SlideShare a Scribd company logo
1 of 18
Download to read offline
Intelligent User
Interfaces
ICS2208
vanessa.camilleri@um.edu.mt
Dr Vanessa Camilleri
Department of AI,
University of Malta
Topic 5: Overview
• Introduction
• NLP in Action
• Generative AI and Textual Interfaces
• LLMs; impact, bene
fi
ts and future
2
• The Natural Language Interface as a user interface
where linguistic phenomena such as verbs,
clauses, phrases act as controls for creating,
selecting and modifying data in applications.
Techniques and
Technologies
• Tokenisation and Text Normalisation
• Part of Speech Tagging (POS) and Named Entity
Recognition (NER)
• Dependency Parsing
• Sentiment Analysis
• Machine Translation
Models and Frameworks
• Rule-based Models
• Statistical Models
• Neural Network Models
• Transformer Models
Applications in Textual
Interfaces
• Chatbots and Virtual Assistants
• Search Engines
• Text Summarisation and Generation
How do NLP models help improve
accuracy of textual interfaces?
• Understanding Context
• Transfer Learning
• Handling Ambiguity
• Entity Recognition and Classi
fi
cation
• Sentiment Analysis
• Continuous Learning and Adaptation
• Integration with Domain Speci
fi
c Knowledge
• Addressing Data Ambiguities
• Predictive Text and Autocorrection
Limitations of NLP Models
• Ambiguity in Language
• Sarcasm and Irony Detection
• Handling Idiomatic Expressions
• Adapting to Language Evolution
• Data Ambiguities
• Information Overload
• Domain-speci
fi
c Language
• Tokenisation Challenges
• Context Dependency
Class Activity
Divide into small groups (ideally 3-4 students each).
Each group takes one text sample
Analyse the text sample assigned to you
Discuss the following questions:
• What is the main topic of the text?
• What are some key entities or phrases mentioned in the text
(e.g., people, organisations, locations)?
• What is the overall sentiment of the text (positive, negative,
neutral)?
Generative AI
• Generative AI refers to a class of artificial
intelligence that specialises in creating content,
which can include text, images, music, and more.
This technology operates by learning from large
datasets to generate new, original material that
resembles the learned content. In the context of
text generation, Generative AI uses models like
Large Language Models (LLMs) to produce human-
like text responses based on the input they
receive.
Generative AI
• Generative AI is different from other types of AI in text
generation through its ability to create new, original content
based on pattern and examples learned from extensive dataset.
• Content creation vs. Task performance
• Data Driven Learning
• Unsupervised Learning Capabilities
• Generative Models
• Creativity and Adaptability
• Versatility and Content Generation
Applications of Generative
AI to Text Generation
• Writing Assistance
• Information Retrieval
• Thought Partnership
• Chatbots and Virtual Assistants
• Language Translation
• Summarisation
• Content Creation for Various Media
Class Activity
Generative AI and Chatbots
Divide into groups - each group discuss one area of research (use references/
sources):
• Group 1: Exploring User Preferences: Research how user expectations and
preferences for chatbot interactions are evolving with the use of Generative AI.
• Group 2: Ethical Considerations: Investigate the ethical considerations
surrounding the use of Generative AI in chatbots, such as bias, transparency,
and user privacy.
• Group 3: The Future of Customer Service: Research how Generative AI
chatbots are transforming the landscape of customer service interactions.
• Group 4: Creative Applications: Explore the use of Generative AI chatbots in
creative domains like storytelling, education, or entertainment.
Large Language Models
• A language model distinguished by its general-
purpose language generation capability.
• Typically built with a transformer-based
architecture, but some implement recurrent neural
network variants or state space models like Mamba.
• Training Process Acquires abilities through learning
statistical relationships from text documents in a
self-supervised and semi-supervised training
process.
How LLMs Work
• Architecture
• Attention Mechanism
• Training Data
• Tokens
• Training Process
Capabilities of LLMs
• Text Generation
• Language Translation
• Content Creation
• Question Answering
• Summarisation
• Sentiment Analysis
Ethical Challenges and
Considerations for LLMs
• Potential Bias in Training
• Risk of Generating Misinformation
• Privacy Concerns
• Data Security
• Environmental Impact of Training Large Models
• Societal Impact
• Legal and Copyright Issues
• Responsibility and Accountability
• Risks of Malicious Use
• Transparency and Control
Online Task
• Choose a specific textual interface (e.g., a virtual
assistant, a search engine) and analyse how it
utilises NLP techniques and/or Generative AI.
• Write a short 300 word description of it, outlining
your observations about it and potential
improvements based on the concepts discussed in
class.

