Artificial Intuition
by Monica Anderson
http://artificial-intuition.com/index.html
Most humans have not been taught logical thinking, but most humans are still intelligent. Most of our
daily actions such as walking, talking, and understanding the world are based on Intuition, not Logic.
I capitalize (for stylistic reasons) all major named memes such as "Intuition" and "Logic"
Others have used the label "Artificial Intuition" for other ideas.I will attempt to show that it is implausible
that the brain should be based on Logic. I believe Intelligence emerges from millions of nested
micro-intuitions, and that true Artificial Intelligence requires Artificial Intuition.
Intuition is surprisingly easy to implement in computers, but requires a lot of memory.
The "N" in the acronym "AN" for Artificial iNtuition is analogous to the Meyers-Briggs use of "N" for
iNtuition
"Artificial Intelligence" is not a technology. It is a problem domain that is delineated by the criterion that
arriving at solutions would require intelligence. We will recognize Intelligence when we see it; a Turing
Test will not be required.
The top of each page has a summary of the page (in italics).I expect Artificial Intuition (AN) to become an
important building block in "AI" systems - Those that aspire to solve problems in domains that we think
require "Intelligence". But many of these domains can likely be handled using Artificial Intuition alone. I
believe AN-based systems will, on their own, be able to provide impressive results in areas like
Document Understanding, Speech Recognition, OCR Correction, Entity Extraction, Machine Translation,
Web Page Quality Analysis, and Semantic Search; in short, in areas that require discovery of Semantics
from lower level representations such as text, DNA sequences, and other streams of spatiotemporal
events.
In what follows I will argue that AN approaches are Biologically Plausible; that they rather elegantly
sidestep many problems and limitations of Logic-based AI approaches; and that they are likely to be
implementable in current or near-future generations of computer hardware.
I will begin by analyzing why certain kinds of problems are thought to "require intelligence"; next I will
contrast Intuition- and Logic-based problem-solving mechanisms; and I attempt to make plausible that
these difficult and important problems require Intuition rather than Logic.
Be forewarned that acceptance of these ideas will likely require a different stance on the nature of
Artificial Intelligence, the nature of Intelligence in general, and on Science itself. Some people, including
scientists in disciplines like Ecology and Systems Biology have adopted this stance; if everything on this
site sounds to you like "motherhood and apple pie", then you know you already have.
The earliest brains might have been nothing more than simple nerve clusters that coordinated the
movement of legs while walking in multi-legged agents, such as arthropods. Walkers keep some of their
legs on the ground at all times which means you only lift one or a few of them at any one time. You will
also want to avoid stepping on your own feet. Such coordination requires control from a central point.
When using a "running gait" all legs will be off the ground at the same time for some part of the running
cycle. When doing that you can no longer rely on feedback from a leg to tell you that the leg is well
positioned on the ground, that the ground under the leg will carry its share of the body mass, and that it
won't slip. You need to be able to predict the spot where the leg will land, and the impact time (with
something like millisecond precision) so that you can start preparing the appropriate muscles for the
landing impact and for the next step. This kind of prediction would also improve your speed in a walking
gait, which means there are continuous rewards for small improvements. This means development of
this ability through Evolution is Biologically Plausible.
In order for a feature to be Biologically Plausible there must exist a way that feature could evolve. This
requires a starting configuration (such as the existence of walking animals) and a gradient of advantage
that provides an evolutionary pressure to develop the feature. A continuous gradient where small
changes yield small improvements is much preferred over steeper saltations where a major change
provides a major improvement with nothing in between.Vision evolved before running. Running requires
vision so that you can run without hitting obstacles. Vision helps you build a model of your environment,
often referred to as a "World Model". If you can remember features of the world from one occasion to
the next you can remember safe and dangerous places to go, safe and treacherous spots to plant your
feet, etc. In essence, you are capable of predicting your environment in order to move faster.
