Current LLMs use overly simplistic reinforcement learning techniques, lacking concepts like value functions that are fundamental to biological learning.
The AI field may be neglecting the importance of complex, specific loss functions, which could be a key to the brain's sample efficiency.
It may be possible to create highly capable AI with a minimal set of drives (e.g., curiosity) that lacks human-like social instincts, posing an alignment risk.
The current AI paradigm, while successful, is architecturally very different from the brain, suggesting a potential performance plateau or a missing fundamental component.
Opportunities Identified
Incorporating principles of 'omnidirectional inference' could lead to AI models with superior generalization capabilities.
Studying the brain's 'steering subsystem' could provide a blueprint for building robustly aligned AI with complex, beneficial reward functions.
Multi-agent, co-evolutionary training may be more compute-efficient for developing intelligent agents than training a single monolithic model.
Neuroscience research, particularly large-scale connectomics, could provide crucial architectural and algorithmic constraints to guide the development of next-generation AI.