What AI Can Teach Us About the Brain: 5 Takeaways

Kari Jaaskelainen

21 Nov 2025 — 1 min read

Generative AI’s recent leaps aren’t due to obscure tricks—they come from clear, testable principles. This paper argues that neuroscience should probe which of these principles the brain may share.

World models: Brains may not build full simulators; they might use task-tuned shortcuts with limited predictive scope.
Thought generation: Like LLMs generating sequences, the brain may compose thoughts step by step, enabling planning and creativity.
Attention: Selection and routing mechanisms in AI hint at how neurons prioritize, bind, and control information flow.
Scaling laws: Performance grows with model size, data, and compute—do similar regularities govern brain learning and development?
Quantization: Low-precision, noisy units can still compute well, echoing energy-efficient neural codes.

Takeaway: ML doesn’t just inspire tools—it offers hypotheses neuroscience can test today. Paper by Claudius Gros (arXiv): https://arxiv.org/abs/2511.16432v1

Paper: https://arxiv.org/abs/2511.16432v1

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#AI #Neuroscience #GenerativeAI #Brain #MachineLearning #CognitiveScience

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