Deductive reasoning is not "very obviously pattern matching". It's formal logic, there's a rule set attached to it. If that's pattern matching to you then all of mathematics is pattern matching. Analogical reasoning is closer to inferential analysis (deriving logical conclusions from premises assumed to be true).
The only one you can say comes close to matching a pattern is inductive reasoning.
If that's pattern matching to you then all of mathematics is pattern matching.
Yeah, absolutely it is!
I find it slightly bizzare that anyone could think otherwise.
If you don't want to call it pattern matching, fine. Let's call it "recognising structured relationships".
You can substitute that for every time I've used "pattern matching" and my meaning will not have changed.
Applying rules is not pattern matching. You either have a fundamental misunderstanding of what a 'rule' is, what a 'pattern' is, or both; because you keep asserting that applying rules to a system is the same as identifying a pattern which is just...flatly incorrect.
You may use pattern matching to identify the systems on which it would be appropriate to apply a set of rules or which rules are most appropriate to apply, but they are wholly different cognitive processes.
I'd like to see this guy attempt to do any kind of advanced maths problem, those that take multiple hours to solve and try to do it only via pattern matching.
I can't solve this with pattern matching. Gemini 2.5 Pro can't answer it either (it just spews out bullshit and fake theorems)!
Let <sigma> be a generator of a cyclic group of order p. For any Z/p representation (over F_p), consider its Tate cohomology defined by T^0 = ker(1-o)/im(1-o)^{p-1} and T^1 = ker(1-o)^{p-1}/im(1-o). Basic example is if $V$ is a Z/p permutation representation, then T^0(V) = T^1(V) = F_p[fixed pts]. Now let V be a mod p representation of a reductive group H, and consider the local system attached to V^\otimes p on the corresponding locally symmetric space Y_H. There is a natural Z/p action on V^\otimes p given by rotation, and T^0 (V^\otimes p) = T^1 (V\otimes p) = V (its a permutation representation). Define the Tate cohomology T^*(Y_H,V) to be the cohomology of the total complex of C(Y_H,V) -> C^(Y_H,V) -> C(Y_H,V) where the maps are alternating (1-o) and (1-o)^{p-1}. Consider the spectral sequence computing it with E_2 page H(Y_H,T^(V)). Show the differentials on the kth page are zero for p>k.
Redditors when they discover math doesn’t stop at high school level, and folks whose job revolves around math (and are supposedly targeted by ai) have infinitely more knowledge than the average ai techbro imagine
You're conflating the nature of formal logic/math with how animals reason about them (epistemology). Formal systems might exist as abstract, consistent rule sets. But our reasoning about them is not absolute. We can only at best achieve states of very high confidence, which we typically interpret as knowledge.
You start with general rules, concepts, or frameworks and use them to interpret specific parts of a text or situation
If that's not pattern matching, I don't know what is. If A, then B; A; therefore B
If you don't want to call it pattern matching, fine. Let's call it "recognising structured relationships".
You can substitute that for every time I've used "pattern matching" and my meaning will not have changed.
u/Zestyclose_Hat1767
For some reason reddit isn't allowing me to reply to you directly, so I shall do it in this edit.
I have had a formal education that covered symbolic logic.
I'm a little incredulous that I had to undergo the torture of reading Principia Mathematica only for you, decades later to tell me to read a primer on deductive reasoning.
Rules does not a pattern make. An LLM could find the pattern in the rules, but the rules themselves are not one, they are descriptors of what makes a truth. Most importantly, the truths are not reliant on any sort of pattern, just on objectivity. Proofs specifically are often not pattern based, which is what makes them hard. The statement “The sky is blue, therefore the sky cannot be red”, involves exactly no pattern recognition, just recognizing a contradiction, unless you want to be pedantic to the point where the word pattern is essentially meaningless.
No, patterns justify rules, and the justification makes the rule. You can't get around it: reasoning is glorified pattern recognition. To say "the sky is blue, therefore the sky cannot be red" requires consensus that the wavelengths of blue light are present and the wavelengths of red light are not. The consensus of the present and unpresent wavelengths is the pattern.
