Conversation-Centered Artificial Intelligence (AI) System
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A Conversation-Centered Artificial Intelligence (AI) System is an interactive AI-based system that can solve conversation-centered AI tasks (designed to engage in conversation-centered dialogs modeled on human-to-human conversations).
- AKA: Conversational AI System, Chatbot System, Conversational Assistant, Dialog System, Conversational Agent.
- Context:
- It can typically process Conversation-Centered Input through conversation-centered natural language understanding, conversation-centered intent recognition, and conversation-centered context interpretation.
- It can typically generate Conversation-Centered Responses via conversation-centered language generation, conversation-centered content synthesis, and conversation-centered answer formulation.
- It can typically have a Conversation-Centered UI that supports conversation-centered user interaction through conversation-centered text inputs or conversation-centered voice inputs.
- It can typically have Conversation-Centered Features, such as conversation-centered conversational abilities, conversation-centered response accuracy, and conversation-centered query handling.
- It can typically be associated with a Conversation-Centered AI Initialization Prompt (like ChatGPT initialization prompt), which sets conversation-centered conversational tone.
- It can typically utilize Conversation-Centered NLP Technologies such as conversation-centered transformer-based models.
- It can typically have a Conversation-Centered AI User Interface that adapts to both conversation-centered structured conversations and conversation-centered unstructured conversations.
- It can typically be developed through a Conversation-Centered Development Task that focuses on conversation-centered model creation and conversation-centered model training.
- It can typically process Conversation-Centered Multi-Turn Dialog through conversation-centered context management.
- It can typically handle Conversation-Centered Intent Recognition through conversation-centered natural language understanding.
- It can typically maintain Conversation-Centered Conversation State through conversation-centered session management.
- ...
- It can often be described in a Conversation-Centered PRD outlining conversation-centered functionality and conversation-centered performance metrics.
- It can often be developed by a Conversation-Centered Development Team including conversation-centered machine learning specialists and conversation-centered NLP specialists.
- It can often be based on a Conversation-Centered 3rd-Party Platform (either conversation-centered custom development platform or conversation-centered configuration-based platform).
- It can often employ Conversation-Centered Multi-Language Support through conversation-centered language detection.
- It can often provide Conversation-Centered Error Recovery through conversation-centered fallback mechanisms.
- It can often enable Conversation-Centered User Feedback Collection through conversation-centered rating systems.
- It can often support Conversation-Centered A/B Testing through conversation-centered response variations.
- It can often implement Conversation-Centered Security Protocols through conversation-centered authentication systems.
- ...
- It can range from being a Freeform Conversation-Centered AI System to being a Structured-Dialog Conversation-Centered AI System, depending on its conversation-centered dialog flexibility.
- It can range from being an Information-Providing Conversation-Centered AI System to being an Action-Taking Conversation-Centered AI System, depending on its conversation-centered functional capability.
- It can range from being a Free-Flowing Dynamic Dialog Conversation-Centered AI System to being a Predefined Response Conversation-Centered AI System, depending on its conversation-centered interaction control.
- It can range from being an Open-Topic Conversation-Centered AI System to being a Domain-Specific Conversation-Centered AI System, depending on its conversation-centered topic scope.
- It can range from being a Data-Driven Conversation-Centered AI System to being a Knowledge-Enriched Conversation-Centered AI System, depending on its conversation-centered knowledge source.
- It can range from being a Memoryless Conversation-Centered AI System to being a Memory-Enabled Conversation-Centered AI System, depending on its conversation-centered context retention.
- It can range from being a Personalized Conversation-Centered AI System to being a Non-Personalized Conversation-Centered AI System, depending on its conversation-centered user adaptation.
- It can range from being a Public Conversation-Centered AI System to being an Enterprise Conversation-Centered AI System, depending on its conversation-centered access model.
- It can range from being a Paid Conversation-Centered AI System to being a Free Conversation-Centered AI System, depending on its conversation-centered commercial model.
