Jackie and the Brain
by Chris McKinstry
Jackie was a very simple computer program that simulated half of a
human conversation , which I wrote in Visual Basic in December of 1994
as an entry for the 1995 Loebner Prize. At her heart was a look up
table that was built up by having numerous people interact with her
converstaionally. The look up table consisted of a stimulus, a response
and a number of supplementary indexes to the stimulus. The key to
Jackie's heart, and her uniqueness, were her supplemental indexes.
When Jackie was in training mode and she was given a new stimulus not
in her stimulus index she asked the trainer for a custom response to
that stimulus. Thereafter when she was in interactive mode and she was
given a stimulus she had seen before, so long as the match was perfect,
she retrieved and displayed the perfect handcrafted response that was
given to her by the first person to expose her to that stimulus. This
is largely how Richard Wallace's Alicebot and kin work today. Given
only a very large number of trainers, both Jackie and Alice appear to
be human-like. However, aside from extreme biological implausability,
there are problems with this strategy.
The first problem with a pure stimulus/response strategy is that there
is no common personality across all stimulus/response pairs. Different
users are not aware of how they are each handling specific areas of the
life of the simulated character. For example, one person may train the
system to respond "Yes, I have a cat named Rufus." to the stimulus "Do
you have any pets?" while another trainer may train the system to
respond "No. I hate cats." to the stimulus "Do you like cats?" -
clearly inconsistent. The solution to this problem is to provide
personality guidelines to the trainers, but unless the guideline forces
a strictly binary response [This is the case with Jackie's little
brother, GAC], the guidelines will always have to be as complex or even
more complex than the simulation itself.
The second problem with the pure stimulus/response strategy is what I
call "match hardness." If the exact stimulus is not in the index, the
system fails catastrophically and must evade the stimulus in an
Eliza-like fashion. Such systems are extremely vulnerable to being
unmasked as simulations by simple binary questioning about common sense
aspects of life. There are two obvious solutions to this problem. The
first is to inject a very large number of common sense propositions
collected via some other manner [This was tried with some success with
Alice and data from the Mindpixel project in the Spring of 2005], or to
"soften" the stimulus matching system.
Jackie used a number of stimulus match softening techniques that vastly
amplified the number of effective items in her primary stimulus index.
The first was simply to convert the stimulus to phonetic codes. One of
Jackie's supplementary indexes was based on the SOUNDEX algorithm. This
converted each word to a standard code that was insensitive to
spelling. The effect of this secondary index was Jackie could find a
match to a given stimulus even if words in it were spelled incorrectly.
Of course, given a large primary index, many spelling mistakes will be
in the index anyway, but this phonetic index expansion technique is
vastly more efficient and keeps the first problem of response
consistency from creeping up again. And of course it is much more
biologically plausable than the biological equivalent of a massive
A second soft matching technique Jackie used was an additional index of
SOUNDEX codes where all the words in each stimulus were sorted
alphabetically. This had the effect of stimulus standardization. With
this index, stimuli with slightly different word ordering could still
be matched and a response retrieved. This was still imperfect as
meaning could be lost or possibly unintentionally created in the
standardization, but it is much preferable to evading a stimulus
Finally, Jackie had a secondary index which was the standardized index
filtered of high frequency words from a hand coded list, though in
theory this should have been machine generated.
The end effect of Jackie's soft matching systems was to amplify the
index footprint of every hand coded stimulus in her primary index - she
appeared to know a great deal more about life than what was put into
her and her behavior became interesting and unpredictable. In fact, the
very first time I exposed Jackie to a person other than myself, she
shocked me by responding to something I knew I did not train her on.
At the time of Jackie's first exposure to a person other than myself,
she was quite small and fit on a 1.44 MB 3 1/2 inch disk. I would train
her at night, teaching her about her own life, which was mostly just
mine, sex shifted, and take a fresh copy of her to work with me the
next day. At the time I worked in the IT department of a large
insurance company, as did David Clemens. David was a Japophile at the
time, and his first question he put to Jackie was "Do you like sushi?"
