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Generative AI models can produce outputs that challenge or exceed human capabilities, but they still make basic errors in understanding. This paradox is due to a divergence in the configuration of intelligence between models and humans, where models can generate expert-level outputs without fully understanding them. Experimental results show that models outperform humans in generation but fall short in understanding, indicating that generative capability is not contingent upon understanding capability. Caution is needed when interpreting AI based on human intelligence.
https://arxiv.org/abs//2311.00059
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
By Igor Melnyk5
33 ratings
Generative AI models can produce outputs that challenge or exceed human capabilities, but they still make basic errors in understanding. This paradox is due to a divergence in the configuration of intelligence between models and humans, where models can generate expert-level outputs without fully understanding them. Experimental results show that models outperform humans in generation but fall short in understanding, indicating that generative capability is not contingent upon understanding capability. Caution is needed when interpreting AI based on human intelligence.
https://arxiv.org/abs//2311.00059
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

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