AI Can See You Through Walls Now

2026-03-11 · Source: There's An AI For That · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Intermediate, long

Summary

MedOS, developed by the Stanford-Princeton AI Coscientist Team and showcased at NVIDIA GTC 2026, is a real-time clinical co-pilot integrating multi-agent AI, XR smart glasses, and collaborative robotics. Currently deployed at Stanford Blood Center and Stanford Department of Pathology, it enhances diagnostic precision for medical students to attending physician levels, restores post-call doctor performance above baseline, and eliminates surgical tremor with intelligent glove and cobot assistance. Other AI developments include ChatGPT's new interactive math visuals for Plus, Pro, and Team users, NVIDIA's release of Nemotron 3 Super, a 120B-parameter open model for agentic AI with 5x higher throughput, and ElevenLabs' launch of ElevenCreative, a comprehensive platform for audio and video generation, editing, and localization.

Key takeaway

For AI architects and product managers developing embodied AI or robotics, you must prioritize practical integration challenges over purely technical ones. Consider the logistical, social, and regulatory vacuums that emerge when AI moves from lab to daily life, such as insurance, transit, and public acceptance. Your deployment plans should include strategies for addressing these "small" questions to ensure successful, ethical, and widely adopted AI systems.

Key insights

Practical considerations, not just grand theories, will shape AI's integration into daily life and regulatory frameworks.

Principles

• AI's real-world deployment reveals unforeseen practical challenges.
• Regulation must evolve to address AI's societal integration.
• Public interaction with AI fosters realistic expectations.

Method

A historian conducted an immersive research project, living with a Unitree G1 EDU-1 humanoid robot for a year to identify thousands of practical questions and regulatory gaps arising from daily cohabitation.

In practice

• Robot-proof homes for obstacle avoidance.
• Develop transit regulations for humanoid robots.
• Formulate community standards for robot presence.

Topics

• AI in Healthcare
• Humanoid Robotics
• Agentic AI
• Through-Wall Sensing
• AI Societal Integration

Code references

• ruvnet/RuView
• AlexsJones/llmfit
• andrewyng/context-hub
• teng-lin/notebooklm-py

Best for: VP of Engineering/Data, Director of AI/ML, AI Architect, AI Engineer, AI Product Manager, General Interest

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Editorial summary, takeaway, and curation by AIssential. Original article published by There's An AI For That.