I expected her to evade that question as I had never mentioned sushi to
her at all, but to my surprise she responded "Of course."
I couldn't believe her response and interrupted David's conversation to
see what happened. She had a soft hit on the secondary phonetic index
to "Do you like sex?" Sushi was phonetically close enough to sex to
satisfy her! This was a major revelation for me and I started spending
a lot of time looking at her phonetic indexes. It was clear that
something profoundly human-like was happening in these indexes. I felt
I was capturing a real model of human experience in the topology of the
indexes. Similar concepts were clustering in phonetic space.
I thought, wow, if I open Jackie up to the Internet - remember this is
1994 and the web is only months old - I could build a massive soft
phonetic index and use it to train a neural network to extract the
underlying phonetic space and make a true synthetic subcognitive
substrate. The problem was how to synthesize responses and how to
quantify the quality of the synthetic responses? The answer I came up
with was to restrict the responses to binary.
If we imagine Jackie's phonetic stimulus index as a multidimensional
sphere, we can imagine each response as either a black or white point
at each stimulus coordinate on its surface - black for false and white
for true. Now if we train a neural net to represent this sphere, novel
stimuli would be points of unknown value on this sphere and we could
interpolate a value from known points near the unknown point, something
that would be difficult if the responses were not restricted to binary.
The important question is of how many dimensions should this sphere be?
I believe George A. Miller unknowingly answered this question in 1956
when he published the landmark psychology paper "The Magical Number
Seven, Plus or Minus Two." Our immediate memory [to use Miller's term]
is about seven items long - that is we can recall easily about seven
unrelated items from a larger list of items that we see or have had
read to us. Thus, we can imagine Jackie's phonetic index to things
people can store in their immediate memories as complex fractal pattern
on the surface of a seven-dimensional hypersphere.
The most remarkable revelation of all occurred when I tried to
visualize this object and figure out why nature would make it
seven-dimensional. Could surface area be maximum at seven-dimensions, I
thought? That seemed unreasonable. Why would it be? More dimensions
would intuitively lead to more surface area, but I had better check
just in case. And guess what? Hypersurface is maximum at about
The revelation that hypersurface was indeed maximum near seven
dimensions, and moreover was maximum at a fractional dimension and
hence fractal, was obviously very powerful for me. I used it to form
what I call the Hypergeometric Hypothesis which states - immediate
memories are points on a maximum hypersurface and complex cognition is
a trajectory on the same hypersurface. I used this hypothesis as a tool
for structuring my initial exploration of real brains.
At first I was quite discouraged when I discovered that the neocortex
was six layered in most animals [some have fewer layers, but it is
important to note that none have more than six layers]. I had predicted
that I would find a seven-layered object in both humans and complex
animals and additionally predicted that we should find in the fossil
records earlier humans and animals with slightly larger brains than
modern brains as evolution would have tried an eight layer system and
rejected it in favor of a system with maximum hypersurface and thus
maximum possible pattern complexity on it surface. It was hard to
believe that we had not yet evolved our seventh layer, so I went
digging deeper into neuroanatomy looking for the seventh layer of the
neocortex. I found it in the thalamus.
The thalamus forms a loop with the neocortex, called the
thalamocortical loop - exactly as one would expect if it were
synthesizing one unified seven-dimensional hyperobeject. I was elated
when I read that in fact the thalamus is considered by some
neuroanatomists to be the seventh layer of the neocortex. The object
The realness of my object became much stronger when I learned that
Neanderthal had slightly larger brains than modern humans and when I
learned that there was no other theory that made this prediction or
that even acknowledged that the difference could have any meaning at
all. It was a glaring fact that science seemed to be ignoring because
it conflicted with the idea that the mental uniqueness of modern humans
derives from our having the largest brains for our size.
A final prediction of the Hypergeometric Hypothesis is that no matter
how advanced a brain is, it should not have a primary loop with more
than seven layers. This appears to be true.
