Allow me to briefly sketch an axiomatic approach to intelligence of
the highest order: real time, "stream" intelligence.  As much as it is
desirable to spawn processes and logical inferences in Lisp and
Prolog-like systems in the pursuit of "better approximations of
reality", there is no escaping the many charms and attractions of the
more streamlined processes typical of biological nervous systems and
electronics: input-to-output in dt, as quickly as possible.

StreamAI would be a strong contender in a lot of domains that use
intelligent agents, including artificial life, financial decision
making and warfare. But there is no reason, and possibly no path, to
fall into the narrow-AI trap if the design is from the beginning
multimodal and ambitious. There is no reason why a financial
streamAgent would not look for clues in the faces on Bloomberg TV in
addition to the Dow Jones ticker.  One architectural problem that
could perhaps be solved in a general way would be how to incorporate
lengthier "thought processes" in StreamAI, two obvious candidates
would be "inlining" (expanding programs with some degree of
parallelism into parallel "hardware") and "externalizing", ie calling
an external prolog or whatever and "knowing what to do" while waiting
for an answer. I would argue that human chess expertise is first a
form of inlining, where a concept like "king safety" has been learned
together with "micro-variations", doing instant checks on the ability
of certain lines and squares been used for an immediate attack on the
king, and then "externalized", passed to the frontal cortex for a more
proper wider and deeper analysis tree.

If you are thinking there is no reason to handicap your AGI quest by
putting all of your eggs in one stream, let me remind you that the
ever expanding range of stream processors, whether GPUs, FPGAs or
whatever are making StreamAI both possible and necessary.

AT


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AGI
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