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Survey Highlights Data Bottlenecks In Physical AI
A survey of 700 professionals reveals critical data gaps slowing the deployment of physical AI. Teams struggle with model failure and inefficient data pipelines during production. This whitepaper identifies where manual labeling and poor data quality stall progress. Practitioners can use these findings to optimize their training sets and reduce deployment friction in IEEE Spectrum research.