csc 242 artificial intelligence - university of rochester · 2018. 4. 10. · welcome • graduate...
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CSC 242 Artificial Intelligence
Henry Kautz Spring 2014
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Welcome• Instructor: Henry Kautz
• Chair, Computer Science
• Director, Institute for Data Science
• Past President, Association for the Advancement of Artificial Intelligence
• Research: combinatorial search algorithms; data mining; human-computer interaction; healthcare
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Welcome• Instructor: Henry Kautz
• Chair, Computer Science
• Director, Institute for Data Science
• Past President, Association for the Advancement of Artificial Intelligence
• Research: combinatorial search algorithms; data mining; human-computer interaction; healthcare
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Welcome
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Welcome• Graduate TA: Xiaowan Dong
• Undergraduate in Tianjin, China
• Research in computer architecture
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Welcome• Graduate TA: Xiaowan Dong
• Undergraduate in Tianjin, China
• Research in computer architecture
• Undergraduate TA’s:
• Sean Esterkin - Class of 2014 - TA’s CSC 282 Algorithms last fall; Did an REU (Research Experience for Undergraduates) at Salisbury University on Fractals
• Daniel Scarafoni - Class of 2015 - Recipient of FIRST Robotics Scholarship; Undergrad research on automated reasoning and crowd intelligence
• Daily office hours will be posted on course home page
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Welcome• Graduate TA: Xiaowan Dong
• Undergraduate in Tianjin, China
• Research in computer architecture
• Undergraduate TA’s:
• Sean Esterkin - Class of 2014 - TA’s CSC 282 Algorithms last fall; Did an REU (Research Experience for Undergraduates) at Salisbury University on Fractals
• Daniel Scarafoni - Class of 2015 - Recipient of FIRST Robotics Scholarship; Undergrad research on automated reasoning and crowd intelligence
• Daily office hours will be posted on course home page
![Page 8: CSC 242 Artificial Intelligence - University of Rochester · 2018. 4. 10. · Welcome • Graduate TA: Xiaowan Dong • Undergraduate in Tianjin, China • Research in computer architecture](https://reader035.vdocument.in/reader035/viewer/2022071419/611827dda1f2c17406131251/html5/thumbnails/8.jpg)
Welcome• Graduate TA: Xiaowan Dong
• Undergraduate in Tianjin, China
• Research in computer architecture
• Undergraduate TA’s:
• Sean Esterkin - Class of 2014 - TA’s CSC 282 Algorithms last fall; Did an REU (Research Experience for Undergraduates) at Salisbury University on Fractals
• Daniel Scarafoni - Class of 2015 - Recipient of FIRST Robotics Scholarship; Undergrad research on automated reasoning and crowd intelligence
• Daily office hours will be posted on course home page
![Page 9: CSC 242 Artificial Intelligence - University of Rochester · 2018. 4. 10. · Welcome • Graduate TA: Xiaowan Dong • Undergraduate in Tianjin, China • Research in computer architecture](https://reader035.vdocument.in/reader035/viewer/2022071419/611827dda1f2c17406131251/html5/thumbnails/9.jpg)
Welcome• Graduate TA: Xiaowan Dong
• Undergraduate in Tianjin, China
• Research in computer architecture
• Undergraduate TA’s:
• Sean Esterkin - Class of 2014 - TA’s CSC 282 Algorithms last fall; Did an REU (Research Experience for Undergraduates) at Salisbury University on Fractals
• Daniel Scarafoni - Class of 2015 - Recipient of FIRST Robotics Scholarship; Undergrad research on automated reasoning and crowd intelligence
• Daily office hours will be posted on course home page
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Honesty• Dishonesty:
• Claiming work by others as your own without attribution
• Unauthorized collaboration with other students
• All cases will be referred to Academic Honesty Board
• No second chances
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Accommodations• I will make appropriate accommodations for
students with learning differences
• Inform me in writing within the first two weeks of class
• Make an appointment to talk with me about differences that require accommodations beyond extra time on tests
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Coursework
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Coursework• 4 team programming projects (40%)
• Teams will be assigned by me (randomly)
• Different teams for each project
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Coursework• 4 team programming projects (40%)
• Teams will be assigned by me (randomly)
• Different teams for each project
• Homeworks: not graded; are preparation for:
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Coursework• 4 team programming projects (40%)
• Teams will be assigned by me (randomly)
• Different teams for each project
• Homeworks: not graded; are preparation for:
• 3 in-class exams plus final exam (50%)
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Coursework• 4 team programming projects (40%)
• Teams will be assigned by me (randomly)
• Different teams for each project
• Homeworks: not graded; are preparation for:
• 3 in-class exams plus final exam (50%)
• Frequent unannounced online quizzes (10%)
• Bring your laptop or smartphone to every class
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Seating• Starting 2nd week of class, seats will be assigned
• Seating chart changed monthly
• Reduce division between talkative students up front and quiet students lurking in the back of the room
• Reduce temptation to copy from your friends on exams
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Programming
• Projects must run on Linux on URCS instructional network
• If you do not have an account (non-majors), contact grad TA Xiaowan Dong
• Can use any language: Java, C, Python, LISP, Prolog
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Prosopagnosia• Face blindness
• Other aspects of visual processing (e.g., object discrimination) remain intact
