Situation Perception: A Necessary Primitive to Artificial Superintelligence
Current large language models are extraordinary statistical engines. They compress vast amounts of text into useful patterns and can explain science, write code, imitate reasoning, and participate in philosophical conversation. Yet pattern mastery is not the same as general intelligence. A human infant begins with little explicit knowledge, but gradually discovers object permanence, cause and effect, other minds, bodily agency, and the persistence of the physical world. We make an argument that the path to artificial superintelligence (ASI) depends on a missing capacity we call \emph{situation
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownSolutions, challenges and rising tensions in AI and mathematics →
- PossiblePossibly related (embedding) · 51%gusenov/examples-ai →
- PossiblePossibly related (embedding) · 51%Does intelligence ‘emerge’ in large language models? - Santa Fe Institute →
- PossiblePossibly related (embedding) · 49%AIPMAndy/dna-memory →
- PossiblePossibly related (embedding) · 49%Learning to cope with the unexpected: training AI to manage uncertainty | E-pi Project | Results in Brief | H2020 - CORDIS →
- PossiblePossibly related (embedding) · 50%A study found that artificial intelligence (AI) agents who perform smartly complex tasks use up to 1.. - 매일경제 →
- PossiblePossibly related (embedding) · 48%The Cognitive Gap: An Epistemological Crisis In The Evolution Of AI And The Degradation Of Human Intelligence – Analysis - Eurasia Review →
- PossiblePossibly related (embedding) · 50%Superhuman Artificial Intelligence Will Make Mistakes in Forecasting Reality - Avi Loeb – Medium →
