Pennsylvania State University

Course Information

Artificial Intelligence: The Very Idea

Syllabus Edits

Please note that the instructor may modify some of the specifics of the Course Syllabus as needed. The instructor will notify students of any changes and students will be responsible for abiding by them.

Detailed course policies covering all aspects of the course from class participation to academic integrity can be found here.

Even if you print this syllabus, please be sure to check the online version for the most current version.

Course Description

DS 197G "Artificial Intelligence: The Very Idea” will cover key concepts needed to understand recent AI advances, as well as the ethical and societal implications of AI technologies. Students will acquire general AI literacy, including:

• Intellectual roots of AI
• Disparate goals of AI
• Many types of AI and their applications
• The risks and benefits of AI technologies
• The societal impacts of AI technologies
• How to tell AI science from science fiction

Learning Objectives

Upon completion of this course, students will be able to:

• Discuss the core intellectual foundations of AI
• Demonstrate an understanding of different types of AI capabilities
• Discuss applications of AI across different areas of human endeavor
• Articulate the benefits of AI
• Discuss the societal impacts of AI
• Articulate the ethical considerations around AI applications
• Distinguish AI science from science fiction
• Effectively communicate about AI with diverse audiences

Course Materials

1. There is no required textbook for the course. A list of recommended references are provided here.
2. Additionally, weekly reading assignments and online readings, lecture slides, and other media will be posted here here.
3. Computers are required for in-class activities, but this course is NOT held in a computer lab. Students are required to their own devices for in-class activities. Penn State is committed to supporting any student who needs a device for use during the semester. Please utilize these resources if you have a need at any time:
   • Penn State’s IT Help Portal During the pandemic, Penn State will provide PC laptops and WIFI hotspots to students who need them. If students are studying remotely, this service offers delivery within the United States. (Web Access login required to fill out the Penn State Mobile Technology Request form)
   • Penn State Library – Wagner Building Annex Students studying at University Park campus can sign out a Mac laptop, PC laptop, iPad with attached keyboard, etc., for all or part of any semester. (Web Access login required to make reservations through the Patron Portal)

Grading Scheme

Final letter grades will be based on the 47-40 Grading System A, A-, B+, B, B-, C+, C, D and F

• A    95%+
• A-   90% – 94.9%
• B+  85% – 89.9%
• B    80% – 84.9%
• B-   75% – 79.9%
• C+  75% – 79.9%
• C    70% – 74.9%
• D    60% – 69.9%
• F    <60%

Course Schedule

Please note that the precise schedule is subject to change. Detailed study guide, including lecture slides, assigned readings can be found here.

There will be two exams: a midterm (roughly midway through the semester) and a final

Assignments are posted typically on a weekly basis on Canvas.

Short in-class quizzes may be given in any class.

• Lecture 1.Course overview. What is AI about? Why should we care? Goals of AI. A little history of artificial intelligence and computer science. Goals of AI. Working hypothesis of AI.
• Lecture 2. Human and Animal Intelligence. Theories of intelligence. Measures and mismeasures of intelligence.
• Lecture 3. The quest for Artificial Intelligence. Turing Test and the Chinese Room Argument.
• Lecture 4: Winograd Schema Challenge, Visual Turing Test, Video Turing Test, Lovelace Test, Physically Embodied Turing Test, Scientific AI Challenge, and the Total Turing Test. The untestability of Strong AI Hypothesis.
• Lecture 5: Origins of the Theory of Computation. Algorithms and Computation. Boolean Logic, Finite Automata and Turing machines, Algorithms, Languages, and Programs. Turing Machines. Universality of Turing Machines. Church-Turing Thesis. Limits of Computation.
• Lecture 6: Solvable and unsolvable problems. Easy versus hard problems.
• Lecture 7: Reactive Machines: Darwinian Minds - Intelligence achieved through (costly) trial and error and Natural selection; Adaptive Machines: Skinnerian minds. Adaptation. Intelligence achieved through Pavlovian conditioning or reinforcement.
• Lecture 8: Deliberative (reasoning) Machines: Popperian minds. Intelligence achieved using models of the world - by substituting "thinking" for "acting", by representing and reasoning about (the relevant aspects) of the world.
• Lecture 9: Nature and Kinds of representation: Ontology and Epistemology. Examples of representation.
• Lecture 10: Deductive Reasoning Machines 1: Logical representation and deductive reasoning: introduction using propositional logic. Beyond propositional logic.
• Lecture 12: Deductive Reasoning Machines 2: Probabilistic representation. Probabilities as subjective measures of belief. Reasoning with probabilities.
• Lecture 12: Deductive Reasoning Machines 3: Decision-theoretic representation. Preferences and utilities. Reasoning about preferences.
• Lecture 13: Mid Term Review
• Mid-term Exam
• Lecture 14: Learning Machines 1: What is machine learning? Simple Neural Networks
• Lecture 15: Learning Machines 2: Naive Bayes classifiers
• Lecture 16: Learning Machines 3: Decision Trees
• Lecture 17: Learning Machines 4: Language Models and Large language models
• Lecture 18: Cooperative and Competitive Machines. Multi-agent coordination, communication, competition.
• Lecture 19: Creative Machines
• Lecture 20: Selected AI Applications 1
• Lecture 21: Selected AI Applications 2
• Lecture 22: Selected AI Applications 3
• Lecture 23: Selected Societal impacts of AI 1
• Lecture 24: Selected Societal impacts of AI 2
• Lecture 25: Selected Societal impacts of AI 3
• Lecture 26: AI governance and regulation
• Lecture 27: Review and wrapup
• Final Exam