Epstein Secrets

k for Natural Language Comprehension Now we move forward in the pipeline from syntax toward semantics. The NL comprehension framework provided with OpenCog at its inception in 2008 is RelEx, an English-language se- mantic relationship extractor, which consists of two main components: the dependency extra

rammar terms, this corresponds loosely to (S (NP The cat) (VP chased (NP a snake)) EFTA00624530 384 44 Natural Language Comprehension but the OpenCog linguistic pipeline makes scant use of this kind of phrase structure rendition (which is fine in this simple example; but in the case of complex sen

current OpenCog NL comprehension pipeline, the RelEx2Frame component uses hand-coded rules to map RelEx output into sets of relationships utilizing FrameNet and other similar semantic resources. This is definitively viewed as a "stopgap" without a role in a human- level AGI system, but it's described her

onents in practical projects, including theme with proto-AGI intentions. The syntax currently used for describing semantic relationships drawn from FrameNet and other sources is exemplified by the example Al_Benefit:Benefitor(give,$varl) The n indicates the data source, where 1 is a number indicating t

his nature that can be effective in this context We have not yet done any sophisticated experiments in this regard - our current experiments using OpenCog to control robots involve cruder integration of OpenCog with perceptual and motor subsystems, rather than the tight hybridization described in Chapt

obabilistic evolutionary program learn- Mg is an effective method The probabilistic evolutionary program learning method we have worked with most in OpenCog is MOSES, and significant evidence has been gathered showing it to be dramatically more effective than genetic programming on relevant classes of p

• Frame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. • Freezing: Saving Atoms from the in-RAM AtomSpace to disk. • General Intelligence: Often used in an informal, commonsen

zed Terms • Abduction: A general form of inference that goes from data describing something to a hypothesis that accounts for the data. Often in an OpenCog context, this refers to the PLN abduction rule, a specific First-Order PLN rule (If A implies C, and B implies C, then maybe A is B), which embodie

bility. EFTA00624100 Appendix A Glossary A.1 List of Specialized Acronyms This includes acronyms that are commonly used in discussing CogPrime, OpenCog and related ideas, plus sonic that occur here and there in the text for relatively ephemeral reasons. • AA: Attention Allocation • ADF: Automatica

• Fl ame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. • Freezing: Saving Atoms from the in-RAM AtomSpace to disk. • General Intelligence: Often used in an informal, commonsen

HOUSE_OVERSIGHT_013240 Appendix A Glossary A.1 List of Specialized Acronyms This includes acronyms that are commonly used in discussing CogPrime, OpenCog and related ideas, plus some that occur here and there in the text for relatively ephemeral reasons. AA: Attention Allocation ADF: Automatically De

alized Terms Abduction: A general form of inference that goes from data describing something to a hypothesis that accounts for the data. Often in an OpenCog context, this refers to the PLN abduction rule, a specific First-Order PLN rule (If A implies C, and B implies C, then maybe A is B), which embodies

e Frame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. e Freezing: Saving Atoms from the in-RAM Atomspace to disk. e General Intelligence: Often used in an informal, commonsen

igence (or any other kind of highly powerful general intelligence), the creation of novel ideas will be paramount. New idea creation takes place in OpenCog via a variety of methods - e.g. inside MOSES which creates new program trees, PLN which creates new logical relationships, ECAN which creates new a

e programmed in. In the latter approach, one may use knowledge from statistical corpus analysis, one may use electronic resources like WordNet and FrameNet, and one may use sophisticated, specialized tools like natural language parsers with hand-coded grammars. Rather than trying to emulate the way a h