1. CS 540 – Introduction to AI Fall 2015
Jude Shavlik TA: Dmitry Basavin

2. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
4/22/2017
Today’s Topics
Administrivia
The Class Home Page
Moodle Piazza?
Java, Eclipse IDE, and CS 367
Course Textbook (read Ch 1 and 2, Sec , Appendices A & B)
Missing class, late HWs, exam dates? TA Office Epic?
A little about me …
Do not me at (use
Three 50-min lectures in 170 minutes
Class Style
Some AI History and Philosophy
Machine Learning (in Lecture 2, Week 1)
For next week: Read the Algorithm section of the Wikipedia page on Random Forests and Pedro Domingos' paper A Few Useful Things to Know About Machine Learning (you can access this paper for free if you are on a UW-Madison network; if you use DoIT's VPN I believe you can also access this from a non-UW network, such as a computer in your home)
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

3. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
AI  Magic
Often mathematically complex algorithms
But lots of data (“big data”) and simple(r) methods can work quite well!
Counting lots of things can lead to intelligent behavior (HW4)
Arguably AI, especially machine learning (ML), most important IT technology currently – still quite exciting after being it in for 30+ years!
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

4. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
Class Style
More about engineering intelligent s/w than modeling human cognition
Concrete focus, to provide context for general AI ideas
“Hands on” – learn more by (actively) doing than (passively) listening
Try to write some notes during class, even though Powerpoint and videos of lectures available (more later)
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

5. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
Work Load
About five HWs (35% of grade), two with substantial programming
Midterm (30%) – a Thursday evening?
Final (35%) – Thursday evening Dec 17?
Will grade more like a grad class than a ugrad class because you all have ugrad degrees
I will teach more than I can test; don’t just focus on what is graded!
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

6. You Remember (Metcalf, 1997 and others)
10% of what you read  
20% of what you hear  
30% of what you see  
50% of what you hear and see together  
70% of what you think and say out loud  
90% of what you do
??% of what you hear, see, and write down
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

7. Programming Knowledge Assumed
For loops, arrays, scanners (to read in data)
Object-oriented design (eg, trees)
Stacks, queues, linked lists, hash tables
Recursion (see “Programming Knowledge Assumed” slide)
Trees and recursion (cs 367 topics) will be a big part of HW1
Math: partial derivatives (for neural nets); mathematical logic and prob covered in class
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

8. HW Schedule (tentative)
HW0 – due next class, read in dataset for ML HW1 – learn decision trees (Java) HW2 – tune sets (Java), ensembles (Java), searching for solutions (paper-and-pencil) Midterm HW3 – probabilistic reasoning (Java) and case-based reasoning (paper-and-pencil) HW4 – artificial neural networks and support vector machines (paper-and-pencil) HW5 – logical rep & reasoning (paper-and-pencil) Final
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

9. Detailed List of Course Topics
Learning from labeled data
Experimental methodologies for choosing parameter settings and estimating future accuracy
Decision trees and random forests
Probabilistic models, nearest-neighbor methods
Genetic algorithms
Neural networks
Support vector machines
Reinforcement learning (if time permits)
Searching for solutions
Heuristically finding shortest paths
Algorithms for playing games like chess
Simulated annealing
Reasoning probabilistically
Probabilistic inference
Bayes' rule
Bayesian networks
Reasoning from concrete cases
Cased-based reasoning
Nearest-neighbor algorithm
Reasoning logically
First-order predicate calculus
Representing domain knowledge using mathematical logic
Logical inference
Problem-solving methods based on the biophysical world
Genetic algorithms
Simulated annealing
Neural networks
Philosophical aspects
Turing test
Searle's Chinese Room thought experiment
The coming singularity
Strong vs. weak AI
Societal impact of AI
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

10. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
Late HW's Policy
HW's due on-line in 11:55pm
You have 5 late days to use over the semester
(Fri 11:55pm → Mon 11:55pm is 1 late "day")
SAVE UP late days!
Penalty points after late days exhausted
Can't be more than ONE WEEK late so solutions can be posted
Since this is a class for working professionals, we’ll be a bit more flexible wrt work travel
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

11. Academic Misconduct (also on course homepage)
All examinations, programming assignments, and written homeworks must be done individually. Cheating and plagiarism will be dealt with in accordance with University procedures (see the Academic Misconduct Guide for Students). Hence, for example, code for programming assignments must not be developed in groups, nor should code be shared. You are encouraged to discuss with your peers, the TAs or the instructor ideas, approaches and techniques broadly, but not at a level of detail where specific implementation issues are described by anyone. If you have any questions on this, please ask the instructor before you act.
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

12. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
Some AI Milestones
Computer beats leading chess grand master (1997)
Computer wins Jeopardy (2011)
Speech recognition in smartphone (2011)
Self-driving cars (2014)
“Star Trek telephone” (2015)
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

13. The “Star Trek” Telephone
Japanese
Speech Recognition
Machine Translation
Speech Generation
English
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

14. CS 540 at the Movies (suggest better/other videos)
AI Algo Beats Kasparov at Chess
IBM Watson Wins Jeopardy
Stanford+Google Car
Microsoft SKYPE Translator XRd4kd2XgZjIbQ8TwTC32Zc9&index=3
CS 540 Nannon© Competition
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

15. DARPA Grand Challenge (2005)
Oshkosh Truck came in 5th
4th: a Louisiana insurance company!
Story about searching for best path through dried lake bed …
Many fine paths, too much time spent thinking!
What is the key difference between Chess and Jeopardy/Car-Driving?
‘closed’ vs. ‘open’ world
Can you write a progam that is smarter than you?
You likely will in cs540
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

16. Some More Videos/Images
Robots Falling Down at the DARPA Robotics Challenge
Google Translate (2015 cellphone app)
9/8/15

17. In which Year will Children Born that Year not Need to Learn How to Drive?
Recently a leading robotics researcher said his answer is ‘2014’
Robots too polite? Eg, never speed, always yield
Will existing cars be retrofitted?
Will airplanes (especially freight) and trucks be first?
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

18. Machine Learning is Becoming Ubiquitous
Search (in the Google/Bing/etc sense)
Credit-card scoring, finance in general
Why might “hadBankruptcy” be the best feature for deciding who gets a credit card?
Personalization/recommendation in various forms
Extracting ‘knowledge’ from ‘natural’ languages (Machine Reading)
Understanding pictures and videos, face recognition
ML large focus of CS 540 (overlap with CS 760, grad ML class)
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

19. An AI Axiom
The easier something is for humans the harder it is for computers
And vice versa
A point I’ve been making for  25 years, but maybe no longer true?
Human-machine cooperation appealing
AI (rapidly) replacing ‘white collar’ jobs? (Robots have been replacing ‘blue collar’ jobs for awhile)
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

20. The Coming Singularity?
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

21. Some Interesting Quotes
“Machine intelligence is the last invention that humanity will ever need to make.”
“When thinking about the future we tend to over estimate the impacts in the near-term and under estimate impacts in the long term.” Roy Amara, Institute for the Future (
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

22. CS 540 - Fall 2015 (Shavlik©), Lecture 1, Week 1
Linear Thinking
How technology actually advances
Predicted Amount of Change
How we tend to predict the future
Time into the Future
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1

23. More AI Philosophy in Final Class this Term
CS 540 Fall 2015 (Shavlik)
4/22/2017
More AI Philosophy in Final Class this Term
Turing Test
Searle’s Chinese Room story
Weak and Strong AI hypotheses
The future of AI including its societal impact
Additional AI classes at Wisconsin
9/8/15
CS Fall 2015 (Shavlik©), Lecture 1, Week 1