We are constantly fed a version of AI that looks, sounds and acts suspiciously like us. It speaks in polished sentences, mimics emotions, expresses curiosity, claims to feel compassion, even dabbles in what it calls creativity.
But what we call AI today is nothing more than a statistical machine: a digital parrot regurgitating patterns mined from oceans of human data (the situation hasn’t changed much since it was discussed here five years ago). When it writes an answer to a question, it literally just guesses which letter and word will come next in a sequence – based on the data it’s been trained on.
This means AI has no understanding. No consciousness. No knowledge in any real, human sense. Just pure probability-driven, engineered brilliance — nothing more, and nothing less.
So why is a real “thinking” AI likely impossible? Because it’s bodiless. It has no senses, no flesh, no nerves, no pain, no pleasure. It doesn’t hunger, desire or fear. And because there is no cognition — not a shred — there’s a fundamental gap between the data it consumes (data born out of human feelings and experience) and what it can do with them.
Philosopher David Chalmers calls the mysterious mechanism underlying the relationship between our physical body and consciousness the “hard problem of consciousness”. Eminent scientists have recently hypothesised that consciousness actually emerges from the integration of internal, mental states with sensory representations (such as changes in heart rate, sweating and much more).
Given the paramount importance of the human senses and emotion for consciousness to “happen”, there is a profound and probably irreconcilable disconnect between general AI, the machine, and consciousness, a human phenomenon.
Exactly. People see “AI” and think LLMs and diffusion models. Those are both probabilistic translation engines. They’re no more intelligent than an AC/DC converter, just a lot more complex.
However, there are neural networks and sense arrays in the field of AI, and those are designed to replicate the process of thought.
The real route to a thinking AI is likely a combination of the two, where a neural network can call on expert systems including translation engines to do the heavy lifting and then run a more nuanced decision tree over the results.
Thing is, modern LLMs and diffusion models are already more complex than a single human mind can fully comprehend, so we default to internally labelling them as either “like us” or “magic”, even when we theoretically know them to be nothing but really deep predictive models.