More Related Content

Similar to ICS 2208 Lecture Slide Notes for Topic 6

Natural Language Processing .pdf
Natural Language Processing .pdfNatural Language Processing .pdf
Natural Language Processing .pdfAnime196637
 
An Abridged Version of My Statement of Research Interests
An Abridged Version of My Statement of Research InterestsAn Abridged Version of My Statement of Research Interests
An Abridged Version of My Statement of Research Interestsadil raja
 
Km cognitive computing overview by ken martin 19 jan2015
Km   cognitive computing overview by ken martin 19 jan2015Km   cognitive computing overview by ken martin 19 jan2015
Km cognitive computing overview by ken martin 19 jan2015HCL Technologies
 
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...mlaij
 
KM - Cognitive Computing overview by Ken Martin 13Apr2016
KM - Cognitive Computing overview by Ken Martin 13Apr2016KM - Cognitive Computing overview by Ken Martin 13Apr2016
KM - Cognitive Computing overview by Ken Martin 13Apr2016HCL Technologies
 
The Revolution Of Cloud Computing
The Revolution Of Cloud ComputingThe Revolution Of Cloud Computing
The Revolution Of Cloud ComputingCarmen Sanborn
 
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and SensemakingAuto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and SensemakingShalin Hai-Jew
 
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLM
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLMCrafting Your Customized Legal Mastery: A Guide to Building Your Private LLM
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLMChristopherTHyatt
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchrohitcse52
 
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...Naseej Academy أكاديمية نسيج
 
best dissertation topics
best dissertation topicsbest dissertation topics
best dissertation topicsPHDAssistance2
 
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...Allison Bloodworth
 
Instructional Design for the Semantic Web
Instructional Design for the Semantic WebInstructional Design for the Semantic Web
Instructional Design for the Semantic Webguest649a93
 
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering Standards
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering StandardsNavigating the Storm: eMOP, Big DH Projects, and Agile Steering Standards
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering StandardsLiz Grumbach
 
Chi2006 trustworkshop
Chi2006 trustworkshopChi2006 trustworkshop
Chi2006 trustworkshopJohn Thomas
 
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptx
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptxARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptx
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptxvijaygondalia86
 
Understanding-Artificial-Intelligence-in-Research (1).pptx
Understanding-Artificial-Intelligence-in-Research (1).pptxUnderstanding-Artificial-Intelligence-in-Research (1).pptx
Understanding-Artificial-Intelligence-in-Research (1).pptxForum of Blended Learning
 

Similar to ICS 2208 Lecture Slide Notes for Topic 6 (20)

Natural Language Processing .pdf
Natural Language Processing .pdfNatural Language Processing .pdf
Natural Language Processing .pdf
 
An Abridged Version of My Statement of Research Interests
An Abridged Version of My Statement of Research InterestsAn Abridged Version of My Statement of Research Interests
An Abridged Version of My Statement of Research Interests
 
Km cognitive computing overview by ken martin 19 jan2015
Km   cognitive computing overview by ken martin 19 jan2015Km   cognitive computing overview by ken martin 19 jan2015
Km cognitive computing overview by ken martin 19 jan2015
 
Ai in Higher Education
Ai in Higher EducationAi in Higher Education
Ai in Higher Education
 
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...
A DEVELOPMENT FRAMEWORK FOR A CONVERSATIONAL AGENT TO EXPLORE MACHINE LEARNIN...
 
