Consequently, it is uncertain whether the generated modalities sufficiently represent the diversity of future behaviors.

The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on different subgroups.

Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status.

This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities.

MAPF focuses on planning collision-free paths for a group of agents. While state-of-the-art MAPF algorithms can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to-use simulator to efficiently test and understand the performance of MAPF algorithms in their specific settings. SMART fills this gap with several advantages: (1) SMART uses a physics-engine-based simulator to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF algorithms and robot models, and (3) SMART scales to thousands of robots. In addition, we use SMART to explore and demonstrate research questions about the execution of MAPF algorithms in real-world scenarios. The code is publicly available at https://jingtianyan.github.io/

Previously, we have outlined three guiding principles for developing embodied artificial intelligence (EAI) systems.1 EAI systems should not depend on predefined, complex logic to handle specific scenarios. Instead, they must incorporate evolutionary learning mechanisms, enabling continuous adaptation to their operational environments. Additionally, the environment significantly influences not only physical behaviors but also cognitive structures. While the third principle focuses on simulation, the first two principles emphasize building EAI foundation models capable of learning from the EAI systems' operating environments. A common approach for EAI foundation models is to directly utilize pretrained large models.

Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent approaches have shown success in perceiving driving topology using vehicle-mounted sensors, their scalability is hindered by the reliance on training data captured by consistent sensor configurations. We identify that the key factor in scalable lane perception and topology reasoning is the elimination of this sensor-dependent feature. To address this, we propose SMART, a scalable solution that leverages easily available standard-definition (SD) and satellite maps to learn a map prior model, supervised by large-scale geo-referenced high-definition (HD) maps independent of sensor settings. Attributed to scaled training, SMART alone achieves superior offline lane topology understanding using only SD and satellite inputs. Extensive experiments further demonstrate that SMART can be seamlessly integrated into any online topology reasoning methods, yielding significant improvements of up to 28% on the OpenLane-V2 benchmark.

Yes, AI can help with programming, but ChatGPT is not ready to be your programming buddy, especially regarding securing your code. Wouldn't it be great to have an AI pair programming friend to help you secure your code? But, while GitHub CoPilot can be handy -- leaving aside whether it's ethical or legal -- AI's new darling chatbot, ChatGPT, isn't ready for programming prime-time. I'll give you that ChatGPT is going to make life much harder for high-school English teachers. Going forward, anyone who assigns a homework paper on To Kill a Mockingbird will be much more likely to get an AI-written document than any real student thought about the literary masterpiece. But programming, especially secure programming, that's another story.

Released by OpenAI, a San Francisco-based company, ChatGPT can write essays, come up with scripts for TV shows, answer math questions and even write code. ChatGPT has inspired awe, fear, stunts and attempts to circumvent its guardrails. The chatbot is suddenly everywhere. Who should decide how it's built? And what could go right?

The Daily is made by Lisa Tobin, Rachel Quester, Lynsea Garrison, Clare Toeniskoetter, Paige Cowett, Michael Simon Johnson, Brad Fisher, Chris Wood, Jessica Cheung, Stella Tan, Alexandra Leigh Young, Lisa Chow, Eric Krupke, Marc Georges, Luke Vander Ploeg, M.J. Davis Lin, Dan Powell, Dave Shaw, Sydney Harper, Robert Jimison, Mike Benoist, Liz O. Baylen, Asthaa Chaturvedi, Rachelle Bonja, Diana Nguyen, Marion Lozano, Corey Schreppel, Anita Badejo, Rob Szypko, Elisheba Ittoop, Chelsea Daniel, Mooj Zadie, Patricia Willens, Rowan Niemisto, Jody Becker, Rikki Novetsky, John Ketchum, Nina Feldman, Will Reid, Carlos Prieto, Sofia Milan, Ben Calhoun and Susan Lee. Our theme music is by Jim Brunberg and Ben Landsverk of Wonderly.