THE SHIFT TO AUTONOMOUS SMART SYSTEMS
The third wave of innovation, which is still evolving, is enabled by cheap, pervasive networked sensors and sensor data fusion which are enabling a new generation of “awareness” applications. As networks continue to invade the “physical” world, developers are working with the growing interactions between sensors, machines, systems and people to detect revelatory patterns in large scale sensor and machine data.
Applications like computer vision are based on methods and algorithms that recognize patterns in the real world and execute an action based on the result. These new capabilities revolve around real-time situational awareness and automated analysis of very large volumes of sensor data. As a result, technology has moved beyond just proposing task solutions—such as executing a work order or a sales order—to sensing what is happening in the world around it, analyzing that new information for patterns, risks and possibilities, presenting alternatives, and automatically taking actions.
Machine data models and analytics allow not only data patterns but a much higher order of intelligence to emerge from large collections of “ordinary” machine and device data—not unlike the workings of neurons of the brain, ants in an anthill, or human beings in a society. The many “nodes” of a network don’t have to be particularly “smart” in themselves. If they are networked in a way that allows them to connect effortlessly and interoperate seamlessly, they begin to give rise to complex, system-wide behavior that usually goes by the name “emergence.” An entirely new order of intelligence “emerges” from the system as a whole—an intelligence that could not have been predicted by looking at any of the nodes individually.
You’ve heard the famous observation by the writer Arthur C. Clarke, “Any sufficiently advanced technology is indistinguishable from magic.” There is a distinct magic to emergence, but it happens only if the network’s nodes are free to share information and processing power.
Many ordinary, non-technical people realized the magic of data from Internet-centric companies like Amazon where, in the late 1990s and early 2000s, they first saw statements like, “People who bought this also bought this.” Later it became “People who clicked on this also clicked on this,” and then it moved beyond “people” and started being about you: “The store you made.”
Soon, Amazon was sitting up on the table and having opinions: “We think you’ll like this.” And to an uncanny degree, the suggestions of this intangible edifice were correct. You’d never even heard of half the things Amazon was recommending, but they were right up your alley. Amazon stopped being a “store” and started being an intelligent entity that—to some very real degree—understood who you were and what you cared about.
Obviously, this was all the result of massive data analysis. And so, with further innovations like vision inspection systems powered by self-organizing sensor networks processing voluminous amounts of data, computers were able for the first time to understand and form associations based on statistical methods. Computers could, all of a sudden, do what we previously thought only humans could do.