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As the Industrial Internet of Things (IIoT) picks up steam, attention is pivoting from connectivity to analytics, flooding manufacturers with a wave of new offerings that all promise to facilitate real business change. Startups as well as familiar automation providers are pulling together new platforms and tools designed to spin the treasure trove of data collected from plant floor equipment and industrial assets into nuggets of actionable insights that can help optimize decision-making. Much of this data has existed in some form for decades, but it's primarily been locked away in siloed and incompatible plant floor systems. As a result, the data has never been fully utilized as part of a broader analytics effort to foster predictive maintenance, optimize energy usage of plant floor assets, or to initiate a response to critical events like a water leak or pump failure to minimize lost production. "It's really easy to capture data, but to then make that data actionable is where companies are really struggling," notes Ryan Lester, director of IoT strategy for Xively, an IoT platform provider.

Inside an abandoned warehouse on the San Francisco docks, as the damp air floods through the holes in its rusted tin roof, Sunny Tang is playing her cello while recovering from the flu. She is 45 percent sad and 0.01 percent disgusted. That, at least, is the read from the AI that's tracking her expressions, gestures, and body language from the other side of the warehouse, flashing these stats on the movie screen behind her. The audience--several hundred people huddled between her and the AI, dressed in scarfs, hats, and overcoats--lets out a collective laugh. Tang is playing alongside the rest of the Kronos Quartet, the iconic San Francisco string ensemble known for its unorthodox experimentation, and the AI is obeying orders from Trevor Paglen, the American artist who poses big questions about technology and surveillance through nearly any medium he can get his hands on. It's all part of Sight Machine, a Paglen-orchestrated performance that explores the rise of computer vision.

This performance by Trevor Paglen explores the way machines see and interpret the experience of watching a musical performance. Kronos Quartet will perform works ranging from Baroque composer J. S. Bach to contemporary minimalist composers Terry Riley and Steve Reich, and American blues to African folk music. A live video feed of the performance will be processed in real time through surveillance AI algorithms, and the resultant machine vision data images will be projected on a screen above the performers. Sight Machine is organized by the Cantor Arts Center. We gratefully acknowledge support from the Palmer Gross Ducommon Fund and from our partners: Meyer Sound, Obscura Digital, Orton Development, and Plant Construction.

Sight Machine Inc., the category leader for manufacturing IIoT, has been named by CB Insights to its Artificial Intelligence 100 list ("AI 100"), a select group of emerging private companies working on groundbreaking artificial intelligence technology. CB Insights revealed the winners during The Innovation Summit, a gathering of leading technology executives and investors to explore the industries of the future. Sight Machine is used by Global 500 companies to make better, faster decisions about their operations. Sight Machine's analytics platform, purpose-built for discrete and process manufacturing, uses artificial intelligence, machine learning, and advanced analytics to help address critical challenges in quality and productivity throughout the enterprise. Sight Machine delivers AI for the plant floor.

Machine-reengineering is a way to automate business processes using machine learning. Although machine-reengineering is new, companies are already seeing striking results with it, particularly in boosts to speed and efficiency. Studying 168 early adopters, we've seen speed improvements of two times or more for most business processes -- and some organizations are reporting speed improvements of 10 times or more. How do companies do it? Our study found that organizations are using machine-reengineering to establish new forms of human-machine collaboration that break through the bottlenecks of complex digital processes.