An Overview of AI

• R.W. "Moose 🫎" Singh
• Associate Professor of Computer Science at JCCC
• Previously worked in web and software development
• I make indie video games in my free time
• Grew up on the "good internet"
• I've been an Open Source enthusiast for a long time, and have spent a lot of time reading, researching, and thinking about ethics in tech

• Let's say computers don't exist and we're inventing the first ones. This computer only understands numbers.

• How would we decide to represent the letters A - Z with numbers?

  A

  B

  C

  D

  E

  F

  G

  H

  I

  J

  K

  L

  M

  N

  O

  P

  Q

  R

  S

  T

  U

  V

  W

  X

  Y

  Z

  
  
  

• Let's encode some data!

  • IT:
  • IS:
  • RAINING:
  • ...What's the next word that should be written?

• How about different colors? Yes, we need numbers to represent those, too! Perhaps we use "1" for on, and "0" for off, for different colors … # RED, # GREEN, # BLUE…

  
  

  100

  010

  001

  110

  101

  011

  111

  000

  
  
  

• What are these images?

  • … … … …
  
  
  
• Last practice. Several people, speak this aloud and we'll compare:
  • "Welcome to Taco Bell would you like to sign in with your app to earn rewards points?"

• AI is a field of Computer Science.
• The goal generally isn't "make a human-like consciousness", it is "make decisions automatically".

• Machine Learning is training computers on patterns:

  • Sets of letters that go together

    • It's raining men cats and dogs
    • Once upon a time midnight dreary
  
  
  
  • Sets of pixels that go together

From Johner Institute

• Sets of sound waves that go together
  • What does "Yes" sound like?
  • What does "No" sound like?
  • What do the numbers "0, 1, 2, 3, 4, 5, 6, 7, 8, 9" sound like?

• We've had "AI" for years:
  • Speech-to-text: typing on phones, or talking to your Siri / Smart Assistant.
  • Text-to-speech: Reading phrases out loud.
  • Optical Character Recognition: Taking scanned books, studying the pixels, turning it into actual text in a text file.
  • Recommendation Algorithms: You've watched Austin Powers 1 and Austin Powers 2. Perhaps the next movie suggested will be…
    • Last decade, the machine learning around recommendation algorithms (whether for movie suggestions or auto-approving loans or ranking teachers) was under the umbrella of "Big Data", as the internet provides huge pipelines of data collection.

• Computers are MATH

• For most people before the 2020s, the term "AI" was something from science fiction, and we have an idea of what we think AI would be like if it were real.

  
  

  Images from IMDB

  But how "AI" is presented in movies is not how real world computer programs labeled AI work, they do not have the same capabilities.

• Computers Are Math - Machine Learning and the Large Language Models we see today are based on statistical probabilities - most likely word to come next, most likely pixel to come next.

  
  

• People like to anthropomorphising things

  • We give boats names, we speak to our pets, we might even thank our Roomba for its work.
  
  

• And we're good at seeing patterns

  
  
  

• Chatbot AIs do not reason or make decisions.

  YouTube short: AI tells me to walk to the car wash

We've seen similar hype before.

… This is the future.

… Those who reject this are are going to get left behind.

• The hype around AI is similar to other 2010s-2020s grifts:
  • Bitcoin
  • NFTs
  • The Metaverse
• Investors are always hungry to jump onto the next hype train in hopes of getting rich quick:
  • After Fortnite and PubG there were SO MANY BATTLE ROYALE GAMES
  • Mortgage-backed Securities (2008 financial crash)
  • Dot Com Bubble (Some websites were good… but investors were handing money over to any "dot com" even if they had no real product)
  • 1980s Video Game crash (too much slop)
  • The California Gold Rush
  • Tulip Mania