1: What is Artificial Intelligence?
- Page ID
- 67023
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\( \newcommand{\dsum}{\displaystyle\sum\limits} \)
\( \newcommand{\dint}{\displaystyle\int\limits} \)
\( \newcommand{\dlim}{\displaystyle\lim\limits} \)
\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)
( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\id}{\mathrm{id}}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\kernel}{\mathrm{null}\,}\)
\( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\)
\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\)
\( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)
\( \newcommand{\vectorA}[1]{\vec{#1}} % arrow\)
\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow\)
\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vectorC}[1]{\textbf{#1}} \)
\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)
\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)
\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\(\newcommand{\longvect}{\overrightarrow}\)
\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Most people using artificial intelligence today began using it before they had any clear idea what it was. A phone finished a sentence, a bank flagged a strange charge, a chat window produced a paragraph that sounded uncomfortably professional, and the technology arrived without an explanation attached. This chapter supplies the explanation. It does not assume any technical background, and it does not require any programming, because the tools this course is about are operated in ordinary language.
Defining Artificial Intelligence
The term artificial intelligence (AI) was coined in 1956 to describe “the science and engineering of making intelligent machines”(opens in new window). Today, artificial intelligence can be defined as machines behaving in ways that humans generally consider intelligent by problem solving or completing tasks. It is a very broad subject that involves computer science, cognitive science, mathematics, philosophy, neuroscience, linguistics and many other disciplines. Over the last decade, AI technologies have been incorporated into a wide range of consumer and enterprise products and services. The rapid development of generative artificial Intelligence (GenAI) — technology capable of generating text, images, video, audio, and code — is transforming how we interact with technology at home and at work. Increasingly, technology companies are discussing the emergence of artificial general intelligence (AGI), where AI tools match or outperform humans across a broad range of tasks.
Domains of AI
Artificial intelligence has several underlying domains or fields that are helpful to understand. You may have heard some of these terms before.
"The Key Domains of Artificial Intelligence: Comprehensive Overview" is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License, except where otherwise noted.
Real-world Applications
You might be using AI technologies already embedded in consumer-facing products and platforms. Your mobile phone may have a virtual assistant (Siri, Gemini, Google Assistant, Alexa) that relies on natural language processing to convert your words into prompts the machine can understand and respond to. Your photo app may use machine learning to improve image recognition, allowing you to search your photo Library for pictures of dogs. It can be used for actions such as shopping, translating text and searching. Your translation app may use a neural network to figure out what word should come next.
Types of Artificial Intelligence
Generative AI
Generative AI is a tool that uses machine learning to create new content from its inputs, ranging from text and images to music. Generative AI (GenAI) is based on the Natural Language Models and is a form of ANI. It creates a series of predictions based on existing online data, to generate new or similar content in response to written prompts. Generative AI such as Midjourney or Chat-GPT has rapidly increased in popularity in recent years, as these AI tools can respond quickly to user prompts, enabling opportunities for real-time application. Generative AI tools are trained using diverse online datasets, including websites and social media conversations. This technology can create contextually relevant, human-like responses to user prompts and is versatile enough to generate software code, images, video, song lyrics and music.
Large Language Models
Since OpenAI launched ChatGPT in 2022, large language models (LLM) have emerged as one of the most visible and widely used generative AI technologies. LLMs are a type of generative AI trained on vast amounts of text using deep learning and neural networks. Users can prompt LLMs, typically via a chat interface and increasingly by voice, to generate sophisticated responses, including text, images, video, audio and code. These chatbots are widely capable and can be applied to a range of tasks. They can:
- engage in conversation
- generate text, images, video, audio and code
- translate text
- summarize content
- identify patterns
- answer questions.
Many of the biggest technology companies have developed LLMs, including Google, Anthropic, XAI, and Meta.
Multimodal AI
Multimodal AI refers to artificial intelligence systems capable of processing, combining, and reasoning across multiple distinct types of data simultaneously. For example, a multimodal AI tool might use a chat interface like an LLM that unifies multiple layers of AI models that are capable of generating text, images, audio, video, etc. Unlike traditional unimodal AI, which operates on a single input format, multimodal models integrate these diverse inputs into a unified framework to capture deeper context and cross-media relationships. This enables the system to generate richer, more accurate outputs and mirror human-like, multisensory comprehension.
Predictive AI
Predictive AI uses machine learning to extrapolate and make predictions from patterns it identifies in data. This allows it to potentially forecast future events and enhance outcomes. This type of AI is can be used in finance to make trades on the stock market, or in science to analyze large amounts of data.
Agentic AI
Many AI companies, including Microsoft, Google, Anthropic, and OpenAI, have created agentic AI tools capable of autonomously executing tasks and completing processes with minimal human oversight, which organizations are using to support customer service inquiries, manage workflows and processes, conduct detailed research and information retrieval, and generate code and software.
Example of using a chatbot vs an AI agent
Using a chatbot:
- Enter your prompt - How do I reference a website in APA 7th?
- Provides an answer.
Using an AI agent set up to perform this task:
- Enter your prompt - Review this assessment draft and identify sources that need APA referencing.
- Scans the document, finds sources, identifies issues and suggests corrections.
Artificial General Intelligence (AGI)
AGI is the type of AI that is typically depicted in movies like I, Robot (2004) with Will Smith. AGI is expected to be able to continuously learn from and use prior knowledge to make decisions, reason, solve problems, make judgments and plan. AGI hypothetically possesses intelligence that equals that of any human in any area. It is purely theoretical at this point and there is debate about how we will know if or when AGI is achieved.
How AI Works
An AI model is, at it's core, a collection of billions or even trillions of stored numerical values called parameters. These values are used to guide large-scale calculations that take place whenever the model runs. So AI models do not "think" the way we do - they treat every task as a math problem and predict their response to every prompt based on the stored values that guide their calculations. These stored values are set through the model training process. When a model "learns," it is not internalizing concepts the way a student does. It is trained by repeatedly predicting the next word in vast quantities of text, comparing that prediction against what the text actually says, and adjusting itself very slightly so that it is a little more likely to be right the next time. Repeat that across trillions of sentences and the model ends up with a detailed statistical map of how language works. Grammar, the relationships between related words, and even the structure of reasoned argument all emerge from this process, not because the model understands any of them, but because those patterns are reliably present in how people write. A model trained this way is knowledgeable but unhelpful. It will continue any text it is given, including text nobody wants. Turning it into an assistant takes a further process in which people compare pairs of possible answers and indicate which they prefer. Those preferences train a separate model that predicts how a person would rate any given answer, and the original model is then nudged, over many cycles, toward answers that score well. This is why current AI assistants are concise, cooperative, and reluctant to produce harmful content: not because they want to be, but because those patterns scored well against recorded human preferences.
Hardware and chips
The massive amount of simultaneous calculations involved in performing dynamic and simultaneous calculations at the scale needed to run AI models requires state-of-the-art chips that are faster and more efficient than typical computer chips. Graphics Processing Units (GPUs), in particular, have become integral to the rollout of AI technologies for their “number-crunching prowess” (What is a GPU? An expert explains the chips powering the AI boom, and why they’re worth trillions).
NVIDIA chip under a CC0 1.0 license.
Chip makers are now among the most valuable publicly traded companies. For instance, the NVIDIA Corporation’s market cap has increased significantly alongside the expansion of GenAI tools, rising from approximately $735 billion (December 2021) to 4.9 trillion (July 2026).