This one important fact about current AI explains almost everything
We recorded 5 claims from this post. 5 can’t be checked. 2 more are held. Read the post at its source.
Claims from this post (5)
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
Causal claim
“If it’s not directly in the training set, but merely superficially like some previous examples, there could be trouble.”
Our reading: When an input is not directly in the training set but only superficially resembles earlier examples, machine learning systems may fail.
No horizon
Qualifier: input not directly in the training set but superficially similar to previous examples
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Everyone commenting on or thinking about AI should learn it by heart, if not verbatim, certainly the gist.”
Our reading: Everyone commenting on or thinking about AI should learn by heart, if not word for word then at least the gist, Koopman's point that machine learning trains on many examples and, when it meets something it lacks many examples of, may have no idea what is going on.
No horizon
Scope: everyone commenting on or thinking about AI
Assessment: Can’t be checkedSaid
Causal claim
“The people who have temporarily gotten rich or famous on AI have done so by pretending that this outlier problem simply doesn’t exist, or that a remedy for it is imminent.”
Our reading: The people who have temporarily gotten rich or famous on AI have done so by pretending that the outlier problem doesn't exist or that a remedy for it is imminent.
No horizon
Scope: people who have temporarily gotten rich or famous on AI
Assessment: Can’t be checkedSaid
Rhetoric
“Once you do, almost everything that people like Altman and Musk and Kurzweil are currently saying about AGI being nigh seems like sheer fantasy, on par with imagining that really tall ladders will soon make it to the moon.”
Our reading: Once one understands that current neural networks struggle with outliers, almost everything that people like Altman, Musk and Kurzweil are currently saying about AGI being near seems like sheer fantasy, like imagining that really tall ladders will soon reach the moon.
No horizon
Qualifier: once you understand that neural networks struggle with outliers
Assessment: Can’t be checkedSaid
Rhetoric
“You can’t think we are close to AGI once you realize that as yet we have no general solution to the outlier problem. You just can’t.”
Our reading: Marcus asserts emphatically that no one who realizes we do not yet have a general solution to the outlier problem can think we are close to AGI.
No horizon
Qualifier: once you realize there is as yet no general solution to the outlier problem
Held from this post (2)
These records did not pass every check, so nothing about them is published. Held is not a finding.
- The two reviews reached different assessments. Our reading, not assessed: LLMs often get large multiplication problems wrong because those problems are effectively outliers relative to their training data.
- The two reviews read the claim differently