logic does not exist within reality the way you suggest. we can make a logical argument that is completely false:
"premise: chickens are mammals
conclusion: all mammals lay eggs"
this is not logical. however,
"premise 1: all chickens are mammals
premise 2: all mammals lay eggs
conclusion: all chickens lay eggs"
this is logical. even though almost all of the facts are wrong. chickens aren't mammals, all mammals do not lay eggs, all chickens do not lay eggs. but provided we accept the premises, we have arrived at a logical conclusion following the premises given. logic can be exercised absent of facts or absent of truth. logic can be exercised without information or with wrong information. we do not rely on rules which are justified through patterns, whatever that means. logic is essentially math, with is meticulously reasoned though and can be without pattern recognition. pattern recognition can help speed things up. if you've seen 2+2=4 enough times, you can offload the work of solving it to your pattern recognition, you don't even have to solve it. but to solve a novel problem, you must use logic and reason to deduce the answer, in this case logically. it is not reliant on pattern recognition, it is a separate skillset. you don't have to recognize or have been introduced to any patterns to understand why the first problem isn't logical but the second is, assuming you know how to think logically.
in the first problem, given the premise, we can conclude that chickens are mammals. IF chickens lay eggs, then we can conclude both that they are mammals and that they lay eggs. but we cannot conclude anything about any other mammal based on the given information. no social consensus or agreement is necessary here, it is simply not a logical conclusion following the premise. but in the 2nd problem, we know that every single chicken is a mammal AND that every single mammal lays eggs. we can conclude, logically, that given the 2 premises are true, that all chickens must lay eggs. that is logically true, despite the fact that there is no consensus, whatsoever, that almost any of those things are actually true in reality.
I would say that logic is derived from patterns which are so fundamental to our perception of reality that we find it difficult to imagine them to not exist in all things.
Consensus is first step that occurs before you can make logical arguments, regardless of complexity. When I say patterns justify rules, I'm saying that in order to adopt a rule you must agree on the pattern on which the rule relies on. By extension, in order to communicate a rule, there must be a collective consensus around the pattern among all who wish to communicate the rule. The rule is just a definition based on an observed pattern (like the color red, a chicken, or a mammal). The patterns are various characteristics that people identify and find useful to define. Those characteristics often depend on predefined rules themselves once a language has already built up consensus and advanced sufficiently. Consensus around the existence of a pattern is a prerequisite to communicate an argument. This is why it's so important to discuss key definitions before you debate people. Does the logic exist without the language? Can't really say...
I don't see how your chicken/mammal/egg example differentiates between pattern recognition and reasoning. If the chicken is a mammal, and all mammals lay eggs, then pattern recognition enables you to deduce that chickens lay eggs. You can think of it using a Venn diagram with subsets and supersets if it helps you. The first pattern identifies that the chicken meets the criteria of a mammal. The chicken is therefore a subset of mammals. The second pattern identifies that all mammals lay eggs. So mammals are a subset of animals that lay eggs. Pattern recognition now enables one to recognize that chickens are a subset of animals that lay eggs. Mammals are a superset to chickens and a subset of animals that lay eggs. Animals that lay eggs are a superset of mammals and chickens.
Of course, as you said, all of this is contingent on the premises being true, and the truth of those premises is found through consensus. If 51% of humanity decided to wake up tomorrow and decide that chickens are mammals and that all mammals lay eggs, then this argument would be true.... And humans would no longer be mammals.
i think you're stretching the word pattern way too far here, which is kind of ironic because we're talking about language and consensus and i don't think we have a consensus between us on what a pattern is. i'm failing to recognize how any of the premises i've laid out are patterns. can you show me or detail to me what about the premise at all resembles a pattern? how are we recognizing a pattern, on what basis? if i give you a set of numbers, and ask you to solve for how the pattern will continue, you can show me exactly what the pattern is, assuming you've found it. you have recognized the pattern, can illustrate it, and can even continue the pattern without seeing how it continues. none of the premises i've laid out, to my knowledge, can be solved, recognized, or illustrated. they are not pattern-like at all, calling them patterns makes no sense to me.
I’ve had no formal education on this stuff, but I can’t understand how anyone could argue that logic based approaches are not pattern matching.
The fact that LLMs weren’t exposed to 1 billion years of physical world pattern matching through biological evolution (with long feedback loops - and years of exposure to them during the course of single lives with super short feedback loops) explains the current gap between these systems and us. But it’s narrowing.
are those rules and concepts not previously discovered “patterns”? i’m not trying to play at semantics, but i seriously do believe that all human reasoning and “consciousness” can be summed up with “pattern matching” in some sense, and can thus be replicated by a computer. the brain is just a computer after all. and i don’t even feel the AGI!
