From: Ben Houston (email@example.com)
Date: Mon Apr 08 2002 - 02:30:38 MDT
Random initial comments.... Quotes from the paper are contained between
>>> <<< demarcations.
Parallelism on the hardware level is currently supported by symmetric
multiprocessing chip architectures [Hwang98], NOW
(network-of-workstations) clustering [Anderson95] and Beowulf clustering
[Becker95], and message-passing APIs such as PVM [Geist93] and MPI
[Gropp94]. However, software-level parallelism is not handled well by
present-day languages and is therefore likely to present one of the
I've seen some truly amazing things done in the computational
pharmacology field dealing with cheap, but massive parallelization.
Basically, a lot of short cuts are available in the parallelization of
an algorithm once you've solidified it. In order words making a
parallel problem solving is difficult and cost in the general case but
in a specific case it can be quite cheap. The field of computational
pharmacology is working with special purpose multi-teraflop machines
that cost less than $1,000,000 US for a year or so now.
Even if software parallelism were well-supported, AI developers will
still need to spend time explicitly thinking on how to parallelize
cognitive processes - human cognition may be massively parallel on the
lower levels, but the overall flow of cognition is still serial.
Cognition, in my opinion, is quite parallel at all levels. There are,
in my understanding, only a few bottlenecks in the brain that forces
things to become serial. An obvious example would be the serial nature
of linguistic output.
We know it is possible to evolve a general intelligence that runs on a
hundred trillion synapses with characteristic limiting speeds of
approximately 200 spikes per second.
200 spikes/sec is probably the median for the brain. Some neurons I've
studied in my courses have upper limits around 1000 spikes/sec.
[Sandberg99] describes a quantity S that translates to the wait time, in
clock cycles, between different parts of a cognitive system - the
minimum time it could take for a signal to travel between the most
distant parts of the system, measured in the system's clock ticks. For
the human brain, S is on the rough order of 1 - in theory, at least.
Let's take for an example a sensory neuron in one's finger and a
alpha-motor neuron in one's thigh. The sensory neuron, as we all know,
will synapse with another neuron in the spine. That spinal neuron will
then project to the lower brain steam where it will synapse with some
neuron. Thus it takes 2 neurons to get to the brain. The alpha-motor
neuron, which will most likely begin in the spine, will receive input
from other spinal neuron(s) -- of which one is probably a projection
from the motor cortex or a lower motor region. Thus there is at least a
2-neuron chain from the brain to a muscle. This means that sensory to
motor neuron round trip is at least 4 neurons in length.
Neglect the sensory and motor systems I believe that in the CNS 'S'
would be upwards of at least 5 as a result of the DAG-like arrangements
of the signal processing pathways -- ignoring backwards, regulatory
Memory association may or may not use a "compare" operation (brute force
or otherwise) of current imagery against all stored memories, but it
seems likely that the brain uses a massively parallel algorithm at one
point or another of its operation; memory association is simply a
It seems plausible that the brain uses a resonance-like compare
function. Basically, a match may be recognized when a neural assembly
finds its group-firing greatly facilitated as a result of the
presented/remembered stimulus. Sort of like how a glass will vibrate
when exposed to its natural resonance frequency.
The human brain's most fundamental limit is its speed. Anything that
happens in less than a second perforce must use less than 200 sequential
operations, however massively parallelized.
Although the simple firing of neurons represents a lot of the
information that the brain is processing probably just as much
information is represented in the dynamic molecular mechanisms of each
cell. Cells constantly change their gene expression on the order of
minutes. On the order of seconds in any one neuron there are probably
dozens on interacting molecular signally cascades that are changing the
neuron's electrophysiological behavior.
As mentioned earlier, primary visual cortex sends massive
corticothalamic feedback projections to the lateral geniculate nucleus
[Sherman86]. Corticocortical connections are also typically accompanied
by feedback projections of equal strength [Felleman91]. There is
currently no standard explanation for these feedback connections.
