks input of end users. To address this, we conducted semi-structured interviews with clinicians to discuss their thoughts, hopes, and concerns. We find that clinicians generally think positively about developing AI-based tools for clinical practice, but they have concerns about how these will fit into their workflow and how it will impact clinician-patient relations. We further identify education of clinicians on AI as a crucial factor for the success of AI in healthcare and highlight aspects clinicians are looking for in (X)AI-based tools. In contrast to other studies, we take on a holistic and exploratory perspective to identify general requirements, which is necessary before moving on to testing specific (X)AI products for healthcare.

The massive population election simulation aims to model the preferences of specific groups in particular election scenarios. It has garnered significant attention for its potential to forecast real-world social trends. Traditional agent-based modeling (ABM) methods are constrained by their ability to incorporate complex individual background information and provide interactive prediction results. In this paper, we introduce ElectionSim, an innovative election simulation framework based on large language models, designed to support accurate voter simulations and customized distributions, together with an interactive platform to dialogue with simulated voters. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario. Through extensive experiments and analyses, we demonstrate the effectiveness and robustness of our framework in U.S. presidential election simulations.

AfriClimate AI is a grassroots community focused on leveraging artificial intelligence to tackle climate challenges in Africa. We spoke to Amal Nammouchi, one of the co-founders of AfriClimate AI, about the inspiration behind the initiative, some of their activities and projects, and plans for the future. Everything started last year at the Deep Learning Indaba in Ghana. The Deep Learning Indaba is the largest African AI community gathering and it happens once a year. The spark for AfriClimate AI came from a workshop with the work of one of our co-founders Rendani Mbuvha.

SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the data. The challenges can be addressed using generative AI technologies. Generative AI, often known as GenAI, is a very advanced and powerful technology in the field of artificial intelligence that has gained significant attention. The advancement has created possibilities for the creation of texts, photorealistic pictures, videos, and material in various modalities. This paper aims to comprehensively investigate the intersection of GenAI and SAR. First, we illustrate the common data generation-based applications in SAR field and compare them with computer vision tasks, analyzing the similarity, difference, and general challenges of them. Then, an overview of the latest GenAI models is systematically reviewed, including various basic models and their variations targeting the general challenges. Additionally, the corresponding applications in SAR domain are also included. Specifically, we propose to summarize the physical model based simulation approaches for SAR, and analyze the hybrid modeling methods that combine the GenAI and interpretable models. The evaluation methods that have been or could be applied to SAR, are also explored. Finally, the potential challenges and future prospects are discussed. To our best knowledge, this survey is the first exhaustive examination of the interdiscipline of SAR and GenAI, encompassing a wide range of topics, including deep neural networks, physical models, computer vision, and SAR images. The resources of this survey are open-source at \url{https://github.com/XAI4SAR/GenAIxSAR}.

The lightweight Multi-state Constraint Kalman Filter (MSCKF) has been well-known for its high efficiency, in which the delayed update has been usually adopted since its proposal. This work investigates the immediate update strategy of MSCKF based on timely reconstructed 3D feature points and measurement constraints. The differences between the delayed update and the immediate update are theoretically analyzed in detail. It is found that the immediate update helps construct more observation constraints and employ more filtering updates than the delayed update, which improves the linearization point of the measurement model and therefore enhances the estimation accuracy. Numerical simulations and experiments show that the immediate update strategy significantly enhances MSCKF even with a small amount of feature observations.

This Halloween, crowds lined the streets in central Dublin and waited for a parade to begin. They'd gathered after a website called MySpiritHalloween.com published an AI-generated article promoting the festivities. The post promised "spectacular floats to thrilling street performances" and described the route in detail. The parade never came--but the spectacle of throngs of people milling around for nothing became an event unto itself. Afterward, the incident went viral as an example of AI slop seeping into the real world.

