Two Nobel Prizes for AI, and Two Paths Forward
We recorded 7 claims from this post. 1 was checked against evidence. 6 can’t be checked. 5 more are held. Read the post at its source.
Claims from this post (7)
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
“Hinton has certainly had a profound influence on machine learning, but it’s still not entirely clear what he specifically won the prize for, or how that has advanced physics.”
What we tested: Hinton has certainly had a profound influence on machine learning, but it is still not entirely clear what he specifically won the physics Nobel Prize for, or how that work has advanced physics.
Assessment: Can’t be checkedSaid
“People will probably be asking questions about this particular award for a long time.”
What we tested: People will probably keep questioning Hinton's Nobel award for a long time.
Assessment: Can’t be checkedSaid
“Hassabis’s win (shared with DeepMind researcher John Jumper), in contrast, is a slam dunk.”
What we tested: Unlike Hinton's prize, the chemistry Nobel won by Hassabis and shared with John Jumper is a slam dunk, meaning clearly deserved.
Assessment: Can’t be checkedSaid
“AlphaFold is a huge contribution to both chemistry and biology. It arguably still hasn’t lived up to the extreme hype that I have sometimes seen, but it’s a fantastic contribution, used widely by biologists, and in my mind one of the two biggest contributions of AI to date, if not the biggest.”
What we tested: AlphaFold is a huge, fantastic contribution to both chemistry and biology and is used widely by biologists. It arguably still has not lived up to the extreme hype Marcus has sometimes seen. In his mind it is one of the two biggest contributions of AI to date, if not the biggest.
Assessment: UnresolvedSaid
“But the anti-neurosymbolic tradition is limited; its most visible manifestation, LLMs, has brought fame and money, but no robust solution to solving any particular problem with great reliability.”
What we tested: The anti-neurosymbolic tradition is limited. Its most visible manifestation, LLMs, has brought fame and money but no robust solution to solving any particular problem with great reliability.
Assessment: Can’t be checkedSaid
“It’s time for a new approach, and Hassabis sees that.”
What we tested: The AI field should move to a new approach beyond pure end-to-end neural networks, as Hassabis recognises.
Assessment: Can’t be checkedSaid
“His open-mindedness will serve the field well.”
What we tested: Hassabis's open-mindedness will serve the AI field well.
Held from this post (5)
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: Hinton did not invent back-propagation, even though the Nobel citation seems to say he won for it.
- The two reviews reached different assessments. Our reading, not assessed: AlphaFold2 does not go directly from sequence to 3D structure with a single massive neural network, as the end-to-end tradition might attempt. It is considerably more structured, with a custom-built neural network embedded in quite a lot of classical symbolic machinery for information integration and search tools.
- The criteria are ambiguous. Our reading, not assessed: As far as Marcus can tell, the AlphaFold chemistry Nobel is the first Nobel Prize for neurosymbolic AI.
- The record does not meet the current rules. Our reading, not assessed: LLMs remain opaque, plagued by hallucinations and bone-headed errors, and extremely greedy for electricity, water, and data.
- The two reviews read the claim differently