CS 540 – Introduction to AI Fall 2016 Jude Shavlik TA: Sam Gelman

Today’s Topics Administrivia –The Class Home PageThe Class Home Page –Moodle Piazza?Moodle –Java, Eclipse IDE, and CS 367JavaEclipse IDE –Course Textbook (skim Ch 1 and 2, read Sec & Appendices A & B)Course Textbook –Also skim Artificial Intelligence and Life in 2030Artificial Intelligence and Life in 2030 –Late HWs, exam dates? –A little about me … –Do not me at (use Class at Capacity – drop soon if you decide to not take; I’ll let more in next Tues Class Style Some AI History and Philosophy HW0 – Reading in a Dataset for Machine Learning (last 15 mins today) Machine Learning (in Lecture 2) Read the Algorithm section of the Wikipedia page on Random Forests (for next week) and (soon) 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)Wikipedia pageA Few Useful Things to Know About Machine Learning 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 2

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! 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 3

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 of lectures available 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 4

Work Load About five HWs (35% of grade), two with substantial programming Midterm (30%) – 90 mins, in class, Oct 20 Final (35%) – Dec 21, 7:45am I will teach more than I can test; don’t just focus on what is graded! 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 5

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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 6

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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 7

HW Schedule (tentative) HW0HW0 – due next Tuesday, 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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 8

Detailed List of Course Topics 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Reasoning from concrete cases Cased-based reasoning Nearest-neighbor algorithm Kernels Reasoning logically First-order predicate calculus Representing domain knowledge using mathematical logic Logical inference Probabilistic logic (if time permits) 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 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, kernels Reinforcement learning (if time permits) Learning from unlabeled data (if time permits) K-Means Expectation-Maximization Searching for solutions Heuristically finding shortest paths Algorithms for playing games like chess Simulated annealing Genetic algorithms Reasoning probabilistically Probabilistic inference Bayes' rule, Bayesian networks Lecture 1, Slide 9

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 ONE 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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 10

9/5/16CS Fall 2016 (Shavlik©), Lecture 1 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.Academic Misconduct Guide for Students Lecture 1, Slide 11

Some AI Milestones Computer beats leading chess grand master (1997) Computer wins Jeopardy (2011) Speech recognition in smartphones (2011) Self-driving cars (2014) ‘Star Trek telephone’ (2015) 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 12

The “Star Trek” Telephone 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Machine Translation Speech Generation Speech Recognition Japanese English Lecture 1, Slide 13

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 XRd4kd2XgZjIbQ8TwTC32Zc9&index=3 CS 540 Nannon © Competition 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 14

DARPA Grand Challenge (2005) Oshkosh Truck came in 5 th 4 th : 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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 15

Some More Videos/Images Robots Falling Down at the 2015 DARPA Robotics Challenge Google Translate (2015 cellphone app) 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 16

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? Cargo ships? 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 17

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) 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 18

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) 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 19

The Coming Singularity? 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 20

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 ( ( 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 21

Linear Thinking Time into the Future Predicted Amount of Change How we tend to predict the future How technology actually advances 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 22

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 9/5/16CS Fall 2016 (Shavlik©), Lecture 1 Lecture 1, Slide 23

HWOHWO – Reading in an Dataset Due in one week (most HWs will have two weeks between when assigned and when due) The Wine Dataset (original version) 9/6/15CS Fall 2015 (Shavlik©), Lecture 224