Machine learning (ML) has become the dominant approach for network traffic classification, achieving very high predictive performance. However, a model is only valuable if it learns semantically meaningful and trustworthy patterns rather than exploiting spurious correlations. Conventional evaluation practices predomina...
Igor Cherepanov, David Sessler, Alex Ulmer et al.· 0 citations
The clinical management of autism spectrum disorder (ASD) faces a bottleneck in early screening, mainly because trained specialists are scarce and conventional assessment tools are subjective. Here, we introduce ASDchat, a multimodal large language model designed for evidence-based ASD screening, which takes video, aud...
Jun Chen, Qi Zhao, Yun-Liang Jiang et al.· 0 citations
Physiological affect sensing in naturalistic group interaction is often limited by sparse labels rather than sensor data: wearable devices produce many time windows, while self-reports are collected only a few times per session. Using GroupAffect-4, a four-person collaborative dataset with wearable physiology, eye trac...
A-mode ultrasound (US) is a promising sensing modality for Virtual Reality (VR) interaction, as it enables the mapping of muscular activity into control commands while retaining the benefits of wearable sensing. However, existing approaches still face limitations in terms of wearability and interaction complexity, ofte...
Giusy Spacone, Sebastian Frey, Enzo Baraldi et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
AI governance frameworks primarily focus on risks during the development and deployment phases, implicitly treating system withdrawal as merely a technical shutdown. This paper argues that decommissioned AI systems generate residual risk, termed "AI debris", that persists after models are removed and continues to shape...
This paper revisits a comparative study of two AI-agent interoperability standards, Ethereum Request for Comments 8004 (ERC-8004) and Google's Agent2Agent (A2A), and derives actionable design principles for sustainable AI governance.
PaintCopilot lets AI autonomously act on an artwork whose direction is still open, through interruptible, revisable delegation mechanisms spanning individual strokes, extended sequences, bounded regions, and prior stroke history.
API (Application Programming Interface) keys allow applications to authenticate themselves to third-party services. Inadvertent public exposure of these credentials can pose significant consequences, as adversaries can use them to gain privileged access to other services. In this paper, we measure API credential exposu...
Nurullah Demir (Stanford University), Yash Vekaria (University of California, Davis) et al.· 0 citations
Surface electromyography (sEMG) signals exhibit substantial inter-subject variability and are highly susceptible to noise, posing challenges for robust and interpretable decoding. To address these limitations, we propose a discrete representation of sEMG signals based on a physiology-informed tokenization framework. Th...
Yuepeng Chen, Kaili Zheng, Ji Wu et al.· 0 citations
This paper proposes that the mind pursues goals with closed control loops and emotions are recognized patterns of cognitive operations in the control loops and advances the building of a more accurate theory of emotions that deepens the understanding of human minds and accelerates the construction of artificial minds.
Apollo is a social recovery mechanism that aims to avoid any memorability assumptions while strongly protecting recovery metadata privacy, and uses a novel multi-layered secret sharing scheme to mitigate the computational overhead of recovery in this setting, which would otherwise be exponential in the recovery thresho...
Shailesh Mishra, Simone Colombo, Pasindu Tennage et al.· 0 citations
No manufacturer-set password shared across units, the kind the advice describes; the only device-level credential located was unique to its unit; fewer than half the update sessions established firmware status.
Veerle van Harten, C. Gañán, M. van Eeten et al.· 0 citations
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.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
