# AI Basics ## **What is Artificial Intelligence?** Regarding the definition of AI, the following are quotes from authoritative organizations or prestigious universities: ### IBM: > **Artificial intelligence, or AI, is technology that enables computers and machines to simulate human intelligence and problem-solving capabilities.** **Source:** ### Stanford HAI (2020): > **Artificial Intelligence (AI), a term coined by emeritus Stanford Professor John McCarthy in 1955, was defined by him as “the science and engineering of making intelligent machines”. Much research has humans program machines to behave in a clever way, like playing chess, but, today, we emphasize machines that can learn, at least somewhat like human beings do.** **Source:** ### MIT Technology Review: > **AI is a catchall term for a set of technologies that make computers do things that are thought to require intelligence when done by people. Think of recognizing faces, understanding speech, driving cars, writing sentences, answering questions, creating pictures.** **Source:** Will Douglas Heaven from MIT Technology Review published on July 10, 2024 ### Google: > **Artificial intelligence is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.** **Source:** ### McKinsey & Company > **Artificial intelligence is a machine’s ability to perform some cognitive functions we usually associate with human minds.** **Source:** {% embed url="" %}

## **Key Concepts in AI**

Concept	Description
Machine Learning (ML)	A subset of AI that involves training algorithms to learn from and make predictions based on data. Can be supervised, unsupervised, or semi-supervised.
Deep Learning	A specialized form of machine learning involving neural networks with many layers. Excels in processing large volumes of data, such as images and speech.
Natural Language Processing (NLP)	Focuses on the interaction between computers and humans through language. Allows machines to understand, interpret, and generate human language.
Computer Vision	Trains computers to interpret and make decisions based on visual data from the world. Powers applications like facial recognition and autonomous driving.
Robotics	Involves the design and creation of robots that can perform tasks autonomously or semi-autonomously, integrating various AI technologies.
Large Language Models (LLM)	AI models that are trained on vast amounts of text data to understand and generate human-like language. Examples include GPT-3 and BERT.
AI-generated Content (AIGC)	The creation of content such as text, images, and videos by AI systems. AIGC is used in areas like marketing, entertainment, and journalism.
Generative AI (GenAI)	Refers to AI systems capable of generating new content, such as images, music, and text. It includes technologies like GANs (Generative Adversarial Networks).

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