Artificial Intelligence and Machine Learning

ARTIFICIAL INTELLIGENCE

is the broader scientific field dedicated to creating intelligent agents that perceive their environment and take actions that maximize their chance of achieving defined goals.

NARROW AI (WEAK AI), GENERAL AI (STRONG)

Types of AI

MACHINE LEARNING (ML)

It's the process by which systems improve their performance on a specific task over time through exposure to data, without being explicitly programmed for every possible scenario.

GENERATIVE AI 

A subset of AI that can generate novel content (text, images, audio, video) that resembles real-world data but is not directly copied from it.

NARROW AI (WEAK AI)

designed to perform specific task within a limited domain (ex. siri and alexa)

GENERAL AI (STRONG AI)

known as artificial general intelligence. Refers to AI systems with human-like cognitive abilities across wide range task and domain

EXPERT SYSTEM, MACHINE LEARNING, NATURAL LANGUAGE PROCESSING, COMPUTER VISION

AI techniques and approaches

EXPERT SYSTEM

rule based system that emulate the decision making processes of human expert 

NATURAL LANGUAGE PROCESSING

techniques for generating and processing human language

COMPUTER VISION

method for enabling computers to interpret and analyze visualize information from the environment

SUPERVISED LEARNING, UNSUPERVISED LEARNING, REINFORCEMENT LEARNING 

3 main types of ML algorithms

SUPERVISED LEARNING

algorithms is trained on a labeled dataset, where each input is associated with a corresponding, 

UNSUPERVISED LEARNING

trained on unlabeled dataset. Where the goal is to discover patterns, structure, or relationships within the data. There is no predefined target. 

REINFORCEMENT LEARNING 

algorithms trained to make sequential decision by interacting with an environment and receiving feedback in the form of reward or penalty