One GenAI image; Ten Errors
We recorded 6 claims from this post. 4 were checked against evidence. 2 can’t be checked. 2 more are held. Read the post at its source.
Claims from this post (6)
In the order they appear in the post. Each quotation is the speaker’s words in this post. A label such as “Supported” or “Can’t be checked” is the claim’s current assessment; its page has the evidence and the reasons.
Assessment: UnresolvedSaid
“By my count (helped considerably by my sharp-eyed followers on X), there are at least ten ways, many subtle, in which the image neither matches the prompt nor what we would expect from the world.”
What we tested: The Midjourney-created image discussed has at least ten ways in which it fails to match the prompt or real-world expectations.
Assessment: Can’t be checkedSaid
“These physical (and in one case conceptual) errors are of a piece with hallucinations and near-plagiarism, too.”
What we tested: The physical errors (and one conceptual error) in the Midjourney image discussed in the post are of a piece with hallucinations and near-plagiarism.
Assessment: UnresolvedSaid
“Hallucinations come from systems exploding data into tiny bits, and then reconstructing them without having a mechanism like a fact-checker to assess the coherence of those reconstructed bits.”
What we tested: Hallucinations result from generative systems breaking data into fragments and reconstructing them without a fact-checking mechanism to assess coherence.
Assessment: UnresolvedSaid
“Near-plagiarism comes from reconstructing those bits in statistically probable ways, without having a mechanism like an originality-assessor to investigate the novelty of the reconstruction.”
What we tested: Near-plagiarism results from generative systems reconstructing data fragments in statistically probable ways without any mechanism to assess originality.
Assessment: SupportedSaid
“What unifies all of the above is that current systems are good at local coherence, between words, and between pixels, but not at lining up their outputs with a global comprehension of the world.”
What we tested: Current systems are good at local coherence, both between words and between pixels, but are not good at lining up their outputs with a global comprehension of the world.
What happened: The authors describe the contemporary contrast between linguistic fluency and explanatory inconsistency. Their experiments concern explanation consistency, not a comprehensive measurement of factual world knowledge. (arXiv; Yanda Chen and colleagues, )
Assessment: Can’t be checkedSaid
“I’ve been worrying about that emphasis on the local at the expense of the global for close to 40 years, and still don’t see a great solution to it.”
What we tested: Marcus says he has been worrying about AI's emphasis on the local at the expense of the global for close to 40 years and still does not see a great solution to it.
Held from this post (2)
These records did not pass every check, so nothing about them is published. Held is not a finding.