Hawkins' proposal is missing the key innovation that Calvin proposes,
which is that learning take place by evolutionary means. But Hawkins'
proposal does fit squarely within current popular ideas around
predictive coding.
The key structures in Hawkins' architecture are cortical columns (CC).
What his VP of Eng (Dileep George) did is to analyze Hawkins' account of
the functionality of a CC, and then say that a CC is a module which must
conform to a certain API, and meet a certain contract. As long as a
module obeys the API and contract, we don't actually care how the CC
module is implemented. In particular, it's actually not important that
the module contain neurons. (Though the connections between the CCs may
still have to look like axons or whatever, I don't know.)
Then Dileep George further figured out that there is an off the shelf
algorithm than works perfectly for the CC module. He selected an
algorithm which is based on statistical learning theory (STL).
STL based algorithms are an excellent choice for the CCs, IMNSHO. They
are fast, theoretically sound, etc. They are also understood in great
mathematical detail, so we can characterize _exactly_ what they can and
can't do. So there is zero mystery about the capabilities of his system
at the CC level. Note that in Hawkins' case, the STL based algorithms
are used for pattern recognition.
Now Hawkin's proposal isn't just a single CC, it's a network of CC's all
connected together in a certain way. My memory is a little hazy at this
point, but as best I recall, his architecture should have no problem
identifying sequences of patterns (as for sound), or spatial patterns
across time (as for vision). And I bought his argument that these could
be combined hierarchically, and that the same structure could also be
used for playing back (outputting) a learned pattern, and for
recognizing cross modal patterns (that is, across sensory modalities).
But is all this enough?
I say no. My reading of evolutionary epistemology suggests to me that
pattern identification is insufficient for making and refuting
conjectures in the most general sense. And ultimately, a system must be
able to create and refute conjectures to create knowledge. Hawkins has a
very weak story about creativity and claims that it can all be done with
pattern recognition and analogy, but I am not convinced. It was the
weakest part of the book. (pp 183-193)
I don't know if it's clear to you or not why pattern recognition is
insufficient for doing general conjectures and refutations. If it's not
clear, I should attempt to expand on that ...
The idea is, that it is not always possible to arrive at a theory by
just abstracting a pattern from a data set. For example:
What set of data could Newton have looked at to conclude that an object
in motion stays in motion? I suppose he knew of 7 planets that stayed in
motion, but then he had millions of counter examples all around him. For
a pattern recognition algorithm, if you feed it a million + 7 data
points, it will conclude that objects in motion always come to a stop
except for a few weird exceptions which are probably noise.
The key structures in Hawkins' architecture are cortical columns (CC). What his VP of Eng (Dileep George) did is to analyze Hawkins' account of the functionality of a CC, and then say that a CC is a module which must conform to a certain API, and meet a certain contract. As long as a module obeys the API and contract, we don't actually care how the CC module is implemented. In particular, it's actually not important that the module contain neurons. (Though the connections between the CCs may still have to look like axons or whatever, I don't know.)
Then Dileep George further figured out that there is an off the shelf algorithm than works perfectly for the CC module. He selected an algorithm which is based on statistical learning theory (STL).
STL based algorithms are an excellent choice for the CCs, IMNSHO. They are fast, theoretically sound, etc. They are also understood in great mathematical detail, so we can characterize _exactly_ what they can and can't do. So there is zero mystery about the capabilities of his system at the CC level. Note that in Hawkins' case, the STL based algorithms are used for pattern recognition.
Now Hawkin's proposal isn't just a single CC, it's a network of CC's all connected together in a certain way. My memory is a little hazy at this point, but as best I recall, his architecture should have no problem identifying sequences of patterns (as for sound), or spatial patterns across time (as for vision). And I bought his argument that these could be combined hierarchically, and that the same structure could also be used for playing back (outputting) a learned pattern, and for recognizing cross modal patterns (that is, across sensory modalities).
But is all this enough?
I say no. My reading of evolutionary epistemology suggests to me that pattern identification is insufficient for making and refuting conjectures in the most general sense. And ultimately, a system must be able to create and refute conjectures to create knowledge. Hawkins has a very weak story about creativity and claims that it can all be done with pattern recognition and analogy, but I am not convinced. It was the weakest part of the book. (pp 183-193)
I don't know if it's clear to you or not why pattern recognition is insufficient for doing general conjectures and refutations. If it's not clear, I should attempt to expand on that ...
The idea is, that it is not always possible to arrive at a theory by just abstracting a pattern from a data set. For example:
What set of data could Newton have looked at to conclude that an object in motion stays in motion? I suppose he knew of 7 planets that stayed in motion, but then he had millions of counter examples all around him. For a pattern recognition algorithm, if you feed it a million + 7 data points, it will conclude that objects in motion always come to a stop except for a few weird exceptions which are probably noise.