How Cognitive Computing Can Be Used in Real Life
Cognitive Computing refers to the next generation of computing systems that will communicate in natural language.
FREMONT, CA: Cognitive computing is a novel synthesis of human science and information technology. Cognitive science studies the human brain and how it operates and integrates this knowledge with computer technologies, most notably artificial intelligence, natural language processing, and coding algorithms that replicate how the human brain works.
Thus, the objective of Cognitive Computing is to imitate human thought processes using self-learning algorithms in a computerized model. The utilization of data mining, speech recognition, vision, dialogue recognition, pattern recognition, machine learning, and natural language processing is critical. As a result, the computer will be able to simulate the way the human brain operates.
Cognitive computing has a plethora of potential applications. It is capable of performing ordinary jobs as well as complex tasks requiring logical thought. The following are some viable business uses for cognitive computing:
Chatbots: Chatbots are computer programs that can replicate human conversations by comprehending the context of the message. This is accomplished through the use of a machine learning approach called natural language processing. Natural language processing enables computers to receive human-generated information (speech or text), analyze it, and produce logical responses. Cognitive computing enables chatbots to communicate with a certain level of intelligence. For example, determining a user's wants based on the previous conversation, making suggestions, and so forth.
Affective analysis: Sentiment analysis is the discipline of deciphering the emotions expressed in written or spoken communication. While it is very simple for humans to comprehend the tone, intent, and other aspects of a conversation, it is significantly more difficult for machines. To train robots to interpret human communication, one must feed them training data from actual human talks and then evaluate the analysis's correctness. Sentiment analysis is frequently applied to social media communications such as tweets, comments, reviews, and complaints.
Facial recognition: Face detection is a more sophisticated form of picture analysis. A cognitive system analyses data about the face's structure, features, and eye color to distinguish it from others. After generating a facial picture, it can identify the subject's face in an image or video. While this was initially accomplished using two-dimensional images, it can now be accomplished utilizing three-dimensional sensors for increased precision. This is useful for security systems such as lockers or even mobile phones.
Assessment of risk: Risk management in financial services entails analyzing market trends, historical data, and other factors to forecast the degree of uncertainty associated with an investment. However, this analysis is not limited to data but includes patterns, gut feelings, and behavioral analytics. As such, it is a combination of art and science. Analyzing big data (i.e., analyzing historical trends alone) is insufficient for risk assessment. Due to the intuition and knowledge required for market forecasting, it is vital to create sophisticated algorithms. Cognitive computing enables the analysis of behavioral data and market trends to derive insights. These can then be analyzed and forecasted further by skilled analysts.