This hub collects plain-language explainers on artificial intelligence: what the term means, how neural networks and classical methods work, where the field came from, and what the evidence says about its uses and limits. It is meant for students, educators, and general readers in Canada and the United States who want a map of the basics before reading individual articles.
Key takeaways
- Definitions of AI vary, so articles here compare how the OECD, NIST, and legal texts describe an AI system.
- Modern AI rests on neural networks, while search, logic, and optimization remain part of the field.
- Survey and official data show how widely AI is used in Canada and the US.
- Claims about AI products, stocks, and viral stories can be checked against primary sources.
- What AI is and how it is defined
- Neural networks, learning, and language models
- Search, logic, and classical problem solving
- History, people, and books
- Courses, careers, and learning with AI
- Robots and autonomous machines
- Creative AI, art, and language
- AI in science and research
- Adoption, government, and measurement
- Society, limits, and claims to check
- FAQ
What AI is and how it is defined
Articles in this group separate artificial intelligence from neighboring terms such as data science, deep learning, and ordinary software. They also cover standard definitions, such as the OECD dimensions for classifying an AI system, and the debate over what counts as AGI.
- How to Tell What Is and Is Not Artificial Intelligence
- Real AI vs Fake AI: How to Test What a Product Claims
- What AI Is Not: Five Common Misconceptions Checked
- Other Names for AI: What Each Term Covers and Where It Came From
- AI vs IT: How They Differ in Definition, Testing, and Risk
- Data Science vs AI: Where Each Term Comes From
- AI vs Data Science: What Official Job Profiles Show
- Deep Learning vs AI: Where Neural Networks Fall Short
- How AI Differs From Conventional Software, Per NIST
- Is AI a Computer? Software, Hardware, and Model Files
- Is AI an Operating System? What Each One Actually Does
- Is AI a Website? Models, Servers, and Web Pages Compared
- Artificial vs Automated Intelligence: How Laws Define Each
- What an AI System Is Made Of: Data, Models, and People
- The Five Dimensions Used to Classify an AI System (OECD)
- What Counts as AGI? Nine Definitions and a Levels Scale
- The Seven Stages of AI: A Popular List, Checked
- Superintelligence vs Today's AI: What the Difference Means
- Artificial vs Natural Intelligence: What Measurements Show
- AI vs Human Intelligence: What Replacement Evidence Shows
- Is AI Part of Computer Science? What CS2023 Says
- Key Topics in AI: How the ACM Classification Maps the Field
- Where AI Fits in Data Science: Six Phases and Test Results
- How Canada's Program Codes Define AI and Data Science
- Two Cultures of Data Work: Explaining vs Predicting
- When to Capitalize Artificial Intelligence and AI in Writing
- Artificial Intelligence as a Noun: Countable or Not?
- Top AI Systems: How Researchers Define the Frontier
- The Most Advanced AI: How Laws Measure It by Training Compute
Neural networks, learning, and language models
These explainers walk through how neural networks compute, how models learn, how text generators choose words, and where the limits of memory, context, and uncertainty lie.
- How Deep Learning Works, From Backpropagation to ImageNet
- How an Artificial Neural Network Computes, Step by Step
- How a Neural Network Works: One Worked Example, Step by Step
- Neural Network Applications: Which Architecture Fits Which Task
- Where Neural Network Research Is Heading: Four Open Lines
- Learning Rate in Machine Learning: What It Does and How to Set It
- Linear Regression in AI: How Least Squares Predicts Outcomes
- What Vectors Do in AI: Embeddings and Similarity
- How Vector Search Works in Retrieval-Augmented AI
- How AI Text Generators Pick Words: Temperature and Top-p
- Long Context in AI: Why Models Miss Text in the Middle
- Trillion-Parameter AI Models: What the Count Means
- Do AI Models Store Their Training Data? What Research Shows
- How AI Systems Estimate Their Own Uncertainty
- Limited Memory AI Examples: Frames, Gates, and Attention
- How AI Learns After Deployment and Where It Stops
- Why AI Repeats Human Myths: What TruthfulQA Measured
- Does AI Struggle With Grammar? What the BLiMP Test Found
- AI Training Datasets: Splits, Label Errors, and Datasheets
- Can AI Do Math Accurately? Calculators, Chatbots, and Tools
- Does AI Depend on Math? What Models and Users Need
- How the Brain and Image Networks See Differently
- Computer Vision in AI: How It Works, Uses, and Limits
- Running AI Offline: On-Device Models, Memory, and Limits
- Apple Intelligence on iPhone: Models, Storage, and Privacy
- AIoT Explained: AI Models on Connected Devices
- Neural Network Cryptography: What Was Tried and What Worked
Search, logic, and classical problem solving
Before large neural networks, AI research centered on search, rules, and logic. These articles explain heuristics, constraint satisfaction, game-playing algorithms, optimization, and how a problem is framed for a machine.
