AI Fundamentals Part 1: Introduction to AI

What is AI?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans.

History of AI

The concept of artificial intelligence dates back to ancient civilizations, but the term "AI" was formally coined in 1956 at the Dartmouth Conference.
AI can throw around some intimidating terms, so let’s break down a few key jargon :

Types of AI

AI can be categorized into Narrow AI (or Weak AI), which is designed for specific tasks, and General AI (or Strong AI), with human-like cognitive abilities.

Note: Chat-GPT is not classified as ASI or Strong AI. A sophisticated illustration of Narrow or Weak AI is GPT-4. Please feel free to get this confirmed from ChatGPT directly.

Now, What is Machine Learning?

Machine Learning is a subset of AI that involves the use of data and algorithms to imitate the way humans learn, gradually improving accuracy.

Types of ML

There are three main types of machine learning: supervised, unsupervised, and reinforcement learning.

Supervised Learning

Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. It's like “Teach me what to learn”.

Unsupervised Learning

Unsupervised learning involves learning patterns from untagged data, used for clustering, association, and dimensionality reduction tasks. It's like “I will find what to learn”.

Reinforcement Learning

Reinforcement Learning is a technique that teaches computer programs to make decisions in order to produce the best possible outcomes. It's like “I’ll learn from my mistakes at every step (Hit & Trial!)”.

Now, What is Deep Learning?

Deep Learning is a subset of machine learning that uses neural networks with many layers (deep nets) to learn from data.

Neural Networks

Neural networks are computing systems vaguely inspired by the biological neural networks that constitute animal brains.

Convolutional Neural Networks (CNNs)

CNNs are deep neural networks used primarily to classify images, cluster them by similarity, and perform object recognition within scenes.

Recurrent Neural Networks (RNNs)

RNNs are networks with loops in them, allowing information to persist, making them ideal for sequence prediction tasks.

AI vs. ML vs. DL