Machine Learning to GenAI- Understanding every popular AI Jargon.

Artificial intelligence (AI)

Artificial intelligence (AI) is essentially the intelligence of machines or systems. It is a branch of computer science that involves creating machines or intelligent agents with human-like thinking and behavior. AI-enabled systems can reason, learn, and act autonomously even going beyond human capability.

AI is a broad term that uses data (information) and algorithms (rules) to allow systems or machines to mimic human intelligence. Typically, AI systems process massive amounts of data and look for patterns to model in their decision-making.

Even though ML and AI are often discussed together and used interchangeably, they don’t mean the same thing.

Machine Learning (ML)

According to Arthur Samuel: “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.”

Machine learning is a subset of the broader category of AI. It is a branch of artificial intelligence (AI) and computer science that focuses on the use of algorithms that learn from data and generalize well on unseen data.

Machine learning is a program or system that trains a model from input data. The trained model can then make predictions or classifications for new (never-seen) data. Machine Learning gives the computer the ability to learn without being explicitly programmed.

Neural network

A Neural Network is a computing model based on how the human brain works- hence the name “neural”. The neural network has a layered structure that resembles the networked structure of neurons in a brain.

Neural networks are also referred to as ‘Artificial’ Neural Network (ANN). The definition provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen is

“…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.

“Neural Network Primer: Part I” by Maureen Caudill, AI Expert, Feb. 1989

Neural networks are typically made of layers. Layers are made up of several interconnected ‘nodes’ which contain an ‘activation function’. 

Deep Learning (DL)

Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks. So the difference between a neural network and deep learning is in the number of nodes, layers, or depth. A deep learning algorithm can have more than three layers.

Deep learning or deep neural networks differ from traditional machine learning techniques in that they can automatically learn representations from data such as images, video, or text, without introducing hand-coded rules or human domain knowledge. The use of artificial neural networks allows them to process more complex patterns than machine learning.

Large Language models (LLMs)

Large Language Models generate human-like text by learning from extensive textual data. The “Large” in Large Language Models refers to the sheer scale of these models—both in terms of the size of their architecture and the vast amount of data they are trained on.

At the core of LLMs is the transformer model that does all the magic.

ChatGPT

ChatGPT is an AI-powered language model developed by OpenAI, capable of generating human-like text based on prompts and past conversations.

Generative AI (GenAI)

Generative Artificial Intelligence (GenAI), a subset of DL, can create diverse content like text, images, videos, or other data based on learned patterns often in response to prompts.