It is ironic that Hilary Putnam used Turing's ideas to create the
functionalism that dominates cognitive psychology today and which is
responsible for the field's near universal ignorance of real brains, as
it was the abstraction of Turing's test to a binary geometric form that
lead me to make structural and functional predictions for real brains
past, present and future.
I found that quite interesting.
"In theory, there is no difference between theory and practice. In practice, there is."
http://annevolve.sourceforge.net is for those seeking something to do with their wetware.
Humans may know that my email address is zenguy at shaw dot ca.
What is nice to know?????
The Hypergeometric Hypothesis (Specific): Evolution maximized
hypersurface in the seven-layer mammalian thalamocortical loop [I am
implicitly defining the thalamus as the seventh layer of the
neocortex.] Immediate memories are points on the maximum hypersurface
of a Milnor-sphere and complex cognition is a trajectory on the same
hypersurface. Falsification of the specific hypothesis would involve
the discovery of an animal with an eight-layer thalamocortical loop.
[Note: I consider the amygdala and hippocampus to be Hopf fibrations of
the thalamocortical Milnor-sphere, but as of yet this is not formally
part of the specific hypothesis as I can think of no way to falsify the
IBM's Custom 7 Qubit Molecule - 8 anyone?
The Hypergeometric Hypothesis (General): Omniconnected systems are
sensitive to hypersurface area. Falsification of the general hypothesis
could be achieved by the creation of a single molecule eight qubit NMR
quantum computer. The current state-of-the-art is IBM's seven qubit
[Note: IBM says "While NMR will continue to provide a testbed for
developing quantum computing tools and techniques, it will be very
difficult to develop and synthesize molecules with many more than seven
qubits." - I think it might be much more difficult than the IBM team
realizes for fundamental geometric reasons.]
On 17 Jun 2005 09:51:05 -0700, "Mindpixel" <mind...@gmail.com>
>Well then, try to falsify this:
>The Hypergeometric Hypothesis (Specific): Evolution maximized
>hypersurface in the seven-layer mammalian thalamocortical loop [I am
>implicitly defining the thalamus as the seventh layer of the
>neocortex.] Immediate memories are points on the maximum hypersurface
>of a Milnor-sphere and complex cognition is a trajectory on the same
>hypersurface. Falsification of the specific hypothesis would involve
>the discovery of an animal with an eight-layer thalamocortical loop.
Ok, first of all, what is the first sentence supposed to be?
Evolution maximized THE hypersurface in the seven-layer mammalian TCL?
Or is it supposed to be Evolution maximized A hypersurface?
In what way is this surface maximized? In any case, a "maximized"
surface is useless to a neural network. A MINIMIZED hypersurface (on
the other hand) can be quite interesting indeed.
In either case, the hypersurface is CHANGED via memories. Do you
really expect me to beleive that all memories any human has ever had
(and can ever have) are all points on the same hypersurface? That's
And anyways, complex cognition is based in language. First show me
the neural substrate of language, before you start making theories
about how it produces cognition.
>[Note: I consider the amygdala and hippocampus to be Hopf fibrations of
>the thalamocortical Milnor-sphere, but as of yet this is not formally
>part of the specific hypothesis as I can think of no way to falsify the
So then you would have a snap-shot description of the brain.
Snap-shot descriptions of the brain are useless and especially in the
thalamocortical system, which is clearly meant to produce a particular
behavior through time. You might want to read on the Dynamic Core
>IBM's Custom 7 Qubit Molecule - 8 anyone?
>The Hypergeometric Hypothesis (General): Omniconnected systems are
>sensitive to hypersurface area. Falsification of the general hypothesis
>could be achieved by the creation of a single molecule eight qubit NMR
>quantum computer. The current state-of-the-art is IBM's seven qubit
What does this have to do with the price of wheat in China?
>[Note: IBM says "While NMR will continue to provide a testbed for
>developing quantum computing tools and techniques, it will be very
>difficult to develop and synthesize molecules with many more than seven
>qubits." - I think it might be much more difficult than the IBM team
>realizes for fundamental geometric reasons.]
What does this have to do with the price of corn in Egypt?