IBM has announced the opening of its global headquarters for Watson Internet of Things (IoT), located in Munich, Germany. At the same time, IBM has launched a series of new IoT offerings, capabilities and ecosystem partners designed to extend the power of cognitive computing to the billions of connected devices, sensors and systems that comprise the IoT.
Munich, Germany will serve as the global headquarters for IBM’s new Watson IoT unit, as well as its first European Watson innovation center. It represents IBM’s largest investment in Europe in more than two decades. The campus environment will bring together 1,000 IBM developers, consultants, researchers and designers to drive deeper engagement with clients and partners. IBM’s IoT headquarters will serve as an innovation lab for data scientists, engineers and programmers building a new class of connected solutions at the intersection of cognitive computing and the IoT.
“The Internet of Things will soon be the largest single source of data on the planet, yet almost 90 percent of that data is never acted upon,” said Harriet Green, general manager, Watson IoT and Education. “With its unique abilities to sense, reason and learn, Watson opens the door for enterprises, governments and individuals to finally harness this real-time data, compare it with historical data sets and deep reservoirs of accumulated knowledge, and then find unexpected correlations that generate new insights to benefit business and society alike.”
IBM Watson IoT Client Experience
IBM has also announced its plans to deliver Watson APIs and services on the Watson IoT Cloud Platform to accelerate the development of cognitive IoT solutions and services, helping clients and partners make sense of the growing volume and variety of data in a physical world that is rapidly becoming digitized.
With these moves, clients, start-ups, academia and an ecosystem of IoT partners – from silicon and device manufacturers to industry-oriented solution providers – will have direct access to IBM’s open, cloud-based IoT platform to test, develop and create the next generation cognitive IoT apps, services and solutions.
IBM also announced that it has opened eight new IBM Watson IoT Client Experience Centers across Asia, Europe and the Americas. Locations include Beijing, China; Boeblingen, Germany; Sao Paulo, Brazil; Seoul, Korea; Tokyo, Japan; and Massachusetts, North Carolina, and Texas in United States.
These IoT Client Experience Centers provide clients and partners access to technology, tools and talent needed to develop and create new products and services using cognitive intelligence delivered through the IBM Watson IoT Cloud Platform.
New Watson IoT Cloud API Services
The new Watson APIs and services on the Watson IoT Cloud Platform are established to accelerate the development of cognitive IoT solutions and services. IBM’s four new IoT API services include:
- The Natural Language Processing (NLP) API Family would enable users to interact with systems and devices using simple, human language. Natural Language Processing helps solutions understand the intent of human language by correlating it with other sources of data to put it into context in specific situations. For example, a technician working on a machine might notice an unusual vibration. He can ask the system “What is causing that vibration?” Using NLP and other sensor data, the system will automatically link words to meaning and intent, determine the machine he is referencing, and correlate recent maintenance to identify the most likely source of the vibration and then recommend an action to reduce it.
- The Machine Learning Watson API Family would automate data processing and continuously monitor new data and user interactions to rank data and results based on learned priorities. Machine Learning can be applied to any data coming from devices and sensors to automatically understand the current conditions, what’s normal, expected trends, properties to monitor, and suggested actions when an issue arises. For example, the platform can monitor incoming data from fleet equipment to learn both normal and abnormal conditions, including environment and production processes, which are often unique to each piece of equipment. Machine Learning helps understand these differences and configures the system to monitor the unique conditions of each asset.
- The Video and Image Analytics API Family would enable monitoring of unstructured data from video feeds and image snapshots to identify scenes and patterns. This knowledge can be combined with machine data to gain a greater understanding of past events and emerging situations. For example, video analytics monitoring security cameras might note the presence of a forklift infringing on a restricted area, creating a minor alert in the system; three days later, an asset in that area begins to exhibit decreased performance. The two incidents can be correlated to identify a collision between the forklift and asset that might not have been readily apparent from the video or the data from the machine.
- The Text Analytics API Family would enable mining of unstructured textual data including transcripts from customer call centers, maintenance technician logs, blog comments, and tweets to find correlations and patterns in these vast amounts of data. For example, phrases reported through unstructured channels – such as “my brakes make a noise”, ”my car seems to slow to stop,” and “the pedal feels mushy” – can be linked and correlated to identify potential field issues in a particular make and model of car.