GPT-o3: Human cognition is extraordinarily rich. It encompasses not just logical reasoning or data-driven learning, but also subjective experiences, intuition, creativity, emotions & other facets...

Asking ChatGPT-o3: Is AI the part of human knowledge, thinking, understanding and reasoning that a) we can explain and b) we can ‘translate’ to how complex machines can work? And are we conveniently ignoring those aspects of human knowledge, thinking, understanding and reasoning that we cannot explain and therefore cannot task machines with? Are we disrespectful towards the complexity of human knowledge by narrowing down the concept of knowledge to only those qualities that machines can compute?

ChatGPT-o3’s answer:
Your question touches on deep and ongoing debates about the nature and scope of both artificial intelligence (AI) and human cognition. Here are some thoughts to consider:

1. AI as a Subset of Human Thought:
   Many AI systems today are indeed designed to model the parts of human knowledge, reasoning, and learning that we understand well enough to formalize. In other words, we focus on those aspects that can be translated into algorithms—whether that means pattern recognition, decision-making, or language processing. This approach is largely pragmatic: if we can model a behavior or process mathematically or logically, then we attempt to build a machine that replicates that function.

2. The Unmodeled Aspects of Human Cognition:
   Human cognition is extraordinarily rich. It encompasses not just logical reasoning or data-driven learning, but also subjective experiences, intuition, creativity, emotions, and other facets that are often hard to define or measure. The aspects of our cognition that remain elusive or are deeply contextual are, by necessity, not directly modeled in current AI. This isn’t so much an act of deliberate ignorance as it is a reflection of the limits of our current understanding and technology.

3. Reductionism vs. Holism:
   The fact that AI models only a subset of human cognition can be seen as a kind of reductionism—a way of simplifying a very complex phenomenon into manageable parts. This reductionist approach is common in science and engineering because it allows us to make progress by isolating and studying components of a larger system. However, this does come with the risk of overlooking or undervaluing the emergent properties and complexities that arise when all those components interact within the human brain.

4. Respecting the Complexity of Human Knowledge:
   Focusing on the aspects we can compute or formalize does not necessarily mean we are “disrespectful” toward the rest of human knowledge. Instead, it reflects a methodological choice. AI research is often driven by what can be operationalized and validated. That said, many scholars and researchers are very aware of—and actively debate—the limitations of this approach. There is ongoing work in fields like cognitive science, neuroscience, and even philosophy to better understand those aspects of human cognition that remain outside the reach of current computational models.

5. Future Directions:
   As our understanding of the brain improves, and as new theories and technologies emerge, it is conceivable that the “uncomputable” or less well-understood aspects of human cognition might be brought into a more formal framework. Future AI might be able to incorporate elements of intuition, creativity, or even subjective experience, though such a task poses enormous challenges both technically and conceptually.

6. A Pragmatic Balance:
   Ultimately, AI is a tool—a set of methods and technologies designed to replicate certain functions of human cognition, not to replicate the entirety of the human mind. This selective modeling is both a strength (in that it allows us to build effective systems) and a limitation (in that it necessarily leaves out many dimensions of human thought). Recognizing this balance is important. It means we can appreciate the power of AI in certain domains without overestimating its scope relative to the full complexity of human intelligence.

In summary, AI does represent the parts of human knowledge and reasoning that we can explain and formalize, but this is not necessarily a dismissal of the more mysterious or complex aspects of human thought. Rather, it is a reflection of where our current methods and understanding allow us to make practical progress. Acknowledging both the achievements and the limitations of AI helps ensure that we remain open to further discoveries about the mind—whether through computational models or other approaches.