1. CS 151: Introduction to Artificial Intelligence Professor Sara Owsley Sood

2. Course Goals  Apply basic Artificial Intelligence techniques…  …to solve real-world (current day) Artificial Intelligence problems, and in the process…  …appreciate how HARD Artificial Intelligence really is (and why)  Apply basic Artificial Intelligence techniques…  …to solve real-world (current day) Artificial Intelligence problems, and in the process…  …appreciate how HARD Artificial Intelligence really is (and why)

3. AI is a HUGE field  Definition of AI?  My Definition  “ Building programs that enable computers to do what humans can do. ”  Ex: read, walk around, play games, solve problems, learn, have conversations.  Definition of AI?  My Definition  “ Building programs that enable computers to do what humans can do. ”  Ex: read, walk around, play games, solve problems, learn, have conversations.

4. Misconceptions  Misconceptions first  AI in movies or TV?  These set expectations high  We can ’ t do all these things  Misconceptions first  AI in movies or TV?  These set expectations high  We can ’ t do all these things

5. What are the goals of AI? How do we measure success? Think like a human Cognitive Modeling Think rationally Logic Act like a human Turing Test Act rationally Rational Agents

6. Natural Language  Understanding  Speech recognition  Natural language processing  Generation  Natural language generation  Speech & gesture generation  LOQUENDO KATE DEMO  Understanding  Speech recognition  Natural language processing  Generation  Natural language generation  Speech & gesture generation  LOQUENDO KATE DEMO

7. Knowledge Representation: Common Sense  What would happen if I dropped my computer on the ground (and how do you think I would react?)  How do you get common sense into a computer?  Opencyc.org and OMCS (OpenMindCommonSense)  How many facts?  What would happen if I dropped my computer on the ground (and how do you think I would react?)  How do you get common sense into a computer?  Opencyc.org and OMCS (OpenMindCommonSense)  How many facts?

8. Automated Reasoning Game playing Planning Route Finding

9.  What does it mean for the computer to learn?  Supervised  Unsupervised  What does it mean for the computer to learn?  Supervised  Unsupervised Learning “circle”“square”“circle”“square” … “group these into two categories”

10. Perception  Visual Image Understanding  Task: find the table

11. Robotics  Do you have to be smart to walk? MIT Leg Lab: D. Paluska et al. http://www.ai.mit.edu/projects/leglab/robots/robots.html Sony QRIO

12. Emotions  Kismet, C. Breazeal, MIT (image courtesy of P. Menzel)

13. Strong AI versus Weak AI

14. Timeline of AI history

15. Course Logistics - syllabus

16. Introduction to Python >>> x = 5 #no variable types or declarations >>> y = 10 #python has implicit typing >>> x + y #meaning that the type is implied by whatever #you do to the variable 15 >>> x = 'hi there' >>> x + y Traceback (most recent call last): File " ", line 1, in TypeError: cannot concatenate 'str' and 'int' objects >>> x + str(y) 'hi there10' >>> x[0] ##treat a string as a list 'h' >>> x[0:4] ##substring of a string is a sublist - slices 'hi t' >>> myL = [2]*5 ##can do powerful things >>> myL ##what do you think this will do? [2, 2, 2, 2, 2] >>> myL[2] = 0

17. Some Python!

18. provide all of the methods and data members available to an object No memorizing! Just use dir & help… help(listSearch) dir(“foo”) help(“foo”.split) dir(str) help(str.split) dir(42) dir([]) dir and help !