What is the difference between Artificial Intelligence (AI), Machine Learning (ML), Neural Networks, Deep Learning, Generative AI, and Large Language Models (LLMs)?

Artificial Intelligence (AI): Artificial Intelligence is a discipline of computer science that deals with creating and developing machines, programs, or systems, that think and act like humans. Examples of AI are Alexa, Siri, Chatbots, etc.

Machine learning (ML): Machine Learning is a subset of AI. ML deals with programming algorithms (ML Models) based on input data, and then the ML models makes predictions or decisions when new input data is fed to it with out the need of explicit instructions from developers.

Neural Networks (NN): Neural Networks is a subset of Machine Learning. Neural Networks contain artificial interconnected neurons organized in layers - input layer, one or more hidden layers, and output layer. Neural Networks mimic the way neurons communicate with each other in the human brain.

Deep Learning: Deep Learning is a subset of Neural Networks and depends on the number of hidden layers in the Neural Network. A Neural Network with more than three hidden layers can be considered as Deep Learning program. Examples of Neural Networks and Deep Learning programs are face recognition programs, image recognition programs, language translation programs, etc.

Generative AI (Gen AI): Generative AI is a subset of Deep Learning. Generative AI is a type of artificial intelligence system that can produce high quality content, specifically text, images, audio, and video - based on what it has learnt from existing content. Example of Generative AI are ChatGBT.

Large Language Models (LLMs): Large Language Models are subset of Generative AI. LLMs specialize in in natural language processing, and generate human-like text based on the patterns learned from vast amounts of textual input data.