The brain has enormous redundancy everywhere. It uses 6 x 10^14 synapses to store 10^9 bits of long term memory and 10^9 bits of inherited knowledge. But parallel systems are like that. Google's server farm of a million CPUs each carry an identical copy of Linux. Each of your 10^13 cell nuclei has an identical copy of your DNA. It's a trade-off between speed and memory.
On Sun, Oct 13, 2019, 12:36 PM James Bowery <[email protected]> wrote: > There is an enormous amount of redundancy in the abstract thalamocortical > architecture evincing small Kolmogorov Complexity in description. While I > understand "the devil is in the details" of this evolved structure (not the > least of which is the fact that it elides that the cerebellum's neuron > count is a super-majority of the brain's), there seems to be a vast > theoretic vacuum of the requisite simplicity. It's the dog that didn't > bark. > > That's why I take Hecht-Nielsen's Confabulation Theory seriously: Not > because I believe, as he did, that he "solved the problem of cognition", > but rather that he has a first order approximation of the neocortex (indeed > thalamcortical) structure -- at least one barking dog -- an _approach_. > It's rather like a framework for compression like mixture of models, rather > than the models themselves. > > On Sun, Oct 13, 2019 at 12:07 PM Matt Mahoney <[email protected]> > wrote: > >> >> >> On Sun, Oct 13, 2019, 10:09 AM <[email protected]> wrote: >> >>> Isn't that massively inefficient? It'd take 100 times more >>> storage/computation to do the same thing as a weighted net no? >>> >> >> The neural models I use in the top ranked text compressors use a lot less >> than 12-24 petaflops and a petabyte of RAM. But the language modeling is >> rather rudamentary, nowhere near AGI. But I would be happy for you to prove >> my estimate wrong. >> >> And one more thing. That's one human brain. To automate all labor, you >> need several billion times that. Current technology uses about 1 megawatt >> per petaflop. Maybe neuromorphic computing could get it down to 100 kW per >> brain. Maybe economy of specialization could reduce it to 1 kW, which is >> 50% of global energy production. But shrinking transistors alone won't do >> it. If we can't do the optimization, it's going to take nanotechnology, >> moving atoms instead of electrons. The brain uses 20 watts. It can be done. >> >>> *Artificial General Intelligence List <https://agi.topicbox.com/latest>* > / AGI / see discussions <https://agi.topicbox.com/groups/agi> + > participants <https://agi.topicbox.com/groups/agi/members> + delivery > options <https://agi.topicbox.com/groups/agi/subscription> Permalink > <https://agi.topicbox.com/groups/agi/Td4a5dff7d017676c-Mb2bcd09cee266e4dd20df9f3> > ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/Td4a5dff7d017676c-Mb6dec56c4ea8c19e7751d482 Delivery options: https://agi.topicbox.com/groups/agi/subscription
