ge2340 artificial intelligence - cityu cscheewtan/lec_ai_courseintro.pdf · 2020. 9. 3. · 1 sep 4...
TRANSCRIPT
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GE2340 Artificial Intelligence Past, Present, Future
Chee Wei Tan
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Requisites and Goals• In this course, you should have some understanding
of freshman basic math (i.e., high-school math). • Preferably, but not a must, you know some basic
computer programming language (since it’s useful for your coursework)
• After this course, you should know– Basic ideas of AI and past achievements– Problem solving skills– Critical thinking and algorithmic thinking (deep thinking)– Creative applications in computer science
Introduction 3
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Reference BooksBertram Raphael. The Thinking Computer. MWH Freeman and Co., 1976. ISBN: 0-7167-0723-3.
Introduction 4https://archive.org/details/thinkingcomputer00raph
Martin Gardner. Mathematical Circus. The Mathematical Association of America, 1992. ISBN: 978-0883855065.
https://archive.org/details/B-001-018-093/page/n287
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Tentative ScheduleWeek Date Topics1 Sep 4 Birth of AI and introduction to computation2 Sep 11 Mathematical logic in AI In-class Quiz Start3 Sep 18 AI games and winning strategies Pre-class Quiz Start4 Sep 25 Advanced AI games and algorithms Written Assignment Out5 Oct 2 No class (Public Holiday) Project Topics Out6 Oct 9 Maze solving and AI applications Written Assignment Due7 Oct 16 Human-assisted computation and AI Chatbot8 Oct 23 Scientific aspects of juggling and AI robots9 Oct 30 AI Chatbot and its case studies10 Nov 6 Searching under uncertainty and neural networks in AI11 Nov 13 Automated reasoning in AI12 Nov 20 Human-compatible AI and the AI Revolution13 Nov 27 Revision and Demo Quiz End, Project Report Due
Introduction 5
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Assessments• Course Work: 60%– Pre-Class Quiz: 15%– In-Class Quiz: 15%– One Written Assignment: 5%– One Project (Individual or Team): 25%
• Final Examination: 40%– For a student to pass the course, at least 30% of the
maximum mark of the examination must be obtained.
• Please adhere to CityU’s Rules on Academic Honesty (http://www.cityu.edu.hk/provost/academic_honesty/rules_on_academic_honesty.htm)
Introduction 6
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Written Assignment and Project
• Written Assignment– A longer version of tutorial questions– Open-book, can discuss questions with classmates but
write up solution individually and submit via Canvas
• Project– Project Topics posted on Canvas in early October– Can be individual or team (may limit no. of students/team)– Project report submission on Canvas by end of semester
(exact date to be posted)– Demo is tentatively optional but recommended with a Call-
for-Demo issued around mid-NovemberIntroduction 7
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Software for Learning
https://xkcd.com/329/
• NemoBot in Facebook Messengerhttps://www.facebook.com/Nemo-Bot-454163798317367To administer pre-class and in-class quizzes that are multiple-choice questions
To start: Subscribe to GE2340 and select “Fetch Message” or text “fetch”!
And we teach you later how to program this chatbot to demonstrate AI ideas
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NemoBot in Telegram• As Telegram is popular
nowadays in Hong Kong and worldwide, we have just added Telegram support!
• To use Nemobot in Telegram, search for @city_nemo_bot in telegram or visit link (https://t.me/city_nemo_bot)
• Then send “/subscribe” in Nemobot who will guide you though the subscription process
Introduction 11
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Pre-Class and In-Class Quizzes• Pre-class Quiz– MCQ and sent out before each Lecture, typically on Thursday– Related to pre-class reading, video watching– Around 2-3 Quiz questions and no time limit
• In-class Quiz – MCQ and sent out during each Lecture in-class– A warm-up poll first, then peer discussions and then quiz sent– Around 2-3 Quiz questions and are time-limited
• Grading of Pre-class and In-class Quizzes – Typically 1 point for each correct quiz answer, and for some of
the Pre-class quizzes, more points may be given (e.g., 2 points or 3 points for a more challenging one)
– Final score is normalized to the highest overall score, the difficulty level of the quiz and the difficulty-level spread
– Try not to miss in-class quiz, as they are not re-issuedIntroduction 12
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Motivation
• “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
– John McCarthy,
When he coined the term “artificial intelligence” in a proposal:
McCarthy, J., Minsky, M., Rochester, N., and Shannon, C., A proposal for the Dartmouth Summer Research Project in Artificial Intelligence, August 31, 1955
Reprint in AI Magazine, Vol 27, Number 4, pp 12-14, 2016. https://doi.org/10.1609/aimag.v27i4.1904
Introduction 13
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Introduction 14
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Motivation• Deep learning - The hottest AI idea in town
Introduction 15https://www.quantamagazine.org/new-theory-cracks-open-the-black-box-of-deep-learning-20170921/