Epstein Secrets

the same Atom from different perspectives may be grouped into CompositeTruthValue objects. • Universal Intelligence: A technical term introduced by Shane Legg and Marcus Hutter, describing (roughly speaking) the average capability of a system to carry out computable goals in computable environments, where

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where "reward" may mean simply the Truth Value of some Atom defined as "reward"). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which

the same Atom from different perspectives may be grouped into CompositeTruthValue objects. • Universal Intelligence: A technical term introduced by Shane Legg and Marcus Hutter, describing (roughly speaking) the average capability of a system to carry out computable goals in computable environments, where

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where "reward" may mean simply the Truth Value of some Atom defined as "reward"). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which

the same Atom from different perspectives may be grouped into CompositeTruthValue objects. e Universal Intelligence: A technical term introduced by Shane Legg and Marcus Hutter, describing (roughly speaking) the average capability of a system to carry out computable goals in computable environments, where g

to learn procedures that will maxi- mize some Goal whose Truth Value is estimated via an expected reward calculation (where *reward” may mean simply the Truth Value of some Atom defined as ”reward”). Goal-driven learning is more general than reinforcement learning as thus defined; and the learning that CogPrime does, which is