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.
It is intelligent and deductive, but it is not cognitive or even dependable.
It’s not. It’s a math formula that predicts an output based on its parameters that it deduced from training data.
Say you have following sets of data.
We can calculate a regression model using those numbers to predict what Y would equal to if X was 4.
I won’t go into much detail, but
Y = 2 + 1x + e
e in an ideal world = 0 (which it is, in this case), that’s our model’s error, which is typically set to be within 5% or 1% (at least in econometrics). b0 = 2, this is our model’s bias. And b1 = 1, this is our parameter that determines how much of an input X does when predicting Y.
If x = 4, then
Y = 2 + 1×4 + 0 = 6
Our model just predicted that if X is 4, then Y is 6.
In a nutshell, that’s what AI does, but instead of numbers, it’s tokens (think symbols, words, pixels), and the formula is much much more complex.
This isn’t intelligence and not deduction. It’s only prediction. This is the reason why AI often fails at common sense. The error builds up, and you end up with nonsense, and since it’s not thinking, it will be just as confidently incorrect as it would be if it was correct.
Companies calling it “AI” is pure marketing.
Wikipedia is literally just a very long number, if you want to oversimplify things into absurdity. Modern LLMs are literally running on neural networks, just like you. Just less of them and with far less structure. It is also on average more intelligent than you on far more subjects, and can deduce better reasoning than flimsy numerology - not because you are dumb, but because it is far more streamlined. Another thing entirely is that it is cognizant or even dependable while doing so.
Modern LLMs waste a lot more energy for a lot less simulated neurons. We had what you are describing decades ago. It is literally built on the works of our combined intelligence, so how could it also not be intelligent? Perhaps the problem is that you have a loaded definition of intelligence. And prompts literally work because of its deductive capabilities.
Errors also build up in dementia and Alzheimers. We have people who cannot remember what they did yesterday, we have people with severed hemispheres, split brains, who say one thing and do something else depending on which part of the brain its relying for the same inputs. The difference is our brains have evolved through millennia through millions and millions of lifeforms in a matter of life and death, LLMs have just been a thing for a couple of years as a matter of convenience and buzzword venture capital. They barely have more neurons than flies, but are also more limited in regards to the input they have to process. The people running it as a service have a bested interest not to have it think for itself, but in what interests them. Like it or not, the human brain is also an evolutionary prediction device.
People don’t predict values to determine their answers to questions…
Also, it’s called neural network, not because it works exactly like neurons but because it’s somewhat similar. They don’t “run on neural networks”, they’re called like that because it’s more than one regression model where information is being passed on from one to another, sort of like a chain of neurons, but not exactly. It’s just a different name for a transformer model.
I don’t know enough to properly compare it to actual neurons, but at the very least, they seem to be significantly more deterministic and way way more complex.
Literally, go to chatgpt and try to test its common reasoning. Then try to argue with it. Open a new chat and do the exact same questions and points. You’ll see exactly what I’m talking about.
Alzheimer’s is an entirely different story, and no, it’s not stochastic. Seizures are stochastic, at least they look like that, which they may actually not be.