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- cross-posted to:
- [email protected]
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.
Your son and daughter will continue to learn new things as they grow up, a LLM cannot learn new things on its own. Sure, they can repeat things back to you that are within the context window (and even then, a context window isn’t really inherent to a LLM - its just a window of prior information being fed back to them with each request/response, or “turn” as I believe is the term) and what is in the context window can even influence their responses. But in order for a LLM to “learn” something, it needs to be retrained with that information included in the dataset.
Whereas if your kids were to say, touch a sharp object that caused them even slight discomfort, they would eventually learn to stop doing that because they’ll know what the outcome is after repetition. You could argue that this looks similar to the training process of a LLM, but the difference is that a LLM cannot do this on its own (and I would not consider wiring up a LLM via an MCP to a script that can trigger a re-train + reload to be it doing it on its own volition). At least, not in our current day. If anything, I think this is more of a “smoking gun” than the argument of “LLMs are just guessing the next best letter/word in a given sequence”.
Don’t get me wrong, I’m not someone who completely hates LLMs / “modern day AI” (though I do hate a lot of the ways it is used, and agree with a lot of the moral problems behind it), I find the tech to be intriguing but it’s a (“very fancy”) simulation. It is designed to imitate sentience and other human-like behavior. That, along with human nature’s tendency to anthropomorphize things around us (which is really the biggest part of this IMO), is why it tends to be very convincing at times.
That is my take on it, at least. I’m not a psychologist/psychiatrist or philosopher.