Vast Topological Learning and Sentient AGI
| Autor: | Stephen L. Thaler |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Cognitive science
03 medical and health sciences 0302 clinical medicine Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Qualia Artificial consciousness 02 engineering and technology 030217 neurology & neurosurgery Associative property |
| Zdroj: | Journal of Artificial Intelligence and Consciousness. :81-111 |
| ISSN: | 2705-0793 2705-0785 |
| Popis: | A novel form of neurocomputing allows machines to generate new concepts along with their anticipated consequences, all encoded as chained associative memories. Knowledge is accumulated by the system through direct experience as network chaining topologies form in response to various environmental input patterns. Thereafter, random disturbances to the connections joining these nets promote the formation of alternative chaining topologies representing novel concepts. The resulting ideational chains are then reinforced or weakened as they incorporate nets containing memories of impactful events or things. Such encodings of entities, actions, and relationships as geometric forms composed of artificial neural nets may well suggest how the human brain summarizes and appraises the states of nearly a hundred billion cortical neurons. It may also be the paradigm that allows the scaling of synthetic neural systems to brain-like proportions to achieve sentient artificial general intelligence (SAGI). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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