- cross-posted to:
- technology@lemmy.ml
- cross-posted to:
- technology@lemmy.ml
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.
That is not really true. Yes, there are jump instructions being executed when you run interference on a model, but they are in no way related to the model itself. There’s no translation of weights to jumps in transformers and the underlying attention mechanisms.
I suggest reading https://en.m.wikipedia.org/wiki/Transformer_(deep_learning_architecture)
The model is data. It needs to be operated on to get information out. That means lots of JMPs.
If someone said viewing a gif is just a bunch of if-else’s, that’s also true. That the data in the gif isn’t itself a bunch of if-else’s isn’t relevant.
Executing LLM’S is particularly JMP heavy. It’s why you need massive fast ram because caching doesn’t help them.
You’re correct, but that’s like saying along the lines of manufacturing a car is just bolting and soldering a bunch of stuff. It’s technically true to some degree, but it’s very disingenuous to make such a statement without being ironic. If you’re making these claims, you’re either incompetent or acting in bad faith.
I think there is a lot wrong with LLMs and how the public at large uses them, and even more so with how companies are developing and promoting them. But to spread misinformation and polute an already overcrowded space with junk is irresponsible at best.