Students’ Corner
A Brief Overview of AI and NLP in Data Science
Mr. Rushikesh Anil Kothawade Student IIM Amritsar
Artificial Intelligence (AI) is the ability of machines to do intelligent activities in the same way that humans do. AI employs intelligence to complete automated tasks.
Machine Learning (ML) is the process of creating systems that can learn from their experiences. Artificial Intelligence is also a subset of it.
Artificial Intelligence consists of 3 Stages mainly: a) Stage 1 - Machine Learning: It is a set of algorithms that intelligent systems use to learn from their experiences.
A biologically inspired network of Artificial Neurons is known as a Neural Network (NN).
b) Stage 2 - Machine Intelligence: This is a sophisticated set of algorithms that computers use to learn from their experiences. Deep Neural Networks, for example. This is where AI is right now. c) Stage 3 - Machine Consciousness: This is when a machine learns from its own experiences rather than relying on external data.
Types of Artificial Intelligence 1) ANI stands for Artificial Narrow Intelligence and includes basic/role duties performed by chatbots and personal assistants such as Apple's Siri and Amazon's Alexa. 2) AGI stands for Artificial General Intelligence, which includes human-level functions such as those done by Uber's self-driving cars and Tesla's Autopilot. It entails the machines' continuous learning. 3) Artificial Super Intelligence (ASI) refers to intelligence that is far superior to that of humans.
Difference Between AI, NLP, ML, NN&DL Artificial Intelligence (AI) is the process of creating systems that can make intelligent decisions. Natural Language Processing (NLP) is the process of creating designs that can understand language. Artificial Intelligence is a subset of it.
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Developing systems that use Deep Neural Networks on a large quantity of data is referred to as Deep Learning (DL). Machine Learning is a subset of it.
What is Natural Language Processing? Natural Language Processing (NLP) is defined as "the ability of machines to comprehend and interpret human language in the way it has been written or spoken." The goal of NLP is to make computers/machines as intelligent as humans in terms of language comprehension. NLP's ultimate goal is to bridge the gap between how humans communicate (natural language) and what computers can understand (machine language). There are 3 different levels of linguistic analysis that can be done before performing NLP a) Syntax : What amount of given text is grammatically true. b) Semantics : What is the correct meaning of the text? c) Pragmatics : What is the actual purpose of the text? NLP deals with various aspects of language such as; a) Phonology: It is the systematic organization of sounds in language. b) Morphology: It is a study of formation of words and how are they related to each other. Approaches of NLP for better understanding semantic analysis; a) Distributional: It employs extensive statistical techniques of Machine Learning and Deep Learning.
