QUESTION:
Mr. Armstrong; you are probably the first pioneer in AI. My professor at
… mentioned you often. My question is straight forward. Do you believe your
system has evolved along the same path as the human mind and if so can it
achieve self-awareness and perhaps consciousness?
ANSWER: I
take it the sudden rush of questions on this topic came from a conference on
Machine Learning in California recently. Forgive me for getting very technical
here for the readers not interested in this subject.
The answer is yes and no. Whenever you create a system be it biological or
artificial, there is an emergent behavior that evolves giving that system the
appearance of intelligence. True, I began to notice that there was an emergent
behavior that seems to evolve from cluster management of volumes of data when
you allow the system to learn and create its own rules for constructing a
knowledge base. These behaviors are fascinating.
I first discovered this when I created a system to mimic humans. I created a
program that would interact with a person creating a dialog while remembering
everything you said. I created this system in the early 1980′s and gave it to my
children as test subjects. My daughter befriended it and to a child it
accomplished everything and fully appeared human. It would ask you a question
like do you have a dog, what is its name, and the next conversation, it would
ask: how is the dog?
Behavior began to appear and this seems to be an emergence that springs from
large distributed systems of data. What happens we also experience in our human
existence. We may taste some food, then we just have to have it as often as
possible for a brief while. We eat too much of it and then suddenly our taste
for it declines. The same will happen with a new song we listen to over and over
again and then cannot stand to hear it one more time. This is a behavior that
emerges and the same appears in computers. It does not make it “alive” but this
is actually cyclical development. In any large-scale distributed system of data,
there will emerge a cyclical pattern of what data is being referred to most
often.
Go to a casino and just watch a roulette table. In theory, every number has
an equal chance of winning. But in reality, the numbers will be cyclical. Some
numbers will never come up while others repeat. It does not matter what system
you look at, it will always revert to a cyclical pattern. This is the secret of
nature. Observe the roulette wheel closely. The reason the house changes dealers
rotating them is because this changes the cycle on that table. The cycle is not
YOUR luck that will emerge from a string of times you might
gamble, The house cycle differs with each dealer and that is the key to running
the casino. This is why the casino rotates dealers because they fall into cycles
and like counting cards, with a keen eye and an understanding of complex
cyclical systems, you can see the the patterns emerge.
(for you information,
if I go to a casino and play roulette, within 15 minutes they come and say they
recognize me as a”player” and want my name; casino understand cycles).
What emerges from any system is the unforeseen behavior arising out of
sufficiently large groups of raw data. The real father of Chaos Theory was
Edward Norton Lorenz (1917-2008) who was an American mathematician and
meteorologist. Lorenz was certainly THE pioneer in Chaos Theory. A professor at
MIT, Lorenz was the first to recognize what is now called chaotic behavior in
the mathematical modeling of weather systems.
During the 1950′s, Lorenz observed that there was a cyclical non-linear
nature to weather yet the field relied upon linear statistical models in
meteorology to do weather forecasting. It was like trying to measure the
circumference of a circle with a straight edge ruler. His work on the topic
culminated in the publication of his 1963 paper Deterministic Non-periodic Flow
in the Journal of the Atmospheric Sciences, and with it, the foundation of chaos
theory. During the early 1960′s, Lorenz had access to early computers. He was
running what he thought would be random numbers and began to observe there was a
duality of a hidden repetitive nature. He graphed the numbers that were derived
from his study of convection rolls in the atmosphere. What emerged has been
perhaps one of the most important discoveries in modern time.
