1. Robotics AI Illusions of intelligence Alan Turing Lab: Combine sound and light exercises. Homework: Identify & describe problems with the automatic solution similar to ‘human’ and problems with the automatic solution very different

2. Artificial Intelligence Definitions? –Machine (computer) simulating human reasoning –Machine (computer) demonstrating surprisingly human intelligence Problem for field: as soon as some AI research proves practical, it isn’t considered AI! –American Association for Artificial Intelligence (AAAI) http://www.aaai.org/AITopics/html/welcome.html http://www.aaai.org/AITopics/html/welcome.html –AAAI/Nova interview with Cynthia Breazeal http://www.pbs.org/wgbh/nova/sciencenow/3318/03.html http://www.pbs.org/wgbh/nova/sciencenow/3318/03.html

3. Different strategies Work to solve problem, using any technique that works Work to understand how humans reason enough to implement these ways using computers (cognitive science) and use implementations to test ideas

4. Methods Symbolic manipulation (as opposed to numerical calculations) Enumerating / expanding trees of possibility: branch-and-bound search Expert systems: states and testing conditions. Neural nets (and other forms of machine learning)

5. Expert systems Collection of rules: If (A, B, C) then do D Using the expert system consists of going through the rules and doing the actions Early example: medical diagnosis. –Do checks –Actions may be do another test –Arrive at diagnosis

6. Neural nets Modeled after how brain may work Define graph (nodes, directed edges) –Nodes conditions –Edge from A to B if A leads to B Different techniques for building and refining net, including trying many cases and putting weight on edge if it leads to good result http://www.popsci.com/science/article/2010- 01/robots-display-predator-prey-co-evolution- evolve-better-homing-techniqueshttp://www.popsci.com/science/article/2010- 01/robots-display-predator-prey-co-evolution- evolve-better-homing-techniques

7. Pattern recognition Need to extract measurable features These constitute the signature Compare to archive Example: facial recognition. Features such as ratio of spaces between eyes to eyes to chin. Need to use ratios for such things.

8. Topics/problems Theorem proving: technique to assume negative and see if you reach a contradiction by trying all combinations. Natural language processing for interface –My abominable abdomen project vs moon rocks query Natural language processing for translation: currently has some success/utility Speech recognition –Depends on size of language, restrictions and/or training

9. Histor Attempt to make use of outtakes from interviews used for documentary of Jacques Lipchitz, sculptor: 1970! Use keywords linking to segments to have Jacques answer questions –Illusion only of natural language –System continually massaged –Updated to work with latest technology If [art, Histor] moves you, you must be satisfied. Quote from Jacques Lipchitz about art in general. Can apply to using Histor. AI?

10. Eliza 1960s program (parady) by Joseph Weizenbaum to emulate a therapist (Rogerian) Relatively simple manipulation of patient’s remarks with randomly inserted stock questions. –“My head hurts.” “Why do you think your head hurts”. –“I feel bad today.” “What do you think about your mother.” Fairly successful!

11. Alan Turing Significant theoretical work on computation (Decision Problem) Worked at Bletchley Park during WWII on decoding German codes (Enigma machine): done by altering a coding machine Worked various places, including Princeton, with Van Neumann, others, on early computers Proposed way to build a chess machine Defined Turing test

12. Decision problem Theoretical problem, set by Hilbert (1900) Entscheidungsproblem: What does it mean for something to be computable? Turing (1936) produced 2 formulations (Turing machines & recursive functions) and proved them equivalent (and later proved these equivalent to a formulation of Post). Also proved limitations http://www.turing.org.uk/turing/http://www.turing.org.uk/turing/ many others

13. Turing machine Infinite strip of tape Machine has finite number of states. A state holds the definition of what to do when reading a 0 or 1 on the tape Machine reading a spot on the tape can –Move (on tape) left or right or stop –Write something on tape (re-write) –Change state Turing machine computes a function on an input if it stops. The result is the number on the tape (technically, answer is number of 1s on the tape or one more)

14. Universal Turing machine Encode a Turing machine to be a single number A Universal Turing machine takes as input the number representing a TM plus input and produces the result that the TM would produce from that input

15. Recursive functions Functions from vectors/tuples to vectors/tuples Built up from basic functions –Constant functions F(x 1, x 2,..x k ) = n –Successor function F(x) = x+1 –Projection F i k (x 1, x 2,..x k ) = x i Using –Composition –Primitive recursion –Inverse (aka μ operator)

16. Turing test Set up a judge to have a ‘conversation’ using text messages back and forth to a machine and to a human. If the judge cannot tell the difference, then the machine has passed the [Turing] test. What about Histor? What about Eliza? Should the test be harder?

17. Robotics Robotics has been considered part of AI in computer science but also in engineering Are AI techniques such as pattern recognition, expert systems, neural networks, especially applicable to physical tasks?

18. Preview / Commercial Fall course Advanced Topics in Computer Science will include computability, AI and encryption (including recent news about vulnerability of current practices), etc. Beautiful Exercise in logical thinking

19. Lab Follow line and turn around when there is a sound. Your own idea. Ideas: –From outside the blue oval, start when someone claps, move to the circle and follow line. –From outside (especially track in the back), go in one direction and keep track of each blue line passed. Use variable (suitcase). Display count. –[Needs singing/hum] Start at rest. When sound is greater than a certain level, go around oval. If and when sound stops (falls), turn to inside of oval and stop leave oval –Add bump sensor (perhaps in back or combined with light?) Your own challenge to the class?

20. Homework Identify a problem/task for which the automated solution/approach is –Similar to the human way –Different from the human way Postings Preview: AI topics are options for research presentation as are 'real world' robotics, real robots versus robots in literature, health, miltiary,ethics, etc.