Epstein Secrets

Blerim Enruli, Kristinn Thorisson, Joscha Bach, Abram Demski, hamar Arel, Mark Waser, Randal Koene, Paul Rosenbloom, Zhongzhi Shi, Steve Omohundro, Bill Hibbard, Eray Ozkural, Brandon Rohrer, Ben Johnston, John Laird, Shane Legg, Selmer Brin&sjord, Anders Sandberg, Alexei Samsonovich, Wlodek Duch, and more

1 The Hopfield neural net model 256 13.4.2 Knowledge Representation via Cell Assemblies 257 13.5 Neural Foundations of Learning 258 13.5.1 Hebbian Learning 258 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies 258 13.5.3 Neural Darwinism 259 13.6 Glocal Memory 260 13.6.1 A Semi-Fo

e some meaningful intermediary between these extremes? • Should governments regulate AGI, with Friendliness in mind (as advocated carefully by e.g Bill Hibbard illib021)? Or will this just cause AGI development to move to the handful of countries with more liberal policies? ... or cause it to move undergrou

e our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

. HHPO12. Adam Hampshire, Roger Highfield, Beth Parkin, and Adrian Owen. Fractionating human intelli- gence. Neuron vol. 76 issue 6, 2012. Hib02. Bill Hibbard. Superintelligent Machines. Springer, 2002. Hof79. Douglas Hofstadter. Cadet, Escher, Bach: An Eternal Golden Braid. Basic, 1979. Hof95. Douglas

parameters and relationships guiding what the system pays attention to, at what points in time. This is a term inclusive of Importance Updating and Hebbian Learning. • Attentional Currency: Short Term Importance and Long Term Importance values are implemented in terms of two different types of artificial money,

Blerim Enruli, Kristinn Thorisson, Joscha Bach, Abram Demski, Itamar Arel, Mark Waser, Randal Koene, Paul Rosenbloom, Zhongzhi Shi, Steve Omohundro, Bill Hibbard, Eray Ozkural, Brandon Rohrer, Ben Johnston, John Laird, Shane Legg, Selmer Bringsjord, Anders Sandberg, Alexei Samsonovich, Wlodek Duch, and more T

3.4.2 Knowledge Representation via Cell Assemblies .................2.05- 257 13.5 Neural Foundations of Learning ..........0 0.000 ee eee 258 13.5.1 Hebbian Learning............ 0.0.00 eee ete eee 258 13.5.2 Virtual Synapses and Hebbian Learning Between Assemblies .......... 258 35:38 Netital DArwitiiSitt +2; case

ere some meaningful intermediary between these extremes? e Should governments regulate AGI, with Friendliness in mind (as advocated carefully by e.g Bill Hibbard [Hib02])? Or will this just cause AGI development to move to the handful of countries with more liberal policies? ... or cause it to move underground

ake our points about glocal knowledge representation in neural net type systems without discussing some aspects of learning in these systems. 13.5.1 Hebbian Learning The most common and plausible assumption about learning in the brain is that synaptic connec- tions between neurons are adapted via some variant of

08. HHPO12. Adam Hampshire, Roger Highfield, Beth Parkin, and Adrian Owen. Fractionating human intelli- gence. Neuron vol. 76 issue 6, 2012. Hib02. Bill Hibbard. Superintelligent Machines. Springer, 2002. Hof79. Douglas Hofstadter. Godel, Escher, Bach: An Eternal Golden Braid. Basic, 1979. Hof95. Douglas Ho

e parameters and relationships guiding what the system pays attention to, at what points in time. This is a term inclusive of Importance Updating and Hebbian Learning. Attentional Currency: Short Term Importance and Long Term Importance values are implemented in terms of two different types of artificial money, ST