This illustration of
the
Lorenz Strange Attractor is incredibly important and was first
reported in 1963. Lorenz’s discovery of a strange attractor was made during an
attempt to create a model of weather patterns. The actual experiment was an
attempt to model atmospheric dynamics of the planet. It involved a truncated
model of the Navier-Stokes equations. It is a visual example of a non-linear
dynamic system corresponding to the long-term behavior in a cyclical manner
revealing a hidden order we cannot otherwise observe. The
Lorenz Strange
Attractor is a 3-dimensional dynamical system that exhibits chaotic
flow, noted for its interesting shape revolving around two invisible strange
points in space-time we call
Strange Attractors. The map shows how
the state of a dynamical system with three variables of a three-dimensional
system evolves over the fourth dimension time in a complex, yet non-repeating
pattern. In other words, here is a visualization of duality – what appears to be
randomness (chaos) yet simultaneously there is a broader clear pattern of order.
The same identical structure appears in light where it is both a wave form and
particle, as we see in the economy where we retain our individuality yet at the
same time we are part of a broader collective pattern. This is the very essence
of the Invisible Hand – or in Lorenz terms, a
Strange
Attractor.
Biology emerges from the laws of chemistry. This is the same structured
emergence. You have the raw behavior patterns that when they combine you get a
functioning biological entity. This is what takes place, It is an emergence or
raw data that within it lies hidden behavioral patterns that on the surface are
complex and we assume is just random chaos. When plotted, what emerges can be
absolutely fascinating behavior revealing ordered systems that lie beneath. This
is what Lorenz discovered we call chaos theory.
There is a natural order of emergent systems within systems. The placement
and prioritization of some data simply cannot be entirely predictable in any
linear model. Systems will reach a certain level of complexity and what will
emerge are these cyclical patterns as in simply observing a roulette wheel and
then it starts to emerge demonstrating emergent behavior. The casino will change
the dealer to stop this cyclical emergent pattern that the players will
eventually adapt to and beat the odds.
The real difficult part in computing is the mere fact that it can be
extremely hard to know what to do with such emergent behavior unless you
understand the complexity of cyclical systems. When you construct such massive
data systems you will create emergent behavior. It is inherent within everything
around us just as we see such complexity in the Mandelbrot Set as seen in this
detail.
This emergent behavior maybe sometimes unexpected.The reason for this is
simply that there is a hidden order in the surface appearing chaos that springs
from the greater cluster of information in a knowledge based system. The sheer
scale of the system at a certain level of complexity allows this cyclical hidden
order to appear and this then demonstrates an emergent behavior. The key is
comprehending this aspect that is totally nonlinear.
I have devoted tremendous resources to develop this knowledge base. The
computer records interactions on a grand scale and this is tested all the time.
I have rejected outside funding because of this emergent behavior that has
allowed the system to even predict war. It took me some time to begin to
comprehend how could this system possibly forecast something I had not
deliberately coded? It took me nearly a decade of observation before even I
understood what was emerging before my eyes. This was in fact emergent behavior
within the system that the computer discovered.
There is an overall software application, but this is then layered over the
hardware containing the cluster of data and then there are sub-applications that
the overall primary layer then arbitrages as they are prioritized with an
innumerable number of tasks according to the size of each economy. It may appear
to be unstable since there is no linear rule actually being followed. However,
everything is changing according to capital flows that result in the movement of
capital internationally that defies creating some rule such as the dollar is the
reserve currency that like eat some recent discovered food causes us to want
more and more until we move on and do not care for it again. Everything is
evolving and nothing is fixed.
There is simply far more complexity involved that we can logically order in
some linear fashion. This is why I say it is a bell curve and at some point the
same fundamental will flip and produce the opposite effect. This complexity
distinguishes the process required and defies linear analysis. Honestly, it may
take more than a $100 million to finish this project. I may not even live to
see the end for it is dynamic in-and-of-itself. My goal has been to provide
predictable behaviors within such complex systems that I can achieve
self-awareness, but unlikely to achieve self-consciousness.
This sort of fuzzy chaos represents the new norm for massive distributed
knowledge base systems. It is a whole new frontier to be discovered. Yes I may
be further advanced than others exploring this field. But it has also been my
curse as people try to just steal such technology failing to realize that they
are unlikely to even comprehend its cyclical complexity with linear thinking. It
requires a change in the way we see the world around us and an understanding of
cyclical behavior.
