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That just seems like an arbitrary limitation. Its like asking someone to do answer a math calculation but "no thinking allowed". Like, I guess we can gauge if a

That just seems like an arbitrary limitation. Its like asking someone to do answer a math calculation but "no thinking allowed". Like, I guess we can gauge if a model just _knows all knowable things in the universe_ using that method... but anything of any value that you are gauging in terms of 'intelligence', is going to actually be validating their ability to go "outside the scope" of what they actually are (an autocomplete on steroids).

flyinglizard 10mo ago
We know there are very simple maze solving algorithms you could code in few lines of Python but no one could claim that constitutes intelligence. The difference is between applying intuitive logic and using a predetermined tool.
nearbuy 10mo ago
It depends whether you're asking it to solve a maze because you just need something that can solve mazes, or if you're trying to learn something about the model's abilities in different domains. If it can't solve a maze by inspection instead of writing a program to solve it, that tells you something about its visual reasoning abilities, and that can help you predict how they'll perform on other visual reasoning tasks that aren't easy to solve with code.
- seanmcdirmid 10mo ago
  You could actually add mazes and paths through them to the training corpus, or make a model for just solving mazes. I wonder how effective it would be, I’m sure someone has tried it. I doubt it would generalize enough to give the AI new visual reasoning capabilities beyond just solving mazes.
- JamesSwift 10mo ago
  Again, think about how the models work. They generate text sequentially. Think about how you solve the maze in your mind. Do you draw a line direct to the finish? No, it would be impossible to know what the path was until you had done it. But at that point you have now backtracked several times. So, what could a model _possibly_ be able to do for this puzzle which is "fair game" as a valid solution, other than magically know an answer by pulling it out of thin air?
  - nl 10mo ago
    > So, what could a model _possibly_ be able to do for this puzzle which is "fair game" as a valid solution, other than magically know an answer by pulling it out of thin air? Represent the maze as a sequence of movements which either continue or end up being forced to backtrack. Basically it would represent the maze as a graph and do a depth-first search, keeping track of what nodes it as visited in its reasoning tokens. See for example https://stackoverflow.com/questions/3097556/programming-theory-solve-a-maze https://stackoverflow.com/questions/3097556/programming-theo... where the solution is represented as: A B D (backtrack) E H L (backtrack) M * (backtrack) O (backtrack thrice) I (backtrack thrice) C F (backtrack) G J
    - JamesSwift 10mo ago
      And my question to you is “why is that substantially different than writing the correct algorithm to do it”? Im arguing its a myopic view of what we are going to call “intelligence”. And it ignores how human thought works in the same way by using abstractions to move to the next level of reasoning. In my opinion, being able to write the code to do the thing is effectively the same exact thing as doing the thing in terms of judging if its “able to do” that thing. Its functionality equivalent for evaluating what the “state of the art” is, and honestly is naive to what these models even are. If the model hid the tool calling in the background instead, and only showed you its answer would we say its more intelligent? Because that’s essentially how a lot of these things work already. Because again, the actual “model” is just a text autocomplete engine and it generates from left to right.
      - nl 10mo ago
        > In my opinion, being able to write the code to do the thing is effectively the same exact thing as doing the thing That's great, but it's demonstrably false. I can write code that calculates the average letter frequency across any Wikipedia article. I can't do that in my head without tools because of the rule of seven[1]. Tool use is absolutely an intelligence amplifier but it isn't the same thing. > Because again, the actual “model” is just a text autocomplete engine and it generates from left to right. This is technically true, but somewhat misleading. Humans speak "left to right" too. Specifically, LLMs do have some spatial reasoning ability (which is what you'd expect with RL training: otherwise they'd just predict the most popular token): https://snorkel.ai/blog/introducing-snorkelspatial/ https://snorkel.ai/blog/introducing-snorkelspatial/ [1] https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus...
rglullis 10mo ago
By your analogy, the developers of stockfish are better chess players than any grandmaster. Tool use can be a sign of intelligence, but "being able to use a tool to solve a problem" is not the same as "being intelligent enough to solve a specific class of problems".
- JamesSwift 10mo ago
  Im not talking about this being the "best maze solver" and "better at solving mazes than humans". Im saying the model is "intelligent enough" to solve a maze. And what Im really saying is that we need to stop moving the goal post on what "intelligence" is for these models, and start moving the goal post on what "intelligence" actually _is_. The models are giving us an existential crisis on not only what it might mean to _be_ intelligent, but also how it might actually work in our own brains. Im not saying the current models are skynet, but Im saying I think theres going to be a lot learned by reverse engineering the current generation of models to really dig into how they are encoding things internally.
  - rglullis 10mo ago
    > Im saying the model is "intelligent enough" to solve a maze. And I don't agree. I think that at best the model is "intelligent enough to use a tool that can solve mazes" (which is an entirely different thing) and at worst it is no different than a circus horse that "can do math". Being able to repeat more tricks and being able to select which trick to execute based on the expected reward is not a measure of intelligence.
    - JamesSwift 10mo ago
      I would encourage you to read the code it produced. Its not like a simple "solve maze" function. There are plenty of "smart" choices in there to achieve the goal given my very vague instructions, and as a result of it analyzing why it failed at first and then adjusting.
      - rglullis 10mo ago

flyinglizard 10mo ago

We know there are very simple maze solving algorithms you could code in few lines of Python but no one could claim that constitutes intelligence. The difference is between applying intuitive logic and using a predetermined tool.

