David beats Go-liath
We recorded 4 claims from this post. 3 were checked against evidence. 1 can’t be checked. 3 more are held. Read the post at its source.
Claims from this post (4)
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: SupportedSaid
Observation
“And the match wasn’t even close; Pelrine beat a top AI system 14 games to 1, in a 15 match series.”
Our reading: An amateur Go player, Kellin Pelrine, beat a top Go AI system 14 games to 1 in a 15-game series.
No horizon Evidence as of
Limitation: The accessible news republication appeared one day after Marcus's post.
Assessment: SupportedSaid
Observation
“Seven years of AI has taught us that deep learning is unpredictable, and not always human like.”
Our reading: The past seven years of AI have shown that deep learning is unpredictable and not always human-like.
No horizon Evidence as of
Limitation: The result depends on reading unpredictable as surprising failures rather than universal unpredictability.
Assessment: SupportedSaid
Observation
““Adversarial attacks” like these have shown remarkable weakness, time and again, repeatedly establishing that what deep learning systems do just isn’t the same as what people do:”
Our reading: Adversarial attacks have repeatedly shown that deep learning systems have remarkable weaknesses and don't work the way people do.
No horizon Evidence as of
Limitation: These behavioral results do not establish that human and deep-learning mechanisms are fundamentally unrelated [e5, e6, e7].
Assessment: Can’t be checkedSaid
Observation
“Pellrine’s victory is a profound reminder that no matter how good deep-learning-driven AI looks when it is trained on an immense amount of data, we can never be sure that systems of this sort really can extend what they know to novel circumstances.”
Our reading: However good deep-learning AI trained on immense data looks, we can never be sure it can extend what it knows to novel circumstances.
No horizon
Qualifier: even when trained on immense data
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: Deep learning is just a technical term for layered neural networks and does not entail conceptual depth.