Since the inception of computer and information technology, artificial intelligence has always been one of our most important goals. With the help of cognitive computing models, we are far closer to achieving our primary aim.
Cognitive computing derives its idea directly from cognitive science where you can study how our human brain works and functions. Cognitive computing can significantly impact our daily lives as well as benefit business, healthcare, and other industries.
Definition of cognitive computing
Cognitive computing is a subtopic of artificial intelligence and it can help machines invigorate the human thought process with the effective utilization of self-instructing algorithms using natural language processing, data mining, and pattern recognition.
Furthermore, neural networks and deep learning algorithms are used in artificial environments to process the data while comparing it with the trained data set. By imitating the human thought process, machines or computers can make better and trouble-free decisions. Due to the machine-human interactions, cognitive computing is also popular as Augmented Intelligence.
The technological aspect of cognitive computing
A lot of people think that cognitive computing is a standalone technology but this common notion is factually incorrect. To be more precise cognitive computing is an amalgamation of multiple technologies which helps it to think like a human mind. Some of the important technologies used in cognitive computing are:
Through machine learning, a system can easily learn even though it is not programmed straightforwardly. Furthermore, machine learning algorithms can be classified in multiple ways such as dimensionality reduction, clustering, anomaly detection, regression, etc. The module of machine learning works as the core whereas the utilized algorithms assist the cognitive system to perform difficult tasks.
Natural Language Processing
Natural Language Processing itself is an amalgamation of artificial intelligence, computer science, and computational linguistics. Its primary aim is to help computers or machines process human languages. Natural Language Understanding NLU and Natural Language Generation NLG are two of the most important subdomains in Natural Language Processing.
With the help of NLP, cognitive systems can decipher the data sources of natural or human language as well as provide perception in the shape of natural language. For applications such as large-scale content analysis, chatbots, text mining, narrative/dialogue generation, sentiment analytics, virtual assistants, etc. NLP is extremely crucial.
Machine reasoning makes use of accessible knowledge while utilizing logical techniques such as induction and deduction to generate a conclusion. In a cognitive system, machine reasoning works as a brain or decision-making engine. The outcomes or results of other modules such as NLP, ML, etc. are validated by machine reasoning.
Apart from the decision-making and validating part, machine reasoning can also act as an independent module to solve different problems. Some of the most popular machine reasoning systems are machine learning systems, procedural reasoning systems, rules engines, deductive classifiers, etc.
Through speech recognition machines or systems can recognize words and phrases from spoken languages and transform them into an arrangement that is readable by the machine. It also has other names such as computer speech recognition, speech to text, etc. Some of the most common applications of speech recognition are home automation, virtual assistant, interactive voice response, voice search, etc.
Important benefits of cognitive computing
Cognitive computing has multiple applications and uses cases catering to different industry requirements. Here are some of the major applications and benefits of cognitive computing:
Better customer experience
Cognitive computing can enhance customer service and customer experience for businesses. Companies can utilize various cognitive computing applications such as behavioral predictions, cognitive assistants, social intelligence, and personalized recommendations to increase customer engagement.
Since cognitive computing can efficiently imitate human tasks and capabilities it can increase the outcome quality and productivity of the employees. Some of the applications of cognitive computing that increase productivity are automated data scientists, cognitive assistants for doctors, robo advisors for wealth management, etc.
99.5 percent of data available in the world is not analyzed and cognitive computing can assist with that. With the help of cognitive computing, you can analyze all these untapped resources to create revenue streams and business opportunities.
Using cognitive computing machines or computers can think like or imitate human beings while expanding further opportunities and creating new scopes for us. We can gather data, make accurate predictions and draw conclusions from the augmentation of human knowledge with cognitive computing.