Ben, et al,

*I think I may finally grok the fundamental misdirection that current AGI
thinking has taken!

*This is a bit subtle, and hence subject to misunderstanding. Therefore I
will first attempt to explain what I see, WITHOUT so much trying to convince
you (or anyone) that it is necessarily correct. Once I convey my vision,
then let the chips fall where they may.

On Sun, Jun 27, 2010 at 6:35 AM, Ben Goertzel <> wrote:

> Hutter's AIXI for instance works [very roughly speaking] by choosing the
> most compact program that, based on historical data, would have yielded
> maximum reward

... and there it is! What did I see?

Example applicable to the lengthy following discussion:
1 - 2
2 - 2
3 - 2
4 - 2
5 - ?
What is "?".

Now, I'll tell you that the left column represents the distance along a 4.5
unit long table, and the right column represents the distance above the
floor that you will be as your walk the length of the table. Knowing this,
without ANY supporting physical experience, I would guess "?" to be zero, or
maybe a little more if I were to step off of the table and land onto
something lower, like the shoes that I left there.

In an imaginary world where a GI boots up with a complete understanding of
physics, etc., we wouldn't prefer the simplest "program" at all, but rather
the simplest representation of the real world that is not physics/math
with our observations. All observations would be presumed to be consistent
with the response curves of our sensors, showing a world in which Newton's
laws prevail, etc. Armed with these presumptions, our physics-complete AGI
would look for the simplest set of *UN*observed phenomena that explained the
observed phenomena. This theory of a physics-complete AGI seems undeniable,
but of course, we are NOT born physics-complete - or are we?!

This all comes down to the limits of representational math. At great risk of
hand-waving on a keyboard, I'll try to explain by pseudo-translating the
concepts into NN/AGI terms.

We all know about layering and columns in neural systems, and understand
Bayesian math. However, let's dig a little deeper into exactly what is being
represented by the "outputs" (or "terms" for died-in-the-wool AGIers). All
physical quantities are well known to have value, significance, and
dimensionality. Neurons/Terms (N/T) could easily be protein-tagged as to the
dimensionality that their functionality is capable of producing, so that
only compatible N/Ts could connect to them. However, let's dig a little
deeper into "dimensionality"

Physicists think we live in an MKS (Meters, Kilograms, Seconds) world, and
that all dimensionality can be reduced to MKS. For physics purposes they may
be right (see challenge below), but maybe for information processing
purposes, they are missing some important things.

*Challenge to MKS:* Note that some physicists and most astronomers utilize "
*dimensional analysis*" where they experimentally play with the dimensions
of observations to inductively find manipulations that would yield the
dimensions of unobservable quantities, e.g. the mass of a star, and then run
the numbers through the same manipulation to see if the results at least
have the right exponent. However, many/most such manipulations produce
nonsense, so they simply use this technique to jump from observations to a
list of prospective results with wildly different exponents, and discard the
results with the ridiculous exponents to find the correct result. The
frequent failures of this process indirectly demonstrates that there is more
to dimensionality (and hence physics) than just MKS. Let's accept that, and
presume that neurons must have already dealt with whatever is missing from
current thought.

Consider, there is some (hopefully finite) set of reasonable manipulations
that could be done to Bayesian measures, with the various competing theories
of recognition representing part of that set. The reasonable mathematics to
perform on spacial features is probably different than the reasonable
mathematics to perform on recognized objects, or the recognition of
impossible observations, the manipulation of ideas, etc. Hence, N/Ts could
also be tagged for this deeper level of dimensionality, so that ideas don't
get mixed up with spacial features, etc.

Note that we may not have perfected this process, and further, that this
process need not be perfected. Somewhere around the age of 12, many of our
neurons DIE. Perhaps these were just the victims of insufficiently precise
dimensional tagging?

Once things can ONLY connect up in mathematically reasonable ways, what
remains between a newborn and a physics-complete AGI? Obviously, the
physics, which can be quite different on land than in the water. Hence, the
physics must also be learned.

My point here is that if we impose a fragile requirement for mathematical
correctness against a developing system of physics and REJECT simplistic
explanations (not observations) that would violate either the mathematics or
the physics, then we don't end up with overly simplistic and useless
"programs", but rather we find more complex explanations that are physics
and mathematically believable.

we should REJECT the concept of "pattern matching" UNLESS the discovered
pattern is both physics and mathematically correct. In short, the next
number in the "2, 2, 2, 2, ?" example sequence would *obviously* (by this
methodology) not be "2".

OK, the BIG question here is whether a carefully-designed (or evolved over
100 million years) system of representation can FORCE the construction of
systems (like us) that work this way, so that our "programs" aren't "simple"
at all, but rather are maximally correct?

Anyway, I hope you grok the question above, and agree that the search for
the simplest "program" (without every possible reasonable physics and math
constraint that can be found) may be a considerable misdirection. Once you
impose physics and math constraints, which could potentially be done with
simplistic real-world mechanisms like protein tagging in neurons, the
problems then shifts to finding ANY solution that fits the complex
constraints, rather than finding the SIMPLEST solution without such

Once we can get past the questions, hopefully we can discuss prospective

Are we in agreement here?

Any thoughts?


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