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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
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Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with a use case based approach to the Design of modern Conversational AI systems.
|
What is the module foundations of conversational AI design about?
|
[
{
"answer_start": 175,
"text": "software and processes by which speech is transformed into input for computers and smart devices"
}
] |
2
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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with a use case based approach to the Design of modern Conversational AI systems.
|
What will I be able to do in the foundations of conversational AI design module?
|
[
{
"answer_start": 313,
"text": "use case based approach to the Design of modern Conversational AI systems"
}
] |
3
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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Module Code - Title: CE2002 - FOUNDATIONS OF CONVERSATIONAL AI DESIGN Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence is the software and processes by which speech is transformed into input for computers and smart devices.This module will provide students with a use case based approach to the Design of modern Conversational AI systems.
|
What is the code of the foundations of conversational AI design module?
|
[
{
"answer_start": 21,
"text": "CE2002"
}
] |
4
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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques. Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.
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What use case will I do in the foundations of conversational AI design module?
|
[
{
"answer_start": 208,
"text": "Use case based example CAI specification"
}
] |
5
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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques.Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.
|
What will I be able to build in the foundations of conversational AI design module?
|
[
{
"answer_start": 468,
"text": "Students will build a functioning CAI system using an enterprise level design tool"
}
] |
6
|
FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Syllabus: Requirements analysis for systems that are based on Human Computer Spoken word interaction.Conversation flow design. The fundamental components of a conversation tree. Branch and bound techniques.Use case based example CAI specification that classifies Agents, Intents, Entities, Contexts, Interactions, reasoning and responses in enterprise level Conversational AI, (CAI) ecosystems. Scripted versus CAI systems for Human Computer spoken word interaction.Students will build a functioning CAI system using an enterprise level design tool. Students will be required to implement a Conceive Design Implement Operate (CDIO) approach to the construction of their CAI system.
|
What will I be required to do in the foundations of conversational AI design module?
|
[
{
"answer_start": 581,
"text": "implement a Conceive Design Implement Operate"
}
] |
7
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FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring
|
What will I be able to learn in the foundations of conversational AI design module?
|
[
{
"answer_start": 169,
"text": "Design a functional conversation narrative"
}
] |
8
|
FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring
|
How the conversational AI design module will be delivered?
|
[
{
"answer_start": 862,
"text": "weekly pre-recorded sessions and live class sessions"
}
] |
9
|
FOUNDATIONS_OF_CONVERSATIONAL_AI_DESIGN
|
Learning Outcomes: Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis) On successful completion of this module, students will be able to: Design a functional conversation narrative that can be successfully implemented in a Conversational AI (CAI) use case. Build a basic CAI application using an enterprise level design tool. Determine the individual component subsystems of a CAI design. Affective (Attitudes and Values) On successful completion of this module, students will be able to: Explain the difference between a scripted Chatbot and a conversational artificial intelligence system. Critically assess the performance of a functioning CAI system. Psychomotor (Physical Skills) How the Module will be Taught and what will be the Learning Experiences of the Students: Materials will be delivered in a blended manner through weekly pre-recorded sessions and live class sessions. The module material will include video recordings as well as readings, exercises, and assignments. The focus is on the reduction of theory to practice so there will be a strong emphasis on developing participants' practical skills. Research Findings Incorporated in to the Syllabus (If Relevant): Prime Texts: Jurafsky, Daniel. and Martin, James H. (2008) Speech and Language Processing: International Edition, Pearson Other Texts: Kamath, Uday. and Liu, John. (2020) Deep Learning for NLP and Speech Recognition, Springer Programmes Semester(s) Module is Offered: Spring
|
In what semester the foundations of conversational AI design module takes place?
|
[
{
"answer_start": 1433,
"text": "Spring"
}
] |
10
|
THEORY_AND_PRACTICE_FOR_CONVERSATIONAL_AI
|
Module Code - Title: CE2003 - THEORY AND PRACTICE FOR CONVERSATIONAL AI Prerequisite Modules: Rationale and Purpose of the Module: Conversational Artificial Intelligence (CAI) is the software and processes by which speech is transformed into input for computers and smart devices. This module will provide students with practical insights regarding the theoretical concepts that underpin modern Conversational AI systems. Syllabus: Introduction to machine learning for Conversational AI, (CAI). Scripted versus CAI systems for Human Computer spoken word interaction. Neural Networks for CAI. The definition and application of RNN, CNN, DNN, xNN subsystems to CAI. Speech recognition, language modeling and language decoding for CAI. Evaluation of Speech recognition tools. Data collection and labelling for training in CAI. Bag of words testing. An introduction to N-gram based modelling of speech. Evaluation of intent in CAI. Development of training sentences. Evaluation of Semantics, context and embedding in CAI systems. Dialog management: Introduction to Reasoning and Response generation in computer-based CAI systems.
|
What is the code of the theory and practice for conversational AI module?
|
[
{
"answer_start": 21,
"text": "CE2003"
}
] |
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