Cognitive computing combines several different technologies to develop its cognitive models.
Natural Language Processing : As its goal is to work side by side with and learn from human intelligence, cognitive systems must be able to understand human speech and written text. Natural language processing (NLP) is the area of computer science concerned with this. Cognitive systems can receive, understand, interpret, and offer feedback in written and spoken forms that emulate human syntax. Although this feat is incredibly difficult given the considerable variances in human communication, NLP technology is advancing at a rapid rate.
NLP is responsible for predictive text, Google’s ability to guess search parameters, communications with Siri and Alexa, and natural-sounding chatbots.
While NLP is the most innovative tool cognitive computing utilizes, other artificial intelligence processes work within its parameters as well.
Machine Learning : ML uses neural networks, a computer system modeled after how the human brain processes information. It is an algorithm designed to recognize patterns, calculate the probability of a certain outcome occurring, and “learn” through error and successes using a feedback loop.
Deep Learning : Deep learning is an approach to machine learning that falls under artificial intelligence (AI), which is most commonly used to label vast and complex data through ANN.
Artificial Neural Networks : An artificial neural network (ANN) is a framework modeled after the human brain. This framework consists of inputs, outputs, and hidden layers. Artificial neural networks have artificial neurons that weigh input data and categorize aspects of that data, connect to other neurons, and feed it further down the classification funnel. ANN is an approach to machine learning that is most commonly used to label data. It is used in computer vision, speech recognition, medical diagnoses, and other ML categorization applications.
