LLMs vs brain function: 5 key similarities and differences

The human brain and Large Language Models (LLMs) share surprising structural similarities, despite fundamental operational differences. Comparing these systems offers valuable insights into artificial intelligence development and helps frame ongoing discussions about machine learning, consciousness, and the future of AI system design. Understanding these parallels and distinctions can guide more effective AI development while illuminating what makes human cognition unique.

The big picture: LLMs and the human cortex share several key architectural similarities while maintaining crucial differences in how they process information and learn from their environments.

Key similarities: Both human brains and LLMs utilize general learning algorithms that can adapt to various information types and show improved performance with increased scale.

• Both systems employ highly adaptable learning mechanisms, with the human cortex processing diverse sensory inputs and neural networks capable of training on various behaviors through appropriate loss functions.
• Scaling principles appear to apply similarly across both systems, with larger networks yielding better performance in LLMs and the human cortex being significantly larger than those of other primates.
• Both employ forms of reinforcement learning and demonstrate the ability to update internal world models based on new information.

Fundamental differences: Unlike LLMs, humans engage in continuous learning throughout their lifetimes and process information through multi-modal sensory inputs rather than primarily through language.

• Human neuroplasticity allows for ongoing learning throughout life, while current LLMs have frozen weights once deployed.
• The human brain processes information from various sensory inputs simultaneously, whereas LLMs primarily specialize in language tokenization.
• Humans incorporate unique “pleasure” and “pain” tokens in their context processing that significantly shape learning and decision-making.

Behind the complexity: Humans possess “reflective learning” capabilities that allow for updating conceptual frameworks through internal thought processes, a sophisticated mechanism current LLMs lack.

• This introspective capability enables humans to reconsider fundamental assumptions and modify their understanding without external feedback.
• Current LLM architectures don’t have similar mechanisms for internal conceptual restructuring without additional training.

Why this matters: Understanding the parallels and differences between human cognition and LLM architecture provides critical insights for AI development, psychology, and addressing alignment problems as AI systems become increasingly sophisticated.