Disclaimer: This post is a summary of a technical roadmap (HTML version, arXiv pre-print, twitter thread) evaluating how neuroscience could contribute to AI safety. The roadmap was recently released by the Amaranth Foundation, a philanthropic organization dedicated to funding ambitious research in longevity and neuroscience. The roadmap was developed jointly by Patrick Mineault*, Niccolò Zanichelli*, Joanne Peng*, Anton Arkhipov, Eli Bingham, Julian Jara-Ettinger, Emily Mackevicius, Adam Marblestone, Marcelo Mattar, Andrew Payne, Sophia Sanborn, Karen Schroeder, Zenna Tavares and Andreas Tolias.

TLDR: We wrote a comprehensive technical roadmap exploring potential ways neuroscience might contribute to AI safety. We adopted DeepMind's 2018 framework for AI safety, and evaluated seven different proposals, identifying key bottlenecks that need to be addressed. These proposals range from taking high-level inspiration from the brain to distill loss functions and cognitive architectures, to detailed biophysical modeling of the brain at the sub-neuron level. Importantly, these approaches are not independent – progress in one area accelerates progress in others. Given that transformative AI systems may not be far away, we argue that these directions should be pursued in parallel through a coordinated effort. This means investing in neurotechnology development, scaling up neural recording capabilities, and building neural models at scale across abstraction levels.

Introduction

The human brain might seem like a counterintuitive model for developing safe AI systems: we engage in war, exhibit systematic biases, often fail to cooperate across social boundaries, and display preferences for those closer to us. Does drawing inspiration from the human brain risk embedding these flaws into AI systems? Naively replicating the human neural architecture would indeed reproduce both our strengths and our weaknesses. Furthermore, pure replication approaches can display unintended behavior if they incorrectly capture physical details or get exposed to different inputs—as twin studies remind us, even genetically identical individuals can have very different life trajectories. Even the idea of selecting an exemplary human mind–say, Gandhi–as a template raises questions, and as history shows us, exceptional intelligence does not guarantee ethical behavior.

We propose instead a selective app...

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