Autonomous Intelligent Machine

An Autonomous Intelligent Machine is an intelligent machine that is an AI system that can perform autonomous operations without continuous human supervision.

• AKA: Non-Human Intelligence, Intelligent Agent System, Autonomous AI System, Self-Governing Machine Intelligence.
• Context:
  • Input(s): autonomous intelligent machine sensor data, autonomous intelligent machine environmental states, autonomous intelligent machine operational goals, autonomous intelligent machine user instructions
  • Output(s): autonomous intelligent machine actions, autonomous intelligent machine decisions, autonomous intelligent machine learning updates, autonomous intelligent machine status reports
  • Autonomous Intelligent Machine Performance Measure(s): autonomous intelligent machine autonomy level, autonomous intelligent machine task effectiveness, autonomous intelligent machine safety metrics, autonomous intelligent machine resource efficiency
  • ...
  • It can typically be based on AI Technologies, such as: autonomous intelligent machine learning, autonomous intelligent machine deep learning, autonomous intelligent machine natural language processing, autonomous intelligent machine computer vision, and autonomous intelligent machine reinforcement learning.
  • It can typically utilize Autonomous Intelligent Machine Computational Resource including autonomous intelligent machine processing power, autonomous intelligent machine memory systems, and autonomous intelligent machine distributed computing.
  • It can typically operate according to Autonomous Intelligent Machine Ethical Framework for autonomous intelligent machine responsible operation and autonomous intelligent machine harm prevention.
  • It can typically establish Autonomous Intelligent Machine Self-Model for autonomous intelligent machine capability assessment and autonomous intelligent machine limitation awareness.
  • ...
  • It can typically demonstrate Capabilities in increasing sophistication:
    • Basic Capabilities, such as:
      • Autonomous Intelligent Machine Environmental Perception through autonomous intelligent machine sensor systems
      • Autonomous Intelligent Machine Action Execution through autonomous intelligent machine actuator systems
      • Autonomous Intelligent Machine Basic Decision Making through autonomous intelligent machine rule systems
      • Autonomous Intelligent Machine State Maintenance through autonomous intelligent machine memory management
      • Autonomous Intelligent Machine Basic Communication through autonomous intelligent machine interface protocols
    • Advanced Capabilities, such as:
      • Autonomous Intelligent Machine Learning through autonomous intelligent machine experience accumulation
      • Autonomous Intelligent Machine Complex Problem Solving through autonomous intelligent machine algorithms
      • Autonomous Intelligent Machine Adaptive Behavior through autonomous intelligent machine feedback systems
      • Autonomous Intelligent Machine Prediction through autonomous intelligent machine predictive modeling
      • Autonomous Intelligent Machine Knowledge Representation through autonomous intelligent machine semantic networks
    • Specialized Capabilities, such as:
      • Autonomous Intelligent Machine Multi-Agent Coordination for autonomous intelligent machine collaborative tasks
      • Autonomous Intelligent Machine Error Recovery for autonomous intelligent machine system resilience
      • Autonomous Intelligent Machine Self-Optimization for autonomous intelligent machine performance improvement
      • Autonomous Intelligent Machine Novel Solution Generation for autonomous intelligent machine creative problem-solving
      • Autonomous Intelligent Machine Human Collaboration for autonomous intelligent machine augmented intelligence
  • ...
  • It can often adapt its Autonomous Intelligent Machine Learning Approach based on autonomous intelligent machine performance metrics and autonomous intelligent machine environmental conditions.
  • It can often develop Autonomous Intelligent Machine Internal Representation of autonomous intelligent machine operational domain and autonomous intelligent machine task structure.
  • It can often modify its Autonomous Intelligent Machine Behavioral Strategy according to autonomous intelligent machine feedback signals and autonomous intelligent machine goal priority.
  • It can often engage in Autonomous Intelligent Machine Resource Management for autonomous intelligent machine operational efficiency and autonomous intelligent machine sustainability.
  • It can often integrate Autonomous Intelligent Machine New Capability through autonomous intelligent machine software updates and autonomous intelligent machine hardware modification.
  • ...
