Information-Providing AI System

A Information-Providing AI System is an automated intelligent system that is an intelligent agent (designed to process and deliver information in response to user requests or system triggers).

• AKA: Intelligent Information Provider, AI Knowledge Assistant.
• Context:
  • Task Input: user query, information request, context parameters
    • Optional Input: user preferences, historical interactions, environmental context, domain constraints, format specifications
  • Task Output: processed information, relevant answers, information summary, knowledge insights, source references
  • Task Performance Measure: response accuracy, information relevance, response time, user satisfaction, information completeness, source reliability
  • ...
  • It can typically process Natural Language Querys through query understanding.
  • It can typically retrieve Relevant Information through knowledge access.
  • It can typically generate Structured Responses through content generation.
  • It can typically maintain Information Quality through validation processes.
  • It can typically handle Contextual Requests through context management.
  • It can typically provide Timely Information through response optimization.
  • It can typically support Interactive Dialogs through conversation management.
  • ...
  • It can often adapt Response Formats through presentation optimization.
  • It can often personalize Information Delivery through user profiling.
  • It can often integrate Multiple Information Sources through information fusion.
  • It can often ensure Information Accuracy through fact verification.
  • It can often update Knowledge Repositorys through continuous learning.
  • It can often filter Irrelevant Information through relevance scoring.
  • It can often summarize Complex Information through abstraction techniques.
  • It can often translate Technical Information through domain adaptation.
  • ...
  • It can range from being a Simple Information-Providing AI System to being a Complex Information-Providing AI System, depending on its capability sophistication.
  • It can range from being a Rule-Based Information-Providing AI System to being a Learning-Based Information-Providing AI System, depending on its knowledge acquisition method.
  • It can range from being a Domain-Specific Information-Providing AI System to being an Open-Domain Information-Providing AI System, depending on its knowledge scope.
  • It can range from being a Static Information-Providing AI System to being a Dynamic Information-Providing AI System, depending on its update frequency.
  • It can range from being a Text-Based Information-Providing AI System to being a Multimodal Information-Providing AI System, depending on its interaction modality.
  • It can range from being a Reactive Information-Providing AI System to being a Proactive Information-Providing AI System, depending on its information delivery approach.
  • It can range from being a Single-Source Information-Providing AI System to being a Multi-Source Information-Providing AI System, depending on its information integration capability.
  • ...
  • It can provide Real-Time Updates through dynamic retrieval.
  • It can handle Multimodal Inputs through modality processing.
  • It can explain Reasoning Processes through transparency mechanisms.
  • It can cite Information Sources through attribution systems.
  • It can detect Information Anomalies through consistency checking.
  • It can support Collaborative Information Sharing through multi-user interfaces.
  • It can generate Information Visualizations through data representation.
  • It can maintain Information Security through access controls.
  • It can integrate with External Knowledge Bases for information enrichment.
  • It can connect to Real-Time Data Feeds for current information.
  • It can interface with Tool-Using AI Systems for enhanced capability.
  • It can collaborate with Information-Processing Tool-Using AI Systems for complex analysis.
  • It can support Action-Taking AI Systems through information provision.
  • ...
• Examples:
  • Information-Providing Conversational AI (Chatbot) Systems, such as:
    • General-Purpose Conversational Assistants, such as:
      • ChatGPT for open-domain conversational information.
      • Claude for analytical conversational information.
      • Google Bard for integrated conversational information.
      • Bing Chat for web-enhanced conversational information.
    • Specialized Conversational Assistants, such as:
      • GitHub Copilot Chat for programming information.
      • Replika for emotional support information.
      • Character.AI for persona-based information.
    • Voice-Based Conversational Assistants, such as:
      • Alexa for voice-activated information.
      • Google Assistant for voice-query information.
      • Siri for device-integrated information.
  • Automated AI-Powered Research Systems, such as:
    • Academic Research Assistants, such as:
      • Semantic Scholar for academic paper information.
      • Elicit for research question information.
