The new Lux report,\u00a0\u201cIntelligent Sensing: The Impact of AI on Sensor Capabilities<\/a><\/strong>,\u201d\u00a0analyzed\u00a0more than\u00a0130,000\u00a0patents and used the Lux Tech Signal to identify the impact of AI on a range of sensor types,\u00a0including\u00a0optical, mechanical, and acoustic sensors.\u00a0Several additional drivers, including falling sensor costs and edge computing,\u00a0are also creating new opportunities for intelligent sensing.\u00a0The report<\/a>\u00a0also evaluates the application of\u00a0AI-defined sensor innovations across industries, revealing\u00a0the\u00a0leading edge for intelligent sensing.<\/p>\n \u201cWith the advent and proliferation of artificial intelligence (AI) technologies \u2013 concentrated in machine learning \u2013 the capabilities of software-defined sensors are increasing at a breakneck pace,\u201d\u00a0said Cole McCollum,\u00a0Research\u00a0Associate in\u00a0Sensors\u00a0at Lux Research and lead author of this report.\u00a0\u201cAI for analyzing sensor data enables far more robust predictions and classifications using sensor signals compared to other methods like physics-based models. We are already seeing this innovation potential with the development of\u00a0applications ranging from medical diagnosis to predictive maintenance.\u201d<\/p><\/blockquote>\n The report provides a detailed analysis of AI innovation activity across sensor types and industry segments. Some of the key takeaways include:<\/p>\n![\"\"](\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/LuxResearch-report-pic.png\")
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