I really enjoyed this 2022 paper by Rose Cao (“Multiple realizability and the spirit of functionalism”). A common intuition is that the brain is basically a big network of neurons with input on one side and all-or-nothing output on the other, and the rest of it (glia, metabolism, blood) is mainly keeping that network running. The paper’s helpful for articulating how that model’s impoverished, and argues that the right level for explaining brain activity (and resulting psychological states) might rely on the messy, complex, biological details, such that non-biological substrates for consciousness are implausible. (Some of those details: spatial and temporal determinants of activity, chemical transducers and signals beyond excitation/inhibition, self-modification, plasticity, glia, functional meshing with the physical body, multiplexed functions, generative entrenchment.) The argument doesn’t necessarily oppose functionalism, but I think it’s a healthy challenge to my previous confidence in multiple realisability within plausible limits of size, speed, and substrate. It’s also useful to point to just how different artificial neural networks are from biological brains. This strengthens my feeling of the alien-ness of AI models, and updates me towards greater scepticism of digital sentience. I think the paper’s a wonderful example of marrying deeply engaged philosophy with empirical reality.
I really enjoyed this 2022 paper by Rose Cao (“Multiple realizability and the spirit of functionalism”). A common intuition is that the brain is basically a big network of neurons with input on one side and all-or-nothing output on the other, and the rest of it (glia, metabolism, blood) is mainly keeping that network running.
The paper’s helpful for articulating how that model’s impoverished, and argues that the right level for explaining brain activity (and resulting psychological states) might rely on the messy, complex, biological details, such that non-biological substrates for consciousness are implausible. (Some of those details: spatial and temporal determinants of activity, chemical transducers and signals beyond excitation/inhibition, self-modification, plasticity, glia, functional meshing with the physical body, multiplexed functions, generative entrenchment.)
The argument doesn’t necessarily oppose functionalism, but I think it’s a healthy challenge to my previous confidence in multiple realisability within plausible limits of size, speed, and substrate. It’s also useful to point to just how different artificial neural networks are from biological brains. This strengthens my feeling of the alien-ness of AI models, and updates me towards greater scepticism of digital sentience.
I think the paper’s a wonderful example of marrying deeply engaged philosophy with empirical reality.