- How an Expert System Works: Rules, Inference, and MYCIN
- How to Formulate an AI Problem: Three Worked Examples
- Alternatives to AI: When Rules and Simple Models Do the Job
- Why Optimization Matters in AI and When a Solver Can Prove It
- What an Optimization Problem in AI Is, With Worked Examples
- How AI Solves Problems: Search, Constraints, and Learning
- AI Problem Space Size: From the 8-Puzzle to Go
- Characteristics of AI Problems: Six Environment Properties
- Heuristics in AI: How a Good Estimate Shrinks the Search
- Constraint Satisfaction Problems in AI, With a Worked Example
- How Minimax and Alpha-Beta Pruning Let AI Play Games
- How Unification Finds the Most General Unifier in Logic AI
- Toy Problems in AI: What They Prove and Where They Mislead
- Intelligent Agents in AI: Four Properties and Five Designs
- Why Domain Knowledge Matters in AI: Three Documented Cases
- TSP Records: From 49 Cities in 1954 to 85,900 in 2006
- AI Contests Compared: Poker, Diplomacy, and Who Wins
- Problem Reduction in AI: How AND/OR Graphs and AO* Work
- What First-Order Logic Lets an AI System Say, and What It Cannot
History, people, and books
The history of the field runs from the first programs through the AI winters to modern deep learning. These articles cover landmark programs and papers, the researchers credited with founding the field, and standard textbooks.
- The First AI Programs: Logic Theorist and Samuel's Checkers
- The First AI Programs: Logic Theorist and the Perceptron
- AI Winters: What the 1966 and 1972 Reports Actually Said
- A Short AI Timeline Told Through Five Landmark Papers
- Famous AI Technologies: Deep Blue, Watson, and the Transformer
- The People Behind Deep Learning: Hinton, Bengio, LeCun
- Marvin Minsky: Career, Perceptrons, and the Father-of-AI Title
- Who Is the Father of AI? The Documented Case for McCarthy
- Stuart Russell: Career, Textbook, and Human-Compatible AI
- Who Created AI? What Turing Awards and a Nobel Recognize
- Leading AI Researchers: How Recognition Is Measured
- Russell and Norvig's AI Textbook: What the 4th Edition Covers
- Mitchell's AI Guide for Thinking Humans: What the Book Covers
- What the AI For Dummies Book Covers, Edition by Edition
- How Canadian Hansards Taught Computers to Translate in 1990
- Artificial Intelligence Meets Natural Stupidity: A 1976 Paper
- AIMA Textbook Reviews: Editions, Translations, and Critiques
- AI Programming Languages: From Lisp to Python and C++
- AI Artificial Intelligence (2001): Facts, Reception, Context
- Where AI Names Come From: Acronyms, People, and Brands
- Where Elon Musk Studied: What Queen's and Penn Records Show
Courses, careers, and learning with AI
These articles cover courses, degree programs, labor data, and whether coding is required, along with research checks on AI tutors, study helpers, and language practice.
- Beginner's Guide to AI: A Free Syllabus and Learning Path
- What Harvard's CS50 Course Teaches About AI, Week by Week
- What Stanford's AI Professional Program Offers and Costs
- UMT BS in Artificial Intelligence: What the Curriculum Contains
- AI Degree Salaries: What Official Data Shows
- Can AI Work Without Coding? What Code Still Does
- Does AI Require Coding? What Labor Data Says by Role
- AI in Computer Science Departments: What CRA's 2025 Data Show
- Who Builds AI: Developer Jobs, Pay, and Tool Use in Data
- AI and IT Work: What Four Developer Studies Show
- How AI Exposure Is Measured in Canadian and U.S. Jobs
- AI Event Ideas for Schools, Built on the AI4K12 Big Ideas
- AI Camp: What the Provider States and What Is Unconfirmed
- AI English Speaking Practice: How It Works and Its Limits
- Can AI Coach or Mentor? Three Randomized Trials Compared
- Khanmigo Explained: Khan Academy's AI Tutor, Cost, and Evidence
- AI Study Helper: What Studies Say About Quizzing Versus Answers
- AI Learning Apps: How to Check a Claim With ESSA Evidence Tiers
- Learning Spanish With AI: What Two Studies Found About Feedback
- Four AI Thesis Ideas Grounded in Published Research
- What Free Means in an Online AI Course: Three Cases Checked
- What an Online AI or ML Course Should Cover, Per ACM CS2023
- Free AI Learning Websites for Students: What Each One States
- Free AI Demos and Courses Worth Trying, With Their Limits
- University Rankings for AI: What Each List Measures
- Citing and Disclosing AI in Coursework: Three Published Rules
- Slides That Explain AI: What Research and Standards Say
Robots and autonomous machines
Robotics articles explain how machines localize, plan, and learn, which standards cover robots at work, and what pilots and competitions have shown.
