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Today’s businesses are slowly shifting towards to a data driven Economy. Advent of emerging and disruptive technology stacks today makes it possible to make People, Processes and Things to not only co-exist but to Co-Interact.
IoT and AI are fast becoming the new IT in today’s modern-day enterprise ecosystem. While IoT enables an enterprise to get data out of the machines and makes it available for consumption, the AI engine leverages out of the box techniques such as Deep Learning (DL) and Advanced Correlations to derive meaning out of the data and then uses techniques such as Robotic Process Automation (RPA), Computer Vision (CV), Natural Language Processing (NLP) and related automation frameworks, to automate the data learning and turn into a business action, driving progress in this modern-day phenomenon of Human-Machine Converged Intelligence.
IoT essentially forms the first step towards ensuring, there are enough devices,
communicators and infrastructure medium that can ensure data availability. To enable IoT, it’s important to have a prelude of Cloud ready environment set up, given the large volume of Data that gets generated out of various smart devices. Typically, AWS, Azure and Google Cloud Platforms are the leading enablers of IoT in today’s enterprise world with a number of ready to use services to extract and accrue data coming out of various smart devices and machines.
Once the Data is available for consumption, typically the next step would be to automate the data into translated business actions. That is where AI comes in substantially handy in realizing the true potential of Data using advanced nuances of Machine Learning, and AI stack of technologies.
Typical business scenarios of today requires an amalgamation of IoT and AI to come to conclusive business actions driven by the learnings from the Data coming from IoT enabled sources. While the use cases can be many, but in the subsequent days, we have had demonstrated success in some of the below automation scenarios:
– Autonomous Image Object Detection in a Smart City
– Autonomous Video Classification in a Smart Surveillance
– Speech Classification for Criminal Offence Profiling
– Virtual Service Agents for Process Automation
– Bots for Incident Records Management
– Audio Classification for Autonomous Reception Service in Smart Offices
– Autonomous Image Segmentation for Smart Buildings
– Autonomous Image Classification for Smart Traffic Management
The decreasing cost of hardware, and more open structure of data consumption, has triggered a number of AIoT possibilities.