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Defining Artificial General Intelligence: Establishing a Consensus on the Meaning and Characteristics of AGI


Core Concepts
Intelligence is the capability for an information system to adapt to the open environment with limited computational resources, and it can be described by a collection of principles.
Abstract
This paper aims to establish a consensus on the definition of Artificial General Intelligence (AGI). The author argues that intelligence can be defined from two perspectives: 1) the capability for an information system to adapt to the environment with limited computational resources, and 2) a collection of principles that describe the system's behavior. The author proposes two axioms as the basis for the definition: 1) for any intelligent system, it must be able to learn and adapt to its environment, and 2) intelligent systems have limited computational resources, including memory and processing speed. Based on these axioms, the author defines "intelligence" as the capability to adapt to the environment with limited resources, and "general intelligence" as the ability to adapt to an open environment with limited resources. Artificial General Intelligence (AGI) is then defined as a computer system that is adaptive to the open environment with limited computational resources and satisfies certain principles. The author acknowledges that the controversial part of the definition lies in the specific principles (denoted as PG) that describe how the AGI system should work. Researchers from different backgrounds, such as cognitive science, neuroscience, and computer science, may have different perspectives on what these principles should be. The paper also compares the proposed definition to other existing definitions of intelligence and AGI, highlighting the similarities and differences. The author argues that the key contribution of this work is to provide a basic specification for AGI that can be used by researchers both inside and outside the community.
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Deeper Inquiries

What are the potential implications of the proposed definition of AGI for the development and evaluation of AGI systems

The proposed definition of AGI, emphasizing adaptation to open environments with limited computational resources and adherence to certain principles, has significant implications for the development and evaluation of AGI systems. Development: Focus on Adaptation: Developers will need to prioritize creating systems that can adapt to a variety of scenarios and challenges without explicit programming for each situation. This shift in focus towards adaptability may lead to the exploration of more dynamic and flexible AI architectures. Principles-based Design: Designing AGI systems based on a set of principles (PG) will guide developers in creating more robust and general systems. Understanding and implementing these principles will be crucial in achieving true AGI capabilities. Evaluation: Performance Metrics: Evaluation criteria for AGI systems will need to include measures of adaptability, learning efficiency, and problem-solving in open-ended environments. Traditional metrics may need to be expanded or adapted to capture the essence of AGI. Benchmarking: Developing standardized benchmarks and tests that assess the system's ability to adapt and learn in diverse situations will be essential. These benchmarks should reflect real-world challenges to ensure the system's general intelligence. Overall, the proposed definition sets a clear direction for the development and evaluation of AGI systems, emphasizing adaptability, resource efficiency, and adherence to fundamental principles.

How might the principles (PG) that describe the behavior of AGI systems evolve as the field of AI progresses and new insights are gained

As the field of AI progresses and new insights are gained, the principles (PG) that describe the behavior of AGI systems are likely to evolve in several ways: Incorporation of New Discoveries: Advances in neuroscience, cognitive science, and computer science may lead to the discovery of new principles that enhance our understanding of intelligence. These insights could be integrated into the existing set of principles guiding AGI development. Refinement and Expansion: The principles may undergo refinement as researchers gain a deeper understanding of intelligence. New dimensions of intelligence, such as emotional intelligence or ethical decision-making, could be included in the evolving set of principles. Interdisciplinary Collaboration: Collaboration between different fields, such as psychology, neuroscience, and AI, may lead to the identification of additional principles that contribute to the development of AGI systems. Cross-disciplinary insights could enrich the principles guiding AGI research. Adaptation to Technological Advances: Technological advancements, such as quantum computing or biological computing, may influence the principles governing AGI systems. New computing paradigms could necessitate the adaptation of existing principles or the formulation of new ones. Overall, the evolution of the principles (PG) will be a dynamic process, shaped by ongoing research, interdisciplinary collaboration, and technological advancements in the field of AI.

What are the potential challenges and limitations in achieving AGI systems that can truly adapt to open-ended environments with limited resources

Achieving AGI systems that can truly adapt to open-ended environments with limited resources poses several challenges and limitations: Complexity of Environments: Open-ended environments present a wide range of unpredictable scenarios, making it challenging for AGI systems to adapt effectively. The complexity and variability of real-world environments can surpass the system's adaptive capabilities. Resource Constraints: Limited computational resources may restrict the system's ability to learn and adapt efficiently. Balancing the trade-off between computational power and adaptability is crucial in developing AGI systems that can operate effectively in resource-constrained settings. Generalization and Transfer Learning: Ensuring that AGI systems can generalize their learning across diverse tasks and environments is a significant challenge. The ability to transfer knowledge and skills from one domain to another while maintaining adaptability is essential for achieving true AGI capabilities. Ethical and Societal Implications: The development of AGI systems raises ethical concerns related to their impact on society, job displacement, privacy, and autonomy. Addressing these ethical considerations while designing adaptive AGI systems is crucial for responsible AI development. Overcoming these challenges will require interdisciplinary collaboration, innovative research approaches, and a deep understanding of the principles that govern intelligence and adaptation in AGI systems.
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