Comparison of AI and Human Thinking

This document explores the differences and similarities between artificial intelligence models, specifically large language models (LLMs), and human thinking across six key areas: learning, processing, memory, reasoning, error, and embodiment.

1. Learning

• Human Learning:

  • Occurs through neuroplasticity, allowing the brain to reorganize its neural networks in response to experience.
  • Can learn from just a few examples or even a single exposure, forming lasting memories.

• AI Learning:

  • Involves backpropagation, adjusting internal weights to minimize prediction error across millions of examples.
  • Requires substantial data and multiple passes to refine predictions.
  • AI model parameters are static post-training, unlike the dynamic learning of humans.

2. Information Processing

• Human Processing:

  • Massively parallel and distributed, with specialized brain regions for different functions.
  • Processes concepts and chunks of meaning, rather than discrete symbols.

• AI Processing:

  • Operates using tokens, converting input into vectors and calculating attention scores.
  • Represents more of a next-step pattern completion compared to human content addressable memory.

3. Memory

• Human Memory:

  • Comprises sensory, working, and long-term memory systems.
  • Associative, linking memories by meaning, context, and emotion.

• AI Memory:

  • Simpler memory architecture, with knowledge encoded in model weights.
  • Context window acts as working memory, limited to input context and forgotten once full.

4. Reasoning

• Human Reasoning:

  • Utilizes both fast intuitive judgments (system one) and slow logical reasoning (system two).

• AI Reasoning:

  • Predominantly trained on structured, explicit system two thinking.
  • Generates plausible token sequences appearing as reasoning, without genuine understanding.

5. Error

• Human Error:

  • Involves confabulation, unknowingly creating false memories believed to be true.

• AI Error:

  • Prone to hallucinations, producing confident but incorrect statements.
  • Occurs due to lack of genuine understanding and context.

6. Embodiment

• Human Embodiment:

  • Interactions with the physical environment deeply influence thoughts and behaviors.
  • Knowledge is tied to sensory experience, like the tactile sensation of wetness.

• AI Embodiment:

  • Disembodied, existing solely as software, lacking direct interaction with the physical world.
  • Knowledge is secondhand, derived from human-written text.

Conclusion

• AI models and human minds can produce similar outputs (e.g., essays and answers) but differ fundamentally in cognition.
• Humans bring genuine comprehension, while AI offers speed and broad knowledge.
• Combining these thinking methods optimally can harness the strengths of both systems.