Cognitive computing is a somewhat ambiguous term, and definitions can vary across industries. It is commonly considered a type of artificial intelligence that can simulate human thought.
While cognitive computing can refer to technology that simulates human capabilities like thought, it can also indicate a machine that simulates human processes and has biological realism. In other words, it does not describe a specific algorithm, technique, or even a core capability.
The goal of cognitive computing is to recreate the human thought process within a computer model. These models can process data as the human brain does through the use of self-learning algorithms that use data mining to recognize patterns. Understanding the context of the information is critical, so cognitive computing models must pull from various data sources to ensure the data is interpreted correctly.
Many view this step as the progression to the third level of computing, where tabulating sums was the first, and programmable systems were second. Historically computers were able to compute calculations and process data faster than humans but struggled to recognize unique objects or interpret natural language.
Some tend to associate cognitive computing with technology that is meant to replace work done by knowledge workers. Still, in reality, it presents an opportunity for employment growth in new areas.
The most well-known example of cognitive computing is IBM’s Watson, which uses neural networks and deep learning algorithms to process information. As Watson is exposed to more data, it can learn better and becomes more accurate.
This has significant implications for industries like healthcare, where a cognitive computing model could interpret patient symptoms and cross-reference their personal medical history with best practices and journal articles.
It doesn’t matter if you only have BI analysts, IT leaders, or executives wanting to leverage cognitive computing or if you have a team of in-house data scientists looking to be more impactful with their analytic output. Any organization looking to start or advance their AI journey can benefit from cognitive computing.
At LogicPlum, our experts have spent years engineering our automated machine learning platform to offer the best possible solution for our clients. Our data science workflow is completely automated, providing you with all the tools that you need to build, deploy, and maintain machine learning models within your business.
Our goal is not to use cognitive computing to replace human capabilities, but rather give your business and analytics professionals the tools and resources they need to be successful in the data science space.
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