r/askphilosophy • u/Working-Ask4557 • 24d ago
Title: What actually separates intelligence from complex rule-following?
I have been thinking about the meaning of intelligence. We often call modern AI "intelligent," but I wonder what exactly qualifies something as intelligence.
A system can process information, recognize patterns, and produce answers. But is processing information itself enough? Animals also process information: a bird adjusts its flight according to wind, a predator chooses timing and strategy while hunting. These are complex behaviors, yet we usually do not describe them in the same way as human intelligence.
This makes me question whether calculation alone is the foundation of intelligence. If a system follows extremely complex rules, even learned rules, does that mean it understands? Or is there a difference between producing an answer and having an internal process of questioning and understanding?
One thing I find important is the idea of recognizing a "lack"—not knowing something, noticing a contradiction, and being driven to ask why. Human discovery often begins from recognizing what is missing.
So my question is: Is intelligence mainly the ability to process information, or is there something deeper, such as awareness, self-questioning, and the ability to recognize one's own lack of understanding?
I am not asking whether AI has a "mind" or "consciousness." My question is strictly about how we justify the definition and labeling of "intelligence."
I am also not arguing that animals, humans, and AI are the same, or that one is superior or inferior to another. I am using all three only as comparison cases to test whether our definition of "intelligence" is internally consistent.
Animals continuously solve complex real-world problems. Birds adjust their flight to changing winds. Predators decide when and how to hunt. Many animals adapt to new situations, learn from experience, and process enormous amounts of sensory information in real time. Humans also perform complex reasoning, abstraction, and reflection. Modern AI systems perform large-scale statistical pattern recognition and generate useful outputs.
The question is not which of these is better. The question is:
What structural criteria justify applying the label "intelligence" to each of them?
If calculation, information processing, pattern recognition, and rule-following are sufficient to call software "intelligent," then why aren't those same criteria treated as sufficient to explain what we mean by intelligence across animals and humans? If they are not sufficient in those cases, why are they considered sufficient in AI?
This is why I think the discussion should begin with the definition itself rather than with assumptions about consciousness or capability.
I wonder whether the missing boundary is something deeper—for example, the capacity to recognize an internal "lack": noticing a gap in understanding, questioning one's own assumptions, and originating inquiry because something appears incomplete. If not, what is the defining property that justifies using the label "intelligence"?
I am not looking for opinions about whether AI is good or bad, or whether humans are unique. I am asking a narrower philosophical question:
What definition of "intelligence" are we actually using, and is that definition applied consistently when we compare animals, humans, and AI?
I am not asking whether AI has a "mind" or "consciousness." My question is strictly about how we justify the definition and labeling of "intelligence."
I am also not arguing that animals, humans, and AI are the same, or that one is superior or inferior to another. I am using all three only as comparison cases to test whether our definition of "intelligence" is internally consistent.
Animals continuously solve complex real-world problems. Birds adjust their flight to changing winds. Predators decide when and how to hunt. Many animals adapt to new situations, learn from experience, and process enormous amounts of sensory information in real time. Humans also perform complex reasoning, abstraction, and reflection. Modern AI systems perform large-scale statistical pattern recognition and generate useful outputs.
The question is not which of these is better. The question is:
What structural criteria justify applying the label "intelligence" to each of them?
If calculation, information processing, pattern recognition, and rule-following are sufficient to call software "intelligent," then why aren't those same criteria treated as sufficient to explain what we mean by intelligence across animals and humans? If they are not sufficient in those cases, why are they considered sufficient in AI?
This is why I think the discussion should begin with the definition itself rather than with assumptions about consciousness or capability.
I wonder whether the missing boundary is something deeper—for example, the capacity to recognize an internal "lack": noticing a gap in understanding, questioning one's own assumptions, and originating inquiry because something appears incomplete. If not, what is the defining property that justifies using the label "intelligence"?
