D'ici la fin de 2026, aurons-nous une transparence sur un quelconque schéma interne utile au sein d'un grand modèle de langage dont la sémantique aurait été inconnue de l'IA et des sciences cognitives en 2006 ?
IAOne-Off4moisManifold MarketsSans KYCDonnées de résolution vérifiéesAssez bien calibréPrévision communautaire actuelleNon 90%
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Play-money forecasting platform
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Mis à jour il y a 3 heures
Obsolète30 sept. 22, 17:54 UTC1 févr. 27, 07:59 UTC
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Questions liées pour le contexte, et non une comparaison de contrats équivalents.
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PolymarketRègles
In "Moving the Eiffel Tower to ROME", a paper claimed to have identified where the fact "The Eiffel Tower is in France" had been stored within GPT-J-6B, in the sense that you could poke the GPT there and make it believe the Eiffel Tower was in Rome.
- "The Eiffel Tower is in France" seems (in my personal judgment) like the sort of fact that early AI pioneers could and did represent within GOFAI systems.
- GPT-J probably does more with that fact - it can for example answer how to get to the Eiffel Tower from Berlin, believing that the Eiffel Tower is in Rome.
- But the paper didn't offer neural transparency into how GPT-J gives directions, we don't know the stored patterns for answering that part - just a neural representation of the brute idea that GOFAI pioneers might've represented with in(Eiffel-Tower, Rome).
- This market reflects the probability that, in the personal judgment of Eliezer Yudkowsky, anyone will have uncovered any sort of data, pattern, cognitive representation, within a text transformer / large language model (LLM), whose semantic pattern and nature wasn't familiar to AI and cognitive science in 2006 (to pick an arbitrary threshold for "before the rise of deep learning").
- Also in 2006, somebody might've represented "the Eiffel Tower is in France" by assigning spatial coordinates to the Eiffel Tower and a regional boundary to France.