IBM Opens Watson IoT Global Headquarters In Germany
IBM said on Tuesday that it will employ 1,000 staff to work with customers on industrial Internet projects in Munich and is opening up eight satellite centers worldwide to help clients create smarter network-connected services. Munich, Germany will serve as the global headquarters for IBM's new Watson IoT unit, as well as its first European Watson innovation center.
The campus environment will bring together IBM developers, consultants, researchers and designers to drive engagement with clients and partners, and will also 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.
IBM also will deliver Watson APIs and services on the Watson IoT Cloud Platform to accelerate the development of cognitive IoT solutions and services.
With these moves, clients, start-ups, academia and IBM's IoT partners 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 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.
Siemens Building Technologies, which makes energy-efficient and environmentally friendly buildings and infrastructures, announced that it is teaming with IBM to bring analytics capabilities together with IBM’s IoT solutions to advance their Navigator platform for energy management and sustainability.
IBM is bringing the power of cognitive analytics to the IoT by making four families of Watson API services available as part of a new IBM Watson IoT Analytics offering.
The four new API services include:
- The Natural Language Processing (NLP) API Family enables users to interact with systems and devices using 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 automates data processing and continuously monitors 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 enables 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 enables 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.
Cognitive computing represents a new class of systems that learn at scale, reason with purpose and interact with humans naturally. Rather than being explicitly programmed, they learn and reason from their interactions with us and from their experiences with their environment, enabling them to keep pace with the volume, complexity, and unpredictability of information generated by the IoT.