781–790 of 998 posts
Walk. It's only about a minute away on foot, and driving such a short distance wastes gas and isn't great for your engine (it won't warm up properly).
*Wait*—if you're taking your car to the car wash, you'll obviously need to drive it there. In that case, yes, drive the 50 meters, even though it's barely worth shifting out of park.
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> so you need to tell them the specifics That is the entire point, right? Us having to specify things that we would never specify when talking to a human. You would not start with "The car is functional. The tank is filled with gas. I have my keys." As soon as we are required to do that for the model to any extend that is a problem and not a detail (regardless that those of us, who are familiar with the matter, do bu…
> > so you need to tell them the specifics > That is the entire point, right? Honestly it is a problem with using GPT as a coding agent. It would literally rewrite the language runtime to make a bad formula or specification work. That's what I like with Factory.ai droid: making the spec with one agent and implementing it with another agent.
If you let the agent go down this path, that's on you not the agent. Be in the loop more
> making the spec with one agent and implementing it with another agent
You don't need a specialized framework to do this, just read/write tools. I do it this way all the time
(using the default selected free model for each)
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I used a paid model to try this. Same deal.
I think the real misleading thing is marketing propping up paid models being somehow infinitely better when most of the time it's the same exact shit.
All the people responding saying "You would never ask a human a question like this" - this question is obviously an extreme example. People regularly ask questions that are structured poorly or have a lot of ambiguity. The point of the poster is that we should expect that all LLM's parse the question correctly and respond with "You need to drive your car to the car wash." People are putting trust in LLM's to provide…
It would be interesting to actually ask a group a people this question. I'm pretty sure a lot of people would fail.
It feels like one of those puzzles which people often fail. E.g: 'Ten crows are sitting on a power line. You shoot one. How many crows are left to shoot?' People often think it's a subtraction problem and don't consider that animals flee after gunshots. (BTW, ChatGPT also answers 9.)
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This is the most important comment in this entire thread IMO, and it’s a bit buried. This is the fundamental limitation with generative AI. It only generates, it does not ponder.
what is the difference between "ponder" and "generate"? the number of iterations?
My take is that an artificial model of true intelligence will only be achieved through emergent complexity, not through Frankenstein algorithms and heuristics built on generative AI.
Generative AI does itself have emergent complexity, but I’m bearish that if we would even hook it up to a full human sensory input network it would be anything more than a 21st century reverse mechanical Turk.
Edit: tl;dr Emergent complexity is a necessary but insufficient criteria for intelligence
Similar "broken" common-sense reasoning also occurs in medical edge-case reasoning ( https://www.nature.com/articles/s41598-025-22940-0 ); e.g. LLMs (o1) gets the following type of question wrong: A 4-year-old boy born without a left arm, who had a right arm below elbow amputation one month ago, presents to your ED with broken legs after a motor vehicle accident. His blood pressure from his right arm is 55/30, and wa…
I'm not a doctor, but am amazed that we've apparently reached the situation where we need to use these kinds of complex edge cases in order to hit the limit of the AI's capability; and this is with o1, released over a year ago, essentially 3 generations behind the current state of the art. Sorry for gushing, but I'm amazed that the AI got so far just from "book learning", without never stepping into a hospital, or ev…
The simulator or world-model approach is being investigated. To your point, textual questions alone do not provide adequate coverage to assess real-world reasoning.
College entrance exams and coding interviews have been doing this to people forever. It's an extremely human kind of mistake.
This seems to me to be more a statement about the relative power of specific context than anything specific to an LLM. Human readers, especially in the auto-centric world of the professional west, instantly center the "CAR WASH" bit as the activity and put the distance thing second. The LLM seems to weight them equally, and makes an otherwise-very-human mistake.
But ask someone who doesn't own a car? Not sure it's as obvious a question as you'd think.
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This is the most important comment in this entire thread IMO, and it’s a bit buried. This is the fundamental limitation with generative AI. It only generates, it does not ponder.
You can define "ponder" in multiple ways, but really this is why thinking models exist - they turn over the prompt multiple times and iterate on responses to get to a better end result.
That is to say, all of that activity I listed is activity I’m confident generative AI is not capable of, fundamentally.
Like I said in a cousin comment, we can build Frankenstein algorithms and heuristics on top of generative AI but every indication I’ve seen is that that’s not sufficient for intelligence in terms of emergent complexity.
Imagine if we had put the same efforts towards neural networks, or even the abacus. “If I create this feedback loop, and interpret the results in this way, …”