Meet the crochet artist who made a 10-foot Appa for'Avatar Legends' in 16 days Shayla Palmer makes crochet look cool and achievable, one stitch at a time. Kristy Puchko is the Entertainment Editor at Mashable. Based in New York City, she's an established film critic and entertainment reporter who has traveled the world on assignment, covered a variety of film festivals, co-hosted movie-focused podcasts, and interviewed a wide array of performers and filmmakers.

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.

This creates a tight feedback loop between representation learning,memory,reinforcementlearning,andsampling-basedplanning.

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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.