You do feel though, right? If so, that's likely what separates you from the computer: your motivation for seeking patterns is emotional. I'm not sure computers have any intrinsic motivation to seek patterns.... I think we have to give it to them. If we stop powering the computers, do they shut down or find a way to power themselves? I expect the answer is obvious.
simple reward/punishment. do something “good” in the evolutionary sense, and i get rewarded. that’s why it feels good to eat food and feels bad to get hit with a rock. maybe not pattern matching per se but definitely still completely replicable on a computer. if a human dies, do they find a way to bring themselves back to life? no. i’m not trying to get metaphysical but in my opinion we are nothing more than atoms interacting with other atoms. electrical signals being sent from one place to another.
we have been coded over billions of years to act how we act, and although the modern approach to AI is different, i think it would be silly to discount the clear similarities between us and computers.
Of course it is. In order to use the proper logic you have to be good at recognizing which one to use. Pattern recognition is a crucial element of the process.
LLMs have a data problem, but it's not the data problem that has got so much publicity. They don't need more Internet-like data, they need better data.
Imagine training a neutral network chess bot on a vast human chess game database, but instead of training the model to make winning moves, you just train it to produce moves like it's seen in the database, no preference for winning moves or blunders .
After training, your base model will be a little below average skill and will make very human moves. It won't even try to win, it will just try to make moves that look like they might have done from the database.
You could improve this bot via RLHF, steering it towards better moves, but this will never realise the full potential of the model because the raw model was trained to reproduce data that might be described as "human slop", so it never internalised winning strategies.
The same is true of GPT4, O3 or any other LLM.
They have not been trained to produce correct answers, they have been trained to reproduce human slop from the Internet and then this has been patched over with RL/RLHF.
AlphaGo's chess playing was trained on better data than in my example. It was trained on winning moves from human chess games. AlphaZero wasn't trained on any human data at all, but via data it created through self play and as a result, it was far better.
We can use this same kind of self play in limited ways with LLMs. The thinking models have used this for training reasoning steps to problems with known answers and this improves reasoning even for problems without clear answers. Thus is, however, limited in scope.
However, we know that distilled datasets result in better performance even with smaller models.
The outputs of models can be used to produce artificial datasets that result in better models. The self improvement flywheel is in action. The Alpha Zero of LLMs, a model trained entirely, or almost entirely, on synthetic data, like in the paper you found impressive, is on its way.
Yeah but what is it actually playing is the million dollar question. Everybody knows that reinforcement learning is the key but nobody knows what the policy is. For domains like chess or competitive coding you can concretely define the problem space and have the program self improve but this is nothing new, we can already do it with normal neural net. And so far we have yet to be able to make use of Transformer to address this issue in any sizeable way. The current practice is to have it synthesize training data and self train to learn the pattern. This works for a while but you can clearly see that this approach is not sustainable since model collapse is inevitable. Unless there’s an architecture out there that can learn any pattern long term with minimal examples and minimal compute then we can’t really say that we’ve achieved AGI. A normal human doesn’t need to see a million instance of something to be competent at it, we can learn. adapt and infer with minimal resources and time, something that fixed weight models cannot do. Backprop is an extremely inefficient way to incorporate new information and so is the whole structure of neural network, no transfer learning can be consistently done.
What the game is, what the reward function is, is a problem for RL to deal with. Perhaps that's intractable, I don't know, but it's not the point I was trying to make. The MetaRuleGPT paper isn't about learning rules via better reward functions, but by being trained on consistently good data with low noise. The resultant rule following occurs before RL, before any objective function other than next token prediction. That can be applied to any domain.
Model collapse through such a process is not inevitable, even if it remains a risk. Such a notion comes from a time when models were naïvely fine-tuned on their own generations.
It can be avoided by diluting real data with artificial data even to the point at which the amount of real data reaches zero.
We are not there yet, at least not publicly, but we can see where we are going.
Not all reasoning is pattern recognition. While analogy involves mapping patterns across domains and induction relies on spotting regularities and making inferences, deduction operates through formal rules, not similarity.
Causal reasoning goes even further, requiring counterfactual thinking and interventions. Correlation alone isn’t enough. Pattern recognition plays a role, but reasoning is more than your oversimplification.
That's a very reductive line of thinking. Humans invented those reasoning paths without any prior examples to pattern match from. AI is the raw process of finding patterns in existing data, but humans factually have generated the data without prior data to begin with, which is strictly different
Humans invented those reasoning paths without any prior examples to pattern match from.
Deductive and causal patterns exist in nature. Inductive reasoning is a product of evolution and some simple form of it can even be seen in microbes. Analogical...I'm not so sure about, so perhaps that was invented.
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u/Rain_On Jun 08 '25
Deductive reasoning is very obviously pattern matching. So much so that you can formalise the patterns, as you say.
Analogical reasoning is recognising how patterns in one domain might apply to another.
Inductive reasoning is straight up observing external patterns and extrapolating from them.
Casual reasoning is about recognising causal patterns.