  • It can typically be composed of Autonomous Intelligent Machine Core System Components:
    • Autonomous Intelligent Machine Cognitive Architecture Components, such as:
      • Autonomous Intelligent Machine Perception Module for autonomous intelligent machine environmental understanding
      • Autonomous Intelligent Machine Decision Engine for autonomous intelligent machine action selection
      • Autonomous Intelligent Machine Learning Module for autonomous intelligent machine knowledge acquisition
      • Autonomous Intelligent Machine Planning System for autonomous intelligent machine sequence generation
      • Autonomous Intelligent Machine Goal Management for autonomous intelligent machine objective prioritization
    • Autonomous Intelligent Machine Physical Architecture Components, such as:
      • Autonomous Intelligent Machine Sensor Array for autonomous intelligent machine data collection
      • Autonomous Intelligent Machine Processing Unit for autonomous intelligent machine computation
      • Autonomous Intelligent Machine Actuator System for autonomous intelligent machine physical interaction
      • Autonomous Intelligent Machine Power System for autonomous intelligent machine energy management
      • Autonomous Intelligent Machine Communication Hardware for autonomous intelligent machine information exchange
    • Autonomous Intelligent Machine Safety Architecture Components, such as:
      • Autonomous Intelligent Machine Monitoring System for autonomous intelligent machine operation oversight
      • Autonomous Intelligent Machine Failsafe Mechanism for autonomous intelligent machine emergency handling
      • Autonomous Intelligent Machine Validation Module for autonomous intelligent machine decision verification
      • Autonomous Intelligent Machine Ethical Filter for autonomous intelligent machine harm prevention
      • Autonomous Intelligent Machine Explainability System for autonomous intelligent machine transparency provision
  • ...
  • It can typically operate within Autonomous Intelligent Machine Operational Contexts:
    • Autonomous Intelligent Machine Safety Context, including:
      • Boundaries for autonomous intelligent machine safe operation
      • Autonomous Intelligent Machine Risk Assessment for autonomous intelligent machine danger mitigation
      • Autonomous Intelligent Machine Emergency Protocols for autonomous intelligent machine critical situations
      • Autonomous Intelligent Machine Human Override for autonomous intelligent machine control transfer
      • Autonomous Intelligent Machine Safety Certification for autonomous intelligent machine regulatory compliance
    • Autonomous Intelligent Machine Performance Context, including:
      • Autonomous Intelligent Machine Efficiency Metrics for autonomous intelligent machine resource utilization
      • Autonomous Intelligent Machine Effectiveness Measures for autonomous intelligent machine goal achievement
      • Autonomous Intelligent Machine Quality Indicators for autonomous intelligent machine output evaluation
      • Autonomous Intelligent Machine Reliability Standards for autonomous intelligent machine consistent operation
      • Autonomous Intelligent Machine Scalability Parameters for autonomous intelligent machine growth capacity
    • Autonomous Intelligent Machine Interaction Context, including:
      • Autonomous Intelligent Machine Human Interface for autonomous intelligent machine operator interaction
      • Autonomous Intelligent Machine Machine Interface for autonomous intelligent machine system integration
      • Autonomous Intelligent Machine Environment Interface for autonomous intelligent machine world interaction
      • Autonomous Intelligent Machine Social Protocol for autonomous intelligent machine human collaboration
      • Autonomous Intelligent Machine Information Exchange for autonomous intelligent machine data sharing
  • ...
  • It can range from being a Conscious Autonomous Intelligent Machine to being a Non-Conscious Autonomous Intelligent Machine, depending on its autonomous intelligent machine awareness level.
  • It can range from being a Simple Autonomous Intelligent Machine to being a Complex Autonomous Intelligent Machine, depending on its autonomous intelligent machine architectural sophistication.
  • It can range from being a Narrow Autonomous Intelligent Machine to being a General Autonomous Intelligent Machine, depending on its autonomous intelligent machine capability breadth.
  • It can range from being a Linguistic Autonomous Intelligent Machine to being a Visual Autonomous Intelligent Machine, depending on its autonomous intelligent machine primary modality.
  • It can range from being a Basic Autonomous Intelligent Machine to being an Advanced Autonomous Intelligent Machine, depending on its autonomous intelligent machine capability level.
  • It can range from being a Single-Domain Autonomous Intelligent Machine to being a Multi-Domain Autonomous Intelligent Machine, depending on its autonomous intelligent machine operational scope.
  • It can range from being a Deterministic Autonomous Intelligent Machine to being a Probabilistic Autonomous Intelligent Machine, depending on its autonomous intelligent machine decision framework.
  • It can range from being a Simple Learning Autonomous Intelligent Machine to being a Complex Learning Autonomous Intelligent Machine, depending on its autonomous intelligent machine learning sophistication.
  • It can range from being a Specialized Autonomous Intelligent Machine to being a Versatile Autonomous Intelligent Machine, depending on its autonomous intelligent machine adaptability range.
