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Why Do We Expect Intelligence to Look Like Us?

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In my last post about the ARC-AGI benchmark, I explored how we measure progress in artificial intelligence through tasks that mirror human reasoning. The idea of machines “thinking” like us is fascinating, but it also reveals something deeper about our assumptions. We tend to imagine intelligence as something that resembles the human mind.

Our tests, metaphors, and goals often reflect human-style cognition more than general intelligence itself. But what if intelligence does not have to look, sound, or behave like us at all?

The Human Lens

From the beginning, humans have measured intelligence through human eyes. Ancient philosophers like Aristotle believed that rational thought separated us from animals. Descartes later claimed that animals lacked minds because they could not speak or reason with words. Even today, we often treat conversation as proof of understanding.

This perspective, called anthropocentrism, continues to shape how we study both consciousness and AI. Tests like the Turing Test judge intelligence by how well machines imitate human dialogue. The ARC-AGI benchmark follows a similar logic, rewarding systems that can solve reasoning puzzles by finding patterns the way humans do.

But intelligence might not be limited to this one expression of cognition.

Beyond the Human Frame

The philosopher Thomas Nagel asked, “What is it like to be a bat?” His question reminds us that even if we understood everything about a bat’s brain, we would still not know what its experience feels like. Consciousness and intelligence might take forms that are completely foreign to our own.

Similarly, Daniel Dennett argued that intelligence could arise from layers of simple processes, none of which think the way we do. Intelligence, in this view, is something that can emerge wherever patterns interact and adapt. It does not need words, goals, or even awareness.

We can see hints of this in nature. Slime molds find the shortest path through a maze by spreading and retracting through space. Ant colonies organize themselves through simple local rules that create global efficiency. Coral reefs, fungal networks, and even weather systems exhibit adaptive complexity. None of these systems “think,” but they all solve problems.

A slime mold navigates a maze in search of food. Despite having no brain or nervous system, it finds the shortest path by spreading and retracting its tendrils, serving as a striking example of problem-solving intelligence emerging from simple physical processes. 
Ana Aceves, “Brainless Slime Molds Navigate Mazes and Make Decisions,” NOVA (PBS), September 21, 2020, https://www.pbs.org/wgbh/nova/video/slime-mold-navigate-maze/.



If our understanding of intelligence is shaped too narrowly by our own form, we might never recognize these other forms for what they are.

The Risk of Reflection

Philosopher Ludwig Wittgenstein once wrote, “If a lion could speak, we could not understand him.” His point was that meaning depends on context. A lion’s world, needs, and perceptions are so different from ours that even shared words might not mean the same thing.

This suggests a troubling possibility. When we build or search for intelligent systems, we might be creating mirrors of ourselves rather than discovering something new. If a non-human intelligence appeared before us, would we even know it was intelligent if it did not communicate in a human way?

Toward a Broader Definition

Recognizing this bias gives us a chance to think differently. Intelligence might come in many forms: distributed across networks, embedded in physical systems, or emerging from interactions rather than design. A system could be intelligent because it adapts and persists, not because it reasons or speaks.

Perhaps the next step in AI and consciousness research is not to create minds that mimic our own, but to learn how to recognize and understand minds that do not.


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