Where’s Waldo? The Elephant in the Room
We recorded 9 claims from this post. 3 were checked against evidence. 6 can’t be checked. 3 more are held. Read the post at its source.
Claims from this post (9)
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: Can’t be checkedSaid
Rhetoric
“Here is a lovely and instructive set of examples just posted on X by the data scientist Colin Fraser, which serve to remind us that the beauty of LLM’s remains skin deep:”
Our reading: The apparent quality of LLMs is only skin deep.
No horizon
Assessment: UnresolvedSaid
Observation
“Examples like these, in which ChatGPT confidently generates an image that does not actually meet requirements, are nicely complement the visual hallucinations that Ernest Davis and I reported here in October, in which visual ChatGPT misinterpreted various images:”
Our reading: In examples like those shown, ChatGPT confidently generated images that did not actually meet requirements, and in examples reported in October, visual ChatGPT misinterpreted various images.
No horizon Evidence as of
Limitation: This is an access and verification gap, not evidence that Marcus's descriptions were false.
Assessment: Can’t be checkedSaid
Observation
“In some ways, the most telling example in the earlier newsletter was from Dave Andress (which Vicki Bier shared with us), highlighting the fact that image generators still don’t really understand how the world works:”
Our reading: Image generators still do not really understand how the world works.
No horizon
Assessment: Can’t be checkedSaid
Causal claim
“When you have terabytes of images in your training set you can generate really cool images, but that doesn’t guarantee that your system will understand the relation between those images and the world.”
Our reading: Training on terabytes of images enables impressive image generation but does not guarantee the system understands how images relate to the world.
No horizon
Assessment: UnresolvedSaid
Forecast
“Such errors clearly aren’t disappearing any time soon.”
Our reading: Errors of discomprehension in neural-network-based systems will not disappear any time soon.
No deadline stated Evidence as of
Limitation: Unresolved does not mean the later evidence runs against the forecast.
Assessment: Can’t be checkedSaid
Forecast
“Sometimes GenAI used for source attribution will work, other times it will probably yield outrageous goofs, same as we have seen over and over in other domains.”
Our reading: Generative AI used for source attribution will sometimes work but will probably sometimes produce outrageous errors.
No horizon
Qualifier: used for source attribution
Assessment: SupportedSaid
Forecast
“We can of course expect related problems of discomprehension in efforts to use these systems to address copyright issues, as I wrote earlier this week.”
Our reading: Efforts to use generative AI systems to address copyright issues will run into problems of discomprehension.
Settled before its deadline Evidence as of
Limitation: The decisive study is a preprint.
Assessment: Can’t be checkedSaid
Forecast
“Unfortunately, errors of discomprehension may soon be even more worrisome in a new context: war.”
Our reading: AI errors of discomprehension may soon become more worrisome as AI is used in war.
No horizon
Qualifier: in military contexts
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Not sure that’s a great idea, in an era in which AI still can’t quite sort out what’s happening with an elephant on a crowded beach.”
Our reading: Marcus is not sure that hooking AI up to military applications such as drones is a great idea in an era in which AI still can't quite sort out what's happening with an elephant on a crowded beach.
No horizon
Qualifier: not sure
Held from this post (3)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The criteria are ambiguous. Our reading, not assessed: OpenAI appears to be opening its systems to military applications, and at least one well-funded startup is connecting AI to military drones.
- The criteria are ambiguous. Our reading, not assessed: Statistical approximation through generative AI is rarely reliable.
- The criteria are ambiguous. Our reading, not assessed: AI still can't quite sort out what's happening with an elephant on a crowded beach.