Epstein Secrets

ble movements. Following a metaphor suggested by Douglas Hofstadter MOSES learning covers both "knob twid

tational resources. And as we have argued in detail in Chapter 7, efficiency is not a side-issue but rather the essence of real-world AGI (since as Hutter has shown, if one casts efficiency aside, arbitrary levels of general intelligence can be achieved via a trivially simple program). The essence of

nce with respect to the distribution v over the space of all computable reward-summable environ- ments, E, that is, TOO a L v(Ii)1'; PEE Legg and Hutter's universal intelligence is obtained by setting v equal to the universal distribution. This framework is more flexible than it might seem. E.g. sup

ible movements. Following a metaphor suggested by Douglas Hofstadter [Hof96], MOSES learning covers both “k

formance with respect to the distribution v over the space of all computable reward-summable environ- ments, E, that is, Y(m) = SO vr pew Legg and Hutter’s universal intelligence is obtained by setting v equal to the universal distribution. This framework is more flexible than it might seem. E.g. supp

putational resources. And as we have argued in detail in Chapter 7, efficiency is not a side-issue but rather the essence of real-world AGI (since as Hutter has shown, if one casts efficiency aside, arbitrary levels of general intelligence can be achieved via a trivially simple program). The essence of t

mpo- rary philosophers Daniel Dennett [Den91] and Douglas Hofstadter [Hof79, Hof96], in Benjamin Whorf’s [W

rization of Real-World General Intelligence ......... 135 7.3.1 Biased Universal Intelligence............ 0.00. ee eee 136 7.3.2 Connecting Legg and Hutter’s Model of Intelligent Agents to the Real World... ee cet ete e tne e eens 137 7.3.3 Pragmatic General Intelligence ............ 00... c eee eee 13