IBM Opens Watson IoT Headquarters in Munich
The second family of APIs is the Machine Learning Watson API Family, which 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, IBM said. 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, IBM said. 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. And 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, IBM said phrases reported through unstructured channels -- such as “my brakes make a noise,” or “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. IBM estimates that there are more than 9 billion connected devices operating in the world today, generating 2.5 quintillion bytes of new data daily. Making sense of data embedded in intelligent devices is creating a significant market opportunity that is expected to reach $1.7 trillion by 2020. Yet without a proper infrastructure in place to analyze all of this data in real-time, its value is minimal.Building on the investment, in October the company revealed plans to acquire the B2B, mobile and cloud-based Web properties of The Weather Channel. Upon completion, the combination of advanced cloud, analytics and security technologies and deep industry expertise is expected to serve as the foundation for the Watson IoT Cloud Platform as well as new services and offerings.
Cognitive systems help overcome this challenge – learning at scale, reasoning with purpose, and interacting with humans naturally. In March, IBM announced that it intended to invest more than $3 billion to address the needs of clients that are looking to capitalize on the increasing instrumentation and interconnection of the world driven by the Internet of Things.