
Iterating Towards Bethlehem - bane
http://www.rifters.com/real/2009/01/iterating-towards-bethlehem.html
======
calydon
Peter Watts became the living master of hard scifi, (imo), with "Blindsight".
In that book he first described (in terms that someone like me could
understand) a cognition that rivals and surpasses human intelligence, by an
alien creature that is 'unconscious'. It's so counter-intuitive to human
thought (and arrogance) that something can be autonomous (unaware of its own
actions) yet still work with purpose and according to a strategy that benefits
its interests. Because the systems that are needed to maintain awareness are
not present, this is in fact a faster and less error-prone method of thinking.
He also does a neat trick where the aliens can communicate with us, despite
having no understanding of what they are saying. I would have to re-read that
part of the book to offer an explanation, but it involves grammar and
information theory.

I would like to know, is this study of crypto-intelligence a recognized field
and is Peter Watts at the forefront of this study, or is he getting his ideas
from a disconnected body of experts?

~~~
barry-cotter
Ctrl + F "Sentience/Intelligence" on the below link and you will get to the
absolutely most relevant bits to your question. Crypto-intelligence isn't big
enough to be a field but cognitive science is, and it covers all the
interesting bits of neuroscience, psychology and philosophy.

<http://www.rifters.com/real/Blindsight.htm#Notes>

------
JoshTriplett
The article seems to use "turing machine" or "emulator" in a more figurative
sense, not to mean that the spider's neural network literally simulates simple
rules that emulate a brain. The analogy to timeslicing makes more sense: the
state of a computation can take less neural-network space than the ongoing
computation, so switch between them and remember the variable values.

Also, the broader speculation in this article reminds me of
<https://www.xkcd.com/505/>

------
huhtenberg
A thought.

Perhaps her neurons are different, i.e. she could rewire some part of her
brain as needed - forgetting past experience to free up the capacity for the
new one - and this is why it takes hours of setup (observation) and this is
why she can do it over and over again within the confines of 600K neurons.

~~~
bl
Synapse "rewiring" is not typically how we think memories are formed in adult
animals. Mostly it is done by modulating the strengths of the existing
connections (this process involves signaling cascades and protein expression,
so it does take some time). So if you want to form a "memory", a particular
connection is strengthened. There isn't a concomitant loss of another
connection. It's not a zero sum game.

All my statements are based on my understanding of mammalian learning and
memory. But I think you hit on the key with "Perhaps her neurons are
different".

Indeed, invertebrate neurons are wildly different from those of mammals. In
fact, if you are accustomed to looking at mammalian neurons [1], invertebrate
neurons can look positively _alien_. For example, check out the Lobula Giant
Movement Detector (LGMD) neuron of the locust [2] and other insects [3].

A) The scale is different: The thickness of some of its branches are about the
size of the cell body on a mammalian neuron.

B) The organization is different: The dendritic arbor is divided into nearly
independent subfields with very independent functions.

C) The behavior is different: The spike output patterns of an LGMD would be
distinguishable to a first-year neuroscience student. And the output
connections are extremely strong, pretty much one-to-one.

Add it all together, and this one neuron does the job of at least a few dozen
mammalian neurons. How many, exactly, is difficult to tell. Not every insect
neuron is as fantastical as the LGMD, but I would say that "600000" value
ought to be scaled by some number greater than five. Given that, one could say
our spider friend has the equivalent of several million mammalian neurons.

Raw neuron count is merely the crudest of measures of neural processing
capability. How sophisticated the processing nodes are (i.e., the neurons) and
how they are wired together (i.e., the network topology) are way more
critical.

[1] Note that <http://en.wikipedia.org/wiki/Neuron> depicts exclusively
mammalian neurons.

[2] Locust version: <http://jn.physiology.org/content/97/1/159/F1.large.jpg> ;
figure from this article: <http://jn.physiology.org/content/97/1/159.full>

[3] Fly version, top row; rat (i.e., representative mammalian neurons) for
comparison, bottom row:
[http://c431376.r76.cf2.rackcdn.com/995/fnsys-03-017/image_m/...](http://c431376.r76.cf2.rackcdn.com/995/fnsys-03-017/image_m/fnsys-03-017-g001.jpg)
; figure from this article:
[http://www.frontiersin.org/systems_neuroscience/10.3389/neur...](http://www.frontiersin.org/systems_neuroscience/10.3389/neuro.06.017.2009/full)

