Artificial Intelligence (AI)
Artificial Intelligence (AI) is comprised of multiple technologies that make machines ‘smarter’ and able to communicate with humans in natural language. ‘Smarter’ means the computer achieving a user’s goal by copying human cognitive functions.
Conversational AI enables machines to understand, process, and respond to voice or texts in natural ways using Natural Language Processing (NLP). These capabilities enable users to interact with enterprise systems in faster and easier ways with a self-service experience. As a result, businesses can deliver personalized experiences and support at scale.
Machine Learning (ML)
Machine Learning is a type of AI that can be used to predict what users will do in the future. It allows software applications to more accurately predict outcomes without being programmed to do so. Machine Learning uses historical data as input to predict new output values.
Deep Neural Networks (DNN)
There are different approaches for building and training neural networks. Deep Learning is a Machine Learning technique that has a set of rules that makes it possible for the neural network to teach itself. This results in the machine being able to achieve success on goals it was never trained to solve.
Data Science (DS)
Data Science is a field of study that combines math and statistics, programming skills and domain expertise to glean meaningful cognizance from data. Data Science integrates AI, Machine Learning and Deep Learning to extract insights from data (exploratory data analysis) and make predictions from very large datasets (predictive analytics).
Predictive analytics is an element of Data Science. It makes predictions from large datasets by analyzing historical data as well as existing external data to find patterns and behaviors. Predictive analysis differs from Machine Learning, which is an AI technique where the algorithms are given data and are asked to process it without a predetermined set of rules and regulations.