Explore UCD

UCD Home >

COMP41760

Academic Year 2024/2025

Artificial/Human Intelligence (COMP41760)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Professor Mark Keane
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

With the advent of Generative Artificial Intelligence (GenAI) and, especially, Large Language Models (LLMs) there is a vigour debate in academia, industry and society about the extent to which Artificial Intelligence may equal or surpass Human Intelligence (as manifested by us). In short, whether Artificial or Human Intelligence will dominate the future. This course will cover the emerging issues currently in this fraught debate. A series of key topics will be reviewed as they are being currently debated in the field, which may cover issues such as whether (i) whether GenAI models can really carry out reasoning and planning, (ii) the analysis of textual sources (in LLMs) is enough for intelligence and the development of a common-sense knowledge of the world, (iii) whether AI models can ever really explain their own processes and predictions (using eXplainable AI methods), (iv) Machine Learning models can get beyond the independent and identically distributed data (i.i.d) assumption to adequately handle what happens outside the data manifold (i.e., surprising and unexpected events), (v) creativity can only be manifested in humans. The course will be run in person with weekly face-to-face lectures and practicals. Students will work in small groups on identified breaking issues and make classroom presentations on their research in these areas. Attendance at lectures and practicals is obligatory in order to learn from in-class discussions (i.e., failure to attend may be judged to constitute an unsuccessful attempt at the module, independent of other graded work). The course aims to help students develop an informed critical appreciation of the claims being made by proponents of both sides of the Artificial or Human Intelligence debate.

About this Module

Learning Outcomes:

At the end of the course students should have a thorough knowledge of some of the major issues being discussed in the Artificial/Human Intelligence debate. Students should have developed a critical insight into how these issues are evolving and be equipped to evaluate future claims made about Artificial Intelligence. Attendance at lectures and practicals is obligatory as in-class debate and discussion plays a key role in the learning outcomes. As such, failure to attend class activities may be deemed a failure to meet the module's learning outcomes and constitute an unsuccessful attempt at the module.

Indicative Module Content:

The course aims to provide students with a firm understanding of the emerging flash-points in the Artificial/Human Intelligence debate, but one not based on hype but on current scientific findings in the area.

Student Effort Hours:
Student Effort Type Hours
Lectures

12

Conversation Class

12

Specified Learning Activities

80

Total

104


Approaches to Teaching and Learning:
The course will rely on lectures, critical writing, group-based work and student presentations.

Requirements, Exclusions and Recommendations
Learning Requirements:

Ideally, students should have covered introductions to (i) Artificial Intelligence akin to that covered in COMP30030, COMP47980, and/or (ii) Machine Learning content akin to that covered in modules such as COMP47490, COMP47750, COMP47460 or COMP47990.

Learning Recommendations:

Students should have prior experience of a introductory course on Artificial Intelligence and/or Machine Learning as it will rely on some knowledge of the history of the discipline and recent advances in the field.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): End of term closed-book, in-person exam in exam centre. End of trimester
Duration:
2 hr(s)
Standard conversion grade scale 40% No
60
No
Assignment(Including Essay): In the weekly practical hour, small groups will make presentations on key emerging topics in the area, with each student submitting an essay on the topic to be corrected. Both will be marked. Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Standard conversion grade scale 40% No
40
No

Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Students will be given feedback on practical work.

Name Role
Saugat Aryal Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 12:00 - 12:50
Autumn Practical Offering 1 Week(s) - Autumn: All Weeks Thurs 13:00 - 13:50