- It can range from being a 3rd-Party Conversation-Centered AI System to being a Custom Conversation-Centered AI System, depending on its conversation-centered development approach.
- It can range from being an Experimental Conversation-Centered AI System to being a Production Conversation-Centered AI System, depending on its conversation-centered deployment maturity.
- It can range from being a Cloud-Based Conversation-Centered AI System to being an On-Device Conversation-Centered AI System, depending on its conversation-centered hosting architecture.
- It can range from being a Skill-Enabled Conversation-Centered AI System to being a Domain-Focused Conversation-Centered AI System, depending on its conversation-centered capability model.
- It can range from being a Task-Supporting Conversation-Centered AI System to being an Open-Ended Conversation-Centered AI System, depending on its conversation-centered interaction purpose.
- It can range from being a Real-Time Response Conversation-Centered AI System to being a Batch Processing Conversation-Centered AI System, depending on its conversation-centered response timing.
- It can range from being a Single-Language Conversation-Centered AI System to being a Multi-Language Conversation-Centered AI System, depending on its conversation-centered language capability.
- It can range from being a Simple Response Conversation-Centered AI System to being a Complex Reasoning Conversation-Centered AI System, depending on its conversation-centered cognitive capability.
- It can range from being a Basic Security Conversation-Centered AI System to being a Zero-Trust Conversation-Centered AI System, depending on its conversation-centered security architecture.
- ...
- It can be an In-Application Conversation-Centered AI Assistant that assists conversation-centered users within conversation-centered software environments.
- It can include Conversation-Centered Personalization Features adapting conversation-centered responses based on conversation-centered user history.
- It can be modeled with a Conversation-Centered System Architecture defining conversation-centered components and conversation-centered workflows.
- It can be evaluated by a Conversation-Centered Evaluation System using conversation-centered evaluation datasets.
- It can create Conversation-Centered Session Log Data for conversation-centered interaction analysis.
- It can integrate with Conversation-Centered External APIs for conversation-centered functionality enhancement.
- It can connect to Conversation-Centered Knowledge Base Systems for conversation-centered information retrieval.
- It can interface with Conversation-Centered Analytics Platforms for conversation-centered performance monitoring.
- It can support Conversation-Centered Enterprise Systems through conversation-centered system integration.
- ...
- ...
- Example(s):
- Historical Conversation-Centered AI System Evolutions, such as:
- Early Rule-Based Conversation-Centered AI Systems (1960s-1990s), characterized by conversation-centered pattern matching and conversation-centered scripted responses.
- ELIZA (1966), demonstrating conversation-centered pattern-matching techniques.
- PARRY (1972), implementing conversation-centered personality simulation.
- ALICE (1995), utilizing conversation-centered AIML pattern matching.
- Statistical NLP-Based Conversation-Centered AI Systems (2000s-2010s), featuring conversation-centered statistical models and conversation-centered machine learning.
- IBM Watson (2011), employing conversation-centered statistical analysis.
- Microsoft XiaoIce (2014), featuring conversation-centered emotional intelligence.
- Neural Network-Based Conversation-Centered AI Systems (2015-2022), utilizing conversation-centered deep learning and conversation-centered neural architectures.
- Google Meena (2020), showcasing conversation-centered open-domain capabilities.
- Large Language Model Conversation-Centered AI Systems (2022-present), leveraging conversation-centered transformer models and conversation-centered massive training data.
- ChatGPT (2022), demonstrating conversation-centered instruction following.
- Claude (2023), featuring conversation-centered constitutional AI approaches.
- Early Rule-Based Conversation-Centered AI Systems (1960s-1990s), characterized by conversation-centered pattern matching and conversation-centered scripted responses.
- Conversational AI Services, such as:
- ...
- General-Purpose Conversational Assistants, such as:
- LLM-based Conversational AI Assistant Systems, such as: OpenAI's ChatGPT, which engages in freeform conversations and integrates plugins to enhance functionality.
