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The Conversational Interface : Talking to Smart Devices / / by Michael McTear, Zoraida Callejas, David Griol
| The Conversational Interface : Talking to Smart Devices / / by Michael McTear, Zoraida Callejas, David Griol |
| Autore | McTear Michael |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (431 p.) |
| Disciplina | 620 |
| Soggetto topico |
Signal processing
Image processing Speech processing systems User interfaces (Computer systems) Natural language processing (Computer science) Signal, Image and Speech Processing User Interfaces and Human Computer Interaction Natural Language Processing (NLP) |
| ISBN | 3-319-32967-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- Part 1: Conversational Interfaces: Preliminaries -- The Dawn of the Conversational Interface -- Towards a Technology of Conversation -- Conversational Interfaces: Past and Present -- Part 2: Developing a Speech-Based Conversational Interface -- Speech Input and Output -- Implementing Speech Input and Output -- Creating a Conversational Interface using Chatbot Technology -- Spoken Language Understanding -- Implementing Spoken Language Understanding -- Dialog Management -- Implementing Dialog Management -- Response Generation -- Part 3: Conversational Interfaces and Devices -- Conversational Interfaces: Devices, Wearables, Virtual Agents, and Robots -- Emotion, Affect, and Personality -- Affective Conversational Interfaces -- Implementing Multimodal Conversational Interfaces Using Android Wear -- Part 4: Evaluation and Future Directions -- Evaluation -- Future Directions. |
| Record Nr. | UNINA-9910254237803321 |
McTear Michael
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Transforming Conversational AI : Exploring the Power of Large Language Models in Interactive Conversational Agents
| Transforming Conversational AI : Exploring the Power of Large Language Models in Interactive Conversational Agents |
| Autore | McTear Michael |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berkeley, CA : , : Apress L. P., , 2024 |
| Descrizione fisica | 1 online resource (235 pages) |
| Altri autori (Persone) | AshurkinaMarina |
| ISBN | 979-88-6880-110-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Contents -- About the Authors -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: A New Era in Conversational AI -- Understanding Key Terms in Conversational AI -- Interacting with ChatGPT and Similar Chatbots -- Using AI-Powered Chatbots: Examples of Some Relevant Application Areas -- Customer Service -- Education -- Healthcare -- Social Companions -- Summary -- Resources -- Podcasts, Blogs, and Social Media -- Online Courses -- Videos -- Conferences -- Chapter 2: Designing Conversational Systems -- Leading a Conversational AI Project -- Roles and Responsibilities in a Cross-functional Team -- Conversational AI Solution Architect -- Conversation Designer -- Conversational AI Developer -- Content Designer or Dialogue Copywriter -- Traditional Conversation Design -- Eliciting User Requirements -- Developing Use Cases -- Designing the System -- Understanding the User's Inputs -- Creating Appropriate System Output -- Creating Effective Conversation Flows -- Using Decision Trees to Implement the Conversation Flow -- Using Forms to Implement Conversation Flow -- Conversation Initiative -- User-Initiative -- System-Initiative -- Mixed-Initiative -- Strategies for Error Handling and Confirmation -- Leveraging Language Models in Conversation Design -- Using LLMs to Create Training Examples for Intents -- Using LLMs to Create the Chatbot's Responses -- Using LLMs to Create Conversation Flows -- Summary -- Resources -- Chapter 3: The Rise of Neural Conversational Systems -- The Encoder-Decoder Architecture -- Encoding -- Decoding -- Training an Encoder-Decoder Architecture -- Transformers and Attention: A High-Level View -- Introducing the Transformer -- Introducing Attention -- Transformers and Attention: A Closer Look -- Tokenization -- Word Embedding -- Positional Encoding -- The Encoding Layers -- The QKV Model.
