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.
All the evidence suggests that our own minds are also nothing more than probability engines.
This completely understates the gulf between what we call AI and how the human brain actually works. The difference is so severe that acting as if they’re quantitatively comparable is basically pseudoscience. You might as well start claiming that we’re not far off from building a Dyson sphere just because we invented solar panels.
Most “AI” these days are built using linear feed forward networks. The brain is constructed using nonlinear recurrent networks which are can do far more with less. Now you could theoretically create the same output from a linear feed forward network but it’s way less efficient and would require many more neurons to achieve such a result. Which is wild when you consider that there are orders of magnitude more synapses in just the regions of the brain associated with language than there are parameters used in even today’s most advanced “AI” models. Now consider that human synapses rely on over a hundred qualitatively different neurotransmitters and not just a single 16-bit number. It’s also not just the scale of the signal that transmits information in a human synapse but the pattern too. Would you be surprised to know that there are a whole variety of signaling patterns neurons use? Because that’s true too. I haven’t even gotten into the differences in complexity in terms of how neurons process the information they receive. As of now there is no “AI” system that comes anywhere close to replicating that kind of complexity. It’s absurd to suggest where dealing with qualitatively similar machines here.