Artificial intelligence is the big, oft-misconstrued catchphrase of the day, making headlines recently with the launch of the new OpenAI organization, backed by Elon Musk, Peter Thiel, and other tech luminaries. AI is neither a synonym for killer robots nor a technology of the future, but one that is already finding new signals in the vast noise of collected data, ranging from weather reports to social media chatter to temperature sensor readings. Today IBM has opened up new access to its AI system, called Watson, with a set of application programming interfaces (APIs) that allow other companies and organizations to feed their data into IBM’s big brain for analysis.
Real AI isn’t about building a know-it-all computer, but rather one that’s a good learner, able to sort overwhelming amounts of data, and diligently catalog recurring patterns. For example, while working with sensor readings and other flight data from airliners, AI might spot the conditions that caused a plane to burn up too much fuel, a project that IBM is already undertaking with plane manufacturer Airbus.
Data-spewing machines such as planes are components of (another catchphrase) the Internet of things (IoT). It’s essentially the online linking of a turbine, water pump, plane, or any other device beyond the usual suspects: computers and smartphones. In this area, IBM is up against Predix, GE’s own new cloud brain for analyzing data from machinery.
IBM is going beyond industrial devices with Watson, though, opening it up to other “things” such as videos, people’s voices, or text from Twitter. Like an isolated sound reading from a turbine, the meaning of a phrase such as “pedal is soft” (an example IBM gave me) isn’t immediately clear to a computer. It’s “unstructured data” that requires sorting out to understand. But after reading enough tweets and other text, AI can figure out that that particular phrase means the brakes aren’t working well.
The opening of Watson’s interface exposes to the world what IBM has already been doing within a few pilot programs. The company has been combining unstructured data with straight-up traditional measurements in a project with the Beijing Environmental Protection Bureau (EPB), to track and forecast air pollution conditions for the city. “Using not just very structured stuff but videos that people are taking, call-center transcripts . . . and blogs, all this unstructured data . . . we’ve been able to identify very accurately exactly where the pollutants are coming from [and] how they are moving,” says Harriet Green, IBM’s general manager for Watson IoT and Education. IBM claims that its efforts have led to a 20% reduction of one pollutant, ultra-fine particulate matter, although Beijing has a long way to go, given recent reports of its most dangerous air pollution ever.
Green will be running outreach from IBM’s new 1000-person Watson IoT Global Headquarters in Munich. The new HQ will develop projects directly with clients, such as one with telecom company Vodaphone and local governments in the Andalucía region of southern Spain. Vodaphone plans to invest, over two years, €243 million ($267 million) on new infrastructure that includes IoT sensors and data analytics.
What exactly IBM and Vodaphone can achieve remains a bit murky. Green describes the undertaking with some vague phrasing like, “Using data about [people’s] moods, their state of being around the services they provide, how they’re texting and blogging–all with permissions naturally–to really develop systems, networks, responses . . . that are in concert with what ordinary people really want.”
Asked for a specific example, she brings up the topic of potholes. People post text, photos, and videos about “enormous numbers of potholes” in southern Spain, she says. Analyzing all those posts could help cities find out where the holes are, and which ones most frustrate drivers. It’s not glamorous, but it’s something that people really do care about.