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
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- 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.
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- It can typically demonstrate Capabilities in increasing sophistication:
  - Basic Capabilities, such as:
  - Advanced Capabilities, such as:
  - Specialized Capabilities, such as:
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- 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.
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- It can typically be composed of Autonomous Intelligent Machine Core System Components:
  - Autonomous Intelligent Machine Cognitive Architecture Components, such as:
  - Autonomous Intelligent Machine Physical Architecture Components, such as:
  - Autonomous Intelligent Machine Safety Architecture Components, such as:
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- It can typically operate within Autonomous Intelligent Machine Operational Contexts:
  - Autonomous Intelligent Machine Safety Context, including:
  - Autonomous Intelligent Machine Performance Context, including:
  - Autonomous Intelligent Machine Interaction Context, including:
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- 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.
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- 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.
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Examples:
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,