ICT L4.pptx
ICT L4.pptxICT L4.pptx
ICT L4.pptx
 
KM - Cognitive Computing overview by Ken Martin 13Apr2016
KM - Cognitive Computing overview by Ken Martin 13Apr2016KM - Cognitive Computing overview by Ken Martin 13Apr2016
KM - Cognitive Computing overview by Ken Martin 13Apr2016
 
The Revolution Of Cloud Computing
The Revolution Of Cloud ComputingThe Revolution Of Cloud Computing
The Revolution Of Cloud Computing
 
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and SensemakingAuto Mapping Texts for Human-Machine Analysis and Sensemaking
Auto Mapping Texts for Human-Machine Analysis and Sensemaking
 
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLM
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLMCrafting Your Customized Legal Mastery: A Guide to Building Your Private LLM
Crafting Your Customized Legal Mastery: A Guide to Building Your Private LLM
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
 
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...
Artificial Intelligence in Library and Educational Settings_Concerns and Oppo...
 
best dissertation topics
best dissertation topicsbest dissertation topics
best dissertation topics
 
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...
Open Source Design Pattern Library, Spreading Communities Thick: Open Source ...
 
Instructional Design for the Semantic Web
Instructional Design for the Semantic WebInstructional Design for the Semantic Web
Instructional Design for the Semantic Web
 
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering Standards
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering StandardsNavigating the Storm: eMOP, Big DH Projects, and Agile Steering Standards
Navigating the Storm: eMOP, Big DH Projects, and Agile Steering Standards
 
Chi2006 trustworkshop
Chi2006 trustworkshopChi2006 trustworkshop
Chi2006 trustworkshop
 
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptx
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptxARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptx
ARTIFICIAL_INTELLIGENCE_AND_HUMAN_AI.pptx
 
Classroom of the futurev3
Classroom of the futurev3Classroom of the futurev3
Classroom of the futurev3
 
Understanding-Artificial-Intelligence-in-Research (1).pptx
Understanding-Artificial-Intelligence-in-Research (1).pptxUnderstanding-Artificial-Intelligence-in-Research (1).pptx
Understanding-Artificial-Intelligence-in-Research (1).pptx
 

More from Vanessa Camilleri

ICS 2208 Lecture 8 Slides AI and VR_.pdf
ICS 2208 Lecture 8 Slides AI and VR_.pdfICS 2208 Lecture 8 Slides AI and VR_.pdf
ICS 2208 Lecture 8 Slides AI and VR_.pdfVanessa Camilleri
 
ICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfVanessa Camilleri
 
ICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfVanessa Camilleri
 
ICS2208 Lecture3 2023-2024 - Model Based User Interfaces
ICS2208 Lecture3 2023-2024 - Model Based User InterfacesICS2208 Lecture3 2023-2024 - Model Based User Interfaces
ICS2208 Lecture3 2023-2024 - Model Based User InterfacesVanessa Camilleri
 
ICS2208 Lecture 2 Slides Interfaces_.pdf
ICS2208 Lecture 2 Slides Interfaces_.pdfICS2208 Lecture 2 Slides Interfaces_.pdf
ICS2208 Lecture 2 Slides Interfaces_.pdfVanessa Camilleri
 
ICS Lecture 11 - Intelligent Interfaces 2023
ICS Lecture 11 - Intelligent Interfaces 2023ICS Lecture 11 - Intelligent Interfaces 2023
ICS Lecture 11 - Intelligent Interfaces 2023Vanessa Camilleri
 
ICS3211_lecture_week72023.pdf
ICS3211_lecture_week72023.pdfICS3211_lecture_week72023.pdf
ICS3211_lecture_week72023.pdfVanessa Camilleri
 
ICS3211_lecture_week62023.pdf
ICS3211_lecture_week62023.pdfICS3211_lecture_week62023.pdf
ICS3211_lecture_week62023.pdfVanessa Camilleri
 
