Evaluating self-improvement in AI is a complex and nuanced task, particularly when we try to relate it to human intelligence, which includes aspects like consciousness, intuition, creativity, and emotional understanding. Here’s a breakdown of how we can approach this concept:

1. Defining Self-Improvement in AI

Self-improvement in AI typically refers to a system’s ability to improve its own performance without explicit human intervention. This could take the form of:

• Optimization of algorithms: An AI might adjust or refine its own models based on new data.
• Autonomous learning: AI systems may evolve or retrain themselves through reinforcement learning or unsupervised learning techniques.
• Adaptation: AI could improve its efficiency or accuracy over time as it interacts with more diverse environments or tasks.

However, self-improvement in AI is quite different from how human intelligence evolves. While humans have the ability to consciously alter their thought processes based on experiences, emotions, and social context, AI systems evolve based on algorithms and data, which may or may not include similar adaptive characteristics such as intuition or ethical reasoning.

2. Challenges in Evaluating AI Self-Improvement

• Objective Performance Metrics: AI self-improvement is generally measured through quantifiable performance metrics. These could include error rates, efficiency, speed, or accuracy on a given task. In contrast, human intelligence is often evaluated subjectively, considering creativity, ethical reasoning, emotional intelligence, and the ability to learn across contexts.
• Lack of Human-Like Consciousness: Human intelligence is influenced by consciousness, subjective experiences, and an ability to intuitively understand the world. AI systems, however, do not have consciousness or the same depth of introspective understanding. While an AI might "improve" at a specific task, it does not "experience" improvement in the way humans might intuitively learn from their mistakes or consider the broader impact of their actions.
• Lack of a Unified Intelligence Metric: There is no single, universally accepted metric of intelligence that encompasses all aspects of human or artificial cognition. Traditional AI evaluation focuses on task-specific benchmarks (e.g., image recognition accuracy, speech-to-text performance), but these do not capture the broad spectrum of human cognitive abilities like reasoning, moral judgment, and adaptation to new environments.

3. Self-Improvement in AI vs Human Intelligence

AI self-improvement often involves iterative optimization, where the system’s algorithm improves by learning from past outputs. While this is effective for many computational tasks, it does not capture aspects of general intelligence in humans, such as:

• Conscious decision-making: Humans can reflect on and intentionally change their decision-making processes based on personal values, ethical considerations, or goals, whereas AI simply adjusts parameters to minimize error or maximize reward.
• Creativity and Intuition: While AI can generate solutions based on data, human intelligence can involve intuition, gut feelings, and creativity, driven by emotional, social, and experiential knowledge that an AI lacks.
• Ethical and Moral Considerations: AI systems do not possess moral reasoning in the way humans do. While AI might improve efficiency or accuracy in specific tasks, the absence of an ethical framework means AI’s self-improvement could potentially lead to harmful outcomes unless carefully regulated.

4. Evaluating AI’s Self-Improvement: Proposed Criteria

• Task-Specific Metrics: The most straightforward evaluation of AI self-improvement would continue to rely on task-specific performance metrics. For example, how much better is the AI at a given task (e.g., playing chess, diagnosing diseases, driving autonomously) after a period of self-directed learning?
• Robustness and Adaptability: Another way to evaluate AI self-improvement is through its robustness and adaptability. Can the AI generalize its learning to new, unseen tasks? A system that can improve in a variety of environments (e.g., novel applications or unpredictable situations) may be considered to have self-improved in a more generalized manner.
• Human-AI Collaboration: AI systems that can improve in ways that complement human intelligence (such as augmenting human decision-making or assisting with complex problem-solving) could be seen as a positive form of self-improvement. In contrast, AI that acts independently and focuses solely on improving its own processes without considering human collaboration could be more problematic.
• Safety and Ethical Considerations: Evaluating AI’s self-improvement should also involve ensuring that its improvements align with ethical guidelines and do not lead to unintended harmful consequences. Ensuring that an AI’s learning process remains aligned with human values is crucial to its safe development.

5. Risks and Concerns

The potential for AI to improve its own performance brings with it several risks:

• Loss of Control: There is a concern that a self-improving AI might surpass human control, leading to unforeseen consequences if the AI’s goals or behaviors deviate from human intent.
• Ethical Concerns: As AI systems improve, their decisions may become more autonomous. Ensuring that these systems operate within ethical boundaries is a significant challenge, especially when they lack human-like reasoning or moral judgments.
• Impact on Human Intelligence: There is an ongoing debate about whether AI could ever degrade or complement human intelligence. The concern is that AI might evolve in ways that make certain human cognitive tasks obsolete or undermine certain aspects of human creativity, problem-solving, or social interaction.

6. How Self-Improvement in AI Relates to Human Intelligence

• AI's self-improvement process is highly task-specific, optimized for performance metrics within certain boundaries. In contrast, human intelligence evolves through a lifetime of experiences, emotions, and social interactions. Human improvement is multidimensional, involving ethical growth, emotional maturity, and creative expression, whereas AI improvement is largely focused on narrow domains.
• While AI can enhance human intelligence by automating certain tasks or providing insights, it does not replicate the richness of human experience and does not involve subjective understanding. AI will likely remain a tool that complements human intelligence rather than a replacement for it.

Conclusion

Self-improvement in AI can be evaluated through task-specific metrics, adaptability, and alignment with ethical principles, but it will always be distinct from the multifaceted nature of human intelligence. While AI systems can excel at optimizing performance in specific domains, they do not possess the holistic, conscious, and moral dimensions that define human intelligence. Thus, AI’s self-improvement will likely continue to complement and augment human intelligence rather than replace it, with careful attention needed to ensure that AI development remains aligned with human values and safety.

AI technologies, such as adaptive learning systems, are revolutionizing education by fostering self-improvement through personalized learning. These systems track individual performance and provide tasks tailored to the learner's current skill level, helping them focus on areas that need growth and enhancing their overall development. For more insights, explore our study titled Policy Guidelines and Recommendations on AI Use in Teaching and Learning: A Meta-Synthesis Study(published in Scopus Q1). Access it here: https://doi.org/10.1016/j.ssaho.2024.101221.