> answer:
> Robert has 30 + 10 = 40 cherries.
> If there are 60 cherries to be shared, then Richard and Jerry will have 60 - 40 = 20 cherries each.
> Robert has 40 - 20 = 20 more cherries than Jerry.
Um, the answer is "correct" but isn't the actual reasoning wrong?
Robert has 30
Richard has 20
Jerry has 10
Hence they split the 60 this way.
> This doesn’t make any sense. When I count the wrong way I get the right answer, and when I count right I get the wrong answer.
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The other story this reminds me of is Abbot and Costello's "7 x 13 == 28" skit.
> If there are 60 cherries to be shared, then Richard and Jerry will have 60 - 40 = 20 cherries each.
"and" here is kind of inexact, it implies a sum, so something else. "If Robert has 30 cherries, 10 more than Richard" would be better.
richard + jerry + robert = 60
robert = 30
robert = richard + 10
[0]: https://www.kaggle.com/c/the-allen-ai-science-challenge/over...
This is still simply a system that is good at guessing. It does not know anything.
I would argue that it "knows" an awful lot, but it can't actually reason with it.
However impressive GPT3 type models are, I am not particularly convinced that they're much more than glorified hashtables.
If the hash table is large enough, it can produce lot of answers to a lot of questions, or approximately imitate a lot of stuff it's seen before.
Whether it can actually combine "knowledge" it has stored in its weights into a pattern it's never seen before ... I'm not convinced.
There is a 1-1 correspondence between data compression and generative models. GPT-2 is a highly effective loseless data compression tool: https://bellard.org/textsynth/sms.html
Always wondered why this insight is not taught as much, especially in the context of things like dimensionality reduction...
You were good at guessing!
See how far randomly guessing an integer 1-1000 gets you with OP's word problems with freeform responses.