Distraction and focus of attention

As Asa H acquires a larger and broader case base memory it tends to attempt to attend to too many things at once.  It may be possible to focus attention by only passing the N most activated concepts (outputs) from each layer of the hierarchy to the next (see my blog of 26 Aug. 2013, lines 1011-1013 of the code).  What value should N have?  Should it be different for different levels?  Should it change as Asa learns more? If so, how should it change?

There is less of an issue for specialized Asa agents.  A generalist supervisor (or network of supervisors) filters input and sends it to the appropriate specialist(s) for action.

The use of the right feature detectors and the right similarity measures should also help.

Conceptual evolution in Asa H

Because of the hierarchical organization of the Asa H memory some concepts/categories evolve (change) substantially and quickly while those at other (higher and/or lower) levels (or the same level) in the hierarchy evolve (change) very little and/or slowly.  "health", "handle", and "house/home" are examples of concepts that we have seen evolve substantially while "direction" and "near" are concepts which we have seen change very little once created.

Conceptual difficulty

We now know how to give Asa H a vocabulary of more than 400 concepts (see my blogs of 1 April 2013, 12 March 2013 and 1 Feb. 2014) but we have found 1-2 dozen concepts that we have been unable to teach, for example, "privacy", "confidential/secret", "embarrassment", .....

Legal roadblocks to driverless cars?

Will (Google's) driverless cars be any more acceptable to our legal system than medical expert systems? (see 3 Dec. 2013 blog)

AI sleep

(REM) sleep is believed to be the brain running "off line," doing cognitive processing while shut off from outside inputs (and output).  Some creative processing is included in this.

Similarly, light versions of Asa H 2.0 suspend I/O while running extrapolation routines (see the code in my blogs of 10 Feb 2011 and 14 May 2012 for one example) and while doing some housekeeping like case sorting/organization and renormalization. 

When Asa is deployed across a large computer network extrapolation (and other creative algorithms) can be run on separate processors and "sleep" can be avoided.

Asa H natural language processing

I have given Asa H enough natural language capability (see blogs of 4 May, 24 May, 1 April and 12 March 2013) that when it is told in natural language that:

man     is_a     mortal

and

Bob     is_a     man

it concludes and records that:

Bob     is_a     mortal