AGI versus “broad, shallow intelligence”
We recorded 14 claims from this post. 2 were checked against evidence. 12 can’t be checked. 3 more are held. Read the post at its source.
Claims from this post (14)
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
Observation
“Nobody serious thinks that we have reached AGI (artificial general intelligence) yet.”
Our reading: No serious person thinks that AGI (artificial general intelligence) has been reached yet.
No horizon
Scope: among people he counts as serious
Assessment: Can’t be checkedSaid
Rhetoric
“Anyone who tries to tell you otherwise is either confused or trying to hype something.”
Our reading: Anyone claiming AGI has been achieved is either confused or hyping something.
No horizon
Assessment: Can’t be checkedSaid
Observation
“Every declaration of us already reaching AGI I have seen rests on redefining the term downwards, as I discussed last week,”
Our reading: Every claim he has seen that AGI has already been reached depends on lowering the definition of AGI.
No horizon Evidence as of
Limitation: The evidence does not show which declarations Marcus saw. It also does not show whether he saw these two.
Assessment: UnresolvedSaid
Observation
“No system has been shown to be robust across all the variations (though specific ones vary across replications, perhaps in response to publicly shared examples).”
Our reading: No system has been shown to be robust across all the variations of the posted LLM failure examples under discussion (such as those from Mahabal and Fraser), though which specific examples fail varies across replications, perhaps in response to publicly shared examples.
No horizon Evidence as of
Limitation: Search failure cannot establish that no qualifying demonstration existed.
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Instead, LLMs are still unpredictable enough that humans should still be in the loop in most case.”
Our reading: Because LLMs remain unpredictable, humans should stay in the loop in most cases.
No horizon
Qualifier: in most cases
Assessment: Can’t be checkedSaid
Causal claim
“Counting on AI that is shallow and not reliable, it should also be said, could cause huge problems.”
Our reading: Relying on shallow, unreliable AI could cause huge problems.
No horizon
Assessment: Can’t be checkedSaid
Observation
“We can never guarantee that broad but shallow AI will be safe, or that it will be human-compatible (in the words of Stuart Russell), or that it will do what we ask of it.”
Our reading: Broad but shallow AI can never be guaranteed to be safe, human-compatible, or to do what we ask.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“For all these reasons, I propose that we call LLMs broad, shallow intelligence..”
Our reading: Marcus proposes that LLMs should be called 'broad, shallow intelligence'.
No horizon
Assessment: Can’t be checkedSaid
Observation
“but that regurgitation is an illusion. Genuine depth is lacking.”
Our reading: The depth LLMs seem to have at times, from regurgitating or replicating deep analyses found on the internet, is an illusion; genuine depth is lacking.
No horizon
Qualifier: at times (per Mahabal's quoted observation)
Assessment: UnresolvedSaid
Observation
“But they remain superficial, in that their responses invariably have the right kind of language for any given problem, but (unlike the hypothetical Star Trek computer) rarely trustworthy.”
Our reading: LLMs remain superficial: their responses invariably have the right kind of language for any given problem, but they are rarely trustworthy.
No horizon Evidence as of
Limitation: The cited evaluations predate the target snapshot and are context only.
Assessment: Can’t be checkedSaid
Observation
“The river crossing example and many others shows that LLMs often use the words without a deep understanding what those words mean.”
Our reading: LLMs often use words without deeply understanding what they mean.
No horizon
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“A system answering a river-crossing problem should not answer by superficial analogy to the words in similar problems found in a giant scrape of the internet, but rather by reasoning through concepts about boats, rivers, people, and so on.”
Our reading: AI systems should solve problems by reasoning over concepts, not by surface analogy to text scraped from the internet.
No horizon
Assessment: Can’t be checkedSaid
Conditional forecast
“Then, and only then, will we have reliable solutions to the unreliability that has plagued recent AI.”
Our reading: AI's unreliability will be reliably solved only once systems reason over concepts with deep understanding.
No horizon
Only if: only if systems achieve deep conceptual understanding
Assessment: Can’t be checkedSaid
Recommendation or value judgment
“Moving past broad but shallow AI and into a new and hopefully safer and more reliable regime should be a major research priority for the human species.”
Our reading: Humanity should make it a major research priority to move beyond broad, shallow AI toward safer, more reliable AI.
No horizon
Held from this post (3)
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 do not meet his AGI definition: they are unreliable and cannot reliably generalize to novel circumstances.
- The two reviews reached different assessments. Our reading, not assessed: Generative AI answers are often superficial, confabulate, fail to self-check, and regularly make basic reasoning and math errors.
- The criteria are ambiguous. Our reading, not assessed: LLMs cannot do basic fact checking or sanity checking, which shows they lack resourcefulness.