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Narrow AI, AGI, and ASI

- **Narrow AI (ANI):** systems built for specific tasks—spam filters, recommenders, translation, chat assistants that still fail outside their comfort zone. This is what ships today. - **AGI (artificial general intelligence):** a researc...

What it is

  • Narrow AI (ANI): systems built for specific tasks—spam filters, recommenders, translation, chat assistants that still fail outside their comfort zone. This is what ships today.
  • AGI (artificial general intelligence): a research / product *goal* of systems that match human-like breadth of competence across many domains. Not a settled, deployed category with a single test everyone accepts.
  • ASI (artificial superintelligence): speculative systems that would surpass humans broadly. Mostly future-facing debate, not a product label you can verify in a store.

Why it matters

Marketing often blurs “smart at this demo” into “generally intelligent.” Keeping the words separate protects your judgment—and connects cleanly to Course 29’s risk literacy without panic theater.

How it works (plain)

Narrow systems can look general because language interfaces hide the limits. Ask them to do something far from training patterns and the mask slips: invented facts, brittle reasoning, or confident nonsense.

AGI/ASI discussions matter for research priorities and safety policy, but they should not erase present-day narrow harms and benefits (bias, scams, job changes).

Everyday example

A coding assistant that writes boilerplate is narrow AI. Calling it “AGI” because it chats fluently is a category error.

Try it

List three AI tools you use. For each, write the narrow task it actually does. If you cannot name the task, the branding is doing the work—not the definition.

Myths

⚠️ Myth: Chatbots are AGI because they talk about many topics.
✓ Reality: Broad chat is still patterned prediction with limits; breadth of conversation ≠ reliable general competence.
⚠️ Myth: Narrow AI is “weak AI” and therefore harmless.
✓ Reality: Narrow systems already move money, jobs, and attention (Course 29).

Sources