On Fri, Jun 26, 2015 at 12:10 AM, Mike Archbold <[email protected]> wrote: > > I've been trying to follow this thread on and off for days... it is > hard to see what is being argued, let alone who is right!
I've been feeling the same way, and I started the thread! When I started it, I simply hoped to point out (and get comments on) what I believe to be a real possibility - namely that whole brain emulation (WBE), one of the most promising and seemingly straightforward paths to AGI (straightforward in the sense of requiring no major insights/breakthroughs into the nature of intelligence, just lots of engineering), may not be so straightforward after all. The reason I suggested was that even if we know in principle how neurons interact, that interaction might not be efficiently computable on any known computer hardware. I made the analogy to how we know in principle how proteins fold into 3D structures but can't in practice figure out even for moderate-length protein sequences how they will actually fold-up into particular 3D structures. The reason protein folding is not efficiently computable (and is in fact, NP-hard) is that the final 3D structure critically depends on interactions between amino acids, and the number of these interactions that must be accounted for grows exponentially with the number of amino acids in the protein sequence. I postulated that interactions in the brain that must be accounted for in order to compute a future state of the brain from a given current state and new (sensory) input could grow similarly rapidly (and therefore not be efficiently computable) if the probability of a neuron firing depends not just on discreet, graph-like patterns of connectivity via synapses (which presumably could be efficiently emulated), but also on local potential fields, which propagate independently of the connectivity pattern of neurons (much like the electrostatic forces that govern interaction between amino acids in protein folding). These local potential fields are both created by, and have influence on, the firing of (groups of) neurons, potentially creating a feedback cascade effect that could be quite sensitive to small changes in the initial configuration and the timing of active neurons. This sensitivity of the final outcome (brain state) to small, non-local changes in the system's initial conditions could make WBE intractable - even if we're one day able to perfectly capture via new scanning methods the 3D structure of neurons and their connectivity pattern. The reason I think this interesting and relevant is that many high profile people (e.g. singularity economist Robin Hanson) see WBE as the most likely path to AGI because on the surface it seems like all that is required is straightforward engineering - all we need is better scanning methods (e.g. extending vitrification techniques already apparently quite good for mice to work for human-sized brains), straightforward extension of current neural modeling techniques (e.g. refinement to Hodgkin-Huxley model of neurons) and more powerful computers on which to run the emulations. If instead the WBE path to AGI proves very difficult or computationally intractable, then alternative methods, like AGI based on cognitive modeling, could be a better path to AGI. I THINK the direction that this thread has taken since my original post was towards a debate about whether certain forms of information (graphs vs. topologies) can be efficiently modeled via data structures and computing technology that exist. My take on the issue is that if the answer you're trying to find depends on interactions whose number grows exponentially with the number of elements in the system you are modeling, than its pretty likely to be intractable for systems of interesting size, like the brain, regardless of the clever data structure you try to use. But I could be wrong, and my perspective may assume that P != NP. Furthermore, its an empirical question whether or not the number of important interactions that must be modeled to accurately emulate the brain is bound by the graph-like connectivity pattern of synapses between neurons, or must include difficult-to-model field-like interactions between neurons without direct synaptic connections between them, via local potential fields or something similar (e.g. non-targeted dispersion of neuromodulators). --Dean ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