      • Research Rabbit for literature connection information.
      • Consensus for scientific consensus information.
    • Market Research Systems, such as:
      • CB Insights for market intelligence information.
      • Crayon for competitive intelligence information.
    • Patent Research Systems, such as:
      • PatSnap for patent landscape information.
      • Ambercite for patent citation information.
  • Domain-Specific Information Systems, such as:
    • Healthcare Information Systems, such as:
      • Medical Diagnosis Assistants, such as:
        • IBM Watson Health for clinical decision information.
        • Babylon Health for symptom assessment information.
        • Ada Health for health condition information.
      • Drug Information Systems, such as:
        • Epocrates for medication information.
        • Micromedex for drug interaction information.
    • Legal Information Systems, such as:
      • Case Law Research Systems, such as:
        • ROSS Intelligence for legal precedent information.
        • Casetext for legal document information.
        • LexisNexis AI for comprehensive legal information.
      • Contract Analysis Systems, such as:
        • Kira Systems for contract clause information.
        • Luminance for contract risk information.
    • Financial Information Systems, such as:
      • Market Analysis Systems, such as:
        • Bloomberg Terminal AI for financial market information.
        • Refinitiv Eikon for financial data information.
        • AlphaSense for financial insight information.
      • Investment Research Systems, such as:
        • Morningstar Direct for investment analysis information.
        • S&P Capital IQ for company financial information.
  • Enterprise Information Systems, such as:
    • Knowledge Management Systems, such as:
      • Microsoft Viva Topics for organizational knowledge information.
      • Confluence AI for documentation information.
      • Notion AI for workspace information.
    • Customer Service Systems, such as:
      • Zendesk Answer Bot for customer support information.
      • Intercom Resolution Bot for customer query information.
      • Salesforce Einstein for CRM information.
  • Educational Information Systems, such as:
    • Intelligent Tutoring Systems, such as:
      • Khan Academy AI for personalized learning information.
      • Duolingo Max for language learning information.
      • Carnegie Learning for mathematics learning information.
    • Academic Writing Assistants, such as:
      • Grammarly for writing improvement information.
      • QuillBot for paraphrasing information.
  • News and Media Information Systems, such as:
    • Artifact for personalized news information.
    • Feedly AI for content curation information.
    • Google News AI for news aggregation information.
  • Technical Documentation Systems, such as:
    • Stack Overflow AI for programming solution information.
    • DevDocs for API documentation information.
    • MDN AI Assistant for web development information.
  • ...
• Counter-Examples:
  • Action-Taking AI Systems, which execute tasks rather than provide information.
  • Decision-Making AI Systems, which generate decision recommendations rather than deliver neutral information.
  • Creative Content Generation Systems, which produce original creative works rather than provide existing information.
  • Predictive AI Systems, which forecast future states rather than report current information.
  • Control Systems, which manage system behavior without providing information services.
  • Data Processing Systems, which transform raw data without information interpretation.
• See: Tool-Using AI System, Information-Processing Tool-Using AI System, Knowledge Base System, Information Retrieval System, Query Processing System, Natural Language Processing System, Conversational AI System, Question Answering System, AI Research Assistant, Action-Taking AI System.

2025-01-27

• Deep Research https://chatgpt.com/s/dr_68467b036588819197ffa03e0bee958c
  • Question: What are Information-Providing AI Systems and how do they differ from other types of AI systems? Provide a comprehensive overview including their characteristics, functionalities, and typologies.
  • Answer: Information-Providing AI Systems are automated intelligent agents designed to process and deliver information in response to user requests or relevant triggers. These AI knowledge assistants answer questions, provide explanations or recommendations, and supply knowledge to users without directly taking physical actions on the user's behalf. They typically interact via natural language (text or voice), enabling users to ask questions as if conversing with an expert.
    • Core Distinctions: Within the broader landscape of intelligent systems, Information-Providing AI sits in contrast to AI agents that act or decide on their own. Rather than executing tasks or making decisions for users, an information provider's primary goal is to equip the user with knowledge.
