A few days ago Numenta sent out a newsletter with a quick update on their work. The newsletter notes that Numenta has now posted the Smith Group lecture on its website (it runs much more smoothly than the version on the University's website). It also announced the first additions/updates to the new learning algorithm documentation. The new additions were helpful, particularly the addition of an appendix that goes into some depth about the neuron model used by the HTM software. It includes some of the graphics used by Numenta in the online lecture.
Sadly, the newsletter noted that Numenta is temporarily deferring its work on computer vision problems in favor of applications that are more focused on temporal patterns, such as web click prediction and credit card fraud prediction. I guess that I can't say that I am too surprised by this. In hindsight, based on the online video and the whitepaper, it is clear that Numenta ran into some problems with its vision experiments with the new algorithms. The current algorithms can model layer three or four of the cortex (layer 3 for variable order time based learning or layer 4 for learning that does not rely on context). The whitepaper hypothesizes that layer four allows the brain to learn spatial invariance while layer three allows the brain to learn temporal invariance but that for vision problems the brain is somehow combining layers 3 and 4 to create spatial and temporal invariance at the same time. Until Numenta figures out how to model both layers at the same time working together like the real brain, computer vision probably isn't going to work terribly well.