Turing trap | Psychology Today

What seems to have happened a lifetime ago, Alan Turing proposed the now famous experiment we all know. It was a conversation between a person and a machine, judged by whether the human could distinguish between the two things. It was practical and inherently binary (yes or no), and it gave early computer science something it needed, a purpose. But it also planted a seed that would grow into a problem we haven’t mentioned yet.

We’ve spent 70 years teaching machines how to behave like humans. And I think we’ve gotten pretty good at that. Linguistic models now write articles and code that sound remarkably human, and perhaps better. they He apologizes When they are wrong and pretend to doubt when the odds are slim. At the heart of this simulation is that they are beyond understanding, and we are letting them get away with it. So, this pseudo-material also fits into the pseudo-style. The models have learned the rhythm and texture of our speech so well that we forget that they are not speaking at all. But something strange happens the better they get that impression. In my opinion, the more human they seem, the less interesting they become.

The cost of imitation

Now, let us consider our current path, which is to extend the scope of the tradition. Today’s large language model predicts the next word based on vast sets of training data. It becomes eloquent, then eloquent, then weak. But remember, it never arrives at understanding. It depicts probability distributions, not meaning. He knows that “the cat sat on” precedes “mat” more often than “sofa,” but it has no image of a cat, no sense of the mat, and no experience of sitting. The sentences it produces are statistically correct and even fantastic, however Emotionally hollow. This is what success looks like when similarity becomes the goal.

Neural computing The same mistake is made in hardware. Engineers are building chips that mimic the structure of our brain, such as spiking neurons and synaptic weights. The results are impressive and tempting. These systems appear to learn faster and use less power than traditional processors. But competence is not insight. A chip that is active like a nerve cell does not think any more than a pianist does. They both reproduce the model but miss the generative process underneath.

Depth through difference

I would argue that the opportunity is real It’s in the differencenot similarity. These differences are interesting. Humans perception travels through the narrative, Passion, intuitionand context. We are slow, biased, and inhuman. Machine perception is driven by pattern, size, speed, and accuracy. It is tireless and relentless, but most importantly, it does not affect. These are not competing situations that need to converge. They are two systems that create depth by separating them. Parallax vision works because your eyes are far apart to produce depth perception. The same principle may apply to intelligence. two Different computational perspectivesWith enough distance between them, it reveals dimensions that neither of them can see alone.

But we keep trying to collapse that distance. Every chatbot trained to look warm and every interface that apologizes for its mistakes aren’t just design choices. They kind of give in to the idea that intelligence only matters when it looks like us. The cost is higher than bad engineering, because we may close the door to forms of cognition that can teach us something new.

Letting machines be weird, really weird

What if we stopped trying to make AI communicable? A quantum computer does not think like a human. It holds multiple cases simultaneously and turns a possibility into an answer. This is not human thinking translated into silicon; It’s a completely different kind of knowledge. Swarm algorithms Solve problems through distributed iteration. No ant alone finds the shortest path to food, but the colony does. Is it possible that intelligence emerges from pattern rather than from parts? These systems do not need to explain themselves in our language or justify their conclusions with our logic. They work on their own terms.

The same can apply to artificial intelligence, if we allow it. Instead of training models to mimic human conversation, we can build systems that show patterns we’d never notice. instead of nervous Networks that approximate brain function, we can explore architectures with no biological counterpart at all. The goal will not be to make machines that think like us, but rather to make machines that think in ways that we can learn from, even if we cannot fully follow them.

The courage to distance ourselves from the center

The Turing Test was not wrong in 1950. It was a clever way to operationalize an intriguing new concept. But was it meant to be a permanent basis on which AI would be judged? The imitation game was the beginning, not the destination. Somewhere along the way, I think we forgot that. We’ve turned methodological comfort into existential ambition, and now we’re stuck improving the wrong thing.

Now, maybe I’m oversimplifying. But for me, the question has never been whether machines can fool us. The question is whether we are brave enough to make them strange. The value of artificial intelligence is not that it makes us feel less alone, but that it may show us how much more awareness there is than we ever imagined. But only if we stop demanding that they look like us.

Author’s Note: The term “Turing Trap” originated in 2022 paper Written by Erik Brynjolfsson, who has studied the economic impacts of human-like artificial intelligence. This post re-examines this concept through an epistemological and philosophical lens.