[ExI] sciam blog article

[Robin D Hanson]

On Mar 26, 2016, at 1:34 AM, John Clark <johnkclark at gmail.com<mailto:johnkclark at gmail.com>> wrote:
On Thu, Mar 24, 2016 at 1:03 AM, Robin D Hanson <rhanson at gmu.edu<mailto:rhanson at gmu.edu>> wrote:

​> ​
You can code an awful lot of complexity into even 100MB of code, and if that is non-modular spaghetti object code instead of modular documented source code, it could take an awful long time to figure out.

​Then it might be better to look for the master learning algorithm directly rather than trying to reverse engineer the biological brain; the recent successes in deep machine learning like
AlphaGo
​ makes me think we might not be too far from finding it.​


Even if there were a single “master” learning algorithm, instead of many more context dependent learning algorithms, there can still be many other relevant design choices, including choices of representations.

On Mar 26, 2016, at 2:06 AM, Rafal Smigrodzki <rafal.smigrodzki at gmail.com<mailto:rafal.smigrodzki at gmail.com>> wrote:
### Some parts of the brain, such as the midbrain and structures inferior to it, are non-modular, spaghetti-like and hardwired in details - genetically determined and running on completely different principles from the cortex. The cortex and parts of the basal ganglia are however highly modular and most likely running a relatively uniform underlying algorithm that determines both short-term function and the longer-term processes, such as rewiring of the cortex.

Yes, some parts may be simple, and even occupy a large fraction of the brain. Even so other parts may no be, and even if they occupy a small fraction of the brain, it may take a long time to figure out how to create systems that substitute effectively for them. I discuss this more at: https://www.overcomingbias.com/2016/03/how-good-99-brains.html

Robin Hanson rhanson at gmu.edu<mailto:rhanson at gmu.edu>
Future of Humanity Inst., Oxford University
Assoc. Prof. Economics, George Mason University
See my new book: http://ageofem.com









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