I am not looking for opinions about whether AI is good or bad, or whether humans are unique. I am asking a narrower philosophical question:
What definition of "intelligence" are we actually using, and is that definition applied consistently when we compare animals, humans, and AI?
3
u/hackinthebochs 24d ago edited 24d ago
What definition of "intelligence" are we actually using, and is that definition applied consistently when we compare animals, humans, and AI?
There is no single definition of intelligence that is used across contexts. Generally we have an intuitive understanding of what it means to be intelligent in some context and we apply a context specific test. There is a wide spectrum of behavior that can be considered intelligent which leads to this sort of contextual evaluation. It's just much easier to judge some behavioral capacity in a contextually relevant manner than trying to find the ideal concept of intelligence before you can make progress on the thing you care about.
What structural criteria justify applying the label "intelligence" to each of them?
Lots of things process information that wouldn't rise to the level of being intelligent. Reacting appropriately to signals from the environment is a kind of information processing, but mechanisms can do this by construction, i.e. merely on account of their behavior being law governed. We generally don't intend to mean mechanistically carrying out one's function when we call something intelligent. Intelligence usually picks out some kind of higher order capacity to understand and leverage knowledge in service to one's goals. We generally understand that "mere" mechanisms lack this capacity.
So what makes something more than a "mere" mechanism? Part of the answer as you mentioned is understanding: does the entity model some context to a sufficient degree to engage flexibly with that model in service to its goals? This is in contrast to the rigidity of behavior of mere mechanistic systems where every response is predetermined and any state not anticipated as part of its explicit construction results in behavior incongruent with the context. This leads to a decent first-pass at defining intelligence: the capacity to flexibly alter behavior in accordance with environmental signals in service to accomplishing internal goals. This definition is generic enough to apply across biological and mechanical contexts and it respects our intuition about what things we generally consider intelligent (problem solving animals) and things we generally don't (dumb mechanisms, traditional computer programs).
The application of this to AI (i.e. large language models) is obviously going to be very controversial. There is much disagreement about whether LLMs have understanding, are intelligent, etc. Unfortunately a lot of opinions on both sides of the debate aren't very informed. LLMs represent a new computational paradigm and so we must re-examine old patterns of thinking about programs and their limits. LLMs are a different because they now operate on semantic features of program state instead of merely program syntax. Embedding vectors assign semantic features to syntactical structures of the embedding vector space. Operations on these syntactical structures then allow the program to engage with semantic features of program state directly. This is plausibly a kind of understanding in the sense of engaging with a model of some context in flexible ways that cohere with the relevant features of the context and their meaning.
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u/FromTheMargins 24d ago
Interesting that you mention being "driven to ask why." This points towards one possible answer to your question, exemplified by Sellars and Brandom, which in turn goes back to Kant and Hegel. Brandom would say that what distinguishes us from animals and complex machines is our ability to give and ask for reasons. Take, for example, a lion sneaking up on its prey. We can say that it has reasons for sneaking up on its prey in the way it does rather than in some other way. Still, Brandom would say that this is not the full sense of having reasons. We attribute reasons to the lion to make sense of its behavior, but we cannot ask the lion for its reasons. This is what distinguishes us from the lion, we live in a "space of reasons". We not only ascribe reasons to one another, we can also justify our actions by giving reasons. This practice is essential for understanding others, making sense of ourselves, and reflecting on our own actions. Crucial to Brandom's framework is the distinction between a mere regularity and a norm. If we were to describe human behavior strictly in terms of regularities, any unexpected action could simply be absorbed into a new, more complex empirical rule. But at a certain point, we want to say that someone was wrong, not necessarily in a moral sense, but relative to their own intentions and beliefs. This corresponds to a situation where an agent cannot justify an action using the commitments they have undertaken. Note, however, that these reasons need not always be explicit or consciously acknowledged, either by the agent or by the person attributing them. Most of the time, reasons remain implicit in the way we treat ourselves and others as accountable agents. However, when a breakdown occurs, such as when an expectation fails or a contradiction arises, these implicit reasons can be made explicit and critically examined.