Happy Groundhog Day, The AI Edition
We recorded 8 claims from this post. 6 were checked against evidence. 2 can’t be checked. 1 more is held. Read the post at its source.
Claims from this post (8)
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
“A subtler problem that I emphasized in 2001, an inability to keep track of knowledge of instances independently of kinds, seems to underlie the rampant hallucinations that allow large language models to say things like “Elon Musk died in a car crash in 2018”, even when nothing in the corpus says so, and massive amounts of data contradict the claim, because they blend together information about kinds with information about specific individuals, in haphazard ways.”
Our reading: LLM hallucinations appear to stem from an inability to track knowledge about individual instances separately from kinds, so the models blend the two haphazardly.
No horizon
Assessment: UnresolvedSaid
Conditional forecast
“Turns out you don’t need to solve them to make beautiful pictures, or decent if error-filled conversation, but anyone who thinks we can get to AGI without solving abstraction, compositionality, and the type-token distinction is kidding themselves.”
Our reading: AGI will not be reached unless abstraction, compositionality, and the type-token distinction are solved, and anyone who thinks otherwise is kidding themselves.
No deadline stated Evidence as of
Only if: if abstraction, compositionality, and the type-token distinction are not solved
Assessment: UnresolvedSaid
Observation
“The measures are new, the model is new, but the stumbling block remains the same.”
Our reading: In the compositionality study he cites, the measures and the model are new, but the stumbling block is the same one neural networks faced in 1993.
No horizon Evidence as of
Limitation: The target period must be refined to the study's actual research period once identified.
Assessment: SupportedSaid
Observation
“Same story; only the architectures and training regimes have changed. The troubles with abstraction persist.”
Our reading: LLMs doing arithmetic still have trouble with abstraction, even with new architectures and training regimes.
No horizon Evidence as of
Limitation: Frequency-associated performance gaps do not prove that models lack all abstraction ability or rely entirely on memorization [e1].
Assessment: UnresolvedSaid
Observation
“Melanie Mitchell’s book, Bender et al’s “Stochastic Parrots”; all of these more recent criticisms of deep learning help establish that some of the basic issues that were known long ago, with predecessors to current models, still remain even today.”
Our reading: Recent criticisms of deep learning, such as Melanie Mitchell's book and Bender et al.'s 'Stochastic Parrots', help establish that some of the basic issues known long ago with predecessors to current models still remain today.
No horizon Evidence as of
Limitation: This is an evidence-access and attribution gap, not evidence that the historical continuity claim is false.
Assessment: UnresolvedSaid
Observation
“Not sure how much has changed in that regard, either, except that the hacks that were once written by graduate students are now farmed out to sweatshops, for a new form of handcrafting:”
Our reading: Marcus is not sure how much AI's reliance on handcrafted hacks has changed since the late 1990s, except that hacks once written by graduate students are now farmed out to sweatshops as a new form of handcrafting.
No horizon Evidence as of
Limitation: The contemporary contractor report does not establish working conditions, while the retrospective allegations concern an earlier employment period [e1, e2].
Assessment: UnresolvedSaid
Observation
“Our new systems work better, but they are still superficial, fine for lab demos but often at a loss in the real world.”
Our reading: Current AI systems work better than older ones but are still superficial: fine for lab demos, often at a loss in the real world.
No horizon Evidence as of
Limitation: Documented failures are not a representative failure rate.
Assessment: Can’t be checkedSaid
Conditional forecast
“Death by a thousand edge cases is still the norm, and probably will be, for another few years, unless and until people learn a little bit of history.”
Our reading: AI systems will probably keep failing on endless edge cases for another few years, unless and until people learn from history.
No horizon
Only if: unless and until people learn a little bit of history
Held from this post (1)
These records did not pass every check, so nothing about them is published. Held is not a finding.