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CS 188: Artificial Intelligence
Introduction
Instructor: Marco Alvarez
University of Rhode Island
(These slides were created/modified by Dan Klein, Pieter Abbeel, Anca Dragan for CS188 at UC Berkeley)
Course Information
▪ Communication:
▪ Announcements/Materials on Piazza and edX edge
▪ Questions? Discussion on piazza
▪ Videos: ai.berkeley.edu
▪ Office Hours: F 2-3p Tyler 257
▪ Course technology:
▪ Piazza, edX edge, Gradescope
▪ Autograded projects, interactive homework
(unlimited submissions!)
▪ Help us make it awesome!
expect a decent load of math and programming
Course Information
▪ Prerequisites:
▪ CSC 301, Python basics
▪ Calculus, Linear Algebra, Probability
▪ Work and Grading:
▪ ~5 programming projects (25%)
▪ ~8 homework assignments (20%):
▪ Online, interactive
▪ One midterm (25%), one final^ (30%)
▪ 1-page cheat sheet allowed
▪ Extra credit for contest participation!
▪ Academic integrity policy
Textbook
▪ Not required, but for students who want to read more we recommend ▪ Russell & Norvig, AI: A Modern Approach, 3rd^ Ed. ▪ Warning: Not a course textbook, so our presentation does not necessarily follow the presentation in the book.
Today
▪ What is artificial intelligence? ▪ What can AI do? ▪ What is this course?
Sci-Fi AI?
Let’s take a (rudimentary) look at hardware Architecture Num neurons Num synapses Fly 100K = 10^5 10M = 10^7 AlexNet 650K = 10^6 600M = 10^8 Mouse 100M = 10^8 100B = 10^11 Human 100B = 10^11 1014 -10^15
If each synapse is 1 FLOP (i.e., can fire / not fire once per second),
Then human brain requires 10^15 flops = 1 petaflop.
100,000 current CPUs
costs $5000 / hr on Amazon’s EC2.
■ (^) My 2002 answer:
■ Largely because you want to learn about AI…
■ … maybe even want to continue to learn even more about AI during a PhD …
■ … but not exactly the class that’s going to maximize your job
opportunities ;)
■ (^) My 2016 answer:
■ I am still hoping because you really want to learn about AI…
■ … but a lot of jobs have started to emerge
Why Take The Class?
`
▪ 1940-1950: Early days ▪ 1943: McCulloch & Pitts: Boolean circuit model of brain ▪ (^) 1950: Turing's “Computing Machinery and Intelligence” ▪ 1950—70: Excitement: Look, Ma, no hands! ▪ 1950s: Early AI programs, including Samuel's checkers program, Newell & Simon's Logic Theorist, Gelernter's Geometry Engine ▪ 1956: Dartmouth meeting: “Artificial Intelligence” adopted ▪ 1965: Robinson's complete algorithm for logical reasoning ▪ 1970—90: Knowledge-based approaches ▪ 1969—79: Early development of knowledge-based systems ▪ 1980—88: Expert systems industry booms ▪ 1988—93: Expert systems industry busts: “AI Winter” ▪ 1990—: Statistical approaches ▪ Resurgence of probability, focus on uncertainty ▪ General increase in technical depth ▪ Agents and learning systems… “AI Spring”? ▪ 2000—: Where are we now?
What Can AI Do?
Quiz: Which of the following can be done at present? ▪ Play a decent game of table tennis? ▪ Play a decent game of Jeopardy? ▪ Drive safely along a curving mountain road? ▪ Drive safely along Telegraph Avenue? ▪ Buy a week's worth of groceries on the web? ▪ Buy a week's worth of groceries at Berkeley Bowl? ▪ Discover and prove a new mathematical theorem? ▪ Converse successfully with another person for an hour? ▪ Perform a surgical operation? ▪ Put away the dishes and fold the laundry? ▪ Translate spoken Chinese into spoken English in real time? ▪ Write an intentionally funny story?
Natural Language
▪ Speech technologies (e.g. Siri)
▪ Automatic speech recognition (ASR) ▪ Text-to-speech synthesis (TTS) ▪ Dialog systems
▪ Language processing technologies
▪ Question answering ▪ Machine translation ▪ Web search ▪ Text classification, spam filtering, etc…
Vision (Perception)
Images from Erik Sudderth (left), wikipedia (right)
▪ Object and face recognition
▪ Scene segmentation
▪ Image classification
Robotics
▪ Robotics ▪ Part mech. eng. ▪ Part AI ▪ Reality much harder than simulations! ▪ Technologies ▪ Vehicles ▪ Rescue ▪ Soccer! ▪ Lots of automation… ▪ In this class: ▪ We ignore mechanical aspects ▪ Methods for planning ▪ Methods for control Images from UC Berkeley, Boston Dynamics, RoboCup, Google
Logic
▪ Logical systems
▪ Theorem provers
▪ NASA fault diagnosis
▪ Question answering
▪ Methods:
▪ Deduction systems
▪ Constraint satisfaction
▪ Satisfiability solvers (huge advances!)
Image from Bart Selman ▪ Classic Moment: May, '97: Deep Blue vs. Kasparov ▪ First match won against world champion ▪ “Intelligent creative” play ▪ 200 million board positions per second ▪ Humans understood 99.9 of Deep Blue's moves ▪ Can do about the same now with a PC cluster ▪ Open question: ▪ How does human cognition deal with the search space explosion of chess? ▪ Or: how can humans compete with computers at all?? ▪ 1996: Kasparov Beats Deep Blue “I could feel --- I could smell --- a new kind of intelligence across the table.” ▪ 1997: Deep Blue Beats Kasparov “Deep Blue hasn't proven anything.” ▪ Huge game-playing advances recently, e.g. in Go!
Game Playing
Text from Bart Selman, image from IBM’s Deep Blue pages
Decision Making
▪ Applied AI involves many kinds of automation
▪ Scheduling, e.g. airline routing, military
▪ Route planning, e.g. Google maps
▪ Medical diagnosis
▪ Web search engines
▪ Spam classifiers
▪ Automated help desks
▪ Fraud detection
▪ Product recommendations
▪ … Lots more!
