Affective Artificial Intelligence
Principal lecturer: Prof Hatice Gunes
Taken by: MPhil ACS, Part III
Code: L344
Term: Michaelmas
Hours: 16
Format: In-person lectures
Prerequisites: Python programming skills (other programming languages are also OK but not always ideal), and basic background in machine learning or signal/image processing.
Moodle, timetable
Synopsis
Affective Artificial Intelligence (Affective AI) aims to imbue machines with social and emotional intelligence (EQ). More specifically, Affective AI aims to create artificially intelligent systems and machines that can recognize, interpret, process, and simulate human social signals and behaviours, expressions, and emotions, to enhance human-AI interaction and communication. \r\n\r\nTo achieve this goal, Affective AI draws upon various scientific disciplines, including machine learning, computer vision, speech / natural language / signal processing, psychology and cognitive science, and ethics and social sciences.
Background & Aims
Affective Artificial Intelligence (Affective AI) aims to imbue machines with social and emotional intelligence (EQ). More specifically, Affective AI aims to create artificially intelligent systems and machines that can recognize, interpret, process, and simulate human social signals and behaviours, expressions, and emotions, to enhance human-AI interaction and communication.
To achieve this goal, Affective AI draws upon various scientific disciplines, including machine learning, computer vision, speech / natural language / signal processing, psychology and cognitive science, and ethics and social sciences.
Affective AI has direct applications in and implications for the design of innovative interactive technology (e.g., interaction with chat bots, virtual agents, robots), single and multi-user smart environments ( e.g., in-car/ virtual / augmented / mixed reality, serious games), public speaking and cognitive training, and clinical and biomedical studies (e.g., autism, depression, pain).
The aim of this module is to impart knowledge and ability needed to make informed choices of models, data, and machine learning techniques for sensing, recognition, and generation of affective and social behaviour (e.g., smile, frown, head nodding/shaking, agreement/disagreement) in order to create Affectively intelligent AI systems, with a consideration for various ethical issues (e.g., privacy, bias) arising from the real-world deployment of these systems.
Syllabus
The following list provides a representative list of topics:
- Introduction, definitions, and overview
- Theories from various disciplines
- Sensing from multiple modalities (e.g., vision, audio, bio signals, text)
- Data acquisition and annotation
- Signal processing / feature extraction
- Learning / prediction / recognition and evaluation
- Behaviour synthesis / generation (e.g., for embodied agents / robots)
- Advanced topics and ethical considerations (e.g., bias and fairness)
- Cross-disciplinary applications (via seminar presentations and discussions)
- Guest lectures (diverse topics – changes each year)
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Hands-on research and programming work (i.e., mini project & report)
Objectives
On completion of this module, students will:
- understand and demonstrate knowledge in key
characteristics of affectively intelligent AI, which include:
- Recognition: How to equip Affective AI systems with capabilities of analysing facial expressions, vocal intonations, gestures, and other physiological signals to infer human affective states?
- Generation: How to enable affectively intelligent AI simulate expressions of emotions in machines, allowing them to respond in a more human-like manner, such as virtual agents and humanoid robots, showing empathy or sympathy?
- Adaptation and Personalization: How to enable affectively intelligent AI adapt system responses based on users' affective states and/or needs, or tailor interactions and experiences to individual users based on their expressivity / emotional profiles, personalities, and past interactions?
- Empathetic Communication: How to design Affective AI systems to communicate with users in a way that demonstrates empathy, understanding, and sensitivity to their affective states and needs?
- Ethical and societal considerations: What are the various human differences, ethical guidelines, and societal impacts that need to be considered when designing and deploying Affective AI to ensure that these systems respect users' privacy, autonomy, and well-being?
- comprehend and apply (appropriate) methods for collection, analysis, representation, and evaluation of human affective and communicative behavioural data.
- enhance programming skills for creating and implementing (components of) Affective AI systems.
- demonstrate critical thinking, analysis and synthesis while deciding on 'when' and 'how' to incorporate human affect and social signals in a specific AI system context and gain practical experience in proposing and justifying computational solution(s) of suitable nature and scope.
Assessment - Part II Students
- Seminar presentation: 20%
- Participating in Q&A and discussions: 10%
- Mid-term mini project report and presentation (as a team of 2): 10%
- Final mini project report & code (as a team of 2): 60%
Assessment - MPhil / Part III Students
- Seminar presentation: 20%
- Participating in Q&A and discussions: 10%
- Mid-term mini project report and presentation: 10%
- Final mini project report & code: 60%
Further Information
Current Cambridge undergraduate students who are continuing onto Part III or the MPhil in Advanced Computer Science may only take this module if they did NOT take it as a Unit of Assessment in Part II.
This module is shared with ACS. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on. More information will follow at the first lecture.
This module is shared with Part II of the Computer Science Tripos. Assessment will be adjusted for the two groups of students to be at an appropriate level for whichever course the student is enrolled on. Further information about assessment and practicals will follow at the first lecture.