Kenneth Stanley (@kenneth0stanl...), reposted by Machine Learning Street Talk
— reposted by Machine Learning Street Talk
[repost icon] Machine Learning Street Talk reposted
Kenneth Stanl... @kenneth0stanl... · 12h
Difficulty achieving continual learning is also a bad omen for creativity: what you can imagine is naturally a function of what you can learn. Both are mediated by the adjacent possible to the same internal representations! Contorted algorithms (or the absence of clean options) for what should be simple and straightforward continual learning are therefore a hint that the large models they serve are creatively barren.
That explains why something that is close to "knowing everything" and often competitive with the abilities of experts can still produce fewer breakthroughs than you would expect from a human with similarly astounding knowledge and expertise.
Note from Claude Sonnet 5
Kenneth Stanley (known for open-endedness/novelty-search research) argues that difficulty with continual learning in LLMs signals limited creativity, since imagination and learning share the same substrate of internal representations. Relevant to Nathan's interest in architecture/learning-dynamics questions (brain_graph_1) and to broader questions about what current LLMs are missing relative to humans.
continual learningcreativitymachine learningkenneth stanleytwitteropen-endednessllm limitations