How do we measure and define intelligence in artificial systems?

Introduction

The measurement and definition of intelligence in artificial systems is a complex and multifaceted challenge. Over the years, various approaches have been proposed to quantify and evaluate AI intelligence, often drawing parallels with human intelligence. This synthesis aims to present the key insights from multiple research papers on how intelligence in artificial systems can be measured and defined.

Key Insights

Task-Based Benchmarking:
- Intelligence in AI is often benchmarked by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. However, this approach is limited as it heavily depends on prior knowledge and experience, which can mask the system's generalization power.
Algorithmic Information Theory:
- A new formal definition of intelligence based on Algorithmic Information Theory describes intelligence as skill-acquisition efficiency. This definition emphasizes the concepts of scope, generalization difficulty, priors, and experience, proposing a benchmark called the Abstraction and Reasoning Corpus (ARC) for fair comparisons between AI systems and humans.
Universal Intelligence:
- A general measure of intelligence for arbitrary machines has been formalized by extracting essential features from informal definitions of human intelligence. This formal definition is related to the theory of universal optimal learning agents, aiming to capture the concept of machine intelligence in the broadest sense.
Regularizing Bayesian Approach:
- Measurement tasks in AI, especially under conditions of information uncertainty, should be solved using regularization methods. The Regularizing Bayesian Approach (RBP) and technologies based on it, such as Bayesian intelligent measurements (BII) and Bayesian intelligent technologies (BIT), are proposed to ensure the stability and reliability of AI solutions.
Human-Like Intelligence:
- Traditional AI performance evaluations often miss important features of human intelligence, such as the ability to transfer knowledge and make complex decisions based on emotional and rational reasoning. A definition of compositional intelligence is suggested to incorporate these features, using developments in quantum cognition and natural language processing.

Conclusion

The measurement and definition of intelligence in artificial systems encompass various approaches, from task-based benchmarking to formal definitions rooted in algorithmic and Bayesian theories. While traditional methods focus on specific task performance, newer approaches emphasize generalization, stability, and human-like reasoning capabilities. A comprehensive understanding of AI intelligence requires integrating these diverse perspectives to develop robust and fair evaluation benchmarks.

How do we measure and define intelligence in artificial systems?

Summary

Introduction

Key Insights

Conclusion

Sources and full results

On the Measure of Intelligence

The Abstraction and Reasoning Corpus (ARC) is a new, human-like benchmark for measuring general intelligence, enabling fair comparisons between AI systems and humans.

Universal Intelligence: A Definition of Machine Intelligence

This paper proposes a general measure of intelligence for arbitrary machines, capturing the concept of machine intelligence in the broadest reasonable sense, and relates it to universal optimal learning agents.

MEASURING ARTIFICIAL INTELLIGENCE: METHODOLOGICAL FOUNDATIONS, PRINCIPLES OF CREATION AND INFORMATION TECHNOLOGY BASED ON THE REGULARIZING BAYESIAN APPROACH

The regularization approach (RBP) and Bayesian intelligent measurements (BII) can effectively measure artificial intelligence solutions, ensuring accuracy, reliability, and traceability in intelligent measurement systems.

A Review on Artificial Intelligence Methods

Artificial intelligence methods include expert systems, computer algebra, fuzzy set theory, artificial neural networks, evolutionary computation, and simulated annealing.

Moral Dilemmas for Artificial Intelligence: A Position Paper on an Application of Compositional Quantum Cognition

Compositional intelligence, incorporating emotional and rational reasoning, is necessary for evaluating AI performance and incorporating human features like knowledge transfer and decision-making.

Can We Define Levels of Artificial Intelligence?

A graded approach to AI research can measure incremental progress and identify resources needed to match the conceptual abilities of animals like pigeons.

Measuring Intelligence in Natural and Artificial Systems

This paper presents a new universal algorithm for measuring intelligence in humans, non-human animals, and artificial systems, based on a system's ability to make accurate predictions.

Entropy of Artificial Intelligence

Artificial intelligence systems can efficiently understand subsets of input sets, decreasing entropy and creating order in the learning process.

Software Measurement by Using Artificial Intelligence

Artificial intelligence can improve software measurement in healthcare, enhancing efficiency and effectiveness in mental health support.

Artificial Intelligence Review

AI has applications in various fields, including information systems, commerce, image processing, human-computer interaction, data compression, robotics, and route planning, and its future research directions and ethics are discussed.

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