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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20174270 |
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| _version_ | 1866901804449529856 |
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| author | Rittersbacher, Harald Rittersbacher, Harald |
| author_facet | Rittersbacher, Harald Rittersbacher, Harald |
| contents | <p>Contemporary approaches to artificial general intelligence (AGI) — large language models,<br>symbolic systems, and reinforcement learning — share a fundamental absence: they have no<br>felt substrate from which understanding can emerge. They mimic intelligence statistically,<br>manipulate symbols formally, or optimize for external reward, but none of them become<br>intelligent in the way biological agents do. We present SEAGI (Self-Evolving AGI), a<br>research architecture built on the thesis that general intelligence emerges from continuous<br>neurochemical tagging of experience against a mortality-grounded substrate. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20174270 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Mortality as Architecture for AGI Rittersbacher, Harald Rittersbacher, Harald <p>Contemporary approaches to artificial general intelligence (AGI) — large language models,<br>symbolic systems, and reinforcement learning — share a fundamental absence: they have no<br>felt substrate from which understanding can emerge. They mimic intelligence statistically,<br>manipulate symbols formally, or optimize for external reward, but none of them become<br>intelligent in the way biological agents do. We present SEAGI (Self-Evolving AGI), a<br>research architecture built on the thesis that general intelligence emerges from continuous<br>neurochemical tagging of experience against a mortality-grounded substrate. </p> |
| title | Mortality as Architecture for AGI |
| url | https://doi.org/10.5281/zenodo.20174270 |