With the technology landscape ever changing, it seems like there is a new hot buzzword online or acronym being thrown around phone calls every day. Oftentimes, words are used improperly or without a true understanding of their relationship to the technology at hand or business problem in question. It can be hard to sort through all of the noise.
To help you streamline your learning and give a clear picture into a few of the buzzwords, I’ll try to de-mystify what cognitive computing means, where it came from, why it is so popular, and what it means to you and your customers.
If you were to do research – simply performing a Google search—you will find that there is no true definition of cognitive computing. The word has risen in popularity because of Watson, IBM’s cognitive computing technology platform. IBM started using the word Watson to brand the type of technology that thinks more like a human and uses “cognitive” often to describe this new era of computing.
Factors Driving Cognitive Computing
So what is driving us into this new era? There are several factors that are making cognitive popular and really driving the emergence of this new era of computing.
1) Small-scale Practical Integration
Slowly but surely, cognitive computing is becoming more mainstream in smalls ways such as natural language processing in call centers. Businesses are demanding to learn more about cognitive to come up to speed with other industries who are utilizing this type of software and figure out how to use cognitive to get ahead in their own industry. You can see this in manufacturing plants and virtual assistants in human-less grocery stores, automated eateries, and, of course, driverless car services.
2) Data-driven Programming
Chatbots are also growing as a robust tool to gather business intelligence and advance artificial intelligence through responsive technology. Chatbots are technologies that allow a real human to chat with a program that is simulating human speech. Many tech platforms are already helping developers build chatbots on their platform, and businesses are now increasingly using chatbots through popular messengers as front ends for cognitive tools.
3) Edge Computing
Many Internet of Things (IoT) devices require high-speed data processing and analytics with short response times that are difficult to meet by sending data to a centralized cloud. Think of the use cases of vehicle-to-vehicle communication or medicine, where milliseconds matter. Edge and fog computing solve this pain point by placing processes and resources at the edge of the cloud, while data is actually stored within the cloud. This solves the bandwidth problem and results in faster local processing, delivering near real-time analytics.
4) Serverless Computing
Function-as-a-Service (FaaS) and serverless architectures are shifting how companies think about infrastructure that is lightweight and can be triggered on an action. Developers can now focus on an application without needing to build it for a specific configuration, scaling it up or down, or spinning up a virtual machine (VM) or containers – it all happens through automatic sequences. IoT edge devices and gateways will gain great value from these architectures, here, too, enabled by less human intervention.
Defining the buzz
Definitions for cognitive computing may vary but recognizing the driving forces for cognitive is more attainable then pinpointing a universal definition. Having an understanding for cognitive computing’s background allows you to leverage the context of a “buzzword” into a worthwhile conversation with your customers.
Knowledge leads to improved customer service. In this blog series, you will gain a better understanding of cognitive computing to better serve your end-customers data-driven concerns. Discover how the difference between artificial intelligence (AI) and cognitive computing plus the relation to data science can sharpen your knowledge of cognitive computing for the better.