I'm going to attack you by questions again :-)

You're more than welcome to, sorry for being brisk. I did reply about RSS on the blog, but for some reason the post never made it through.
I don't how RSS works, but you can subscribe via bloglines.com.

What are 'range' and 'complexity'? Is there a specific architecture of
'levels'? Why should higher-level concepts be multidimensional?

Levels are defined incrementally, a comparison adds a set of derivatives to the syntax (complexity) of the template pattern. After sufficient accumulation (range) the pattern is evaluated & selectively transfered to a higher level, where these derivatives are also compared, forming yet another level of complexity. Complexity generally corresponds to the range of search (& resulting projection) because it adds "cost", which must be justified by the "benefit": accumulated match (one of the "derivatives"). The levels may differ in dimensionality (we do live in a 4D world)b or modality integration, but this doesn't have to be designed-in, the differences can be discovered by the system.

What is the dynamics of system's operation in time? Is inference
feed-forward and 'instantaneous', measuring by external clock? Can
capture time series?

Temporal, as well as spatial, range of search (& duration is storage) increases with the level of generality, the feedback (projection) delay increases too.

By 'what prediction is for?' I mean connection to action. How does
prediction of inputs or features of inputs translate into action? If
this prediction activity doesn't lead to any result, it may as well be
absent.

The "intellectual" part of action is planning, which technically is a self-prediction. Prediction is a pattern with adjusted focus: coordinates & resolution, sent down the hierarchy the changes act as a motor feedback. Using the feedback the system will focus on areas of the environment with the greatest predictive potential.
To do so, it will eventualy learn to use "tools".

Boris, http://scalable-intelligence.blogspot.com/

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agi
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