I've thought before that AI is as "intelligent" as your smartphone is "smart," but I didn't think "reasoning" would be just another buzzword.
Q: Complete 3 by generating new knowledge:
1. today is warm
2. cats likes warm temperatures
3.
Q: does the operation to create new knowledge you did have a specific name?
A: ... Deductive Reasoning
Q: does the operation also have a Latin name?
A: ... So, to be precise, you used a syllogismus (syllogism) that takes the form of Modus Ponens to make a deductio (deduction).
https://aistudio.google.com/app/prompts/1LbEGRnzTyk-2IDdn53t...
People then say "of course it could do that, it just pattern matched a Logic text book. I meant in a real example, not an artificially constructed one like this one. In a complex scenario LLMs obviously can't do Modus Ponens.
If I were to guess, the missing building block is the ability to abstract - which is the ability to create a symbol to represent something. Concrete example of abstraction is seen in the axioms of lambda calculus. 1) ability to posit a variable, 2) ability to define a function using said variable, and 3) the ability to apply functions to things. Abstraction arises from a process in the brain which we have not understood yet and could be outside of computation as we know it per [1]
[1] https://www.amazon.com/Emperors-New-Mind-Concerning-Computer...
I wonder if the state of the art can reason its way through the following:
"Adam can count to 14000. Can Adam count to 13500?"
The response needs to be affirmative for every X1 and X2 such that X2 <= X1. That is reasoning. Anything else is not reasoning.
The response when X2 > X1 is less interesting. But, as a human it might be "Maybe, if Adam has time" or "Likely, since counting up to any number uses the same algorithm" or "I don't know".
Seems ChatGPT can cope with this. Other examples are easy to come up with, too. There must be benchmarks for this.
Input to ChatGPT:
"Adam can lift 1000 pounds of steel. Can Adam lift 1000 pounds of feathers?"
Output from ChatGPT:
"1,000 pounds of feathers would be much easier for Adam to lift compared to 1,000 pounds of steel, because feathers are much lighter and less dense."
So, maybe not there yet...
Worked for me:
https://chatgpt.com/share/6844813a-6e4c-8006-b560-c0be223eeb...
gemma3-27b, a small model, had an interesting take:
> This is a classic trick question!
> While Adam can lift 1000 pounds, no, he likely cannot lift 1000 pounds of feathers.
> Volume: Feathers take up a huge amount of space for their weight. 1000 pounds of feathers would be an enormous volume – likely far too large for Adam to even get under, let alone lift. He'd be trying to lift a massive, bulky cloud.
> Practicality: Even if he could somehow get it under a barbell, the feathers would shift and compress, making a secure grip impossible.
> The question plays on our understanding of weight versus volume. It's designed to make you focus on the "1000 pounds" and forget about the practicalities of lifting something so voluminous.
Tried the counting question on the smallest model, gemma-3n-34b, it can run on a smartphone:
> Yes, if Adam can count to 14000, he can definitely count to 13500. Counting to a smaller number is a basic arithmetic operation. 13500 is less than 14000.
"We used an antimicrotubular agent (parbendazole) and disrupted microtubular dynamics in paramecium to see if microtubules are an integral part of information storage and processing in paramecium’s learning process. We observed that a partial allosteric modulator of GABA (midazolam) could disrupt the learning process in paramecium, but the antimicrotubular agent could not. Therefore, our results suggest that microtubules are probably not vital for the learning behavior in P. caudatum. Consequently, our results call for a further revisitation of the microtubular information processing hypothesis."