6.3 Current and Prior Applications of OpenCog 109
SPACETIME DIMENSIONAL BACKUP SERVER EMBEDDING STORE SPACE
ASSOCIATIVE EPISODIC MEMORY REPOSITORY
/
\
FORMATION
MaSES/ WORLD
CLIMBING ‘errr
PROCEDURE AGING Te
EMBEDDING ATTENTION AGRA ALLOCATION
EPISODIC ENCODING / RECALL
FORGETTING/
FREEZING / DEFROSTING
LEARNING
BLENDING [ey ~DECLARATIVE/ y | SEMANTIC ATOMS cuusTerine |S | :
\, a HEBBIAN \, ©) ATOMS ARE \ oy atoms \ . SELECTIVELY PLN A/T \ 9 \ “FORGOTTEN” PROBABILSTIC / he pee h \ 4 INFERENCE / PROCEDURE Se | 23 f ATOMS Es
7) NEW atoms = \ | ARE FORMED
i A 2 |e ‘ | ot ALL ATOMS | / oe. @\| piatocve =| Pal HAVE SHORT | f s PROCEDURE / a ager AND LONG-TERM i 4 TOMS ( i2? IMPORTANCE VALUES ( jf MOTOR ¥ ¥ i f PROCEDURE f | / ATOMS _——
NY SOME ATOMS HAVE “N (UNCERTAIN) GOAL | (@ FEELING TRUTH VALUES
ATOMS e ATOMS
ATOM SPACE
PATTERN MINER PATTERN IMPRINTER
PERCEPTION HIERARCHY
LANGUAGE LANGUAGE COMPREHENSION GENERATION
HERARCHY HIERARCHY,
SENSORS ~ ACTUATORS
Fig. 6.1: High-Level Architecture of CogPrime. This is a conceptual depiction, not a
detailed flowchart (which would be too complex for a single image). Figures 6.2 , 6.4 and 6.5 highlight specific aspects of this diagram.
e Learning to build structures resembling structures that it’s shown (even if the available materials are a bit different)
e Learning how to build bridges to cross chasms
Of course, the AI significance of learning tasks like this all depends on what kind of feedback the system is given, and how complex its environment is. It would be relatively simple to make an AI system do things like this in a trivial and highly specialized way, but that is not the intent of the project the goal is to have the system learn to carry out tasks like this using general learning mechanisms and a general cognitive architecture, based on embodied experience and
HOUSE_OVERSIGHT_013025