Artificial Intelligence General Terms

This glossary is not exhaustive, but it provides a starting point for understanding some of the key terms and concepts in AI. As the field continues to evolve, new terms and concepts will emerge, so look out for updates on the latest developments.

Artificial Intelligence (AI)

The simulation of human intelligence processes by machines or computer systems.

In simpler terms, making machines think and act like us (humans).

Machine Learning

A subfield of AI that allows computers to learn from data without being explicitly programmed.

In simpler terms, computers getting smarter by learning from examples, like studying for a test.

Deep Learning

A type of machine learning that uses artificial neural networks with multiple layers to learn complex patterns from data.

In simpler terms, this is just extra fancy machine learning, inspired by the brain, to find hidden patterns in information.

Natural Language Processing (NLP)

A subfield of AI that deals with the interaction between computers and human language.

In simpler terms, helping computers understand and chat with us in our own language.

Computer Vision

A subfield of AI that allows computers to extract information from images and videos.

In simpler terms, it is like giving computers eyes to see and understand pictures and videos.

Robotics

The field of engineering that deals with the design, construction, operation, and application of robots.

In simpler terms, It is the building and using of robots, like machines that can move and do things on their own.

AI Ethics

The study of the ethical implications of AI development and deployment.

In simpler terms, thinking about what's right and wrong when building and using AI, to make sure it's fair and safe for everyone.

Big Data

Large and complex datasets that are difficult to process using traditional methods.

Responsible AI

The development and use of AI in a way that is fair, transparent, and accountable.