  • It can range from being a Physical Autonomous Intelligent Machine to being a Virtual Autonomous Intelligent Machine, depending on its autonomous intelligent machine embodiment type.
  • It can range from being a Supervised Autonomous Intelligent Machine to being an Unsupervised Autonomous Intelligent Machine, depending on its autonomous intelligent machine human oversight requirement.
  • It can range from being a Reactive Autonomous Intelligent Machine to being a Proactive Autonomous Intelligent Machine, depending on its autonomous intelligent machine initiative capacity.
  • It can range from being a Static Autonomous Intelligent Machine to being a Dynamic Autonomous Intelligent Machine, depending on its autonomous intelligent machine adaptability rate.
  • It can range from being an Individual Autonomous Intelligent Machine to being a Collective Autonomous Intelligent Machine, depending on its autonomous intelligent machine collaborative structure.
  • ...
  • It can implement Autonomous Intelligent Machine Governance Protocol for autonomous intelligent machine ethical operation and autonomous intelligent machine compliance management.
  • It can establish Autonomous Intelligent Machine Trust Framework for autonomous intelligent machine reliability assurance and autonomous intelligent machine predictability enhancement.
  • It can utilize Autonomous Intelligent Machine Verification Process for autonomous intelligent machine behavior validation and autonomous intelligent machine safety confirmation.
  • It can deploy Autonomous Intelligent Machine Explainability System for autonomous intelligent machine decision transparency and autonomous intelligent machine action justification.
  • It can maintain Autonomous Intelligent Machine Operational Log for autonomous intelligent machine activity recording and autonomous intelligent machine audit trail provision.
  • ...
• Examples:
  • Autonomous Intelligent Machine Types by Autonomous Intelligent Machine Capability Level, such as:
    • Advanced Autonomous Intelligent Machines, such as:
      • AI-Powered Autonomous Intelligent Machine Robots, such as:
        • Boston Dynamics Atlas (2023) for autonomous intelligent machine dynamic movement and autonomous intelligent machine obstacle navigation.
        • Tesla Optimus (2024) for autonomous intelligent machine general-purpose manipulation and autonomous intelligent machine human assistance.
        • Google DeepMind Lab Robot (2024) for autonomous intelligent machine reinforcement learning and autonomous intelligent machine generalization capability.
      • Autonomous Intelligent Machine Vehicles, such as:
        • Waymo Self-Driving Car (2024) for autonomous intelligent machine urban navigation and autonomous intelligent machine passenger transport.
        • Amazon Prime Air Drone (2023) for autonomous intelligent machine package delivery and autonomous intelligent machine route optimization.
        • Clearpath Robotics OTTO (2023) for autonomous intelligent machine industrial transport and autonomous intelligent machine warehouse logistics.
      • Autonomous Intelligent Machine Industrial Systems, such as:
        • FANUC Intelligent Robot (2024) for autonomous intelligent machine manufacturing process and autonomous intelligent machine assembly task.
        • ABB YuMi Collaborative Robot (2023) for autonomous intelligent machine precision work and autonomous intelligent machine human collaboration.
        • Siemens Industrial Edge System (2024) for autonomous intelligent machine process optimization and autonomous intelligent machine predictive maintenance.
    • Specialized Autonomous Intelligent Machines, such as:
      • Autonomous Intelligent Machine Medical Systems, such as:
        • Intuitive Surgical da Vinci System (2024) for autonomous intelligent machine surgical assistance and autonomous intelligent machine medical procedure.
        • Siemens AI-Rad Companion (2023) for autonomous intelligent machine diagnostic imaging and autonomous intelligent machine medical analysis.
        • Care.ai Autonomous Monitoring System (2024) for autonomous intelligent machine patient observation and autonomous intelligent machine healthcare alert.
      • Autonomous Intelligent Machine Financial Systems, such as:
        • JPMorgan LOXM Trading System (2023) for autonomous intelligent machine market operation and autonomous intelligent machine transaction execution.
        • BlackRock Aladdin System (2024) for autonomous intelligent machine portfolio management and autonomous intelligent machine risk assessment.
        • SymphonyAI Sensa (2023) for autonomous intelligent machine fraud detection and autonomous intelligent machine financial security.
      • Autonomous Intelligent Machine Agricultural Systems, such as:
        • John Deere Autonomous Tractor (2024) for autonomous intelligent machine field operation and autonomous intelligent machine precision farming.
        • Blue River See & Spray (2023) for autonomous intelligent machine targeted application and autonomous intelligent machine crop management.
        • Abundant Robotics Harvesting System (2023) for autonomous intelligent machine produce collection and autonomous intelligent machine yield optimization.
