This paper presents a control interface that uses a commercial XR pen to command a semi-autonomous mobile robot in Augmented Reality (AR), and shows that the XR pen significantly outperforms the other methods in task selection time with the most consistent selections, supporting XR-based control as an intuitive alterna...
Alicia Torc, Carl Tornberg, Éric Piette et al.· 0 citations
This work proposes Multi-Objective Human-in-the-loop Bayesian Optimization (MO-HILBO), which builds on explicit multi-objective Bayesian optimization to efficiently infer a personalized set of Pareto-optimal controllers.
Neil C. Janwani, Matthew T. Lerner, Aaron J. Young et al.· 0 citations
Human judgement is the reference measure for evaluating generative models, yet the software used to collect it lags behing the methodology. Researchers adapt listening-test frameworks designed for perceptual protocols such as MUSHRA, rely on closed commercial survey platforms, or implement single-use web applications....
Matteo Spanio, Andrea Poltronieri, Mart\'{\i}n Rocamora· 0 citations
With the prevalence of Attention Deficit Hyperactivity Disorder (ADHD) over the past decades, healthcare systems across the globe face critical diagnostic challenges due to long diagnostic waiting times and a reliance on subjective behavioural assessments that cannot distinguish ADHD from comorbid psychiatric disorders...
C.-W. Peng, Tony Russell-Rose· 0 citations
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AI assistance can improve performance without improving self-assessment. We report a study (N=366) comparing Human alone and Human+AI performance on reasoning tasks, for which the AI model is benchmarked on the same items. Participants estimated global and block performance and rated confidence in their answers. Human+...
Daniela Fernandes, Michelle Rausch, A. M. Kloft et al.· 0 citations
Electroencephalography (EEG)-based emotion recognition is important for affective computing and human-computer interaction, yet most existing methods divide a long trial into short segments and assign each segment the label of its source trial. Although this strategy increases the number of training samples, it reduces...
Chi Yang, Jihong Wang, Chengxi Xie et al.· 0 citations
Generalizing Electroencephalography (EEG)-based mental workload recognition to unseen subjects remains a formidable challenge due to severe inter-subject variability. While functional brain graphs effectively model distributed cognitive dynamics, their inherent subject-specificity induces two coupled distribution shift...
Yuzhe Zhang, Wenmin Zhou, Chengxi Xie et al.· 0 citations
Anthropomorphic artificial intelligence systems increasingly remember personal details, display empathy, and are engaged with as social counterparts, creating forms of risk that emerge from the evolution of the user-system relationship over time. Existing safeguards largely operate at the level of individual conversati...
Large language models (LLMs) have become fixtures of academic work even as their users describe them as degrading their writing, thinking, and skills. Dominant adoption frameworks read continued use as evidence of satisfaction, and cannot explain continued use of a distrusted tool. We interviewed 36 graduate student wo...
Matt Viana, Patrick Erickson, Shomir Wilson et al.· 0 citations
Recent generative video editing models enable video content modification (e.g., changing a character) but target short clips. Extending them to full multi-shot videos requires tedious work to locate relevant content across shots, segment it into clips, craft context-aware editing prompts for each clip, and repeatedly a...
Boyu Li, Yu-Qian Zhou, Duo-Tun Wang et al.· 0 citations
Mobile virtual reality (MVR) provides low-cost access to extended reality (XR), but its limited input restricts use of common locomotion techniques such as head-decoupled and velocity-controlled steering. Using a low-cost controller with an audio-based button, we compared gaze- and controller-directed steering and tele...
Kristen Grinyer, Daniel Zielasko, Robert J. Teather· 0 citations
Findings show that researchers leaned on GenAI to fill knowledge gaps while maintaining epistemic agency for novelty discovery, and an expertise paradox was uncovered: GenAI outputs were hardest to verify when most needed.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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