Autonomous Intelligent Machine

An Autonomous Intelligent Machine is an intelligent machine that is an AI system that can perform autonomous operations.

• AKA: Non-Human Intelligence, Intelligent Agent System.
• Context:
  • Input(s): sensor data, environmental states, operational goals
  • Output(s): autonomous actions, decisions, learning updates
  • Performance Measure(s): autonomy level, task effectiveness, safety metrics
  • ...
  • It can (typically) be based on AI Technologies, such as: machine learning, deep learning, natural language processing, and computer vision.
  • It can (typically) demonstrate Capabilities in increasing sophistication:
    • Basic Capabilities, such as:
      • Environmental Perception through sensor systems
      • Action Execution through actuator systems
      • Basic Decision Making through rule systems
    • Advanced Capabilities, such as:
      • Autonomous Learning through experience accumulation
      • Complex Problem Solving through AI algorithms
      • Adaptive Behavior through feedback systems
    • Specialized Capabilities, such as:
      • Multi-Agent Coordination for collaborative tasks
      • Error Recovery for system resilience
      • Self-Optimization for performance improvement
  • ...
  • It can (typically) be composed of Core System Components:
    • Cognitive Architecture Components, such as:
      • Perception Module for environmental understanding
      • Decision Engine for action selection
      • Learning Module for knowledge acquisition
    • Physical Architecture Components, such as:
      • Sensor Array for data collection
      • Processing Unit for computation
      • Actuator System for physical interaction
    • Safety Architecture Components, such as:
      • Monitoring System for operation oversight
      • Failsafe Mechanism for emergency handling
      • Validation Module for decision verification
  • ...
  • It can (typically) operate within Operational Contexts:
    • Safety Context, including:
      • Boundaries for safe operation
      • Risk Assessment for danger mitigation
      • Emergency Protocols for critical situations
    • Performance Context, including:
      • Efficiency Metrics for resource utilization
      • Effectiveness Measures for goal achievement
      • Quality Indicators for output evaluation
    • Interaction Context, including:
      • Human Interface for operator interaction
      • Machine Interface for system integration
      • Environment Interface for world interaction
  • ...
  • It can (often) exhibit System Behaviors, such as: adaptive learning, decision making, problem solving, and autonomous navigation.
  • It can range from being a Conscious Intelligent Machine to being a Non-Conscious Intelligent Machine.
  • It can range from being a Linguistic AI Machine to being a Visual AI Machine.
  • It can range from being a Basic Autonomous Machine to being an Advanced Autonomous Machine, depending on its capability level.
  • It can range from being a Single-Domain Machine to being a Multi-Domain Machine, depending on its operational scope.
  • It can range from being a Simple Learning Machine to being a Complex Learning Machine, depending on its learning sophistication.
  • It can operate in a variety of domains, from Autonomous Vehicles and Medical Diagnosis Systems to Automated Financial Trading Systems and Smart Home Technologies.
  • ...
• Examples:
  • Advanced Autonomous Machines, such as:
    • AI-Powered Robots, such as:
      • HAL 9000 in science fiction
      • Auto-GPT-based implementation
      • LLM-based Agents like Voyager AI agent
    • Autonomous Vehicles, such as:
      • Self-Driving Cars utilizing AI algorithms
      • Autonomous Drones for aerial tasks
      • Autonomous Robot Vehicles for industrial transport
    • Smart Industrial Machines, such as:
      • Predictive Maintenance Robots for equipment monitoring
      • Manufacturing Robots for assembly tasks
      • Quality Control Robots for inspection tasks
  • Specialized Intelligence Machines, such as:
    • Medical Machines, such as:
      • Surgical Robots for medical procedures
      • Diagnostic Systems for medical analysis
      • Care Robots for patient assistance
    • Financial Machines, such as:
      • Trading Robots for market operations
      • Risk Assessment Systems for financial analysis
      • Fraud Detection Systems for transaction monitoring
  • ...
• Counter-Examples:
  • a Non-Intelligent Mechanical Agent, such as a Roomba robot or an ASIMO robot.
  • an Intelligent Virtual Assistant, due to its limited autonomy and dependence on human-defined rules.
  • an Intelligent Living Agent, an intelligent person.
  • a Heuristic-based Autonomous System, as it operates based on set rules rather than learning from data.
  • a Corporation, because it is not a machine.
• See: Intelligent System, Moral Machine, AI Emotion, Advanced AI System, Machine Ethics, Human-AI Collaboration, Artificial General Intelligence.

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References

2024

• (AI Open Letter, 2024-06-04) ⇒ 13 American Industry AI Researchers. (2024). “A Right to Warn About Advanced Artificial Intelligence.”
  • NOTE: It highlights the potential benefits of AI technology while acknowledging serious social risks such as societal inequalities, misinformation, and the potential for autonomous AI systems to pose existential threats.

2017

• (Institute, 2017) ⇒ The Future of Life Institute. (2017). “Asilomar AI Principles.”
  • QUOTE: ...
    • 8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
    • …
    • 10) Value Alignment: Highly autonomous AI systems should be designed so that their goals and behaviors can be assured to align with human values throughout their operation.

2006

• Stan Franklin, and F. G. Patterson Jr. (2006). “The LIDA architecture: Adding new modes of learning to an intelligent, autonomous, software agent.” In: Integrated Design and Process Technology, IDPT-2006

2001

• (Addlesee et al., 2001) ⇒ Mike Addlesee, Rupert Curwen, Steve Hodges, Joe Newman, Pete Steggles, Andy Ward, and Andy Hopper. (2001). “Implementing a Sentient Computing System.” In: Computer Journal, 34(8). doi:10.1109/2.940013
  • QUOTE: Sentient computing systems, which can change their behavior based on a model of the environment they construct using sensor data,

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