Source: Deep Learning on Medium
Cognitive Computing is often used interchangeably with artificial intelligence, not that it’s entirely incorrect. MIT Sloan School of Business, for instance, defines the term as a system that adaptively utilizes underlying algorithms or processing with exposure to new data.
It wasn’t always a hot trend though. The present dictates, otherwise, with much of the steam being attributed to the:
1. Massive Computing Power of New-Generation Computers
2. Greatly Increased Capacity of Storage Devices (Petabytes of Data on Cloud)
3. Effortless and Fluid Access to Digital Data
This was an expected outcome, with the concept and potential of cognitive computing having existed for decades. Today, Amazon Alexa, Apple Siri, IBM Watson, Google Go, and more stand to drive awareness around ideas of cognitive computing. While applications exist for both the end consumer and enterprise, the latter requires a heightened focus on precision and accuracy.
Is Cognitive Computing Business Ready?
Cognitive Computing isn’t a universal solution to problems, neither is it omniscient. The technology is most effective with narrow and well-defined tasks. As an example, take an insurance company amid auditing its reimbursement claims; while the rules for reimbursement are largely exhaustive and complex for human comprehension, they are well-defined and therefore well-suited to Cognitive Computing.
How Data Defines Outcomes
Data is critical to successful cognitive outcomes — the more qualitative the data, the more successful the outcome. At the same time, low-quality data is bound to produce unreliable or ineffective outcomes.
Cognitive Computing also becomes ineffective in the face of dynamic or rapidly changing data, which implies the possibility of relevant data becoming irrelevant quickly. This diminishes the value and credibility of the insights generated. Presently, use cases with rapidly fluctuating data are not considered good for Cognitive Computing.
Identifying the Human Component
Cognitive Systems is an augmentative technology and requires human support to become effective. During infancy, the technology is known to produce incorrect outcomes, which is why human intervention mandates itself to a 4–6-month training period depending on the situation.
At the end of the day, humans teach Cognitive Systems right from wrong, allowing better learning and growth for more accurate future predictions.
Cognitive Technology is part of a larger digital evolutionary cycle. It starts with a company seeking digital transformation; perhaps with the identification of manual processes that require automation. With an evolving marketplace, several technology companies — such as IBM — offer automation engines designed to convert manual processes into automated processes. You can call this the foundation for a digital enterprise.
In time, and with a certain level of maturity, organizations can apply analytics to derive insights and improve outcomes. It might start with being descriptive, providing insights into past events. But with enough data sets in addition to analytic-insights, data sets transform, becoming patterns and then sets of patterns usable to predict future events and outcomes. With predictive analytics, Cognitive Systems start to emerge.
As a technology, Cognitive Computing is more robust than it was in the past, expanding rapidly into both end-consumer and enterprise avenues. We now see positive use of the technology in financial, healthcare, and other fields. This is just the beginning. It holds the potential to support humans in enabling effectiveness and efficiency around important and creative work.
Does that sound like too daunting a task? Well, you could always start by improving mundane processes within your firm and look ahead to a powerful, limitless future.