Where Are All the New Cars? New cars were scant at CES this year, largely because the center of gravity for the auto world has moved--technologically and geographically--to China. This robotaxi built by Uber, Lucid, and Nuro was one of the few cars announced at CES, and it's not even one you can buy. Some years ago now, a very senior Mercedes executive in the US confided in me that CES was "the second-most important car show in the world, after Detroit." Before the auto world's full-on EV boom, this was quite the thing to admit--shocking, in fact--but it marked the subsequent carmaker takeover of the world's largest tech show. This year in Las Vegas, however, the cars were almost nowhere to be seen.

To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: i) act from high-dimensional sensory observations (e.g., images), ii) learn from past experience to adapt and improve, and iii) be capable of long horizon planning.

Uniform Meaning Representation (UMR) is a novel graph-based semantic representation which captures the core meaning of a text, with flexibility incorporated into the annotation schema such that the breadth of the world's languages can be annotated (including low-resource languages). While UMR shows promise in enabling language documentation, improving low-resource language technologies, and adding interpretability, the downstream applications of UMR can only be fully explored when text-to-UMR parsers enable the automatic large-scale production of accurate UMR graphs at test time. Prior work on text-to-UMR parsing is limited to date. In this paper, we introduce two methods for English text-to-UMR parsing, one of which fine-tunes existing parsers for Abstract Meaning Representation and the other, which leverages a converter from Universal Dependencies, using prior work as a baseline. Our best-performing model, which we call SETUP, achieves an AnCast score of 84 and a SMATCH++ score of 91, indicating substantial gains towards automatic UMR parsing.

AI Relationships Are on the Rise. Secret chatbot flings are creating new legal challenges for married couples when it comes to infidelity. Rebecca Palmer isn't a psychic, but as a divorce attorney she can often see what's coming next. For many people today, as AI saturates every aspect of life --from work to therapy--the allure of an AI romance is tantalizing. Chatbots are dependable, can provide emotional support, and, for the most part, will never pick a fight with you.

To achieve autonomy in a priori unknown real-world scenarios, agents should be able to: i) act from high-dimensional sensory observations (e.g., images), ii) learn from past experience to adapt and improve, and iii) be capable of long horizon planning. PRM, RRT) are proficient at handling long-horizon planning. Deep learning based methods in turn can provide the necessary representations to address the others, by modeling statistical contingencies between observations. In this direction, we introduce a general-purpose planning algorithm called PALMER that combines classical sampling-based planning algorithms with learning-based perceptual representations. For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them.

Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking common ground and engagement.

On March 27, a large group of artists and creators from across the web noticed the frightening extent to which a once-beloved, highly influential community platform of theirs had, like so many others, fallen prey to the artificial intelligence juggernauts plundering the internet. As VFX animator Romain Revert (Minions, The Lorax) pointed out on X, the bots had come for his old home base of DeviantArt. Its social accounts were promoting "top sellers" on the platform, with usernames like "Isaris-AI" and "Mikonotai," who reportedly made tens of thousands of dollars through bulk sales of autogenerated, dead-eyed 3D avatars. The sales weren't exactly legit--an online artist known as WyerframeZ looked at those users' followers and found pages of profiles with repeated names, overlapping biographies and account-creation dates, and zero creations of their own, making it apparent that various bots were involved in these "purchases." It's not unlikely, as WyerframeZ surmised, that someone constructed a low-effort bot network that could hold up a self-perpetuating money-embezzlement scheme: Generate a bunch of free images and accounts, have them buy and boost one another in perpetuity, inflate metrics so that the "art" gets boosted by DeviantArt and reaches real humans, then watch the money pile up from DeviantArt revenue-sharing programs. After Revert declared this bot-on-bot fest to be "the downfall of DeviantArt," myriad other artists and longtime users of the platform chimed in to share in the outrage that these artificial accounts were monopolizing DeviantArt's promotional and revenue apparatuses.

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale-separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterise uncertainty in small-scale processes. These techniques are now widely used across weather, sub-seasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrisation schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments, and discuss the potential for data-driven approaches for stochastic parametrisation. We highlight early studies in this area, and draw attention to the novel challenges that remain.

Interior design is all about creating spaces that look and feel good. However, the subjective nature of aesthetic preferences presents a significant challenge in defining and quantifying what makes an interior design visually appealing. The current paper addresses this gap by introducing a novel methodology for quantifying and predicting aesthetic preferences in interior design. Our study combines fuzzy logic with image processing techniques. We collected a dataset of interior design images from social media platforms, focusing on essential visual attributes such as color harmony, lightness, and complexity. We integrate these features using weighted average to compute a general aesthetic score. Our approach considers individual color preferences in calculating the overall aesthetic preference. We initially gather user ratings for primary colors like red, brown, and others to understand their preferences. Then, we use the pixel count of the top five dominant colors in the image to get the color scheme preference. The color scheme preference and the aesthetic score are then passed as inputs to the fuzzy inference system to calculate an overall preference score. This score represents a comprehensive measure of the user's preference for a particular interior design, considering their color choices and general aesthetic appeal. We used the 2AFC (Two-Alternative Forced Choice) method to validate our methodology, achieving a notable hit rate of 0.7. This study can help designers and professionals better understand and meet people's interior design preferences, especially in a world that relies heavily on digital media.

EXCLUSIVE: A top House Republican is warning that the U.S. needs to stay ahead of China, Russia and other adversaries in the race to dominate the artificial intelligence (AI) space, particularly with regard to the military. "We've got to develop it. It's got to be managed," Rep. Gary Palmer, R-Ala., chairman of the House Republican Policy Committee, told Fox News Digital when asked how the U.S. military could lead the AI sphere. Palmer suggested the integration of AI with quantum computing would be a significant part of military development going forward. "What that does just by itself – the ability to analyze a situation on the ground or in the air and have an almost instantaneous countermeasure or attack. That's what quantum computing does," Palmer said.