  • Autonomous Intelligent Machine Types by Autonomous Intelligent Machine Implementation, such as:
    • Physical Autonomous Intelligent Machines, such as:
      • Robotic Autonomous Intelligent Machines, such as:
        • Embodied Autonomous Intelligent Machine for autonomous intelligent machine physical interaction.
        • Mobile Autonomous Intelligent Machine for autonomous intelligent machine environment navigation.
        • Stationary Autonomous Intelligent Machine for autonomous intelligent machine fixed operation.
      • Vehicular Autonomous Intelligent Machines, such as:
        • Ground-Based Autonomous Intelligent Machine for autonomous intelligent machine terrestrial movement.
        • Aerial Autonomous Intelligent Machine for autonomous intelligent machine flight operation.
        • Marine Autonomous Intelligent Machine for autonomous intelligent machine water navigation.
    • Virtual Autonomous Intelligent Machines, such as:
      • Software-Based Autonomous Intelligent Machines, such as:
        • Cloud Autonomous Intelligent Machine for autonomous intelligent machine distributed processing.
        • Edge Autonomous Intelligent Machine for autonomous intelligent machine localized computation.
        • Embedded Autonomous Intelligent Machine for autonomous intelligent machine device integration.
      • AI-Based Autonomous Intelligent Machines, such as:
        • LLM-Based Autonomous Intelligent Machine for autonomous intelligent machine natural language task.
        • Computer Vision Autonomous Intelligent Machine for autonomous intelligent machine visual processing.
        • Multi-Modal Autonomous Intelligent Machine for autonomous intelligent machine cross-domain operation.
  • Autonomous Intelligent Machine Types by Autonomous Intelligent Machine Learning Approach, such as:
    • Supervised Learning Autonomous Intelligent Machines for autonomous intelligent machine guided development.
    • Reinforcement Learning Autonomous Intelligent Machines for autonomous intelligent machine experience-based improvement.
    • Self-Supervised Learning Autonomous Intelligent Machines for autonomous intelligent machine independent pattern discovery.
    • Transfer Learning Autonomous Intelligent Machines for autonomous intelligent machine cross-domain knowledge application.
    • Meta-Learning Autonomous Intelligent Machines for autonomous intelligent machine learning optimization.
  • Autonomous Intelligent Machine Types by Autonomous Intelligent Machine Decision Framework, such as:
    • Rule-Based Autonomous Intelligent Machines for autonomous intelligent machine predefined response.
    • Statistical Autonomous Intelligent Machines for autonomous intelligent machine probability-based decision.
    • Neural Network Autonomous Intelligent Machines for autonomous intelligent machine pattern recognition.
    • Symbolic Autonomous Intelligent Machines for autonomous intelligent machine logic reasoning.
    • Hybrid Autonomous Intelligent Machines for autonomous intelligent machine multi-paradigm processing.
  • ...
• Counter-Examples:
  • Non-Intelligent Autonomous Machine, which can perform self-directed operations but lacks autonomous intelligent machine learning capability and autonomous intelligent machine adaptive decision making.
  • Intelligent Non-Autonomous Machine, which possesses machine intelligence but requires continuous human direction rather than autonomous intelligent machine independent operation.
  • Unintelligent Mechanical Agent, such as a basic Roomba robot (2010) or a simple ASIMO robot (2000), which follows programmed patterns without autonomous intelligent machine learning or autonomous intelligent machine contextual adaptation.
  • Intelligent Virtual Assistant, such as basic voice assistant (2018), due to its limited autonomy and dependency on human-defined rules rather than autonomous intelligent machine independent decision making.
  • Intelligent Living Agent, such as an intelligent person, which is a biological intelligence rather than a machine-based intelligence.
  • Heuristic-based Autonomous System, as it operates based on fixed rule sets rather than learning from data and lacks autonomous intelligent machine adaptive capability.
  • Rule-Based Expert System, which applies predefined knowledge rather than developing autonomous intelligent machine evolving understanding.
  • Teleoperated Robot, which relies on remote human control rather than autonomous intelligent machine independent operation.
  • Corporation, because it is a social entity rather than a technological system or machine.
• See: Intelligent System, Moral Machine, AI Emotion, Advanced AI System, Machine Ethics, Human-AI Collaboration, Artificial General Intelligence, Robot, Autonomous Agent, Machine Consciousness, AI Safety, Explainable AI, Trustworthy AI, AI Alignment, Machine Learning System, Neural Architecture, Cognitive Computing.

---

---

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,

---