AI「看见」实验，哈佛颠覆性突破，一副AR眼镜，新手秒变资深专家

Core Insights - The article discusses the emergence of Human-AI Co-Embodied Intelligence systems, which integrate human researchers with AI and mixed reality technologies to enhance scientific experimentation and manufacturing processes. This paradigm shift aims to redefine the collaboration between humans and AI, allowing for real-time interaction and decision-making in physical environments [1][13][39]. Group 1: Challenges in Scientific Research and Manufacturing - Common issues in scientific research include human errors during complex procedures, leading to wasted resources and time. For instance, a novice may input incorrect parameters, resulting in the loss of an entire batch of devices [5][11]. - The lack of detailed documentation and the reliance on implicit knowledge make it difficult for new researchers to replicate successful experiments, often requiring extensive trial and error [7][10]. - Traditional AI systems are limited in their ability to perceive and interact with the physical world, which hampers their effectiveness in real-time experimental settings [12][18]. Group 2: Human-AI Co-Embodied Intelligence Systems - The APEX system developed by Harvard's Liu Jia team utilizes mixed reality (MR) technology to capture human actions and environmental changes, allowing AI to understand and respond to real-world scenarios [21][22]. - APEX consists of four collaborative intelligent agents that work together to plan, track, and analyze experimental processes, significantly improving accuracy and efficiency in manufacturing environments [23][24]. - The Agentic Lab system applies similar principles in biological research, enabling AI to assist researchers in real-time by providing visual and auditory feedback during experiments [25][28]. Group 3: Benefits of the New Paradigm - APEX provides real-time error correction, alerting researchers immediately when incorrect parameters are set, thus preventing potential losses [34]. - The systems automatically record and analyze experimental data, creating structured digital logs that enhance traceability and facilitate knowledge transfer [36]. - New researchers can quickly acquire skills through guided interactions with the systems, reducing the time required to reach expert-level proficiency [38]. Group 4: Future Implications - The integration of AI into both manufacturing and biological research signifies a shift from viewing AI as a replacement for human labor to recognizing it as a collaborative partner [39][43]. - This new paradigm is expected to expand across various fields, including flexible electronics and biomedicine, fostering a cross-disciplinary and self-evolving experimental ecosystem [42][45].