- Freeform Casual Conversation Chatbots such as XiaoIce (open-ended chit-chat systems), that engages users in casual conversations about a wide range of topics.
- Multi-Modal Open-Ended Action-Taking Chatbot such as OpenAI's ChatGPT with Plugins (multi-functional assistants), that supports a wide range of conversation topics and actions, including integrating with third-party services through plugins.
- LLM-based Chatbot such as Google Bard (conversational assistants), that provides users with detailed information and creative content through natural language conversation.
- Conversation-Centered AI System Interaction Patterns, such as:
- Turn-Taking Conversation-Centered AI Systems, such as:
- Meta's BlenderBot, implementing conversation-centered natural dialog flow.
- Multi-Modal Conversation-Centered AI Systems, such as:
- Memory-Enhanced Conversation-Centered AI Systems, such as:
- Personal History-Aware Conversational Systems, retaining conversation-centered user preferences across sessions.
- Character.AI, maintaining conversation-centered persistent persona.
- Goal-Oriented Conversation-Centered AI Systems, such as:
- Ada, optimizing for conversation-centered customer support resolution.
- Drift, facilitating conversation-centered sales qualification.
- Turn-Taking Conversation-Centered AI Systems, such as:
- Personal Assistants, such as:
- Conversational Instruction-Taking Chatbots such as Amazon Alexa (voice assistants), that takes user instructions to perform tasks like playing music or controlling smart home devices.
- Conversational Personal-Assistant such as Siri (virtual assistants), that performs various tasks including managing calendars, sending texts, and setting alarms.
- Action-Taking (Agent) Chatbot such as Google Assistant (smart assistants), that can book a taxi ride or set reminders based on conversational commands.
- Domain-Specific Assistants, such as:
- Knowledge Management Chatbots, such as:
- GM-RKB Task-Supporting Chatbot, which helps with GM-RKB tasks.
- Wikipedia Task-Supporting Chatbot, which helps with Wikipedia tasks.
- Domain-Specific Chatbots, such as DoNotPay (legal assistants), that assists users in generating legal documents or contesting parking tickets.
- Task-Specific Information-Providing Dialog-Centered Chatbots, such as Bank of America's Erica (financial assistants), that provides users with banking information, transaction details, and account management assistance.
- Healthcare Conversational AI System such as Nuance's Dragon Medical Assistant (medical assistants), that helps healthcare providers document patient interactions, manage records, and enhance the accuracy of clinical documentation.
- Educational AI Chatbot such as Quizlet's Learning Assistant (education assistants), that helps students study by generating practice quizzes, answering questions, and conversationally explaining concepts.
- Product-Related Chatbots, such as:
- Product Usage Assistants, such as Microsoft Office Copilot, providing conversation-centered feature guidance.
- Pre-Purchase Product Advisors, such as Sephora Virtual Artist, offering conversation-centered product recommendations.
- Product Onboarding Specialists, such as Slack Onboarding Bot, facilitating conversation-centered feature discovery.
- Product Support and Service Chatbots, such as Apple Support Chatbot, delivering conversation-centered troubleshooting assistance.
- Legal Tech-Product Chatbots, such as Contract Analysis Conversational System, providing conversation-centered legal document review.
- Knowledge Management Chatbots, such as:
- Custom RAG Chatbots, such as: OpenAI CustomGPT chatbot.
- Enterprise Assistants, such as:
- Process Automation Chatbots such as Blue Prism Assistant (RPA assistants), that automates repetitive business processes through conversational interfaces.
- Compliance Assistant Chatbots such as RegBot (regulatory assistants), that helps ensure regulatory compliance in financial institutions.
- Resource Planning Chatbots such as SAP Conversational AI (ERP assistants), that enables natural language interactions with enterprise systems.
- Task-Specific Dialog-Centered Agent such as Connie the Hilton Robot (hospitality assistants), that helps guests with booking rooms, checking hotel amenities, and answering frequently asked questions.