There is a strong evolutionary pressure to get better and better at predicting your environment. You
would benefit from developing models of other agents so that you can predict how predators or prey will
act, and you would want to predict other members of your tribe such as potential mates and rivals. The
single skill of prediction, even if it often fails, yields a big advantage in how well you survive and how
likely you are to breed. The better you can predict the near future compared to your immediate rivals,
the more offspring you will have.
Jeff Hawkins also believes Intelligence is defined by Prediction. You may enjoy this excellent video. A
small detail to note is that that he seems to indicate only higher levels of Intelligence use Prediction but I
believe Prediction is a fundamental low level operation for ALL Intelligence.To summarize, the purpose
of Intelligence is Prediction, and the above argument shows that the capability to make predictions is a
Biologically Plausible feature.
Cascading and nesting predictions
Evolution rarely throws anything away. I believe Prediction, not Logic, is still the most important low level
primitive in human brains.
William Calvin has discussed in several books how prediction of limb movement might be a reason we
evolved brains large enough to handle language. I believe we evolved discovery of Semantics as a way
to widen and generalize the patterns for events that trigger predictions, i.e. as a way to generalize
predictions without having to create Logic-based models; such a development would predate the
development of language, would be a reason to enlarge the brain, and would provide the capability to
handle semantics that would later enable the development of language.
Friedrich Hayek observed that when sensory input events reach the brain they have all been converted
to the same kind of nerve signals that the brain uses everywhere else. So there is no real difference
between sensory input and other processing in the brain. Predictions in the brain are events like any
other.
It is easy to imagine a layered brain architecture where sensory input arrives "at the bottom layer".
Predictions of these inputs, or lower level predictions in general, could be predicted by higher level
predictions in several layers. I believe that this kind of nesting of predictions is the origin of higher level
semantics, which would allow an agent to generalize the event that triggers the prediction. The
development of Semantics as a nesting of predictions is Biologically Plausible.
Longer term predictions are possible by cascading predictions. Interaction with higher-level predictions
that maintain the context (the semantics) could be used to limit the combinatorial explosion that results
from cascading.
Seems like a reasonable approach.
donbot
Next
by Monica Anderson
http://artificial-intuition.com/index.html
Most humans have not been taught logical thinking, but most humans are still intelligent. Most of our
daily actions such as walking, talking, and understanding the world are based on Intuition, not Logic.
I capitalize (for stylistic reasons) all major named memes such as "Intuition" and "Logic"
Others have used the label "Artificial Intuition" for other ideas.I will attempt to show that it is implausible
that the brain should be based on Logic. I believe Intelligence emerges from millions of nested
micro-intuitions, and that true Artificial Intelligence requires Artificial Intuition.
Intuition is surprisingly easy to implement in computers, but requires a lot of memory.
The "N" in the acronym "AN" for Artificial iNtuition is analogous to the Meyers-Briggs use of "N" for
iNtuition
"Artificial Intelligence" is not a technology. It is a problem domain that is delineated by the criterion that
arriving at solutions would require intelligence. We will recognize Intelligence when we see it; a Turing
Test will not be required.
The top of each page has a summary of the page (in italics).I expect Artificial Intuition (AN) to become an
important building block in "AI" systems - Those that aspire to solve problems in domains that we think
require "Intelligence". But many of these domains can likely be handled using Artificial Intuition alone. I
believe AN-based systems will, on their own, be able to provide impressive results in areas like
Document Understanding, Speech Recognition, OCR Correction, Entity Extraction, Machine Translation,
Web Page Quality Analysis, and Semantic Search; in short, in areas that require discovery of Semantics
from lower level representations such as text, DNA sequences, and other streams of spatiotemporal
events.
In what follows I will argue that AN approaches are Biologically Plausible; that they rather elegantly
sidestep many problems and limitations of Logic-based AI approaches; and that they are likely to be
implementable in current or near-future generations of computer hardware.
I will begin by analyzing why certain kinds of problems are thought to "require intelligence"; next I will
contrast Intuition- and Logic-based problem-solving mechanisms; and I attempt to make plausible that
these difficult and important problems require Intuition rather than Logic.