Actually, there is quite a collection of papers in PubMed discussing the
evidence that the corticothalamic feedback projections play a role in
image contrast control.
DGI16 requires sensory modalities with feature controllers that are the
inverse complements of the feature detectors; this fits with the
existence of the feedback projections. However, it should be noted that
this assertion is not part of contemporary neuroscience. The existence
of feature controllers is allowed for, but not asserted, by current
theory; their existence is asserted, and required, by DGI. (The
hypothesis that feedback projections play a role in mental imagery is
not limited to DGI; for example, [Kosslyn94] cites the existence of
corticocortical feedback projections as providing an underlying
mechanism for higher-level cognitive functions to control depictive
It is an accepted fact that working memory, both verbal and spatial, is
maintained by mutual stimulation between the lateral prefrontal cortex
and certain posterior association areas:
Postle BR, Stern CE, Rosen BR, Corkin S. 2000. "An fMRI investigation
of cortical contributions to spatial and nonspatial visual working
memory." Neuroimage. May;11(5 Pt 1):409-23.
Diwadkar VA, Carpenter PA, Just MA. 2000. "Collaborative activity
between parietal and dorso-lateral prefrontal cortex in dynamic spatial
working memory revealed by fMRI."
SARNTHEIN J, et al. 1998. "Synchronization between prefrontal and
posterior association cortex during human working memory." PNAS, Vol.
95, pp. 7092–7096.
Concepts act as verbs, adjectives, and adverbs as well as nouns.
I'm not sure if you mentioned it but did you know that 'verbs' seems to
be stored in a different brain region that 'nouns'? And that 'noun'
storage in the brain seems to be organized in a categorized spatial
manner? Neat stuff eh?
The thought level lies above the learned complexity of the concept
level. Thoughts are structures of combinatorial concepts that alter
imagery within the workspace of sensory modalities. Thoughts are the
disposable one-time structures implementing a non-recurrent mind in a
non-recurrent world. Modalities are wired; concepts are learned;
thoughts are invented.
In your section on "thought" why don't you mention the cognitive
psychology construct of "working memory"? You seem to describe its two
part structure perfectly: (1) phonological loop and (2) visuospatial
And at this point, I must stop since I have some things that I must
4th Year Cognitive Science/Neuroscience
Carleton University, Ottawa, Canada
( firstname.lastname@example.org / 613-266-0637 )
> -----Original Message-----
> From: email@example.com [mailto:firstname.lastname@example.org] On
> Of Eliezer S. Yudkowsky
> Sent: Sunday, April 07, 2002 5:14 PM
> To: email@example.com
> Cc: SL4
> Subject: PAPER: Levels of Organization in General Intelligence
> A draft of the paper "Levels of Organization in General Intelligence",
> appear in Ben Goertzel and Cassio Pennachin (eds.), "Real AI: New
> to Artificial General Intelligence", is now available online.
> It has not been linked in to SIAI's main site or announced anywhere.
> should probably do that after I answer my accumulated email, do my
> tax returns, and get some sleep. I probably shouldn't say anything
> until I have some idea of the audience's reactions.
> http://intelligence.org/DGI/ (multi-file)
> http://intelligence.org/DGI.html (single file, 382K)
> LEVELS OF ORGANIZATION IN GENERAL INTELLIGENCE
> Part I discusses the conceptual foundations of general intelligence as
> discipline, orienting it within the Integrated Causal Model of Tooby
> Cosmides. Part II constitutes the bulk of the paper and discusses the
> functional decomposition of general intelligence into a complex
> of interdependent internally specialized processes, and structures the
> description using five successive levels of functional organization:
> sensory modalities, concepts, thoughts, and deliberation. Part III
> discusses probable differences between humans and AIs and points out
> fundamental advantages that minds-in-general potentially possess
> current evolved intelligences, especially with respect to recursive
> -- -- -- -- --
> Eliezer S. Yudkowsky http://intelligence.org/
> Research Fellow, Singularity Institute for Artificial Intelligence
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