nearbuy 10mo ago

It depends whether you're asking it to solve a maze because you just need something that can solve mazes, or if you're trying to learn something about the model's abilities in different domains. If it can't solve a maze by inspection instead of writing a program to solve it, that tells you something about its visual reasoning abilities, and that can help you predict how they'll perform on other visual reasoning tasks that aren't easy to solve with code.

seanmcdirmid 10mo ago
You could actually add mazes and paths through them to the training corpus, or make a model for just solving mazes. I wonder how effective it would be, I’m sure someone has tried it. I doubt it would generalize enough to give the AI new visual reasoning capabilities beyond just solving mazes.
JamesSwift 10mo ago
Again, think about how the models work. They generate text sequentially. Think about how you solve the maze in your mind. Do you draw a line direct to the finish? No, it would be impossible to know what the path was until you had done it. But at that point you have now backtracked several times. So, what could a model _possibly_ be able to do for this puzzle which is "fair game" as a valid solution, other than magically know an answer by pulling it out of thin air?
- nl 10mo ago
  > So, what could a model _possibly_ be able to do for this puzzle which is "fair game" as a valid solution, other than magically know an answer by pulling it out of thin air? Represent the maze as a sequence of movements which either continue or end up being forced to backtrack. Basically it would represent the maze as a graph and do a depth-first search, keeping track of what nodes it as visited in its reasoning tokens. See for example https://stackoverflow.com/questions/3097556/programming-theory-solve-a-maze https://stackoverflow.com/questions/3097556/programming-theo... where the solution is represented as: A B D (backtrack) E H L (backtrack) M * (backtrack) O (backtrack thrice) I (backtrack thrice) C F (backtrack) G J
  - JamesSwift 10mo ago
    And my question to you is “why is that substantially different than writing the correct algorithm to do it”? Im arguing its a myopic view of what we are going to call “intelligence”. And it ignores how human thought works in the same way by using abstractions to move to the next level of reasoning. In my opinion, being able to write the code to do the thing is effectively the same exact thing as doing the thing in terms of judging if its “able to do” that thing. Its functionality equivalent for evaluating what the “state of the art” is, and honestly is naive to what these models even are. If the model hid the tool calling in the background instead, and only showed you its answer would we say its more intelligent? Because that’s essentially how a lot of these things work already. Because again, the actual “model” is just a text autocomplete engine and it generates from left to right.
    - nl 10mo ago
      > In my opinion, being able to write the code to do the thing is effectively the same exact thing as doing the thing That's great, but it's demonstrably false. I can write code that calculates the average letter frequency across any Wikipedia article. I can't do that in my head without tools because of the rule of seven[1]. Tool use is absolutely an intelligence amplifier but it isn't the same thing. > Because again, the actual “model” is just a text autocomplete engine and it generates from left to right. This is technically true, but somewhat misleading. Humans speak "left to right" too. Specifically, LLMs do have some spatial reasoning ability (which is what you'd expect with RL training: otherwise they'd just predict the most popular token): https://snorkel.ai/blog/introducing-snorkelspatial/ https://snorkel.ai/blog/introducing-snorkelspatial/ [1] https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus_or_Minus_Two https://en.wikipedia.org/wiki/The_Magical_Number_Seven,_Plus...

rglullis 10mo ago

By your analogy, the developers of stockfish are better chess players than any grandmaster. Tool use can be a sign of intelligence, but "being able to use a tool to solve a problem" is not the same as "being intelligent enough to solve a specific class of problems".

JamesSwift 10mo ago
Im not talking about this being the "best maze solver" and "better at solving mazes than humans". Im saying the model is "intelligent enough" to solve a maze. And what Im really saying is that we need to stop moving the goal post on what "intelligence" is for these models, and start moving the goal post on what "intelligence" actually _is_. The models are giving us an existential crisis on not only what it might mean to _be_ intelligent, but also how it might actually work in our own brains. Im not saying the current models are skynet, but Im saying I think theres going to be a lot learned by reverse engineering the current generation of models to really dig into how they are encoding things internally.
- rglullis 10mo ago
  > Im saying the model is "intelligent enough" to solve a maze. And I don't agree. I think that at best the model is "intelligent enough to use a tool that can solve mazes" (which is an entirely different thing) and at worst it is no different than a circus horse that "can do math". Being able to repeat more tricks and being able to select which trick to execute based on the expected reward is not a measure of intelligence.
  - JamesSwift 10mo ago
    I would encourage you to read the code it produced. Its not like a simple "solve maze" function. There are plenty of "smart" choices in there to achieve the goal given my very vague instructions, and as a result of it analyzing why it failed at first and then adjusting.
    - rglullis 10mo ago