Announcements

• Midterm is one week from today (Tuesday, October 30) during class in STLC 115. If you have an exam accommodation and haven't received an email from Shreya, please email us.
• Practice midterm on website
• Review session in class Thursday
• No homework on this material, but homework from last week due Wednesday

Plan for Today

• Last week, we saw how we can easily visualize data in spreadsheets.
• Today, we'll see how computers can learn from data in a field called Artificial Intelligence.
  • What is AI
  • Some AI algorithms
  • Branches of AI
  • Challenges

AI: A Brief Overview

• Input: data
• Output: model used to make predictions
• Predictions make it seem as if the computer is thinking
  • Makes guesses about new data

History of Artificial Intelligence

• Fascination since early humanity of forging intelligence like the gods
• Formal logic in mathematics - mechanically reasoning about math (ancient days through 1920s and 30s)
• 1940s and 50s: "Neural Nets": Try to duplicate the way the human brain works
• 1956: Dartmouth Conference: belief that machines could simulate human thought
• 1987: Deep Blue beats the world chess champion
• 2011: Watson wins at Jeopardy!
• 2012: Image classifiers get really good
• 2014: Deep learning hype builds

Turing Test

• What does it mean to have truly intelligent AI?
• AI: good at performing computationally intensive tasks. Anything we can do with a calculator or do in a couple seconds, AI can do.
• AI: bad at doing things we can easily and passively do, like understand a book or carry a conversation about lots of things.
• Turing test: two participants (one machine and one human), and a human evaluator. Human evaluator has to decide which participant is the machine, and which participant is the computer.
• Trace

Machine Learning

• Algorithms that improve over time with more observations (data)
• Idea: the model will be better/smarter as it is used in the real world
• Basically fancy statistics :)
• Example: Netflix's recommendations become better the more you watch
• Trace

Technique: Linear Regression

• Linear regression: try to predict one variable's value based on other (known) variables
• Idea: make a line "fit" the data, then use the line to make our predictions
• Example: given age and gender, can generally predict height fairly accurately (within a couple inches); as age goes up, height goes up too

Technique: Predict by Grouping

• Idea: sort a new piece of data into the same category as its "nearest neighbors", already categorized data that is similar to the new sample
• You're likely to live in the same country as your three closest friends on Facebook
• Automatically "reading" addresses on envelopes

Natural Language Processing

• Build probability models for language
• Google autocomplete: "guess" the next word
• Chatbots/Siri/Alexa
• Turing Test (The Imitation Game)
• Trace

Computer Vision

• Challenge: extract visual meaning from grid of pixels
• Self-driving cars (actually supplement with LIDAR)
• Handwriting recognition
• Auto-captioning images on Facebook (for visually impaired)
• Trace

Robotics

• Challenge of incorporating senses and movement
• Prosthetic limbs
• Defusing Bombs
• Trace

Safety

• How do you develop AI that doesn't take over the world?
• AI is a tool. Researchers can't prevent bad people from being bad, but we can prevent AI from acting badly when good people use it.
• Value alignment: align AI systems with our values (Trace)
• Reward hacking: find easy or trivial solutions that weren't intended (Trace)
• Security: AI models are vulnerable to attacks (adversarial patch)

Challenges

• How do we measure "better"?
• Loss of jobs due to automation (similar to Industrial Revolution)
• Machine learning might not be fair (AI detecting toxicity, face recognition bias)
• What checks do we have on AI?

Recap

• AI is all around us and affects our lives in innumerable ways.
• However, we must carefully think about how we can mitigate its negative impacts.

Crazy things AI can do