      • Information-Providing vs Tool-Using AI: A tool-using or action-taking AI (like a robot or an autonomous software agent) manipulates tools or performs tasks in the environment, whereas an information provider delivers information without tool manipulation.
      • Information-Providing vs Decision-Making AI: A decision-making AI might output a recommendation or choose an option (say, approving a loan or selecting a strategy), whereas an information provider strives to give neutral information for a human to consider.
    • Applications: Information-Providing AI Systems are ubiquitous in modern life.
      • Digital Assistants: Siri, Alexa, and Google Assistant answer everyday queries.
      • Advanced Chatbots: ChatGPT and Claude engage in open-domain dialogue to provide explanations or advice.
      • Domain-Specific Systems: Medical or legal assistants help professionals by supplying relevant facts or research findings.
    • Key Dimensions of Information-Providing AI Systems:
      • Capability Sophistication (Simple vs. Complex): This dimension describes the complexity and sophistication of the AI's capabilities.
        • Simple Example: A rule-based FAQ chatbot that uses pattern matching to answer common questions is a simple information provider. For instance, an IT helpdesk bot that recognizes a few keywords ("password reset") and returns a canned answer has minimal understanding.
        • Complex Example: ChatGPT, powered by a large language model, is a complex information-providing system. It can interpret a wide range of questions, context, and even hold multi-turn conversations with reasoning.
        • Near Counter-Example: AlphaGo, DeepMind's game-playing AI, is an extremely complex AI system but is not an information-providing system – it does not supply answers or knowledge to a user.
      • Knowledge Acquisition Method (Rule-Based vs. Learning-Based): This dimension captures how the system acquires and represents knowledge.
        • Rule-Based Example: Expert systems are classic rule-based information providers. MYCIN, for example, was an early medical diagnosis assistant that used ~500 handcrafted rules to diagnose blood infections and recommend treatments.
        • Learning-Based Example: Modern AI assistants like those powered by GPT-3/GPT-4 (e.g. ChatGPT or Google Bard) are learning-based. They have ingested enormous datasets and learned statistical associations.
        • Near Counter-Examples: A rule-based industrial control system might open or close valves based on sensor readings – it uses if-then rules but its outputs are physical actions, not information for a user.
      • Knowledge Scope (Domain-Specific vs. Open-Domain): This refers to how broad the subject matter of the AI's knowledge is.
        • Domain-Specific Example: Healthcare and legal AI assistants exemplify domain-specific info providers. IBM Watson for Oncology, for instance, was designed to help doctors by providing information on cancer treatment options.
        • Open-Domain Example: General-purpose assistants like Google Assistant, Bing Chat, or ChatGPT are open-domain. They draw from broad knowledge sources and are designed to handle unstructured queries on any subject.
      • Knowledge Update Frequency (Static vs. Dynamic Knowledge): This dimension indicates whether the AI's knowledge base is fixed (static) or continually updating (dynamic).
        • Static Knowledge Example: Many offline or self-contained AI assistants operate on a static knowledge base. For instance, the original GPT-3 model had a training cutoff of 2021.
        • Dynamic Knowledge Example: Bing Chat and Google Bard exemplify dynamic systems – they have live access to the web. If you ask Bing Chat for today's news or the current stock price of a company, it actually performs a web search and includes current information in its answer.
      • Interaction Modality (Text-Based vs. Multimodal): This dimension concerns the input/output channels through which the AI interacts.
        • Text-Based Example: A large portion of knowledge assistants are purely text-based. For example, web chatbots on support websites or a command-line QA system accept questions as text and return text answers.
        • Multimodal Example: Voice assistants like Amazon Alexa, Apple's Siri, or Google Assistant are multimodal information providers. They take voice input and often respond in a synthesized voice.
      • Information Delivery Approach (Reactive vs. Proactive): This dimension describes whether the AI only provides information upon user request (reactive) or whether it can initiate information delivery on its own based on context or triggers (proactive).
        • Reactive Example: The vast majority of QA bots and search engines are reactive. Google Search or a customer service chatbot will sit idle until the user enters a query, then return the relevant info.
        • Proactive Example: Some modern assistants incorporate proactivity. A familiar example is Google Now or smartphone assistants offering "cards" or notifications: your assistant might ping you, "Your flight tomorrow is at 9am; traffic is heavy, consider leaving by 7am."
      • Information Integration (Single-Source vs. Multi-Source): This dimension concerns how many data/knowledge sources the AI draws from.
        • Single-Source Example: An enterprise knowledge base assistant that only pulls answers from a specific internal wiki or document repository is single-source.
        • Multi-Source Example: Web search engines with AI (like Bing or Google's search-integrated chat) are inherently multi-source – they crawl and draw from millions of websites.
      • Context Handling and Memory (Stateless vs. Contextual): This dimension addresses whether the AI remembers context from previous interactions.
        • Stateless Example: Traditional search engines and many simple chatbots are stateless. Each query stands alone.
        • Contextual Example: Newer conversational AI systems maintain a memory of the dialogue – they have multi-turn conversation capability. ChatGPT and similar chatbots are designed to keep a running context of everything said so far.
      • Personalization (Generic vs. Personalized): This dimension covers whether the AI tailors its responses to a specific user's preferences, history, or profile.
        • Generic Example: Most public search engines and QA systems initially were one-size-fits-all. Both users get the same list.
        • Personalized Example: Modern systems increasingly personalize information. For instance, news recommendation AI in your phone will learn what topics you read often and prioritize those.
      • Deployment Environment (Cloud-Based vs. On-Device): This dimension pertains to where the AI system is hosted and run.
        • Cloud-Based Example: Most high-end information-providing AIs today are cloud-based. ChatGPT, for instance, runs on OpenAI's cloud servers.
        • On-Device Example: With advances in model optimization, some information assistants can run partially or entirely on-device. For example, Apple's Siri in recent versions processes certain commands on-device.
      • User Audience & Access (Public vs. Enterprise): This dimension looks at who the system is built for and how it is accessed.
        • Public Example: Wikipedia's AI search, Google Search with AI snippets, or Bing Chat are available to anyone with an internet connection.
        • Enterprise Example: An enterprise knowledge assistant might be deployed within a corporation for employees only.
      • Transparency and Explainability (Opaque vs. Explainable): This dimension describes the degree to which the AI can explain its reasoning or cite sources for its answers.
        • Opaque Example: Many end-to-end neural network-based assistants are initially opaque. Early versions of ChatGPT would just give you a fluent answer, but you had to take it on faith.
        • Explainable Example: There is a growing trend toward explainable AI in information provision. For example, Bing Chat and Google Bard cite their sources with each piece of information.
    • Conclusion and Outlook: Information-Providing AI Systems form a distinct class of AI: their raison d'être is to empower users with knowledge and insights, rather than autonomously acting on the world. They are essentially the heirs to libraries and search engines in the AI era – aimed at satisfying our curiosity and information needs.
  • Citations:

[1] https://www.gabormelli.com/RKB/Information_Providing_AI_System - GM-RKB definition and characteristics of Information-Providing AI Systems
[2] https://www.gabormelli.com/RKB/Tool-Using_AI_System - GM-RKB contrasting AI system type focused on tool manipulation
[3] https://www.gabormelli.com/RKB/Conversational_AI_System - GM-RKB subcategory of memory-enabled conversation-centered AI
[4] https://www.britannica.com/technology/MYCIN - MYCIN expert system, early rule-based medical diagnosis assistant with explainability features
[5] https://www.theverge.com/2023/9/27/23892781/openai-chatgpt-live-web-results-browse-with-bing - OpenAI enabling Browse with Bing for real-time web access in ChatGPT
[6] https://en.wikipedia.org/wiki/GPT-3 - GPT-3 technical specifications and training details

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