[ExI] sciam blog article

Tomaz Kristan protokol2020 at gmail.com
Sun Mar 27 13:39:58 UTC 2016


> Yes, some parts may be simple, and even occupy a large fraction of the
brain. Even so other parts may no be

Or may be simple enough as well. No ireducible complexity, at least.

On Sun, Mar 27, 2016 at 5:32 AM, Robin D Hanson <rhanson at gmu.edu> wrote:

> On Mar 26, 2016, at 1:34 AM, John Clark <johnkclark at gmail.com> wrote:
>
> On Thu, Mar 24, 2016 at 1:03 AM, Robin D Hanson <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>
> 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
> Future of Humanity Inst., Oxford University
> Assoc. Prof. Economics, George Mason University
> See my new book: http://ageofem.com
>
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