705.640.01VL - Cognitive and Behavioral Foundations for Artificial Intelligence

Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the required readings and assignments. You are encouraged to preview all sections of the module before starting. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

• General history of robots and AI
• Building a human-like machine
• Evolution of the brain and intelligence
• Human vs. computer vision
• Human vs. computer audition
• National language understanding
• Learning
• Emotion, social cognition, and theory of mind
• Creativity
• Consciousness
• Biases and ethics
• Brain-computer interfaces

Course Goals

To identify and describe the characteristics of human intelligence, and then apply that knowledge to analyze and challenge current machine intelligence capabilities and propose a human-centered approach to AI in a semester long group project.

Course Learning Outcomes (CLOs)

• List and explain the main questions, theories, and experiments central to the human mind/brain sciences.
• Summarize different experimental methods and identify their impact on our current understanding of the human mind/brain.
• Identify and challenge current assumptions about and techniques utilized in the development of artificial intelligence.
• Discover and evaluate biologically inspired solutions for real-world AI-enabled smart technology needs.
• Develop a research proposal that formulates critical arguments for human-like intelligence and propose a novel R&D solution to a real-world smart technology problem of interest.
• Create written and oral communications understandable by a diverse audience.

Textbooks

None.

Other Materials & Online Resources

Readings subject to change. Any changes will be made in a timely manner and announced accordingly.

References of Course Readings (in alphabetical order)

• Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. Ai Magazine, 26(4), 53-60.
• de Haan, E.H.F., Corballis, P.M., Hillyard, S.A. et al. Split-Brain: What We Know Now and Why This is Important for Understanding Consciousness. Neuropsychol Rev 30, 224–233 (2020). https://doi.org/10.1007/s11065-020-09439-3.
• Dupoux, E. (2018). Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner. Cognition, 173, 43-59.
• Francl, A. F., & McDermott, J. H. (2020). Deep neural network models of sound localization reveal how perception is adapted to real-world environments. bioRxiv.
• Funke, C. M., Borowski, J., Stosio, K., Brendel, W., Wallis, T. S., & Bethge, M. (2021). Five points to check when comparing visual perception in humans and machines. Journal of Vision, 21(3), 16-16.
• Gamez, D. (2020). The relationships between intelligence and consciousness in natural and artificial systems. Journal of Artificial Intelligence and Consciousness, 7(01), 51-62.
• Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021). Embodied intelligence via learning and evolution. Nature communications, 12(1), 1-12.
• Howard, Ayanna & Borenstein, Jason. (2018). The ugly truth about ourselves and our robot creations: The problem of bias and social inequity. Science and Engineering Ethics, 24(5), 1521-1536.
• Lake, Brenden M., Ullman, Tomer, D., Tenenbaum, Joshua B., & Gershman, Samuel J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40:e253. DOI: 10.1017/S0140525X16001837
• Leeb, R., & Pérez-Marcos, D. (2020). Brain-computer interfaces and virtual reality for neurorehabilitation. Handbook of Clinical Neurology, 168, 183-197.
• Livingstone, M. S. & Hubel, D. (1988). Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science, 240(4853), 740-749.
• López-González, M. (2015). Cognitive psychology meets art: Exploring creativity, language, and emotion through live musical improvisation in film and theatre. Proceedings of SPIE 9394, Human Vision and Electronic Imaging XX, 939403 (March 17, 2015); DOI: 10.1117/12.2083880.
• McCarthy, J. (2007). What is artificial intelligence?. 2-15.
• McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (2019). Extending machine language models toward human-level language understanding. arXiv preprint arXiv:1912.05877.
• Moxon, K., Saez, I., & Ditterich, J. (2019, May). Mind Over Matter: Cognitive Neuroengineering. In Cerebrum: the Dana Forum on Brain Science (Vol. 2019). Dana Foundation.
• Oxenham, Andrew J. (2018). How we hear: The perception and neural coding of sound. Annual Review of Psychology, 69, 27-50. DOI: 10.1146/annurev-psych-122216-011635.
• Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54-71.
• Pease, A., & Colton, S. (2011, April). On impact and evaluation in computational creativity: A discussion of the Turing test and an alternative proposal. In Proceedings of the AISB symposium on AI and Philosophy (Vol. 39).
• Roth, G., & Dicke, U. (2005). Evolution of the brain and intelligence. Trends in cognitive sciences, 9(5), 250-257.
• Schuller, D., & Schuller, B. W. (2018). The age of artificial emotional intelligence. Computer, 51(9), 38-46.
• Shah, Vikas, “A conversation with Nobel prize winner Prof. Daniel Kahneman and Prof. Cass R. Sunstein on ‘Noise’ and the flaws in human judgement,” ThoughtEconomics - Articles & Interviews, 8 June 2021, https://thoughteconomics.com/noise/.
• Shelley, M. W. (2018). Frankenstein: The 1818 Text. Penguin.
• Solé R, Moses M, Forrest S. (2019). Liquid brains, solid brains. Phil. Trans. R. Soc. B 374: 20190040. http://dx.doi.org/10.1098/rstb.2019.0040
• Storm, Johan F., Boly, Mélanie, Casali, Adenauer G., Massimini, Marcello, Olcese, Umberto, Pennartz, Cyriel M. A., & Wilke, Melanie. (2017). Consciousness regained: disentangling mechanisms, brain systems, and behavioral responses. Journal of Neuroscience, 37(45), 10882-10893.
• Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011). How to grow a mind: Statistics, structure, and abstraction. science, 331(6022), 1279-1285.
• Winfield, A. F. (2018). Experiments in artificial theory of mind: From safety to story-telling. Frontiers in Robotics and AI, 5, 75.

Student Coursework Requirements

Grades will be determined by the following weighted breakdown:

• Class attendance (5%)
• In-class oral participation (10%)
• Five in-class quizzes (25%)
• Take-home midterm (20%)
• Final group project (30%)
• In-class final group project oral presentation (10%)

Grading Policy

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.