Patrick Mineault

@patrickmineault.bsky.social

4580 Followers 580 Following

NeuroAI, vision, open science. NeuroAI researcher at Amaranth Foundation. Previously engineer @ Google, Meta, Mila. Updates from http://neuroai.science

A call to action All of that is a long-winded way of saying that our ambitions in neuroscience–solving all neurological disease, understanding intelligence and consciousness, etc.–are mismatched with our tools and datasets. Foundation models are one area of opportunity: leverage existing and future datasets to find good representations of neural data, make predictions, and optimize in a closed loop, leveraging the differentiability of deep learning models. That cannot occur in a vacuum, however: the datasets that we collect, and the ecosystem of tools to read and write neural activity, cell identity, synapses, and connections should be matched in a virtuous circle. That will, almost assuredly, involve doing large-scale, hypothesis-free neuroscience focused on tooling and data, including in innovative structures like focused research organizations and coordinated research programs. Hypothesis-free science sometimes gets a bad reputation, but I think it can be most easily justified by epistemic humility: with billions of neurons, trillions of connections, thousands of cell types, hundreds of areas and receptors, maybe the first thing we should do is catalog these dang things, and then figure out how to poke at them to obtain causal models. I can foresee, 10 years from now, many more neural atlases and databases, distilled into multiple application-specific foundation models and biophysically detailed models, cross-linked and validated through new neurotechnology. At the Amaranth Foundation, we philanthropically fund ambitious neuroscience projects led by ambitious mission-driven individuals; and my role is to lead our efforts in NeuroAI. Of course, I’m not a neutral party in all of this, but I think it’s a compelling vision for neuroscience.
The old one
When a fly's compound eye spots a mate in the sky, that's /amore/
The 7 proposed ways in which neuroscience might help AI safety: Reverse-engineer representations of sensory systems, embodied digital twins, biophysically detailed models, develop better cognitive architectures, use brain data to finetune AI systems, infer the loss functions of the brain, leverage neuroscience-inspired methods for mech interp
Three levels of safety where neuroscience might help: robustness, specification and assurance
Different paths toward safe AI at different Marr's levels
102 pages