- Conversational Enterprise Search System such as IBM Watson Assistant (enterprise assistants), that is queried by employees to find documents, data, and answers within an organization.
- Customer Service AI Assistant such as Zendesk's Answer Bot (customer support assistants), that resolves customer inquiries and automates common support tasks, improving efficiency and customer satisfaction.
- Sales Assistant AI Chatbot such as HubSpot's Chatbot Builder (sales assistants), that interacts with potential leads, qualifies them, and directs them to the appropriate sales channels.
- Specialized Tool Assistants, such as:
- Data Analytics Conversational AI System such as Yellowfin's Signal Chatbot (data analytics assistants), that generates insights and data visualizations from user queries.
- Application Copilot Chatbot such as GitHub Copilot (coding assistants), that assists developers by providing code suggestions and completing functions in real-time as they write code.
- Job Interview AI Chatbot such as HireVue (recruitment assistants), that conducts initial candidate screenings and provides recruiters with insights on potential hires.
- Industry-Specific Conversation-Centered AI System Implementations, such as:
- Social Interaction Assistants, such as:
- Conversational Social Robots such as Jibo (social interaction assistants), that carries on open-ended chit-chat with users to build emotional connections and provide companionship.
- ...
- Historical Conversation-Centered AI System Evolutions, such as:
- Example(s):
- Historical Conversation-Centered AI System Evolutions, such as:
- Early Rule-Based Conversation-Centered AI Systems (1960s-1990s), characterized by conversation-centered pattern matching and conversation-centered scripted responses.
- ELIZA (1966), demonstrating conversation-centered pattern-matching techniques.
- PARRY (1972), implementing conversation-centered personality simulation.
- ALICE (1995), utilizing conversation-centered AIML pattern matching.
- Statistical NLP-Based Conversation-Centered AI Systems (2000s-2010s), featuring conversation-centered statistical models and conversation-centered machine learning.
- IBM Watson (2011), employing conversation-centered statistical analysis.
- Microsoft XiaoIce (2014), featuring conversation-centered emotional intelligence.
- Neural Network-Based Conversation-Centered AI Systems (2015-2022), utilizing conversation-centered deep learning and conversation-centered neural architectures.
- Google Meena (2020), showcasing conversation-centered open-domain capabilities.
- Large Language Model Conversation-Centered AI Systems (2022-present), leveraging conversation-centered transformer models and conversation-centered massive training data.
- ChatGPT (2022), demonstrating conversation-centered instruction following.
- Claude (2023), featuring conversation-centered constitutional AI approaches.
- Early Rule-Based Conversation-Centered AI Systems (1960s-1990s), characterized by conversation-centered pattern matching and conversation-centered scripted responses.
- Conversational AI Services, such as:
- ...
- General-Purpose Conversational Assistants, such as:
- LLM-based Conversational AI Assistant Systems, such as: OpenAI's ChatGPT, which engages in freeform conversations and integrates plugins to enhance functionality.
- Freeform Casual Conversation Chatbots such as XiaoIce (open-ended chit-chat systems), that engages users in casual conversations about a wide range of topics.
- Multi-Modal Open-Ended Action-Taking Chatbot such as OpenAI's ChatGPT with Plugins (multi-functional assistants), that supports a wide range of conversation topics and actions, including integrating with third-party services through plugins.
- LLM-based Chatbot such as Google Bard (conversational assistants), that provides users with detailed information and creative content through natural language conversation.
- Conversation-Centered AI System Interaction Patterns, such as:
- Turn-Taking Conversation-Centered AI Systems, such as:
- Meta's BlenderBot, implementing conversation-centered natural dialog flow.
- Multi-Modal Conversation-Centered AI Systems, such as:
- Memory-Enhanced Conversation-Centered AI Systems, such as:
- Personal History-Aware Conversational Systems, retaining conversation-centered user preferences across sessions.
