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 conversations modeled on human-to-human discussion).
- Context:
- input: Conversational Request.
- Optional Input: Data Items, such as: document files or image files.
- output: Conversational Response.
- measures: Chatbot Performance Measures like response relevance, user engagement, and the human-likeness of their conversations.
- ...
- It can (typically) have a Conversation-Centered UI that supports user interaction through text or voice inputs, simulating natural conversations.
- It can (typically) have Chatbot Features, such as: conversational abilities to respond fluidly, accuracy of responses to user queries, user satisfaction measured by feedback, and the ability to handle different types of queries effectively in diverse contexts.
- It can (typically) be associated with a AI Chatbot Initialization Prompt (like ChatGPT initialization prompt), which sets the conversational tone and guides initial interactions.
- It can (typically) utilize NLP Technologies such as transformer-based models to enable natural conversational abilities.
- It can (typically) have a Conversational AI User Interface that adapts to both structured and unstructured conversations.
- It can (typically) be developed through a Chatbot Development Task that focuses on creating, training, and fine-tuning conversational models.
- It can (typically) process Multi-Turn Dialog through context management.
- It can (typically) handle Intent Recognition through natural language understanding.
- It can (typically) maintain Conversation State through session management.
- ...
- It can (often) be described in a Chatbot PRD (Product Requirements Document), outlining key functionality and performance metrics.
- It can (often) be developed by a Chatbot Development Team (with chatbot developers), including specialists in machine learning, NLP, and user experience design.
- It can (often) be based on a Conversation-Centered 3rd-Party Platform (either a custom chatbot development platform tailored for specific needs or a configuration-based chatbot development platform that uses prebuilt modules).
- It can (often) employ Multi-Language Support through language detection.
- It can (often) provide Error Recovery through fallback mechanisms.
- It can (often) enable User Feedback Collection through rating systems.
- It can (often) support A/B Testing through response variations.
- It can (often) implement Security Protocols through authentication systems.
- ...
- It can range from being an Freeform Chatbot that engages in open-ended, dynamic conversations to being a Structured-Dialog Chatbot with predefined conversational paths.
- It can range from being an Information-Providing Chatbot System that delivers factual answers to being an Action-Taking Conversation AI System capable of executing tasks like booking appointments or making purchases.
- It can range from being a Conversational AI System with Free-Flowing Dynamic Dialog that responds to user input flexibly to being a Conversational AI System with Predefined Responses for specific, controlled interactions.
- It can range from being an Open-Topic Conversational AI System that discusses a wide array of topics to being a Domain-Specific Conversation AI System or a Task-Specific Conversational AI-based System (such as a customer service conversational AI system) that focuses on a narrow domain or specific tasks.
- It can range from being a Data-Driven Conversational AI System that learns from large datasets to being a Knowledge-Enriched Conversational AI System, based on knowledge sources like expert systems or specialized databases.
- It can range from being a Memoryless Conversational AI System that handles each query independently to being a Conversational AI System with Memory that retains context across sessions.
- It can range from being a Personalized Conversational AI System that tailors responses based on user history to being a Non-Personalized Conversational AI System, based on whether it uses a user context to guide responses.
- It can range from being a Public Conversational AI System accessible to anyone to being an Enterprise Conversational AI System used within an organization for internal operations.
- It can range from being a Paid Conversational AI System that requires subscriptions to being a Free Conversational AI System available at no cost.
- It can range from being a 3rd-Party Conversational Chatbot integrated into external applications to being a Custom Conversational Chatbot developed in-house for proprietary use.
- It can range from being an Experimental Conversational AI System used for research and testing to being a Production Conversational AI System deployed for widespread use.
- It can range from being a Cloud-Based Conversational AI System hosted remotely to being an On-Device Conversational AI System installed locally on a user's device to being a Hybrid Conversational AI System, combining both architectures.
- It can range from being a Skill-Enabled Conversational AI System to being a Domain-Specific Conversational AI System focused on specialized knowledge.
- It can range from being a Task-Supporting Chatbot focused on accomplishing specific tasks to being an Open-Ended Conversation Chatbot engaging in general discussions.
- It can range from being a Real-Time Response System to being a Batch Processing System, depending on its response timing.
- It can range from being a Single-Language System to being a Multi-Language System, depending on its language capability.
- It can range from being a Simple Response System to being a Complex Reasoning System, depending on its cognitive capability.
- ...
- It can be an In-Application Conversational AI Assistant that assists users within a specific software or app environment.
- It can include Personalized Conversational Feature, adapting responses based on the user's previous interactions or user context within the domain.
- It can be modeled with a Chatbot System Architecture that defines the components and workflows of the system.
- It can be evaluated by a Chatbot Evaluation System (possibly using a chatbot evaluation dataset) to measure performance metrics like accuracy, engagement, and naturalness.
- It can create Chatbot Session Log Data used to analyze user interactions and improve future performance.
- It can integrate with External APIs for enhanced functionality.
- It can connect to Knowledge Base Systems for information retrieval.
- It can interface with Analytics Platforms for performance monitoring.
- It can support Enterprise Systems through system integration.
- It can enhance Customer Service Platforms through automated support.
- …
- input: Conversational Request.
- Example(s):
- 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.
- 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.
- Knowledge Management Chatbots, such as:
- Enterprise Assistants, such as:
- 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.
- 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.
- …
- General-Purpose Conversational Assistants, such as:
- Counter-Example(s):
- A Rule-based Conversational System, such as an early chatbot like ELIZA, which only responds to specific patterns rather than truly understanding context.
- A Menu-based AI-based System, such as Grammatical Correction Systems that guide users through fixed menus rather than engaging in natural conversation.
- A Wizard-based System that does not accept conversational input, such as a wizard-based personal tax analysis system (such as TurboTax) where users answer predefined questions without engaging in open-ended dialogue.
- A Form-based System, such as a e-commerce checkout form that relies on user input through fixed fields instead of dynamic conversation.
- A Domain-Specific Information-Providing System such as a weather chatbot that can provide forecasts but cannot engage in back-and-forth conversation.
- See: Conversational AI, Conversational Agent, Intelligent Virtual Assistant, Interactive AI-based Application, Chatbot API Design, Generative Artificial Intelligence, Software Agent, Chatbot Analytics.
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. ...