Statistics versus Understanding: The Essence of What Ails Generative AI
We recorded 6 claims from this post. 3 were checked against evidence. 3 can’t be checked. 3 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: Can’t be checkedSaid
Observation
“The problem with “Foundation Models” (a common term for Generative AI) is that they have never provided the firm, reliable foundation that their name implies.”
Our reading: Foundation models (generative AI) have never provided the firm, reliable foundation their name implies.
No horizon
Assessment: UnresolvedSaid
Observation
“In our own brief experiments with GPT-3 (OpenAI has refused us proper scientific access for over a year) we found cases like the following, which reflects a complete failure to understand human biology.”
Our reading: In Marcus and Davis's experiments, GPT-3 produced outputs showing a complete failure to understand human biology.
No horizon Evidence as of
Limitation: The local-error reading is narrower than a claim that GPT-3 understood nothing about biology.
Assessment: Can’t be checkedSaid
Observation
“GPT-3 has no idea what grape juice is, or what cranberry juice is, or what pouring, sniffing, smelling, or drinking are, or what it means to be dead.”
Our reading: GPT-3 has no idea what grape juice or cranberry juice is, what pouring, sniffing, smelling or drinking are, or what it means to be dead.
No horizon
Scope: GPT-3, in the grape-juice example
Assessment: Can’t be checkedSaid
Observation
“Generative AI systems have always tried to use statistics as a proxy for deeper understanding, and it’s never entirely worked.”
Our reading: Generative AI systems have always tried to use statistics as a proxy for deeper understanding, and it has never entirely worked.
No horizon
Qualifier: always
Assessment: UnresolvedSaid
Causal claim
“That’s why statistically improbable requests like horses riding astronauts have always been challenging for generative image systems.”
Our reading: Because they rely on statistics instead of understanding, generative image systems have always struggled with statistically improbable requests such as horses riding astronauts.
No horizon Evidence as of
Limitation: The most directly relevant study concerns one model.
Assessment: UnresolvedSaid
Causal claim
“Once again statistical frequency (most guitarists play righthanded) won out over linguistic understanding, to the consternation of Hendrix fans everywhere.”
Our reading: In Wyatt Walls's left-handed guitarist example, statistical frequency (most guitarists play right-handed) once again won out over linguistic understanding.
No horizon Evidence as of
Limitation: Image-based tweets could not be inspected.
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 read the claim differently
- The two reviews read the claim differently
- The criteria are ambiguous. Our reading, not assessed: The critic's objection that the system had not been taught relevant information about left-handed drawing is nonsense, because a few seconds on Google Images turns up lots of people drawing with their left hand.