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As the Internet of Things continues to accelerate, the sheer volume and variety of data requires that we get smarter about how we process it and respond to insights. We need tools and capabilities that capture the human ability to understand intent, reason through options, and learn from successes and failures. If we don’t take this next step, we’ll be stuck using yesterday’s tools to solve problems that are orders of magnitude harder to solve. Enter Cognitive IoT. What do I mean by Cognitive IoT? It’s about building smarter solutions that interact naturally with humans through voice and textual commands and understand intent; infusing them with intelligence to rank and prioritize responses based on the most likely outcomes; and incorporating feedback loops that help systems learn about changing environments, changing intent, and the efficacy of specific actions.

Cognitive Analytics for IoT

Today, IBM announced it is enabling exactly these sorts of capabilities by bringing the power of cognitive analytics to IoT. IBM will offer a set of Watson APIs as part of its IoT Foundation platform. As the physical world of devices and systems becomes increasingly digitized, these capabilities will allow clients, partners and developers to make greater sense of this data through machine learning and correlation with unstructured textual, video, and image data.

The APIs will enable capabilities in four key areas:

  • Natural Language Processing (NLP) enables users to interact with systems and devices using simple, human language. Natural Language Processing helps solutions understand the intent of human language by correlating with other sources of data to put interactions into the context of specific situations. For example, a technician working on a machine notices an unusual vibration. He can ask the system “What is causing that vibration?”. Using NLP and other sensor data, the system automatically links words to meaning and intent, determines the machine he is referencing, and correlates recent maintenance to identify the most likely source of the vibration and recommend an action to reduce the vibration.
  • Machine Learning 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 current conditions, what’s normal, expected trends, properties to monitor, and suggested actions when an issue arises. For example, the IBM IoT Foundation platform can monitor incoming data from a fleet of equipment and learn both normal and abnormal conditions. These conditions are often unique to each piece of equipment and its usage conditions, including environment and production processes. Machine Learning helps understand those differences and configures the system to monitor the unique conditions of each asset.
  • Video and Image Analytics enables monitoring of unstructured data from video feeds and image snapshots to identify scenes and patterns in video data. This can also be combined with machine data to gain a greater understanding of past events and emerging situations. For example, video analytics monitoring security cameras note the presence of a forklift infringing on a restricted area which creates a minor alert in the system. Three days later, the asset in the restricted area begins to exhibit decreased performance. The two incidents can now be correlated to identify a collision between the forklift and asset that was not readily apparent from the video or the data from the machine.
  • Text Analytics enables mining of unstructured textual data including transcripts from customer calls at call center, maintenance technician logs, blog comments, and tweets to find correlations and patterns in these vast amounts of data. For example, phrases such as “my brakes make a noise”, ”my car seems to slow to stop,” and “the pedal feels mushy,” reported through unstructured channels can be linked and correlated to identify potential brake issues in a particular make and model of car.

    Getting Smarter

    Cognitive analytics enable a new class of systems that interact naturally with end users and understand the intent of both verbal and textual input. These cognitive systems also help us make decisions through reasoning and advanced analytics that assert priorities and probable outcomes. And instead of being programmed with explicit logic, they learn from their interactions with us and the surrounding environment, enabling them to keep pace with the volume, variety, and unpredictability of information generated by IoT. Finally, cognitive systems understand unstructured data, like text, video, and images, and can correlate it with machine data to provide greater insights and help us make better, faster decisions.

    The next few months will be exciting indeed! How will you use cognitive capabilities in your IoT solutions?

    Want to learn more?

  • Press release for today’s announcement.
  • Detailed article about using cognitive capabilities in an IoT app.
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