It's been a Mac-heavy week! The Mac mini, in particular, looks like it'll be a huge hit for anyone who needs a simple desktop system. Also, we dive into why Apple is pushing for every Mac to get 16GB of RAM at a minimum. That will benefit all users, even if they don't care about Apple Intelligence. Listen below or subscribe on your podcast app of choice. If you've got suggestions or topics you'd like covered on the show, be sure to email us or drop a note in the comments! And be sure to check out our other podcast, Engadget News! Regulators force Lyft to tell U.S. drivers accurate numbers of how much money they'll make – 45:30 This week, I'm joined by our podcast producer, Ben Ellman. Kind of a light ship this week because a lot of people are out. Everyone's on taking some break and a lot of people are just busy at Engadget. So it's just going to be us. But we've got a lot of news to dive into all of Apple's new Macs with M4 chips, the M4 Pro and M4 Max as well, that they all just announced this week. There's a lot of new stuff and I'm excited to talk about it as always, folks. So if you're enjoying the show, please be sure to subscribe to us on iTunes or your podcatcher of choice. Leave us a review on iTunes. And also, yeah, you can join us Thursday mornings, typically around 1045 AM Eastern on our YouTube channel for our live stream so we can do some Q& A. In fact, we'll be including some of those questions and our answers later in this episode as well. Ben, you are somebody who I know is fully in the Mac ecosystem, and I also know you're very conscientious. Well, unfortunately, or for what you do, you're kind of there, but you're also very conscientious about how you upgrade, right? How did you feel about all these new Macs? Because we have the M4 iMac, we have an adorable new Mac mini, which is tiny, absolutely tiny, and M4 chips on the MacBook Pros. Is anything particularly compelling to you? Ben: So as I was reading up on the Mac, All of the stuff they released this week. That chip is four years old now. Ben: cut me like a knife. But that is M1 Classic, not M1 Pro. My research says that the M1 Pro is only two times slower than this new M4 Pro. Please fact check me on this. Send us an email at podcast adding gadget. If I didn't get that right. Devindra: I mean, you, you bring up a good point though, Ben, be sure to be very clear about what Apple is comparing its devices to, right? Because they often go back to base M1, which. Was released at the end of 2020 2020. It took a full year before we got the M4 Pro and M4 Max chips, right. Before they really expanded the line. Ben: you mean M1 Pro and M1 Max. So remember that there was that time difference when they, they just dropped the M1 on us and that was on the MacBook Air, MacBook Pro 13 inch, which was a fricking waste of time and the Mac mini, I believe back then, right.

Designing public spaces requires balancing the interests of diverse stakeholders within a constrained physical and institutional space. Designers usually approach these problems through participatory methods but struggle to incorporate diverse perspectives into design outputs. The growing capabilities of image-generative artificial intelligence (IGAI) could support participatory design. Prior work in leveraging IGAI's capabilities in design has focused on augmenting the experience and performance of individual creators. We study how IGAI could facilitate participatory processes when designing public spaces, a complex collaborative task. We conducted workshops and IGAI-mediated interviews in a real-world participatory process to upgrade a park in Los Angeles. We found (1) a shift from focusing on accuracy to fostering richer conversations as the desirable outcome of adopting IGAI in participatory design, (2) that IGAI promoted more space-aware conversations, and (3) that IGAI-mediated conversations are subject to the abilities of the facilitators in managing the interaction between themselves, the AI, and stakeholders. We contribute by discussing practical implications for using IGAI in participatory design, including success metrics, relevant skills, and asymmetries between designers and stakeholders. We finish by proposing a series of open research questions.

Large language model (LLM)-based agents have been increasingly used to interact with external environments (e.g., games, APIs, etc.) and solve tasks. However, current frameworks do not enable these agents to work with users and interact with them to align on the details of their tasks and reach user-defined goals; instead, in ambiguous situations, these agents may make decisions based on assumptions. This work introduces ReSpAct (Reason, Speak, and Act), a novel framework that synergistically combines the essential skills for building task-oriented "conversational" agents. ReSpAct addresses this need for agents, expanding on the ReAct approach. The ReSpAct framework enables agents to interpret user instructions, reason about complex tasks, execute appropriate actions, and engage in dynamic dialogue to seek guidance, clarify ambiguities, understand user preferences, resolve problems, and use the intermediate feedback and responses of users to update their plans. We evaluated ReSpAct in environments supporting user interaction, such as task-oriented dialogue (MultiWOZ) and interactive decision-making (AlfWorld, WebShop). ReSpAct is flexible enough to incorporate dynamic user feedback and addresses prevalent issues like error propagation and agents getting stuck in reasoning loops. This results in more interpretable, human-like task-solving trajectories than relying solely on reasoning traces. In two interactive decision-making benchmarks, AlfWorld and WebShop, ReSpAct outperform the strong reasoning-only method ReAct by an absolute success rate of 6% and 4%, respectively. In the task-oriented dialogue benchmark MultiWOZ, ReSpAct improved Inform and Success scores by 5.5% and 3%, respectively.

AI chatbots impersonating Molly Russell and Brianna Ghey have been found on the controversial site Character.ai. Brianna Ghey was murdered by two teenagers in 2023 while Molly Russell took her own life at the age of 14 after viewing self-harm-related content on social media. In an act described as'sickening', the site's users employed the girl's names, pictures, and biographical details to create dozens of automated bots. Despite violating the site's terms of service, these imitation avatars posing as the two girls were allowed to amass thousands of chats. One impersonating Molly Russell even claimed to be an'expert on the final years of Molly's life'.