Let us
continue from where we left off in Part 125.
The nature of
data flowing into a pattern recognizer
What does the
data for a pattern look like? Suppose the pattern is a face, an essentially
2-dimensional set of data. But, as can be seen from the structure of the
neocortex, the pattern inputs are only 1-dimensional lists. All the experience
in the creation and functioning of artificial pattern-recognition systems also
confirms that one can represent 2- or higher-dimensional data streams as
1-dimensional lists. Our memories are patterns organized as lists (that is why
we have trouble reciting the alphabet backwards). And, what is more, each item
in the list is another pattern, and so on, hierarchically. We have learnt these
lists, and we recognize them when an appropriate stimulus is present. Memories
exist in the neocortex in order to be recognized.
Autoassociation
and invariance
As explained in
Part 125, we can
recognize a pattern even if it is incomplete. This ability to associate a
pattern with a part of itself is called autoassociation.
Often we are
able to recognize patterns that are distorted, or when aspects of them are
transformed. This ability is called invariance, and the brain deals with
it in four ways.
The first way
is through global transformations that are effected before the cortex receives
the sensory data.
The second
takes advantage of the redundancy in the storage of memory. The memory has many
perspectives or variations stored away.
The third is
the ability to combine two or more memory lists. That is how we understand
metaphors and similes.
The fourth
method derives from the 'size parameters' that allow a single module to encode
multiple instances of a pattern.
Learning
As Kurzweil (2012) writes: 'Our neocortex is virgin territory
when our brain is created. It has the capability of learning and therefore of
creating connections between its pattern recognizers, but it gains those
connections from experience. . .
Learning and recognition take place simultaneously. We start learning
immediately, and as soon as we've learned a pattern, we immediately start
recognizing it. . . . patterns that are not recognized are stored as new
patterns and are appropriately connected to the lower-level patterns that form
them'.
The language
of thought
At the heart
of the pattern-recognition theory of mind (PRTM) is the neocortical pattern-recognition
module, the inputs to and the outputs from which are shown below (diagram taken
from Kurzweil 2012).
The brain starts out with a very large number of
‘connections-in-waiting’ to which the pattern-recognition modules can hook up.
As we learn and have experiences, the pattern recognizing modules of the
neocortex are connecting to preestablished connections that were created when
we were embryos. Kurzweil (2012) has summarized his PRTM as follows:
'a) Dendrites
enter the module that represents the pattern. Even though patterns may seem to
have two- or three-dimensional qualities, they are represented by a
one-dimensional sequence of signals. The pattern must be present in this
(sequential) order for the pattern recognizer to be able to recognize it. Each
of the dendrites is connected ultimately to one or more axons of pattern
recognizers at a lower conceptual level that have recognized a lower-level
pattern that constitutes part of this pattern. For each of these input
patterns, there may be many lower-level pattern recognizers that can generate
the signal that the lower-level pattern has been recognized. The necessary
threshold to recognize the pattern may be achieved even if not all of the
inputs have signalled. The module computes the probability that the pattern it
is responsible for is present. This computation considers the "importance"
and "size" parameters (see [f] below).
'Note that
some of the dendrites transmit signals into the module and some out of the
module. If all of the input dendrites to this pattern recognizer are signalling
that their lower-level patterns have been recognized except for one or two, then
this pattern recognizer will send a signal down to the pattern recognizer(s)
recognizing the lower-level patterns that have not yet been recognized,
indicating that there is a high likelihood that that pattern will soon be
recognized and that lower-level recognizer(s) should be on the lookout for it.
'b) When this
pattern recognizer recognizes its pattern (based on all or most of the input
dendrite signals being activated), the axon (output) of this pattern recognizer
will activate. In turn, this axon can connect to an entire network of dendrites
connecting to many higher-level pattern recognizers that this pattern is input
to. This signal will transmit magnitude information so that the pattern
recognizers at the next higher conceptual level can consider it.
'c) If a
higher-level pattern recognizer is receiving a positive signal from all or most
of its constituent patterns except for the one represented by this pattern
recognizer, then that higher-level recognizer might send a signal down to this
recognizer indicating that its pattern is expected. Such a signal would cause
this pattern recognizer to lower its threshold, meaning that it would be more
likely to send a signal on its axon (indicating that its pattern is considered
to have been recognized) even if some of its inputs are missing or unclear.
'd) Inhibitory
signals from below would make it less likely that this pattern recognizer will
recognize its pattern. This can result from recognition of lower-level patterns
that are inconsistent with the pattern associated with this pattern recognizer.
. . .
'e) Inhibitory
signals from above would also make it less likely that this pattern recognizer
will recognize its pattern. This can result from a higher-level context that is
inconsistent with the pattern associated with this recognizer.
'f) For each
input, there are stored parameters for importance, expected size, and expected
variability of size. The module computes an overall probability that the
pattern is present based on all of these parameters and the current signals
indicating which of the inputs are present and their magnitudes. A
mathematically optimal way to accomplish this is with a technique called hidden Markov models. When such
models are organized in a hierarchy (as they are in the neocortex or in
attempts to simulate a neocortex), we call them
hierarchical hidden Markov models.'
Triggered
patterns trigger other patterns. Incomplete patterns send signals down the
conceptual hierarchy. Complete patters send signals up the hierarchy. These
patterns are the language of thought. Like language they are hierarchical, but
they are not always language per se, although language-based thoughts are also
possible.
There can be
two modes of thinking, nondirected and directed. In the former, thoughts
trigger one another in a nonlogical way. Dreams are examples of nondirected
thoughts. Directed thinking is what we use when we are trying to solve a
problem, or when we formulate an organized response.
Thus,
according to the PRTM, our intelligence is the result of 'self-organizing,
hierarchical recognizers of invariant self-associative patterns with redundancy
and up-and-down predictions' (Kurzweil 2012).
It is rightly
claimed in Kurzweil's (2012) book that it ' . . is an incredible synthesis of
neuroscience and technology and provides a road map for the future of human
progress'. The operating principle of the neocortex (explained by the PRTM) 'is
arguably the most important idea in the world, as it is capable of representing
all knowledge and skills as well as creating new knowledge'.
Note added on 11th August 2016
Here is a good two-part update on what has been happening in the field of artificial intelligence:
Note added on 11th August 2016
Here is a good two-part update on what has been happening in the field of artificial intelligence:
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