- How a Robot Finds Its Way: Localization, Maps, and A*
- How AI Robots Learn: Simulation, Demonstrations, Shared Data
- How AI Controls Robots: Vision-Language-Action Models
- Most Advanced Robot? What the DARPA Finals Scored
- AI Robots of 2021: What Was Real and What Was a Concept
- Robots and AI in Industry: Counts, Definitions, Limits
- Robot Safety at Work: What the 2025 Standards Cover
- Humanoid Robots: What Pilots and the Draft Safety Standard Show
- AI Companion Robots for Older Adults: What the Evidence Shows
- Five Concrete AI Safety Problems, Explained With One Robot
- Self-Driving Cars: SAE Levels and What Waymo Data Shows
- How AI Chatbot Toys Work and What Parents Should Check
- AI Toys: Which Safety Rules Cover Electronic Toys
- Smart Home AI: What Labels, Standards, and Orders Cover
- Hoori AI: A Viral Robot Hoax and a Hong Kong Company
Creative AI, art, and language
This group covers image, music, and text generation, how researchers measure creativity, and the rules that apply to AI-made work.
- How AI Paintings Are Made: Diffusion, Data, and the Law
- Can AI Be Creative? What Three Experiments Measured
- Creative AI Examples and Who Gets Credit for Them
- Can People Tell AI Art From Human Art? What a 2024 Study Found
- AI and Human Creativity: Contracts, Studies, and Freelance Data
- AI and Fragrance: What Is Documented About Machine-Made Scents
- Best AI Image Generator? What Evaluation Research Shows
- AI Art Rules at the Oscars, Grammys, and Arts Funders
- AI Singing Voices: How They Work and Which Rules Apply
- Best Humans vs AI on a Creative Thinking Test: What Was Found
- Writing Letters With AI: What Works, What to Check, What to Hide
- How AI Is Changing English Word Choice: Three Studies
- Machine Translation: What AI Changed and What Still Needs Review
- How Conversational Interfaces Work, From ELIZA to Chatbots
- Interactive AI: What Design Research Says About Human Control
- AI Icons: What the Sparkles Symbol Means and Where It Fails
- How AI Helped Rebuild the Missing Edges of The Night Watch
- How AI Helps Read Ancient Texts: Ithaca and the Scroll Prize
- AI and Sanskrit: How Software Splits and Reads the Language
AI in science and research
Articles here describe what AI has and has not done in science, from protein structure to physics data, and how to judge AI research claims and compare systems.
- How AlphaFold Shows What AI Can and Cannot Do in Science
- How AI Solves Complex Problems: The AlphaFold Case
- How AI Filters Huge Data: The Large Hadron Collider Case
- How AI Tackles Weather Forecasting, a Problem Too Big to Solve
- AI and Nanotechnology: What GNoME and A-Lab Showed So Far
- AI in Electrical Engineering: Grid Uses and Limits
- How Cognitive Science Uses AI Models to Study the Mind
- Why AI Research Errors Are Hard to Find and Fix
- How AI Research Is Published and Checked, With 2025 Data
- How to Judge an AI Article: Preprints, Peer Review, Press
- Most-Cited AI Papers and How Citation Counts Are Built
- How to Judge an AI Newsletter: Disclosures and Email Law
- Comparing AI Systems: What Benchmarks and Leaderboards Show
- AI and Natural Product Drug Discovery: What Has Been Shown
Adoption, government, and measurement
These articles rely on survey and official data to show how widely AI is used in Canada, the US, and elsewhere, and how governments plan, list, and regulate it.
