Cognitive Computing

Cognitive Computing has brought the power of Artificial Intelligence (AI) to enterprises across the globe, letting them make better decisions and that too, at a lightning-fast pace. Combined with the power of the cloud, Cognitive becomes more accessible and easy-to-implement.

So, what does this mean for enterprises undergoing digital transformation? What would an implementation roadmap look like, if you’re looking to get measurable ROI from your cognitive systems? In this series of blogs, we consider these critical questions and offer answers, as well as a glance at future possibilities.

What is Cognitive Computing?

Cognitive Computing refers to machine systems that can mimic human understanding of the environment, bringing an immense level of contextualization and intelligence to business processes. Cognitive Computing is closely related to artificial intelligence and its multiple subsumed technologies (image recognition, pattern recognition, machine learning, natural language processing, and the like).

It differs from traditional data analytics, owing to its agile, interactive and contextual properties. A cognitive algorithm, for example, can intuitively change in response to real-time data and help you make more accurate decisions. And, the interfaces used for Cognitive are very high on interactability, letting users deep-dive into the insights and multiple predictive scenarios available. But the biggest USP of Cognitive is possibly its power to contextualize information. Equipped with in-the-moment data on customers/users/machines, algorithms can make decisions that are relevant and effective.

So, how is Cognitive Computing related to the cloud? In many ways, their intersection is the next logical move in the progression of digital technology. Let’s unravel this further.

Cognitive Computing on the Cloud: An Intelligent Combination

Expectedly, Cognitive is highly resource-intensive, requiring powerful servers, deep technical skillsets, and often leading to a high degree of technical debt. For example, developing machine learning models not only consumes significant computing power but also adds technical debt with every learning cycle. That’s why, for a long time, Cognitive was limited to large enterprises such as the Fortune 500s.

This has been completely overturned by the cloud. The Cloud allows developers to build Cognitive models, test solutions, and integrate with existing systems without needing physical infrastructure. While there are still resource costs involved, enterprises can flexibly subscribe to Cloud resources for Cognitive development and downscale as and when necessary.

Traditionally, Cognitive would only make sense for large enterprises from a purely ROI standpoint. They would commit sizeable time, effort, and investments in R&D, and could afford delays/uncertainties in value generation. Now, even small-to-mid-sized businesses can utilize the Cognitive Cloud to apply AI as part of their day-to-day IT ecosystem, rapidly generating value without infrastructure of vendor dependencies.

 

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