There are no pre-requisites for this module to be added

Conversational AI has attracted increasing attention in academia and industry in the past five years. There have been over 100 companies working on voice-AI or Conversational AI-based products, including the world’s most valuable brands (e.g. Apple, Google, Amazon, Huawei, etc.) and start-ups (e.g. Emotech, Alana). On the other hand, there are global competitions organised by Amazon, which focus on state-of-the-art technologies in diverse conversational AI tasks, e.g. social conversation and task-oriented conversation. This module can help students learn about conversational AI, related NLP and ML/DL methodologies, and state-of-the-art research topics. It also can provide students with the experience of team-level project work on realistic human daily problems, which offers a good insight into academic and industrial perspectives. The module covers the following topics: • Data types and formats: numerical and time series, graph, textual, unstructured, • Data sources and interfaces: open data, APIs, social media, web-based • NoSQL databases such as document (MongoDB), graph and key-value pair • Techniques for dealing with large data sets, including Map Reduce • Developing Data-Driven Applications in Python The Benchmark Statement for Computing specifies the range of skills and knowledge that should be incorporated in computing courses. This module encompasses cognitive skills in Computational Thinking, Modelling and Methods and Tools, Requirements Analysis and practical skills in specification, development and testing and the deployment and use of tools and critical evaluation, and providing useful generic skills for employment.

Upon completion of this module you will be able to

LO1: Critically analyse and compare state-of-the-art and off-the-shelf approaches to Conversational AI, including methods for natural language understanding, language generation, dialogue management, large language models, and multimodal interaction.

LO2: Critically evaluate the suitability, limitations, and trade-offs of Conversational AI techniques and tools for complex real-world applications, considering factors such as performance, robustness, scalability, usability, ethics, and responsible AI.

LO3: Design and implement an advanced conversational AI system for a complex or open-ended problem, integrating appropriate data, models, tools, and system components.

LO4: Design and conduct rigorous empirical experiments in Conversational AI, critically analyse results, compare alternative approaches, and draw evidence-based conclusions.

LO5: Communicate technical and research contributions effectively through academic writing and scholarly presentation, including the preparation and presentation of work in a format appropriate for an academic conference paper.