Undergraduate Course: Artificial Intelligence, Present and Future (INFR11180)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 5 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | AI systems now outperform humans on tasks that were once taken to show great intelligence when undertaken by people (for example, playing chess). How far can this go in the future? What are the assumptions behind different approaches to AI? What dangers can there be from AI systems, and how should AI practitioners take these into account? The course gives a quick overview of the background and of contemporary work in symbolic AI, and looks at the relationship between statistical and 2 logical approaches to AI. It also addresses some of the philosophical and ethical issues that arise. |
| Course description |
The course surveys the state of the art in current AI, looking at systems and techniques in various subfields (eg, agents and reasoning; planning, constraints and uncertainty; google search and the semantic web; dialogue and machine translation; varieties of learning).
Throughout, relationships between different approaches to AI will be explored, especially the symbolic/sub-symbolic split at the representation level. Philosophical and ethical issues in AI issues will be introduced.
Typical topics include:
Reasoning agents
Logic and inference via Logic Programming
Linked data, semantic net and internet search
Monte Carlo Tree Search
Planning under uncertainty
Adversarial search, game playing
Probabilistic inference
Inductive Logic Programming
Natural language processing, approaches to machine translation
Approaches to machine learning
AI prospects and dangers
Ethical and Philosophical issues.
|
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2019/20, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 18,
Supervised Practical/Workshop/Studio Hours 8,
Feedback/Feedforward Hours 2,
Summative Assessment Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
50 )
|
| Assessment (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written exam: 75%
Practical Examination: 0
Coursework: 25%
One formative assessment involving programming; one assessed coursework.
Time spent on assessments 30 hours |
| Feedback |
Students will receive written feedback from the formative assessment, and feedback on the summitive assessment in the normal way. The lab sessions will allow more immediate feedback on ideas coming from the students. The lecturers will be available at advertised office hours. There will be a Piazza page to support day-to-day queries. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge that covers and integrates the current main conceptual frameworks at use in AI;
- Compare and contrast competing approaches towards the construc- tion of AI artefacts;
- Understand and make use of computational reasoning techniques to solve AI problems;
- Clearly present and justify considered opinions on major debates in the field.
|
Reading List
Russell and Norvig: Artificial Intelligence: a Modern Approach, 3rd edition, Prentice Hall, 2016
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, M. Brundage et al, 2018. |
Additional Information
| Graduate Attributes and Skills |
Apply critical analysis, evaluation and synthesis to issues that are informed by forefront developments in the subject/discipline/sector.
Demonstrate and work with a critical understanding of the principal concepts and principles |
| Keywords | Artificial Intelligence; Reasoning; |
Contacts
| Course organiser | Dr Jacques Fleuriot
Tel: (0131 6)50 9342
Email: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: |
|
|
University Homepage