IBM ahead of curve
IBM probably has the deepest understanding of smart systems today. Dozens of its so-called smarter-planet systems are already solving widespread infrastructure problems worldwide, including a smart transportation system in Stockholm, Sweden; a national smart grid—the world’s first—in Malta; and a smart wireless sensor network that protects paintings at the New York Metropolitan Museum of Art.
“Smart systems are generating eight times more data every day than there is in all the U.S. libraries combined—85 percent of which is unstructured,” said Jai Menon, IBM fellow, chief technology officer and vice president of technical strategy for the company’s systems group. “Business intelligence has the problem of using analytics to derive value from all that unstructured data, and Watson is a good example of how to answer questions about unstructured data very quickly.”
Traditional IT analytics were run on structured data that was carefully tailored by database experts into neat, isomorphic containers that could be easily searched, sorted and analyzed using well-known mathematical formulas. But Watson proved that messy, unstructured data can also be easily searched, by virtue of cleverly crafted analytics designed to run on optimized system architectures that preposition the technological capabilities needed to address specific unstructured problem domains.
“Analytics for the financial markets—such as predicting commodity prices—is a cyclical phenomenon driven by well-known patterns. But predicting the risk of failure in infrastructure—say, how long a water pipe will last—is what we call an unstructured problem,” said Arun Hampapur, distinguished engineer and director of business analytics at IBM Research. “And analytics for unstructured problems is best done by instrumenting a strategy that custom-tailors the analytics and architecture for a particular problem domain.”
IBM’s latest foray into addressing unstructured problem domains with smart systems is aimed at using Watson to create automated advisers for apps in health care, banking and finance, retailing, law and governmental regulation. “We get calls every day from industry leaders who want to repurpose Watson for new applications, such as helping to make airline reservations faster, better, easier,” said Hampapur.
For instance, Wellpoint Inc. (Indianapolis), the nation’s largest health-care provider, recently announced that it would use a Watson-derived smart system to simplify and speed medical diagnoses by matching patients’ symptom sets with data from millions of medical records, journal articles and late-breaking medical-research results.
Watson is based on technologies that IBM created to solve unstructured problems in smart-city projects. IBM started its quest for such smart systems by leveraging its strengths in the data center, where traditional analytics are run. But it has been steadily working out toward the edge of the connectivity network, where analytics can be run on the embedded processors themselves. Menon noted that Chicago police “use smart analytics at the edge to automatically turn surveillance cameras toward a gunshot, so that by the time the 911 call comes in they already have a readout of the caliber of gun that was used and a camera pointing at the location from which it was fired.”
IBM has spent more than $15 billion in the past few years acquiring companies with specialized analytics expertise, and it is building a new generation of cognitive-computer chips for smarter systems that can fuse the inputs from multiple sensors.