Core Concepts

A glossary to understand the fundamental pillars of Artificial Intelligence.

Foundations

Artificial Intelligence (AI)

A discipline of computer science focused on creating systems capable of performing tasks that typically require human intelligence.

Artificial Neural Network (ANN)

A computational model inspired by the human brain, composed of interconnected nodes (neurons) that process information and learn patterns.

Machine Learning (ML)

A subset of AI that allows machines to learn from data and improve their performance on a task over time without being explicitly programmed.

Symbolic AI (GOFAI)

The classical approach to AI, based on the idea that intelligence can be replicated by manipulating symbols and explicit logical rules.

Connectionism

The paradigm that suggests intelligence emerges from the interaction of many simple, interconnected units, forming the philosophical basis for neural networks.

Weak AI vs. Strong AI

Weak (or Narrow) AI is specialized for one task. Strong (or General) AI aspires to a versatile, human-like intelligence.

Turing Test

A test proposed by Alan Turing to assess a machine's ability to exhibit intelligent behavior indistinguishable from that of a human.

Expert Systems

Programs from the symbolic AI era designed to emulate the decision-making of a human expert in a specific domain.

Backpropagation

The fundamental algorithm that enables the training of deep neural networks by calculating and propagating errors backward through the network.

Generative AI

A branch of AI focused on models that can create new, original content, such as text, images, music, or code.

Artificial General Intelligence (AGI)

A hypothetical type of AI that possesses the ability to understand, learn, and apply its intelligence to solve any problem, much like a human being.

Artificial Superintelligence (ASI)

A hypothetical form of intelligence that would vastly and profoundly surpass the brightest human intellects in virtually every domain.