Cognitive computing (CC) refers to technology platforms that simulate human thinking in order to improve human decision-making. It is a combination of cognitive science and computer science.
Cognitive computing platforms are self-learning systems that combine machine learning, reasoning, natural language processing, speech and vision recognition, human-computer interaction, dialog and narrative generation, as well other technologies.
In this article, we will take a deep dive into what cognitive computing is and how it works.
What is Cognitive Computing?
Cognitive computing is a term used to describe technology platforms that mimic human behavior and reasoning to help regular computing better solve problems and analyze data. These systems are designed to learn at scale, reason with purpose, and interact with humans naturally.
The goal of CC devices is to simulate the human brain’s thought process in a computerized model. It tries to replicate how humans would solve problems and performs specific tasks that facilitate human intelligence.
AI and Cognitive Computing
Because it has the ability to solve complex problems, cognitive computing is often used interchangeably with AI, but the two are fundamentally different.
Cognitive computing attempts to mimic human behavior in order to help humans solve problems. AI solves problems using the best possible algorithm with the goal of minimizing the role of humans in the decision-making process.
They both utilize technologies such as machine learning, deep learning, neural networks, but instead of making a decision, CC supplements information for humans to make it easier for them to make a decision.
How Does Cognitive Computing Work?
Cognitive computing is accomplished by combining cognitive and computer sciences.
Engineers build and code cognitive functions into computer systems to create technology platforms that can solve complicated problems without constant human intervention.
These systems contain self-learning technologies that use data mining, pattern recognition, and natural language processing (NLP) to mimic the way the human brain works.
A long-term goal for CC is to facilitate the creation of automated IT models capable of solving problems without human assistance.
Features of Cognitive Systems
Cognitive computing is a broad term and one that does not yet have a universally agreed-upon definition in either academia or the technology industry.
However, CC devices usually possess five key capabilities in order to accurately simulate human thinking.
Those five features are:
- Adaptive: CC devices must be adaptive systems that are capable of learning as information changes and as goals and requirements evolve. They should be able to resolve ambiguity and tolerate unpredictability.
- Interactive: These devices should be able to interact with other processors, devices, cloud services, and humans as well.
- Iterative and stateful: They are able to define a problem by posing questions or finding additional source input if a problem statement is ambiguous or incomplete. They are also able to recall previous interactions in a process and return information that is suitable for the specific application at a given point in time.
- Contextual: CC devices should be able to understand, identify, and extract contextual elements such as syntax, meaning, time, location, appropriate domain, regulations, user profiles, process, task and goal. They can pull from multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).
Cognitive computing involves using computerized models to simulate human thinking.
To accomplish this, CC technology platforms are adaptive, interactive, interactive and stateful, and contextual.
While it is different from AI, CC provides a realistic roadmap for a way to achieve true artificial intelligence.
To learn how CC devices are able to run several calculations or processes simultaneously, read about GPU Parallel Computing.