Multiheaded Attention -- The Feedforward Network -- The Decoding Layers -- Pros and Cons of Neural Conversational Systems -- Summary -- Resources -- Chapter 4: Large Language Models -- Introduction -- What Is a Large Language Model? -- Large Language Models and Traditional Search Engines -- Acquiring the Knowledge -- Representing the Knowledge -- Using the Knowledge -- Different Types of LLMs -- Training LLMs -- Training BERT -- Training the GPT Models -- Is Bigger Better? -- Extending Pre-trained LLMs and Enhancing their Performance -- Combining LLMs with External Knowledge Sources -- Fine-tuning -- Fine-tuning ChatGPT -- Using Plug-ins to Access External APIs -- Challenges and Limitations of LLMs -- Summary -- Resources -- Chapter 5: Introduction to Prompt Engineering -- Getting Started -- Basic Definitions -- LLM Web Interfaces -- Ready-to-Use Prompts -- What Tasks Can Be Solved with LLMs -- Text Summarization -- Sentiment Analysis -- Translation -- Other Applications -- Crafting Clear and Effective Prompts -- Define the Use Case -- Start Small, Iterate, and Experiment -- Use Building Blocks, Patterns, and Their Combinations -- Prompt Building Blocks -- Role and Personality -- Task, Goal, and Objective -- Tone of Voice, Style, and Language -- Audience and Channels -- Format and Limits -- Prompt Patterns -- Flipped Interaction -- Infinite Conversation -- Top-Down Pattern -- Fact Check -- In-Context Learning -- Adding Variables -- Combining Techniques -- Challenges and Limitations -- Hallucinations -- Knowledge Cut-off -- Bias -- Limited Context Window -- Prompt Brittleness -- Summary -- Resources -- Chapter 6: Advanced Prompt Engineering -- Large Language Model Applications -- System Prompts -- Prompt Settings -- Temperature -- TopP and TopK -- Repetition Penalties -- Stop Sequence -- Maximum Length -- Other Settings. Creating Combinations of Parameters -- Playgrounds, Consoles, and APIs -- Prompt Hacking -- Advanced Prompt Patterns -- Chain-of-Thought -- ReAct -- Self-consistency -- Prompt Chaining -- Summary -- Resources -- Chapter 7: Conversational AI Platforms -- Traditional Conversational Platforms -- Hybrid Conversational Platforms -- Dynamic AI Responses -- The Assistant's Persona -- Dynamic Decisions -- External Data Sources -- Conversation Memory -- Emerging LLM Platforms -- Managing Prompts -- Uploading Documents -- Creating Workflows -- Using Different LLMs -- Validating Output -- LangChain Framework -- Retrieval Augmented Generation -- Summary -- Resources -- Chapter 8: Evaluation Metrics -- Key Factors to Consider When Evaluating Conversational Systems -- Why Evaluate -- Where to Conduct the Evaluation -- What Sorts of Users Should Conduct the Evaluation? -- Evaluating the System as a Whole or Evaluating Its Individual Components -- Evaluating Complete Dialogues vs. Individual Turns -- Qualitative or Quantitative Metrics? -- Task-Oriented vs. Open Domain Conversational Systems -- Manual and Automated Testing -- Evaluating Intent-Based Dialogue Systems -- Evaluating Large Language Models -- What Areas of LLM Usage to Evaluate -- How to Conduct the Evaluations -- Frameworks for LLM Evaluation -- Metrics for Evaluating Systems as a Whole -- Using LLMs as Tools to Evaluate Dialogues -- Practical Examples of Using Metrics to Evaluate Conversational Applications -- Summary -- Resources -- Chapter 9: AI Safety and Ethics -- What Risks Can Generative AI Bring to Conversational Interfaces? -- LLMs Safety and Challenges -- Guardrails -- Responsible AI -- The Open Voice Network -- Summary -- Resources -- Chapter 10: Final Words -- Recent Developments in Technology -- Multimodal Capabilities -- Large Language Models. Using Generative AI to Empower Conversational AI Systems -- Browsing the Web and Accessing Apps -- Technical Improvements -- Usage -- Recent Developments in Applications -- GPTs -- Copilots and AI Assistants for Business -- Conversational AI in Augmented and Mixed Reality -- Personal AI Agents -- Autonomous Agents -- Transforming the Role of the Conversation Designer -- Summary -- Resources -- Appendix A -- Index. |
| Record Nr. | UNINA-9910841867903321 |
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McTear Michael
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| Berkeley, CA : , : Apress L. P., , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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