- Character.AI, maintaining conversation-centered persistent persona.
- Goal-Oriented Conversation-Centered AI Systems, such as:
- Ada, optimizing for conversation-centered customer support resolution.
- Drift, facilitating conversation-centered sales qualification.
- Turn-Taking Conversation-Centered AI Systems, such as:
- Personal Assistants, such as:
- Conversational Instruction-Taking Chatbots such as Amazon Alexa (voice assistants), that takes user instructions to perform tasks like playing music or controlling smart home devices.
- Conversational Personal-Assistant such as Siri (virtual assistants), that performs various tasks including managing calendars, sending texts, and setting alarms.
- Action-Taking (Agent) Chatbot such as Google Assistant (smart assistants), that can book a taxi ride or set reminders based on conversational commands.
- Domain-Specific Assistants, such as:
- Knowledge Management Chatbots, such as:
- GM-RKB Task-Supporting Chatbot, which helps with GM-RKB tasks.
- Wikipedia Task-Supporting Chatbot, which helps with Wikipedia tasks.
- Domain-Specific Chatbots, such as DoNotPay (legal assistants), that assists users in generating legal documents or contesting parking tickets.
- Task-Specific Information-Providing Dialog-Centered Chatbots, such as Bank of America's Erica (financial assistants), that provides users with banking information, transaction details, and account management assistance.
- Healthcare Conversational AI System such as Nuance's Dragon Medical Assistant (medical assistants), that helps healthcare providers document patient interactions, manage records, and enhance the accuracy of clinical documentation.
- Educational AI Chatbot such as Quizlet's Learning Assistant (education assistants), that helps students study by generating practice quizzes, answering questions, and conversationally explaining concepts.
- Product-Related Chatbots, such as:
- Product Usage Assistants, such as Microsoft Office Copilot, providing conversation-centered feature guidance.
- Pre-Purchase Product Advisors, such as Sephora Virtual Artist, offering conversation-centered product recommendations.
- Product Onboarding Specialists, such as Slack Onboarding Bot, facilitating conversation-centered feature discovery.
- Product Support and Service Chatbots, such as Apple Support Chatbot, delivering conversation-centered troubleshooting assistance.
- Legal Tech-Product Chatbots, such as Contract Analysis Conversational System, providing conversation-centered legal document review.
- Knowledge Management Chatbots, such as:
- Custom RAG Chatbots, such as: OpenAI CustomGPT chatbot.
- Enterprise Assistants, such as:
- Process Automation Chatbots such as Blue Prism Assistant (RPA assistants), that automates repetitive business processes through conversational interfaces.
- Compliance Assistant Chatbots such as RegBot (regulatory assistants), that helps ensure regulatory compliance in financial institutions.
- Resource Planning Chatbots such as SAP Conversational AI (ERP assistants), that enables natural language interactions with enterprise systems.
- Task-Specific Dialog-Centered Agent such as Connie the Hilton Robot (hospitality assistants), that helps guests with booking rooms, checking hotel amenities, and answering frequently asked questions.
- Conversational Enterprise Search System such as IBM Watson Assistant (enterprise assistants), that is queried by employees to find documents, data, and answers within an organization.
- Customer Service AI Assistant such as Zendesk's Answer Bot (customer support assistants), that resolves customer inquiries and automates common support tasks, improving efficiency and customer satisfaction.
- Sales Assistant AI Chatbot such as HubSpot's Chatbot Builder (sales assistants), that interacts with potential leads, qualifies them, and directs them to the appropriate sales channels.
- Specialized Tool Assistants, such as:
- Data Analytics Conversational AI System such as Yellowfin's Signal Chatbot (data analytics assistants), that generates insights and data visualizations from user queries.
- Application Copilot Chatbot such as GitHub Copilot (coding assistants), that assists developers by providing code suggestions and completing functions in real-time as they write code.
