Last week at CES, IBM’s CEO Ginni Rometty offered a few impressive examples about how partners like Under Armour or Softbank are leveraging IBM Watson in their mobile and wearable solutions. This announcement, represents one of the latest examples of how cognitive computing is transforming mobile and Internet of Things (IoT) solutions.
Cognitive computing is one of the most exciting developments in software technology in the past few years. Conceptually, cognitive computing focuses on enabling software models that simulate the human thought process. More specifically, cognitive computing enables capabilities that simulate functions of the human brain such as voice, speech, and vision analysis. From this perspective, cognitive computing is becoming an essential element to enable the next wave of data intelligence for mobile and IoT solutions.
Text, vision, and speech are common sources of data used by mobile and IoT solutions. However, until recently, developers didn’t have the mechanisms to programmatically interpret and interact with those data sources. By bridging the gap, cognitive computing offers new possibilities for IoT and mobile solutions.
Cognitive computing for enterprise mobile
Cognitive computing provides enterprise mobile solutions with interfaces for interacting with voice, video, and audio programmatically. Beyond the example of mobile digital assistants that everyone is familiar with, mobile cognitive computing is relevant in almost every industry. Imagine an insurance mobile app that allows clinical personnel to collect pictures and text to describe a medical condition and use cognitive APIs to analyze the data real time and provide recommendations of actions that should be taken. Similarly, in many verticals, such as construction or public safety, voice interfaces are a more natural way to interact with mobile applications than touch interfaces.
To be effective in the enterprise mobile world, cognitive computing platforms should provide interfaces optimized for each mobile OS. Those interfaces should enable abstract capabilities such as the interaction with cognitive computing APIs, picture encoding optimization, and data caching among other relevant elements of cognitive mobile applications.
Cognitive computing for enterprise IoT
Similar to mobile, enterprise IoT scenarios can greatly benefit from cognitive computing interfaces. These days, a large percentage of the data collected from IoT sensors is completely thrown away because of the difficulty to analyze it. Cognitive computing will enable scenarios like next generation car telematics applications to improve voice interaction dialogs with the passengers or analyze data collected from the vehicle sensors in real time. Similarly, industrial scenarios can benefit from gathering insights from voice, vision, and text data collected by sensors.
To be effective in enterprise IoT scenarios, cognitive computing platforms should provide specific solutions for vertical scenarios as well as SDKs for different device manufacturers. As the popularity of cognitive IoT interfaces evolves, we should expect to see new sensors and smart devices being created with embedded cognitive capabilities.
Making cognitive mobile and IoT computing a reality today
The last few years have brought us tremendous advancements in cognitive computing producing a series of platforms that developers can leverage today in mobile and IoT solutions. While IBM Watson remains the most sophisticated and best known cognitive computing platform in the space, recent efforts by Microsoft, Google, and new startups are accelerating the evolution of cognitive computing in the mobile and IoT space.
The most popular cognitive computing platform in the market, IBM Watson provides a diverse number of APIs to enable capabilities such as vision, speech, text, and data analysis. Watson is now available to developers as part of the Watson developer cloud included in Bluemix distributions. IBM is rapidly expanding Watson’s features in mobile and IoT scenarios and recently added Watson to its IoT Foundation to expand the cognitive capabilities of industrial IoT applications.
Microsoft’s Project Oxford provides a new series of cognitive computing APIs in areas such as face, speech, and computer vision. Currently, Project Oxford is available as part of Microsoft’s Cortana Analytics suite. Even though Project Oxford is relatively new compared to IBM Watson, it can become rapidly relevant as Microsoft integrates its capabilities across its products and platform suite. From that perspective, we should expect Project Oxford to be integrated into Microsoft technologies such as the advanced analytics suite, line of business solutions such as Dynamics, application platforms like Azure, or even consumer devices like Xbox or HoloLens.
Qualcomm Zeroth™ is a cognitive computing platform optimized for on-device intelligence. From a functional perspective, Qualcomm Zeroth™ enables capabilities such as visual perception, always-on awareness, speech recognition, intuitive security, and other cognitive computing features that are common in mobile and IoT solutions. One of the great advancements of Qualcomm Zeroth™ is the ability to execute large scale cognitive computing models on the Qualcomm Snapdragon mobile system-on-chip (SOC) providing, arguably, the first iteration of on-device cognitive computing models that can be embedded in mobile and IoT devices.
Google is actively expanding its cognitive computing capabilities. The recent release of Google’s Cloud Vision API expands Google Cloud with robust image recognition and analysis capabilities. Two years ago, Google acquired artificial intelligence startup DeepMind for $400 million. Even though DeepMind has remained very secretive about the feature set of its platform, it is expected that many of the capabilities will expand Google Cloud’s cognitive computing stack.
A recent acquisition by Intel, Saffron provides cognitive computing capabilities to make sense of data and predict future trends, events, and outcomes. Saffron Natural Intelligence Platform applies human-reasoning techniques like autonomous learning or contextual analysis to data collected from heterogeneous sources in order to continuously gather insights and predict outcomes. From that perspective, the Saffron platform effectively mimics human reasoning and memory.
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