Zullen we tegen het einde van 2026 transparantie hebben over enig nuttig intern patroon binnen een Large Language Model waarvan de semantiek in 2006 onbekend zou zijn geweest voor AI en cognitieve wetenschap?
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Verouderd30 sep 22, 17:54 UTC1 feb 27, 07:59 UTC
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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.