14013 - Conversational Artificial Intelligence: History, Application, Future Modulübersicht

Module Number: 14013
Module Title:Conversational Artificial Intelligence: History, Application, Future
  Konversations Künstliche Intelligenz: Geschichte, Anwendung, Zukunft
Department: Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
Responsible Staff Member:
  • Prof. Dr. rer. nat. Langendörfer, Peter
Language of Teaching / Examination:English
Duration:1 semester
Frequency of Offer: Every winter semester
Credits: 4
Learning Outcome:After successfully completing the module, students have a comprehensive overview of the field of Conversational AI. Studnets know the history of NLP and chatbots, including the meaning of NLP, NLU, and NLG. They know text mining techniques using Matlab/Simulink, Python and R learn. They also have knowledge of large language models and multimodal models, and familiarity with Transformer models for deep learning. They know the limitations of models and new platforms such as ChatGPT, Baidu's Ernie, and Google's Bard and are able to build and maintain conversation-based agents.
Contents:The module provides a comprehensive overview of the field of Conversational AI.
  • History of natural language processing (NLP) and chatbots, including the meaning of NLP, NLU,
  • Introduction to text mining using Matlab/Simulink, Python, R
  • Large language models (LLM) and multimodal models
  • Transformer models for deep learning (DL), including generative pre-trained transformer (GPT-3), GPT-3.5, bidirectional encoder representations from transformers (BERT), Turing natural language generation (Turing-NLG), and Wu Dao 2.0
  • Creating and managing a conversational agent with Google Dialogflow CX including flows, pages, intents, entities, fulfillments, and webhooks)
  • Cybersecurity aspects of chatbots, misinformation, and data biases
Recommended Prerequisites:
  • Basic knowledge of Python
  • Basic knowledge of machine learning
Mandatory Prerequisites:None
Forms of Teaching and Proportion:
  • Seminar / 2 Hours per Week per Semester
  • Self organised studies / 90 Hours
Teaching Materials and Literature:
  • Will be handed out at the beginning of the semester / during the first seminar meeting.
Module Examination:Continuous Assessment (MCA)
Assessment Mode for Module Examination:
  • Seminar presentation, 5-10 minutes (20%)
  • Paper/Essay, 10-15 pages (30%)
  • Successful completion of bi-weekly exercises (50%)
Evaluation of Module Examination:Study Performance – ungraded
Limited Number of Participants:30
Part of the Study Programme:
  • Master (research-oriented) / Artificial Intelligence / PO 2022
  • Master (research-oriented) / Informatik / PO 2008 - 2. SÄ 2017
  • Master (research-oriented) / Künstliche Intelligenz Technologie / PO 2022
Remarks:
  • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Seminars or Laboratories“
  • Study programme Künstliche Intelligenz Technologie M.Sc.: Compulsory elective module in complex „Software-basierte Systeme“
  • Study programme Informatik M.Sc.: Compulsory elective module in complex „Seminare oder Praktika“ (level 400)
Module Components:
  • Seminar: Conversational AI: History, Application, Future
Components to be offered in the Current Semester: