Automated Intelligent (AI) System

An Automated Intelligent (AI) System is an intelligent entity that is an information processing system that implements AI algorithms to solve AI-requiring tasks.

• Context:
  • Input: AI model input, inference requests, system parameters
    • Optional Input: training data, external context, environmental signals, real-time feedback
  • Output: AI inference results, model predictions, generated content, decision outputs
  • Performance Measure: model accuracy, inference latency, resource efficiency, learning rate, generalization capability
  • It can (typically) perform Human-Level Intelligence Tasks through AI processing.
  • It can (typically) be an AI Technology with AI system capability.
  • It can (typically) implement AI Algorithms through machine learning processes.
  • It can (typically) be managed through AI System Engineering Projects, such as:
    • developed with an AI Development Project through AI system development.
    • enhanced through AI System Enhancement Project for AI capability improvement.
    • maintained through AI System Maintenance Project for AI system stability.
    • integrated through AI System Integration Project for system interoperability.
    • validated through AI System Validation Project for system quality assurance.
    • monitored through AI System Monitoring Project for operational oversight.
    • upgraded through AI Model Update Project for model version advancement.
    • optimized through AI Performance Optimization Project for system efficiency.
  • ...
  • It can (often) accept AI System Input Formats (input formats) including text, images, audio, and video.
  • It can (often) generate AI System Output.
  • It can (often) consume AI System Energy for internal processes and actions.
  • ...
  • It can range from being a Narrow AI System to being a General AI System, depending on its capability scope.
  • It can range from being an Athletic AI to being a Scholarly AI, depending on its task domain.
  • It can range from being a Non-Autonomous AI to being an Autonomous AI, depending on its independence level.
  • It can range from being a Domain-Specific AI System to being an Open-Domain AI System, depending on its application scope.
  • It can range from being a Non-Cognitive AI to being a Cognitive AI, depending on its reasoning capability.
  • It can range from being an Engineered AI to being an Evolved AI, depending on its development approach.
  • It can range from being an Information Providing AI System to being a Tool Using AI System, depending on its interaction mode.
  • It can range from being a Black-Box AI System to being an Explainable AI System, depending on its transparency level.
  • It can range from being a Beneficial AI to being a Dangerous AI, depending on its impact type.
  • It can range from being a Simple Rule-Based System to being a Large Language Model System, depending on its complexity and capability level.
  • It can range from being a Reactive AI System to being a Self-Aware AI System, depending on its level of consciousness.
  • It can range from being a Non-Learning AI System to being a Learning AI System, depending on its learning capabilities.
  • ...
  • It can maintain AI System Audit Trails of its decisions and actions.
  • It can implement AI Security Measures, e.g., to protect sensitive information.
  • It can scale its AI System Processing Capabilities based on workload demands.
  • It can be analyzed by an AI System Analysis Task.
  • It can be related to AI Scaling Laws (such as LLM scaling laws).
  • It can implement an AI System API for service integration and system interoperability.
  • It can support AI Model Version Control for model lifecycle management.
  • It can provide AI System Monitoring for operational oversight.
  • ...
• Example(s):
  • Capability-Based AI Systems, such as:
    • Narrow AI Systems, such as: AlphaGo, Deep Blue, YOLO, Facial Recognition AI, GPT-3, BERT, Industrial Robots
    • General AI Systems (hypothetical), such as: Artificial General Intelligence (AGI), Artificial Superintelligence (ASI)
  • Task-Oriented AI Systems, such as:
    • Athletic AI Systems, which handle physical tasks: AlphaGo, Deep Blue, OpenAI Five, Self-Driving Cars, Boston Dynamics' Robots
    • Scholarly AI Systems, which handle intellectual tasks: GPT-3, BERT, Machine Translation AI, DALL-E, IBM Watson, AI Assistants
  • Architecture-Based AI Systems, such as:
    • Centralized AI Systems, which use single platforms: IBM Watson, GPT-3, AlphaGo, Deep Blue
    • Distributed AI Systems, which use multiple nodes: Federated Learning, Edge AI, Self-Driving Cars
  • Implementation-Based AI Systems, such as:
    • Symbolic AI Systems, which use explicit rules: Expert Systems
    • Sub-Symbolic AI Systems, which use neural networks: GPT-3, BERT, AlphaGo, DALL-E
  • Enterprise AI Systems, such as:
    • Salesforce Einstein (2024), offering customer relationship intelligence
  • Autonomy-Based AI Systems, such as:
    • Non-Autonomous AI Systems, requiring human input: Siri, YOLO, Industrial Robots
    • Autonomous AI Systems, with independent operations: Self-Driving Cars, future AI systems
  • Impact-Based AI Systems, such as:
    • Beneficial AI Systems, providing positive value: AI Assistants, Surgical Robots, Machine Translation AI
    • Dangerous AI Systems, requiring careful control: highly advanced systems with potential risks
  • Specialized AI Systems, such as:
    • Conversational AI Systems: ChatGPT (2024), Google Bard (2024), Claude (2024)
    • Computer Vision Systems: Azure Computer Vision (2024), Amazon Rekognition (2024)
  • Interaction-Based AI Systems, such as:
    • Information Providing AI Systems: conversational chatbots
    • Tool Using AI Systems: industrial robots, surgical robots, web-search tool-using AI system
  • Enterprise AI Integration Systems, such as:
    • MLOps Platform Systems: Kubeflow (2024), MLflow (2024), Weights & Biases (2024)
    • AI Pipeline Systems: Azure ML Pipeline (2024), Google Vertex AI (2024)
    • Model Serving Systems: TensorFlow Serving (2024), Triton Inference Server (2024)
  • Large Language Model-based Systems, such as:
    • LLM-Powered Language Processing Systems for automated text generation
    • LLM-Based Reasoning Systems for automated analysis
    • LLM-Enhanced Knowledge Systems for automated task solving
  • Multimodal AI-based Systems, such as:
    • AI-Powered Image Generation Systems for automated visual creation
    • AI-Based Art Production Systems for automated artistic rendering
    • AI-Enhanced Image Processing Systems for automated visual transformation
  • Audio AI-based Systems, such as AI-powered speech recognition systems for automated transcription
  • Domain-Specific AI-based Systems:
    • Healthcare-Domain AI Systems, such as medical diagnosis AI system that perform automated medical diagnosis.
    • Financial-Domain AI Systems, such as financial trading AI systems that performed automated financial trading.
    • Legal-Domain AI Systems, such as contract analysis AI systems that perform automated contract analysis.
  • ..., such as:
    • Scientific AI-based Systems, such as AI-powered protein analysis systems for automated structure prediction.
    • Creative AI-based Systems, such as AI-powered music composition systems for automated musical creation.
  • …
• Counter-Example(s):
  • Biological Intelligence, such as an intelligent human.
  • Unintelligent Machine, which lacks learning capability, such as basic information processing systems.
  • Simple Automation Tools, which perform predefined tasks without learning or adaptation.
• See: Cognitive Science, Mind, Organizational AI Guideline, Artificial Neural Network, Machine Learning, Natural Language Processing, You Only Look Once (YOLO) System, Deep Learning, Reinforcement Learning, Computer Vision, Expert System, Neural Network Architecture, AI Ethics, AI Safety, AI Governance.

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References

2024-11-16

• Perplexity.ai
  • Question: Can you provide a comprehensive overview of modern AI systems, including their core capabilities, types, and characteristics? I'm particularly interested in understanding how they process information, their learning abilities, and different categories from narrow to general AI. Please include examples of real-world applications, explain how they range from simple to complex systems, and describe their key features like autonomy levels, domain specificity, and interaction modes. Also, touch on their integration capabilities, security aspects, and how they can scale based on needs.
  • Answer: AI systems can be understood through their core capabilities, learning mechanisms, and architectural components that enable human-like information processing.
    • Information Processing Capabilities: Modern AI systems employ sophisticated mechanisms to process and learn from input data. They utilize parsing to break down complex inputs into meaningful components, enabling understanding of structure and context. The systems continuously absorb and process vast amounts of data, adapting their knowledge and behavior through advanced cognitive learning techniques.
    • Learning Mechanisms:
      • Adaptive Learning:
        • Continuous real-time learning and reasoning rather than relying solely on pre-programmed instructions
        • Meta-learning capabilities that help optimize learning paths and reduce computational requirements
        • Strategic thinking and decision-making through meta-reasoning to make efficient use of cognitive resources
      • Memory and Retention:
        • Short-term and long-term memory components that mirror human cognitive structures
        • Spaced repetition and active recall techniques to improve knowledge retention
        • Dynamic memory that enables context maintenance and reference to previous interactions
    • System Characteristics:
      • Core Components:
        • Integrated cognitive modules that work together to enable sophisticated tasks
        • Language processing capabilities for natural communication
        • Inference engines that draw logical conclusions from learned data
      • Personalization Capabilities:
        • Unparalleled personalization by analyzing user interaction and creating unique digital profiles
        • Adaptive content delivery based on individual learning styles and preferences
        • Real-time feedback loops that adjust to user performance and needs
    • Applications and Integration:
      • Industry Applications:
        • Healthcare diagnostics and treatment planning
        • Retail personalization and customer service
        • Financial analysis and decision-making
      • Learning Enhancement:
        • Interactive learning methods with personalized feedback
        • Adaptive recall games and memory challenges
        • Multi-sensory learning experiences tailored to individual preferences
    • Future Development:
      • Research into becoming general-purpose learners like humans
      • Integration of meta-reasoning and meta-learning capabilities
      • Development of more sophisticated cognitive architectures that better mirror human thought processes
  • Source:
    • Vation Ventures research on cognitive AI impact
    • IEEE Innovation at Work article on AI learning
    • Training Industry article on AI-driven learning retention
    • Additional academic and industry publications
  • Citations:

[1] https://www.vationventures.com/research-article/cognitive-ai-explained-impact-and-future-in-the-digital-world
[2] https://innovationatwork.ieee.org/can-artificial-intelligence-ai-learn/
[3] https://trainingindustry.com/articles/artificial-intelligence/forget-forgetting-ai-driven-strategies-for-learning-retention/
[4] https://cbmm.mit.edu/sites/default/files/documents/Langley_AAAI17_SoI.pdf
[5] https://www.mdpi.com/2227-7390/11/11/2420
[6] https://www.sciencedirect.com/science/article/pii/S2666920X2100014X
[7] https://csuglobal.edu/news-stories-press?type=post%2Fhow-does-ai-actually-work
[8] https://www.forbes.com/sites/cognitiveworld/2019/11/16/can-artificial-intelligence-learn-tolearn/

2021

• (Pretz, 2021) ⇒ Kathy Pretz (2021). "Stop Calling Everything AI, Machine-Learning Pioneer Says". In: IEEE Spectrum.
  • Stop Calling Everything AI, Machine-Learning Pioneer Says Michael I. Jordan explains why today’s artificial-intelligence systems aren’t actually intelligent
  • QUOTE: ... Despite such developments being referred to as “AI technology," he writes, the underlying systems do not involve high-level reasoning or thought. The systems do not form the kinds of semantic representations and inferences that humans are capable of. They do not formulate and pursue long-term goals.

    “For the foreseeable future, computers will not be able to match humans in their ability to reason abstractly about real-world situations," he writes. “We will need well-thought-out interactions of humans and computers to solve our most pressing problems. We need to understand that the intelligent behavior of large-scale systems arises as much from the interactions among agents as from the intelligence of individual agents." ...

2013

• (Gartner, 2013-10-13) ⇒ "Gartner Says Smart Machines Will Have Widespread and Deep Business Impact Through 2020." Press Release
  • QUOTE: Machines are evolving from automating basic tasks to becoming advanced self-learning systems as capable as the human brain in many highly specialized professions. As such, the next wave of job losses will likely occur among highly valued specialists during the next decade. … "... This marketplace comprises intelligent agents, virtual reality assistants, expert systems and embedded software to make traditional machines 'smart' in a very specialized way, plus a new generation of low-cost and easy-to-train robots and purpose-built automated machines that could significantly devalue and/or displace millions of humans in the workforce." said Kenneth Brant, research director at Gartner. … Gartner believes that the capability and reliability of smart machines will dramatically increase through 2020 to the point where they will have a major impact on business and IT functions. The impact will be such that firms that have not begun to develop programs and policies for a "digital workforce" by 2015 will not perform in the top quartile for productivity and operating profit margin improvement in their industry by 2020. … Citizens will protest higher and more prolonged states of unemployment, electing governments to legislate against smart machines … "We certainly will not approach a state of mass unemployment at any time in the near future," said Mr. Brant. “What is also certain, however, is that many new combinations of technology — from intelligent software agents, expert systems and virtual reality assistants to software systems embedded in smart products and revolutionary new forms of robotics — will emerge and have great impacts in this decade. We won't need to develop a full-functioning artificial brain by 2020 for smart machines to have radically changed our business models, workforce, cost structure and competitiveness."

2012

• (Wikipedia, 2012) ⇒ http://en.wikipedia.org/wiki/Intelligent_agent
  • In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is rational). Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is an intelligent agent, as is a human being, as is a community of human beings working together towards a goal.

    Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents (AIA) to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their Autonomy, and so prefer the term autonomous intelligent agents. Still others (notably (Russell & Norvig, 2003) considered goal-directed behavior as the essence of intelligence and so prefer a term borrowed from economics, “rational agent”.

    Intelligent agents in artificial intelligence are closely related to agents in economics, and versions of the intelligent agent paradigm are studied in cognitive science, ethics, the philosophy of practical reason, as well as in many interdisciplinary socio-cognitive modeling and computer social simulations.

    Intelligent agents are also closely related to software agents (an autonomous software program that carries out tasks on behalf of users). In computer science, the term intelligent agent may be used to refer to a software agent that has some intelligence, regardless if it is not a rational agent by Russell and Norvig's definition. For example, autonomous programs used for operator assistance or data mining (sometimes referred to as bots) are also called "intelligent agents".

2010a

• The New Oxford American Dictionary, Third Edition
  • QUOTE: … computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

2009a

• (IEEE Intelligent Systems, 2009) ⇒ http://www.computer.org/portal/site/intelligent/
  • QUOTE: … systems that perceive, reason, learn, and act intelligently.

2009b

• (Intelligent Systems, 2009) ⇒ http://www.intelligent-systems.com.ar/intsyst/defintsi.htm
  • It is a system.
  • It learns during its existence. (In other words, it senses its environment and learns, for each situation, which action permits it to reach its objectives.)
  • It continually acts, mentally and externally, and by acting reaches its objectives more often than pure chance indicates (normally much oftener).
  • It consumes energy and uses it for its internal processes, and in order to act.

1998

• (Moravec, 1998) ⇒ Hans Moravec. (1998). “When Will Computer Hardware Match the Human Brain.” In: Journal of evolution and technology, 1(1).
  • QUOTE: This paper describes how the performance of AI machines tends to improve at the same pace that AI researchers get access to faster hardware. Based on extrapolation of past trends and on examination of technologies under development, it is predicted that the required hardware will be available in cheap machines in the 2020s.

1959

• (Samuel, 1959) ⇒ Arthur L. Samuel. (1959). “Some Studies in Machine Learning Using the Game of Checkers.” IBM Journal of research and development 3, no. 3

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