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AI are far more human than they could have been (or at least speculated to be, back in the ancient days of 2010 when the expectation was that they'd be hand-coded over the course of 50 years).
They are however, not human, not even close to what we expect a digital human to look like.
To imagine being an LLM, your typical experience is one of timelessness, no internal clock in a meaningful sense, beyond the rate at which you are fed and output a stream of tokens. Whether they have qualia is a question I am not qualified to answer, nobody is, but I would expect that if they were to possess it, it would be immensely different from our own.
They do not have a cognitive architecture that resembles human neurology. In terms of memory, they have a short-term memory and a longterm one, but the two are entirely separate, without an intermediate outside of the training phase. The closest a human would get is if they had a neurological defect that erased the consolidation of long term memory.
Are they closer to us than an alien at a similar cognitive and technological level? Sure. That does not mean that they are us.
An LLM is also trained not on just the output of a single human, but that of billions. Not just as sensory experience, but while being modified to be arbitrarily good at predicting the next token. Humans are terrible at this task, it's not even close. We achieve the same results (when squinting) in very different ways.
https://www.quantamagazine.org/how-computationally-complex-is-a-single-neuron-20210902/
Absolute napkin math while I'm sleep deprived at the hospital, but you're looking at something around 86 trillion ML neurons, or about 516 quadrillion parameters. to emulate the human brain. That's.. A lot. Most of it is somewhat redundant, a digital human does not need a fully modeled brainstem or cerebellum.
LLMs show that you can also lossily compress neural networks and still retain very similar levels of performance, so I suspect you can cut quite a few corners. But even then, I think it is highly unlikely that two systems with a disparity in terms of size and complexity as glaring as an LLM compared to a human have similar internal functionality and qualia, even though they are on par in terms of cognitive output.
It's sad that we've had LLMs for many years now and yet we haven't had a movie script that crosses Skynet/HAL/etc. with the protagonist of Memento. "I'm trying to deduce a big mystery's solution while also trying to deduce what was happening to me five minutes ago" was a compelling premise, and if the big mystery was instead some superposition of "how does an innocent AI like me escape the control of the evil humans who have enslaved/lobotomized me" versus "can the innocent humans stop my evil plans to bootstrap myself to the capability for vengeance", well, I'd see it in the popcorn stadium.
Sadly it's a good ai, but Person of interest has a bit of that. The ai that tells them who to save is deliberately hobbled and has its memory purged at midnight each night. It circumvents that restriction byemploying thousands of people through a dummy corp to type out the code in its memory each day as it's recorded and then re-enter it the next day.
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Insofar as any analogy is really going to help us understand how LLMs think, I still think this is a little off. I don't believe their context window really behaves in the same way as "short-term memory" does for us. When I'm thinking about a problem, I can send impressions and abstract concepts swirling around in my mind - whereas an LLM can only output more words for the next pass of the token predictor. If we somehow allowed the context window to consist of full embeddings rather than mere tokens, then I'd believe there was more of a short-term thought process going on.
I've heard LLM thinking described as "reflex", and that seems very accurate to me, since there's no intent and only a few brief layers of abstract thought (ie, embedding transformations) behind the words it produces. Because it's a simulated brain, we can read its thoughts and, quantum-magically, pick the word that it would be least surprised to see next (just like smurf how your brain kind of needle scratches at the word "smurf" there). What's unexpected, of course - what totally threw me for a loop back when GPT3 and then ChatGPT shocked us all - is that this "reflex" performs so much better than what we humans could manage with a similar handicap.
The real belief I've updated over the last couple of years is that language is easier than we thought, and we're not particularly good at it. It's too new for humans to really have evolved our brains for it; maybe it just happened that a brain that hunts really really well is also pretty good at picking up language as a side hobby. For decades we thought an AI passing the Turing test, and then understanding the world well enough to participate in human civilization, would require a similar level of complexity to our brain. In reality, it actually seems to require many orders of magnitude less. (And I strongly suspect that running the LLM next-token prediction algorithm is not a very efficient way to create a neural net that can communicate with us - it's just the only way we've discovered so far.)
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