ICS3211_lecture_week52023.pdf
ICS3211_lecture_week52023.pdfICS3211_lecture_week52023.pdf
ICS3211_lecture_week52023.pdfVanessa Camilleri
 

More from Vanessa Camilleri (20)

ICS 2208 Lecture 8 Slides AI and VR_.pdf
ICS 2208 Lecture 8 Slides AI and VR_.pdfICS 2208 Lecture 8 Slides AI and VR_.pdf
ICS 2208 Lecture 8 Slides AI and VR_.pdf
 
ICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdfICS2208 Lecture6 Notes for SL spaces.pdf
ICS2208 Lecture6 Notes for SL spaces.pdf
 
ICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdfICS2208 Lecture4 Intelligent Interface Agents.pdf
ICS2208 Lecture4 Intelligent Interface Agents.pdf
 
ICS2208 Lecture3 2023-2024 - Model Based User Interfaces
ICS2208 Lecture3 2023-2024 - Model Based User InterfacesICS2208 Lecture3 2023-2024 - Model Based User Interfaces
ICS2208 Lecture3 2023-2024 - Model Based User Interfaces
 
ICS2208 Lecture 2 Slides Interfaces_.pdf
ICS2208 Lecture 2 Slides Interfaces_.pdfICS2208 Lecture 2 Slides Interfaces_.pdf
ICS2208 Lecture 2 Slides Interfaces_.pdf
 
ICS Lecture 11 - Intelligent Interfaces 2023
ICS Lecture 11 - Intelligent Interfaces 2023ICS Lecture 11 - Intelligent Interfaces 2023
ICS Lecture 11 - Intelligent Interfaces 2023
 
ICS3211_lecture 09_2023.pdf
ICS3211_lecture 09_2023.pdfICS3211_lecture 09_2023.pdf
ICS3211_lecture 09_2023.pdf
 
ICS3211_lecture 08_2023.pdf
ICS3211_lecture 08_2023.pdfICS3211_lecture 08_2023.pdf
ICS3211_lecture 08_2023.pdf
 
ICS3211_lecture_week72023.pdf
ICS3211_lecture_week72023.pdfICS3211_lecture_week72023.pdf
ICS3211_lecture_week72023.pdf
 
ICS3211_lecture_week62023.pdf
ICS3211_lecture_week62023.pdfICS3211_lecture_week62023.pdf
ICS3211_lecture_week62023.pdf
 
ICS3211_lecture_week52023.pdf
ICS3211_lecture_week52023.pdfICS3211_lecture_week52023.pdf
ICS3211_lecture_week52023.pdf
 
ICS3211_lecture 04 2023.pdf
ICS3211_lecture 04 2023.pdfICS3211_lecture 04 2023.pdf
ICS3211_lecture 04 2023.pdf
 
ICS3211_lecture 03 2023.pdf
ICS3211_lecture 03 2023.pdfICS3211_lecture 03 2023.pdf
ICS3211_lecture 03 2023.pdf
 
ICS3211_lecture 11.pdf
ICS3211_lecture 11.pdfICS3211_lecture 11.pdf
ICS3211_lecture 11.pdf
 
FoundationsAIEthics2023.pdf
FoundationsAIEthics2023.pdfFoundationsAIEthics2023.pdf
FoundationsAIEthics2023.pdf
 
ICS3211_lecture 9_2022.pdf
ICS3211_lecture 9_2022.pdfICS3211_lecture 9_2022.pdf
ICS3211_lecture 9_2022.pdf
 
ICS1020CV_2022.pdf
ICS1020CV_2022.pdfICS1020CV_2022.pdf
ICS1020CV_2022.pdf
 
ARI5902_2022.pdf
ARI5902_2022.pdfARI5902_2022.pdf
ARI5902_2022.pdf
 
ICS2208 Lecture10
ICS2208 Lecture10ICS2208 Lecture10
ICS2208 Lecture10
 
ICS2208 lecture9
ICS2208 lecture9ICS2208 lecture9
ICS2208 lecture9
 

Recently uploaded

An Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxAn Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxCeline George
 
