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Module Number:
| 14013
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| Module Title: | Conversational Artificial Intelligence: History, Application, Future |
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Konversations Künstliche Intelligenz: Geschichte, Anwendung, Zukunft
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| Department: |
Faculty 1 - Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology
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| Responsible Staff Member: | -
Prof. Dr. rer. nat. Langendörfer, Peter
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every winter semester
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| Credits: |
4
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| 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
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| Recommended Prerequisites: | - Basic knowledge of Python
- Basic knowledge of machine learning
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| Mandatory Prerequisites: | None |
| Forms of Teaching and Proportion: | -
Seminar
/ 2 Hours per Week per Semester
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Self organised studies
/ 90 Hours
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| Teaching Materials and Literature: | - Will be handed out at the beginning of the semester / during the first seminar meeting.
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| 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%)
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| Evaluation of Module Examination: | Study Performance – ungraded |
| Limited Number of Participants: | 30 |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
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Master (research-oriented) /
Informatik /
PO 2008
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| Remarks: | - Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Seminars or Laboratories“
- Study programme Informatik M.Sc.: Compulsory elective module in complex „Seminare oder Praktika“ (level 400)
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| Module Components: | - Seminar: Conversational AI: History, Application, Future
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| Components to be offered in the Current Semester: | |
