Another subtle point (one of many) involves transfer of knowledge. My most recent system "learned" by self-modification (similar in concept to growing new synap

Another subtle point (one of many) involves transfer of knowledge. My most recent system "learned" by self-modification (similar in concept to growing new synapse connections but done by self-modifying code). This kind of modification will eventually arrive with Neural Nets as they move beyond simple 'dropout' techniques to more context-specific additions of specialized subnets and/or cooperating subnets (e.g. the room-recognizer adds a 'table'-specific subnet). Assume two systems start out in a similar state. After interactions the systems are no longer identical and they gradually diverge over time (think twins). One is used in accounting and thinks of a 'table' as a kind of spreadsheet. Another is used in woodworking and thinks of a 'table' as a wooden object. The implication is that one cannot just 'copy' what the first system knows to 'teach' the second system. There would have to be some sort of teaching mechanism (aka college) to transfer the information.