Artificial intelligence is the ability of a machine to do tasks that normally seem to need human intelligence (recognising a face, translating a sentence, recommending a film).
AI finds patterns in large data and predicts a likely answer. When it labels a photo it is doing statistical matching, with no grasp of what the image means.
Words like learns, sees, and understands make AI sound conscious. Think of it instead as a capable pattern-matcher, which keeps clear what it can do and who is responsible when it fails.
Narrow AI: the AI we actually have
Narrow AI (or weak AI) is built for one task or a small set of related tasks. It can be superb in its lane but cannot step outside it.
IBM's Deep Blue beat world champion Garry Kasparov at chess in 1997 but could do nothing else. Some narrow systems are domain-specific, tuned to one field like medical scans or speech.
Recommendation, fraud flagging, and speech-to-text are separate narrow systems: swapping tasks means building a new model, not reusing an old one.
Because each system is built for a single task, its failures stay within that narrow scope rather than spreading into unrelated abilities. That does not make them transparent: many are black boxes, and who is accountable when one makes a mistake is often disputed.
General AI (AGI): the flexible mind that does not exist yet
Artificial general intelligence (AGI), also called strong or full AI, is a single system that could understand, learn, and apply knowledge across any task as flexibly as a human.
AGI does not exist. A model that writes fluently still has no general world model and fails in ways no human would (confident nonsense, forgetting what it just said).
Contested: whether AGI is years away, decades away, or impossible is genuinely disputed among experts.
Blurring AGI with narrow AI has real stakes: firms may oversell narrow tools, the public may over-trust them, and policymakers may target a science-fiction future while missing present harms. The companies and labs that get to define intelligence hold real power over what counts as progress.
Super AI and the debate over the future
Super AI (superintelligence) would surpass the best human minds across virtually every field, from science to creativity. Like AGI, it is entirely hypothetical.
Contested: one camp treats a future superintelligence as an uncontrollable existential risk worth preparing for; another says such fears are speculation that distracts from documented narrow AI harms today (bias, job loss, misinformation).
The Turing test
In 1950 Alan Turing proposed the Turing test: if a human judge chatting by text cannot reliably tell a machine from a person, the machine passes.
It sidesteps what thinking is and asks only whether a machine can convincingly imitate a human. Today's language models can fool people at least some of the time.
Passing the test does not require understanding. A system predicts likely words without grasping meaning, so many argue the test measures imitation rather than intelligence. When something seems to understand, people over-trust it, which is a values and ethics risk.
Types of AI, hype, and power
Because AI covers everything from a spam filter to imagined superintelligence, companies and headlines can blur the categories, overselling narrow tools or stoking fear of machines that do not exist.
Generative AI feels startlingly general. It is still narrow AI, predicting patterns without understanding them.
Practical habit of mind: when you meet a bold AI claim, ask which type it describes. Almost always it is narrow AI. The companies that blur the line usually profit from the confusion, so ask who benefits and who holds that power.
Case study
Generative AI (case study)
System: large language models and image generators, trained on vast text and images to predict and produce new content. Powerful narrow AI (AI and data topics), not general intelligence.
Specifics: since 2022, adopted by over a billion people for writing, coding, art, and study, marketed as human-like reasoning. In practice they make confident errors, invent facts, and reflect training-data bias.
Impacts and implications: Impacts: productivity and creativity gains alongside job disruption, misinformation, and copyright disputes. Implications: framing narrow tools as near-general shapes investment, policy, and public fear, concentrating power in a few companies.
Concepts: power (who owns and defines AI), change (evolution some read as transformation toward AGI), values and ethics (honesty and accountability). Economic and political contexts.
Theory of Knowledge
TOK
If a system convincingly imitates human output, does it matter whether it truly understands?
Who benefits when narrow AI is described as almost human, and who is harmed by the exaggeration?
Active recall
Self review
What is AI, and why is calling it a pattern-matcher more accurate than calling it a mind?
What is narrow AI, and why is it the only kind we actually have?
What would general AI (AGI) be, and does it exist?
What is super AI, and how does the idea shape debate about AI's future?
What does the Turing test measure, and why does confusing the types of AI fuel hype, fear, and power?