- How Fast AI Use Has Grown in Canada and the US
- The True Cost of Implementing AI, Using Canadian Survey Data
- Which AI Technologies Do Businesses Use? 18 Measured
- Which AI Tools Businesses Use: Canada's 2026 Survey Data
- Which Everyday Tools Use AI? A Pew Quiz and Maps Example
- AI by Industry: What U.S. Census Data Show About Sector Use
- Is AI Available to Everyone? Internet Access in Canada and the US
- AI Development vs Adoption by Country: What Data Compare
- Who Leads AI? Model Counts, Compute, and Transparency
- AI Research Agendas in the US and Canada: What Is Planned
- Artificial Intelligence in New York: Rules and Public Programs
- Taiwan's AI Basic Act: What Its 20 Articles Do and Do Not Say
- Canada's Federal AI Register: What It Lists and Omits
- How Statistics Canada Uses Machine Learning on Jobs Data
- How the UK Plans, Regulates, and Measures AI Adoption
- What U.S. Agencies Report Using AI For, by Technique and Topic
- What Two UK Government Trials Found About an AI Work Assistant
- Generative AI Patent Growth: What WIPO's 2026 Update Counts
- Big Tech and AI Developers: What Regulators Found
- What Happened to Three Canadian AI Companies After Acquisition
- How AI Chooses Who Sees an Ad, and Where Rules Step In
- What Studies Show AI Improves and Where Gains Are Small
- Canada's AI Research Network: CIFAR Chairs and Three Institutes
- How People Use AI Chatbots for News, per 2026 Survey Data
- What Canadians Told a 47-Country AI Survey in 2025
- AI Summaries in Search and Clicks: What Pew Measured
Society, limits, and claims to check
Articles in this group test popular claims about AI against studies and official documents, including the generative AI risks named in a NIST profile. Several also show how to check product claims, stock labels, and viral stories against primary sources and regulators.
- The Impact of AI on Modern Society: Evidence by Area
- Artificial Intelligence and Religion: What Faith Bodies Say
- Does AI Make People Lazy? What Studies Actually Show
- AI and Indigenous Data Sovereignty: What OCAP and CARE Say
- Positive Social Impact of AI: Two Cases With Public Evidence
- AI Targeting of Cash Aid in Togo: What Nature Reported
- How to Weigh AI's Pros and Cons: NIST and Canada's AIA
- The 12 Generative AI Risks Named in NIST's AI 600-1 Profile
- What AI Cannot Do: Limits Courts and NIST Have Documented
- Controversial AI Debate Topics, Each Tested Against Evidence
- The Biggest AI Trends of 2022, Counted in the AI Index
- 2025 AI Predictions: Which Forecasts Can Be Checked Now
- Is AI a Revolution? What the Electricity Precedent Shows
- Could AI Take Over a Psychiatrist's Work? The Evidence So Far
- What an AI Stock Label Means and How to Check the Claim
- A 2021 AI Stock List Read Later: Rules on Past Performance
- AI Stocks Under $10: What a Low Share Price Does Not Show
- AI Claims in Low-Priced Stocks: What Regulators Warned
- The WisdomTree AI UCITS ETF: Index, Fees, and Where It Is Sold
- AI Stocks to Buy Right Now? What 10-K Filings and the SEC Show
- Is Satoshi Nakamoto an AI? What the Record Shows
- Ethereum and AI: What Smart Contracts Can and Cannot Run
- AI GE: What the FDA List Shows About GE-Named Devices
- What Does "É Isso Aí" Mean? A Brazilian Phrase, Not AI
- Free AI Apps: What Privacy Regulators Say to Check First
- Free AI Apps: What the Word Free Means Under Advertising Law
- What U.S. Surveys Show About AI and Religion
FAQ
How does the OECD define an AI system?
The OECD describes an AI system as a machine-based system that, for explicit or implicit objectives, infers from its inputs how to generate outputs such as predictions, content, recommendations, or decisions. The 2023 revision is the current wording.
Is there one official definition of AI?
No single definition is used everywhere. The OECD, NIST, and national laws each word it differently, which is why articles in this hub compare them.
Is the NIST AI Risk Management Framework mandatory?
No. NIST describes the framework, published on January 26, 2023, as intended for voluntary use by organizations.
Where can reliable data on AI development and use be found?
The Stanford HAI AI Index collects and organizes data on AI for policymakers, researchers, and the public. Official statistics offices also publish data on business use, as the articles on adoption show.
Related reading
More in AI Blog.
Last verified: October 2026