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community PartnershipsSpring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community Partnershipsexpandedwebsite
 
The Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxThe Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxNehaChandwani11
 
Championnat de France de Tennis de table/
Championnat de France de Tennis de table/Championnat de France de Tennis de table/
Championnat de France de Tennis de table/siemaillard
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024Borja Sotomayor
 
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya - UEM Kolkata Quiz Club
 
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxHVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxKunal10679
 
The Last Leaf, a short story by O. Henry
The Last Leaf, a short story by O. HenryThe Last Leaf, a short story by O. Henry
The Last Leaf, a short story by O. HenryEugene Lysak
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽中 央社
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjMohammed Sikander
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxneillewis46
 
Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024CapitolTechU
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...Krashi Coaching
 
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...Nguyen Thanh Tu Collection
 
REPRODUCTIVE TOXICITY STUDIE OF MALE AND FEMALEpptx
REPRODUCTIVE TOXICITY  STUDIE OF MALE AND FEMALEpptxREPRODUCTIVE TOXICITY  STUDIE OF MALE AND FEMALEpptx
REPRODUCTIVE TOXICITY STUDIE OF MALE AND FEMALEpptxmanishaJyala2
 
An overview of the various scriptures in Hinduism
An overview of the various scriptures in HinduismAn overview of the various scriptures in Hinduism
An overview of the various scriptures in HinduismDabee Kamal
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...Nguyen Thanh Tu Collection
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxheathfieldcps1
 

Recently uploaded (20)

An Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptxAn Overview of the Odoo 17 Discuss App.pptx
An Overview of the Odoo 17 Discuss App.pptx
 
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community PartnershipsSpring gala 2024 photo slideshow - Celebrating School-Community Partnerships
Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
 
The Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptxThe Ball Poem- John Berryman_20240518_001617_0000.pptx
The Ball Poem- John Berryman_20240518_001617_0000.pptx
 
Championnat de France de Tennis de table/
Championnat de France de Tennis de table/Championnat de France de Tennis de table/
Championnat de France de Tennis de table/
 
“O BEIJO” EM ARTE .
“O BEIJO” EM ARTE                       .“O BEIJO” EM ARTE                       .
“O BEIJO” EM ARTE .
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024
 
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General QuizPragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
 
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptxHVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
 
The Last Leaf, a short story by O. Henry
The Last Leaf, a short story by O. HenryThe Last Leaf, a short story by O. Henry
The Last Leaf, a short story by O. Henry
 
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
Operations Management - Book1.p  - Dr. Abdulfatah A. SalemOperations Management - Book1.p  - Dr. Abdulfatah A. Salem
Operations Management - Book1.p - Dr. Abdulfatah A. Salem
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptx
 
Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
 
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
 
REPRODUCTIVE TOXICITY STUDIE OF MALE AND FEMALEpptx
REPRODUCTIVE TOXICITY  STUDIE OF MALE AND FEMALEpptxREPRODUCTIVE TOXICITY  STUDIE OF MALE AND FEMALEpptx
REPRODUCTIVE TOXICITY STUDIE OF MALE AND FEMALEpptx
 
An overview of the various scriptures in Hinduism
An overview of the various scriptures in HinduismAn overview of the various scriptures in Hinduism
An overview of the various scriptures in Hinduism
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
 

