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| Format: | Recurso digital |
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Zenodo
2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.20174270 |
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Inhaltsangabe:
- <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>