Epstein Secrets

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

pause to briefly discuss some of the practicalities of work done with the OpenCog system currently implementing parts of the CogPrime architecture. OpenCog, the open-source software framework underlying the "OpenCogPrime" (currently partial) implementation of the CogPrime architecture, has been used for

cessful, this will provide an outstanding platform for ongoing AGI development, as well as a visually appealing and immediately meaningful demo for OpenCog. Specific, particularly simple tasks that are the focus of this project team's current work at time of writing include: • Watch another character

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

ing used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as described here. But if OpenCog continues to attract sufficient funding or volunteer inte

at the ideas presented here are not mere armchair speculation — they are currently being used as the basis for an open-source software project called OpenCog, which is being worked on by software developers around the world. Right now OpenCog embodies only a percentage of the overall CogPrime design as des

be validated or refuted via practice. (As a related note: here and there in this book, we will refer to the "current" CogPrime implementation (in the OpenCog framework); in all cases this refers to OpenCog as of late 2013.) To state one believes one knows a workable path to creating a human-level (and pot

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

the present work, it has also been used to control virtual agents in virtual worlds [GEA08]. Prototype work done during 2007-2008 involved using an OpenCog variant called the Open- PetBrain to control virtual dogs in a virtual world (see Figure 6.6 for a screenshot of an OpenPetBrain-controlled virtual d

level of capability will be achievable by simply interposing DeS- TIN (or some other system in its place) as a perception /action “black box” between OpenCog and a robot. Some preliminary experiments in this direction have already been carried out, con- necting the OpenPetBrain to a Nao robot using simpler