@evan@cosocial.ca just as an aside, I think it's important to distinguish cognition and intelligence, since obviously even trivial neural nets have some level of cognition, as do life forms that don't even have a nervous system, or only exist as single cells.

As to whether hardware needs to be brainlike, I lean towards the existence proof, but also with the proviso that neural net software is too abstract and deliberately elides potentially important physical properties of a brain. Some examples:
* neurons have chemical signalling that propagates at the speed of sound, in a volume, beyond the synaptic transmission.
* neurons have internal state due to their epigenome.
* neurons have complex, mesh-like connections between anatomical regions.
* neural information is inherently sensitive to time in a variety of ways, including those mesh-like connections.
* non-neural tissue in the brain also affects neuronal behaviour.
* there are unknown nonclassical properties of neurons.

Maybe you could throw enough matrix munching transistors at a neural net and get a life-like mind that is superior to life in all domains, but I doubt it, not with current software models of neural nets. They are only able to reproduce particular kinds of cognitions, and certainly nothing like a mind.

@evan@cosocial.ca Unknown, but current hardware and software can only barely clear a minimal bar of cognition; forget any kind of intelligence. Conversational LLMs piggy back on human mentalisation cognitions to create the illusion of a mind, but it's even less real than the "people" that used to live in my head when I was a teenager.

We don't even have neuromorphic electronics that can remotely function like a nervous system. We can perform basic cognitions on neuromorphic hardware, but there is no reason to believe that anything like intelligence will magically emerge if you throw enough transistors at the problem.

A further problem is the gordian knot of trying to understand the brain, from its underlying physics to needing to understand the dynamical aspects of its anatomy; currently we're mostly stuck in correlating environmental or internal, conscious stimuli to metabolic activity in particular regions. Lesion studies are more problematic in terms of the accuracy of anatomical knowledge they provide.

Side thought, I think LLMs show one of the biggest weaknesses of attempting to create true AI. As soon as we have a system that can mimic the form of human speech, we are biased to inferring a mind into it through mentalisation cognition. It is going to be an important problem to be able to prove a priori that a system that produces language is doing so as a result of spontaneous self-reflection and motivated by social cognition.