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.