Overview

Artificial Intelligence (AI) is the quest to build a brain. It is the science of making machines smart.

Core Idea

The core idea is Simulation. Can we simulate the cognitive functions of a human mind (learning, reasoning, problem-solving) in a machine?

Formal Definition

The study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

Intuition

  • The Calculator vs. The Poet: A calculator is better than you at math, but it’s not “intelligent” in the AI sense. AI aims for the messy, fuzzy stuff: recognizing a face, driving a car, writing a poem.
  • Narrow vs. General AI:
    • Narrow AI (ANI): Good at one thing (Chess, Spam filtering). We have this now.
    • General AI (AGI): Good at everything (like a human). We don’t have this yet.

Examples

  • Deep Blue: Beat Kasparov at Chess (1997). (Brute force search).
  • AlphaGo: Beat Lee Sedol at Go (2016). (Deep Learning + Reinforcement Learning).
  • ChatGPT: Generates human-like text. (Large Language Model).

Common Misconceptions

  • Misconception: AI is conscious.
    • Correction: Current AI is just math. It doesn’t “know” anything; it just predicts the next word or pixel.
  • Misconception: It will kill us all (Terminator).
    • Correction: The risk is real (Alignment Problem), but it’s more about “unintended consequences” than “evil robots.”

Applications

  • Healthcare: Diagnosing diseases from X-rays.
  • Transportation: Self-driving cars.
  • Creativity: AI art and music.

Criticism and Limitations

  • Hallucination: AI models often confidently state falsehoods.
  • Job Displacement: Automation of cognitive labor.

Further Reading

  • Artificial Intelligence: A Modern Approach by Russell and Norvig
  • Superintelligence by Nick Bostrom