ICS 2208 Lecture Slide Notes for Topic 6

  • 1. Intelligent User Interfaces ICS2208 vanessa.camilleri@um.edu.mt Dr Vanessa Camilleri Department of AI, University of Malta
  • 2. Topic 5: Overview • Introduction • NLP in Action • Generative AI and Textual Interfaces • LLMs; impact, bene fi ts and future 2
  • 3. • The Natural Language Interface as a user interface where linguistic phenomena such as verbs, clauses, phrases act as controls for creating, selecting and modifying data in applications.
  • 4. Techniques and Technologies • Tokenisation and Text Normalisation • Part of Speech Tagging (POS) and Named Entity Recognition (NER) • Dependency Parsing • Sentiment Analysis • Machine Translation
  • 5. Models and Frameworks • Rule-based Models • Statistical Models • Neural Network Models • Transformer Models
  • 6. Applications in Textual Interfaces • Chatbots and Virtual Assistants • Search Engines • Text Summarisation and Generation
  • 7. How do NLP models help improve accuracy of textual interfaces? • Understanding Context • Transfer Learning • Handling Ambiguity • Entity Recognition and Classi fi cation • Sentiment Analysis • Continuous Learning and Adaptation • Integration with Domain Speci fi c Knowledge • Addressing Data Ambiguities • Predictive Text and Autocorrection
  • 8. Limitations of NLP Models • Ambiguity in Language • Sarcasm and Irony Detection • Handling Idiomatic Expressions • Adapting to Language Evolution • Data Ambiguities • Information Overload • Domain-speci fi c Language • Tokenisation Challenges • Context Dependency
  • 9. Class Activity Divide into small groups (ideally 3-4 students each). Each group takes one text sample Analyse the text sample assigned to you Discuss the following questions: • What is the main topic of the text? • What are some key entities or phrases mentioned in the text (e.g., people, organisations, locations)? • What is the overall sentiment of the text (positive, negative, neutral)?
  • 10. Generative AI • Generative AI refers to a class of artificial intelligence that specialises in creating content, which can include text, images, music, and more. This technology operates by learning from large datasets to generate new, original material that resembles the learned content. In the context of text generation, Generative AI uses models like Large Language Models (LLMs) to produce human- like text responses based on the input they receive.
  • 11. Generative AI • Generative AI is different from other types of AI in text generation through its ability to create new, original content based on pattern and examples learned from extensive dataset. • Content creation vs. Task performance • Data Driven Learning • Unsupervised Learning Capabilities • Generative Models • Creativity and Adaptability • Versatility and Content Generation
  • 12. Applications of Generative AI to Text Generation • Writing Assistance • Information Retrieval • Thought Partnership • Chatbots and Virtual Assistants • Language Translation • Summarisation • Content Creation for Various Media
  • 13. Class Activity Generative AI and Chatbots Divide into groups - each group discuss one area of research (use references/ sources): • Group 1: Exploring User Preferences: Research how user expectations and preferences for chatbot interactions are evolving with the use of Generative AI. • Group 2: Ethical Considerations: Investigate the ethical considerations surrounding the use of Generative AI in chatbots, such as bias, transparency, and user privacy. • Group 3: The Future of Customer Service: Research how Generative AI chatbots are transforming the landscape of customer service interactions. • Group 4: Creative Applications: Explore the use of Generative AI chatbots in creative domains like storytelling, education, or entertainment.
  • 14. Large Language Models • A language model distinguished by its general- purpose language generation capability. • Typically built with a transformer-based architecture, but some implement recurrent neural network variants or state space models like Mamba. • Training Process Acquires abilities through learning statistical relationships from text documents in a self-supervised and semi-supervised training process.
  • 15. How LLMs Work • Architecture • Attention Mechanism • Training Data • Tokens • Training Process
  • 16. Capabilities of LLMs • Text Generation • Language Translation • Content Creation • Question Answering • Summarisation • Sentiment Analysis
  • 17. Ethical Challenges and Considerations for LLMs • Potential Bias in Training • Risk of Generating Misinformation • Privacy Concerns • Data Security • Environmental Impact of Training Large Models • Societal Impact • Legal and Copyright Issues • Responsibility and Accountability • Risks of Malicious Use • Transparency and Control
  • 18. Online Task • Choose a specific textual interface (e.g., a virtual assistant, a search engine) and analyse how it utilises NLP techniques and/or Generative AI. • Write a short 300 word description of it, outlining your observations about it and potential improvements based on the concepts discussed in class.