A new paper argues that achieving artificial general intelligence (AGI) requires more than just advancements in computational architecture or scaling up existing models. The research proposes that the constraints on AGI exist across distinct, non-reducible levels of description, drawing evidence from AI systems research, anthropology, law, and economics. This framework identifies twenty-three structural constraints, organized into eight clusters, suggesting that progress at one level does not automatically translate to others. The paper concludes by offering five falsifiable predictions with named benchmarks to guide future research beyond the current scaling hypothesis. AI
IMPACT Suggests current AI scaling approaches may be insufficient for AGI, necessitating a broader research agenda.
RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- artificial general intelligence
- arXiv
- CatalyzeX
- DagsHub
- g factor
- Gotit.pub
- Hugging Face
- ScienceCast
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