Be forewarned that acceptance of these ideas will likely require a different stance on the nature of
Artificial Intelligence, the nature of Intelligence in general, and on Science itself. Some people, including
scientists in disciplines like Ecology and Systems Biology have adopted this stance; if everything on this
site sounds to you like "motherhood and apple pie", then you know you already have.
The earliest brains might have been nothing more than simple nerve clusters that coordinated the
movement of legs while walking in multi-legged agents, such as arthropods. Walkers keep some of their
legs on the ground at all times which means you only lift one or a few of them at any one time. You will
also want to avoid stepping on your own feet. Such coordination requires control from a central point.
When using a "running gait" all legs will be off the ground at the same time for some part of the running
cycle. When doing that you can no longer rely on feedback from a leg to tell you that the leg is well
positioned on the ground, that the ground under the leg will carry its share of the body mass, and that it
won't slip. You need to be able to predict the spot where the leg will land, and the impact time (with
something like millisecond precision) so that you can start preparing the appropriate muscles for the
landing impact and for the next step. This kind of prediction would also improve your speed in a walking
gait, which means there are continuous rewards for small improvements. This means development of
this ability through Evolution is Biologically Plausible.
In order for a feature to be Biologically Plausible there must exist a way that feature could evolve. This
requires a starting configuration (such as the existence of walking animals) and a gradient of advantage
that provides an evolutionary pressure to develop the feature. A continuous gradient where small
changes yield small improvements is much preferred over steeper saltations where a major change
provides a major improvement with nothing in between.Vision evolved before running. Running requires
vision so that you can run without hitting obstacles. Vision helps you build a model of your environment,
often referred to as a "World Model". If you can remember features of the world from one occasion to
the next you can remember safe and dangerous places to go, safe and treacherous spots to plant your
feet, etc. In essence, you are capable of predicting your environment in order to move faster.
There is a strong evolutionary pressure to get better and better at predicting your environment. You
would benefit from developing models of other agents so that you can predict how predators or prey will
act, and you would want to predict other members of your tribe such as potential mates and rivals. The
single skill of prediction, even if it often fails, yields a big advantage in how well you survive and how
likely you are to breed. The better you can predict the near future compared to your immediate rivals,
the more offspring you will have.
Jeff Hawkins also believes Intelligence is defined by Prediction. You may enjoy this excellent video. A
small detail to note is that that he seems to indicate only higher levels of Intelligence use Prediction but I
believe Prediction is a fundamental low level operation for ALL Intelligence.To summarize, the purpose
of Intelligence is Prediction, and the above argument shows that the capability to make predictions is a
Biologically Plausible feature.
Cascading and nesting predictions
Evolution rarely throws anything away. I believe Prediction, not Logic, is still the most important low level
primitive in human brains.
William Calvin has discussed in several books how prediction of limb movement might be a reason we
evolved brains large enough to handle language. I believe we evolved discovery of Semantics as a way
to widen and generalize the patterns for events that trigger predictions, i.e. as a way to generalize
predictions without having to create Logic-based models; such a development would predate the
development of language, would be a reason to enlarge the brain, and would provide the capability to
handle semantics that would later enable the development of language.
Friedrich Hayek observed that when sensory input events reach the brain they have all been converted
to the same kind of nerve signals that the brain uses everywhere else. So there is no real difference
between sensory input and other processing in the brain. Predictions in the brain are events like any
other.
It is easy to imagine a layered brain architecture where sensory input arrives "at the bottom layer".
Predictions of these inputs, or lower level predictions in general, could be predicted by higher level
predictions in several layers. I believe that this kind of nesting of predictions is the origin of higher level
semantics, which would allow an agent to generalize the event that triggers the prediction. The
development of Semantics as a nesting of predictions is Biologically Plausible.
Longer term predictions are possible by cascading predictions. Interaction with higher-level predictions
that maintain the context (the semantics) could be used to limit the combinatorial explosion that results
from cascading.
Seems like a reasonable approach.
donbot
Next
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