IBM Watson and Four New IOT Services - New APis include:
- Natural Language Processing - Allows users to interact with systems and devices using simple human language.
- Machine Learning - Automates data processing by continuously learning from every interaction with data
- Video and Audio Analytics - Finds correlations and anomalies in unstructured data.
- Text Analytics - Draws insights from Twitter feeds, blogs, call center transcripts
New Watson IoT Services Accelerate Cognitive IoT
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. As the physical world of devices and systems are becoming highly digitized, these capabilities will allow clients, partners and developers to make greater sense of this data through machine learning and correlation with unstructured data.
The four new API services include:
The Natural Language Processing (NLP) API Family 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 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.
Going to poke at these in the next week or so...
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. Cognitive systems can make sense of the 80 percent of the world’s data that computer scientists call “unstructured,” which means they can illuminate aspects of the world that were previously invisible, allowing users to gain greater insight and to make more informed decisions.
About this blog
This is an informal blog that explores tools, code and tricks that group members have developed to engage IBM Watson cognitive computing services - from the R Programming Language. Packages include RCURL to access Watson APIs - for services that include Natural Language Classifier and Speech to Text. THIS IS MY PERSONAL BLOG - it does not represent the views of my employer. Code is presented as 'use at your own risk' (it has lots of bugs)
Created: September 13, 2015English