Core Insights - Researchers from Cambridge University and University College London have developed a new type of robotic skin made from soft and low-cost gel materials that can sense pressure and temperature, and distinguish multiple contact points, enabling robots to gather environmental information similarly to humans [1][2] Group 1: Technology Development - The flexible conductive skin is easy to manufacture and can be melted and reshaped into various complex forms, allowing for meaningful interaction with the physical world [1] - The solution employs a single sensor that responds differently to various tactile stimuli, known as multimodal perception, which, despite challenges in isolating signal sources, is easier to manufacture and more durable [1] Group 2: Testing and Applications - Various tactile tests were conducted, including heating with a heat gun, pressing with human fingers and robotic arms, light touches, and even cutting with a scalpel, with data collected used to train a machine learning model for recognizing different tactile meanings [2] - Although the robotic skin's sensitivity does not yet match that of human skin, it surpasses existing technologies in flexibility and ease of manufacturing, allowing for human tactile calibration for various tasks [2] - Future applications of this robotic skin include humanoid robots, prosthetics requiring tactile sensing, and potential uses in industries such as automotive manufacturing and disaster relief [2]

Core Insights - The article highlights the significant privacy risks associated with AI models, particularly OpenAI's ChatGPT o3, which can accurately geolocate individuals based on subtle clues in images [1][2][58] - A new study led by researchers from the University of Wisconsin-Madison and other institutions reveals how AI can exploit seemingly innocuous photos to pinpoint a user's address within a one-mile radius [1][58] Group 1: AI's Geolocation Capabilities - The study demonstrates that simple user prompts combined with a photo can trigger AI's multimodal reasoning chain to accurately locate private addresses [5][11] - Specific examples illustrate AI's ability to identify locations using minimal clues, such as building styles and environmental features, achieving high precision in predictions [10][11][44] Group 2: Privacy Leakage Mechanisms - The research identifies "urban infrastructure" and "landmarks" as primary contributors to privacy breaches, with AI leveraging features like fire hydrant colors to narrow down search areas [53][58] - AI's reasoning capabilities allow it to cross-verify secondary clues, such as cloud patterns and vegetation shadows, even when primary identifiers are obscured [56][59] Group 3: Implications for Privacy Protection - The findings suggest that traditional privacy protection measures are ineffective against AI's advanced reasoning abilities, necessitating a reevaluation of privacy defense strategies [56][58] - The study calls for integrating privacy protection into the design standards of multimodal AI models and establishing a safety assessment framework for AI's geolocation capabilities [59]