What is AI?

Joseph C. Osborn (joseph.osborn@pomona.edu)

Artificial Intelligence

  • What even is it?

Bad news

  • No such thing as AI
  • Bye

Artificial

  • “Made by people”?

Intelligence

  • …?
  • Uh oh

It gets worse

  • Can we achieve human-level generalization and abstraction?
    • Can we do that without human-level error and cognitive hazards?
    • (Prejudice, change blindness, sloppy reasoning…)

Ethical issues

  • This conception of AI has ethical problems
  • Sure, displacing human labor…
    • Technical change without social change reinforces power structures

Really big ethical issues

  • Worst of all: “hard AI” is a fantasy of slavery
    • The worker who never tires or strikes
    • The “lover” who can’t say “no”
  • “R.U.R.” written in 1920
    • “Robota” means “forced (serf) labor” in Czech

AI’s “utopia”

Within every dystopia, there’s a little utopia —Margaret Atwood


Now what?

  • So, to recap:
  • No such thing as AI
  • It’s also impossible
  • Even if there were and it were, it should be illegal
  • But we have a way out:

Artificial what now?

The question of whether Machines Can Think… is about as relevant as the question of whether Submarines Can Swim. —Edsger W. Dijkstra


Automated Decision-Making

  • Let’s try it this way:
  • Lowercase-ai systems make decisions about problems
    • Important: Who acts on them?
  • Successful ai systems make “better” decisions?

Types of decisions

  • I like to break it down into three types of decisions:
  • Satisfaction
  • Optimization
  • Prediction

Types of problems

  • Addressing three types of problems:
  • Synthesis
  • Dynamics
  • Agent behavior

Russell & Norvig

  • Here’s another common breakdown
    • Source: “AI: A Modern Approach”, Russell & Norvig
  Human Rational
Thinking Think like a human Think rationally
Acting Act like a human Act rationally

Symbolic vs Non-Symbolic

  • A different breakdown
  • Symbolic: Machines reason over (human readable) symbolic representations
    • E.g. deriving a correct world model by analyzing experts’ processes
  • Non-Symbolic: Machines reason over opaque,(usually) learned models
    • E.g. getting correct inputs and outputs by analyzing data
    • Almost purely learning correlations

State of the Field


Challenges

  • Perception
  • Computer vision
  • Natural language processing/understanding/synthesis
  • Knowledge representation/common-sense
  • Learning
  • Reasoning & problem-solving
  • Robotics
  • …

Achievements

  • Capturing spoken language as text is getting pretty okay
    • Is this the same as understanding spoken language?
  • AI can speak and sing (easier in some languages, interestingly)
  • Self-driving cars
    • Good on highways
    • Okay off-roading
    • Urban driving very hard
      • Why?

Self-driving cars

  • Computer-assisted driving is pretty great already
  • Anti-lock brakes
  • Automatic braking
  • Pedestrian detection
  • Blind spot warnings
  • Lane drift avoidance
  • Smart cruise control
  • …

Emotions

  • It’s generally not too hard for humans to read emotions
  • It’s super hard for computers
    • People have different faces
    • People emote with their whole body
    • People don’t always say what they mean
  • Limited success so far

Automated Reasoning

  • Really good on isolated problems
    • Chess, Go, Starcraft, …
  • Really hard in general

Robots

  • Walking with two legs is hard
  • “Let robots be robots”

Fundamental issue

  • AI isn’t just one thing or one technique
  • Figuring out what approach to use is more than half the challenge!

Automated Decision-Making

  • AI is most useful (to me) as a method of inquiry
    • “Build it to understand it”
  • I’m an old-fashioned symbolic AI guy
    • Of course my research also incorporates modern tools