Testing it with more variations until one fails feels a bit like p-hacking. You'd need to engage in actual statistics to get reliable results from that, beyond "If I really try, I can make it fail". Which would be a completely different hypothesis than the one presented at the start
In fact, in my use of o1 it's incredibly clear that it still has the same problems. It's incredibly common that the second I ask for someone even slightly outside the training set, it's more likely to "round" to some wrong solution in the training set, rather than use any sort of human-like reasoning to figure out the right answer (often the right answer isn't hard to get, just not found in a Google search).
May still get it wrong in more subtle ways, though. Personally, I think it'll continue to get physics wrong until someone builds it some robot arms so it can train on actually interactive physical spaces and behavior.
People, including around HN, constantly argue (or at least phrase their arguments) as if they believed that LLMs do, in fact, possess such "knowledge". This very comment chain exists because people are trying to defend against a trivial example refuting the point - as if there were a reason to try.
> That doesn’t mean that LLMs aren’t incredibly powerful; it may not even mean that they aren’t a route to AGI.
I don't accept your definition of "intelligence" if you think that makes sense. Systems must be able to know things in the way that humans (or at least living creatures) do, because intelligence is exactly the ability to acquire such knowledge.
It boggles my mind that I have to explain to people that sophisticated use of language doesn't inherently evidence thought, in the current political environment where the Dead Internet Theory is taken seriously, elections are shown over and over again to be more about tribalism and personal identity than anything to do with policy, etc.
According to whom? There is certainly no single definition of intelligence, but most people who have studied it (psychologists, in the main) view intelligence as a descriptor of the capabilities of a system - e.g., it can solve problems, it can answer questions correctly, etc. (This is why we call some computer systems "artificially" intelligent.) It seems pretty clear that you're confusing intelligence with the internal processes of a system (e.g. mind, consciousness - "knowing things in the way that humans do").
LLMs get things wrong due to different factors than humans (humans lose focus, LLMs have randomness applied when sampling their responses to improve results). But clearly we have to choose a goal somewhat below 100% if we want a test that doesn't conclude that humans are incapable of reasoning.
There's deeper philosophical questions about what reasoning actually _is_, and LLMs have made those sharper, because they've shown it's clearly possible for a complex statistical model to generate words that look like reasoning, but the question is whether there's a difference between what they're doing and what humans are doing, and evidence that they're _not_ reasoning - evidence that they're just generating words in specific orders - weighs heavily against them.
But more importantly, if you want to show that LLMs can't reason you obviously have to use a test that when applied to humans would show that humans can reason. Otherwise your test isn't testing reasoning but something more strict.
Apple AI researchers released a paper on it. They say no.
You can make mistakes and still reason. Very often people given the same premises will disagree in thier reasoning as we are doing right here.
Nothing capable of reasoning would contradict itself so blatantly and in such a short span while failing to indicate any kind of uncertainty.
And failure modes of other types of reasoners do not need to be the same as the failure modes of humans.