- Job Interview AI Chatbot such as HireVue (recruitment assistants), that conducts initial candidate screenings and provides recruiters with insights on potential hires.
- Industry-Specific Conversation-Centered AI System Implementations, such as:
- Social Interaction Assistants, such as:
- Conversational Social Robots such as Jibo (social interaction assistants), that carries on open-ended chit-chat with users to build emotional connections and provide companionship.
- ...
- Historical Conversation-Centered AI System Evolutions, such as:
- Counter-Example(s):
- Rule-Based Dialog Systems, which use fixed pattern matching without conversation-centered contextual understanding or conversation-centered adaptive responses.
- Menu-Based Interactive Systems, which navigate through predetermined options rather than conversation-centered natural language exchange.
- Form-Based Input Systems, which collect structured data through fixed fields instead of conversation-centered flexible dialog.
- Static FAQ Systems, which display predefined answers without conversation-centered interactive engagement or conversation-centered context awareness.
- Command-Line Interfaces, which require specific syntax rather than conversation-centered natural language input.
- See: Interactive AI-Based System, Natural Language Processing System, Dialog Management System, Conversational AI, Human-Computer Interaction, Voice User Interface, Chatbot Development Platform.
References
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/chatbot Retrieved:2023-11-21.
- A chatbot (originally chatterbot[1]) is a software application or web interface that aims to mimic human conversation through text or voice interactions.[2] [3] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such technologies often utilize aspects of deep learning and natural language processing, but more simplistic chatbots have been around for decades prior.
As of 2022, the field has gained widespread attention due to the popularity of OpenAI's ChatGPT (using GPT-3 or GPT-4), released in 2022, followed by alternatives such as Microsoft's Bing Chat (which uses OpenAI's GPT-4) and Google's Bard. Such examples reflect the recent practice of such products being built based upon broad foundational large language models that get fine-tuned so as to target specific tasks or applications (i.e. simulating human conversation, in the case of chatbots). Chatbots can also be designed or customized to further target even more specific situations and/or particular subject-matter domains.[4]
A major area where chatbots have long been used is in customer service and support, such as with various sorts of virtual assistants. Companies spanning various industries have begun using the latest generative artificial intelligence technologies to power more advanced developments in such areas.[4]
- A chatbot (originally chatterbot[1]) is a software application or web interface that aims to mimic human conversation through text or voice interactions.[2] [3] Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such technologies often utilize aspects of deep learning and natural language processing, but more simplistic chatbots have been around for decades prior.
- ↑ Mauldin, Michael (1994), "ChatterBots, TinyMuds, and the Turing Test: Entering the Loebner Prize Competition", Proceedings of the Eleventh National Conference on Artificial Intelligence, AAAI Press, archived from the original on 13 December 2007, retrieved 5 March 2008.
- ↑ "What is a chatbot?". techtarget.com. Archived from the original on 2 November 2010. Retrieved 30 January 2017.
- ↑ Caldarini, Guendalina; Jaf, Sardar; McGarry, Kenneth (2022). "A Literature Survey of Recent Advances in Chatbots". Information. MDPI. 13 (1): 41. arXiv:2201.06657. doi:10.3390/info13010041.
- ↑ 4.0 4.1 "GPT-4 takes the world by storm - List of companies that integrated the chatbot". 21 March 2023.
2023
- (IBM, 2023) => https://www.ibm.com/topics/conversational-ai
- QUOTE: ... Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms."
2023
- (Google Cloud, 2023) => https://cloud.google.com/conversational-ai
- QUOTE: ... Conversational AI works by using a combination of natural language processing (NLP) and machine learning (ML). Conversational AI systems are trained on large amounts of data, such as text and speech. ...
2023
- (TechTarget, 2023) => https://www.techtarget.com/searchenterpriseai/definition/conversational-AI
- QUOTE: ... Natural language processing (NLP) is the current method of analyzing language with the help of machine learning used in conversational AI. ...