• Acquired or congenital (development) damage to fusiform gyrus
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Prosopagnosia• Face blindness
• Other aspects of visual processing (e.g., object discrimination) remain intact
• Acquired or congenital (development) damage to fusiform gyrus
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Living with Prosopagnosia• No effective therapies
• Matching to photographs unsuccessful
• Subjects try to consciously use visual clues to identify people
• Distinctive hair, glasses, ways of dressing, place where seen
• Can sometimes learn to recognize a small number of people after months or years of frequent interaction
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Oliver Sacks• Physician, best-selling
author, and professor of neurology at the NYU School of Medicine
• Books: The Mind's Eye, Musicophilia, Awakenings, The Man Who Mistook His Wife For A Hat, Hallucinations
• Employs a full-time assistant to help recognize people
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Oliver Sacks• “I am much better at recognizing
my neighbors dogs (they have characteristic shapes and colors) than my neighbors themselves. Thus when I see a youngish woman with a Rhodesian ridgeback hound, I realize that she lives in the apartment next to mine. If I see an older lady with a friendly golden retriever, I know this is someone from down the block. But if I should pass either woman on the steer without her dog, she might as well be a complete stranger.”
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Mike May• Blinded age 3, sight restored at
43
• Had to consciously learn to recognize objects - visual neurons had been repurposed
• Biography: Crashing Through
• Rode a bike as a blind child
• Became champion blind skier
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Mike MayNear the back of the store, May spotted a large object at the end of the aisle. He moved near it and put his reasoning powers to work: The object wasn't moving. It was large and squarish. It was positioned near pallets.
"Is that a forklift?" he asked Jennifer.
Her face went white. She waited for a moment, then leaned in to May's ear.
"No," she whispered. "That's a very, very heavy woman."
Excerpt From: Robert Kurson. "Crashing Through." iBooks. https://itun.es/us/oPYez.l
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Henry Kautz• Researcher in artificial intelligence
• Moderate prosopagnosia
• After 7 years, can recognize about 20 people at UR
• Relies on email and notes to deduce names
• Worked with two graduate students for 6 months without realizing they were the same person who sometimes used a nickname
Zimmerman Dylan
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The Brains’ Two Systems• System 1: operates automatically and
quickly, with little or no effort and no sense of voluntary control
• Normal perception, making stereotypical judgements
• Face recognition, speech recognition
• System 2: allocates attention to the effortful mental activities that demand it, including complex computations.
• Conscious reasoning, self-monitoring
• Chess, computer programming
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Test• Say the COLOR of each of the following words:
• CAR!
• TREE!
• BANK!
• TIME
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Test• Say the COLOR of each of the following words:
• RED!
• BLUE!
• BROWN!
• ORANGE
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ar·ti·fi·cial in·tel·li·gence
noun
1. the theory and development of computer systems able to perform tasks that normally require human intelligence
What kind of intelligence(s)?
What kind of computer systems?
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Origins of the Field of AI
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Origins of the Field of AI• From its start, research in AI was divided by the kinds
of intelligence and computer systems studied
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Origins of the Field of AI• From its start, research in AI was divided by the kinds
of intelligence and computer systems studied
• Perceptron (Rosenblatt 1957): artificial neural networks for pattern recognition
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Origins of the Field of AI• From its start, research in AI was divided by the kinds
of intelligence and computer systems studied
• Perceptron (Rosenblatt 1957): artificial neural networks for pattern recognition
• Expert Systems (Feigenbaum 1965): logical rule-based systems for medicine and industry
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Origins of the Field of AI• From its start, research in AI was divided by the kinds
of intelligence and computer systems studied
• Perceptron (Rosenblatt 1957): artificial neural networks for pattern recognition
• Expert Systems (Feigenbaum 1965): logical rule-based systems for medicine and industry
• Bayesian Networks (Pearl 1998): brought probability and decision theory into AI - provided the mathematics needed to (slowly) unify the field
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Breakthroughs• For each of the following stories, think about:
• Which specific kinds of intelligence behavior were achieved? In humans, would they be System 1 (automatic) or System 2 (conscious)?
• How did the systems achieve this behavior? Could the operation of the system be plausibly analogous to the operation of the human brain?
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Shakey the Robot1972
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Deep Blue 1997
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Stanley 2005
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Watson 2011
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The Assistant 2013
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Syllabus• Heuristic Search
• Project: Othello Tournament • Logical Reasoning
• Project: Automated Planning • Probabilistic Reasoning
• Project: Email Spam Detection • Machine Learning
• Project: Face Recognition