Comment by TeMPOraL
2 hours ago
Sure! But if I experience it, and then write about my experience, parts of it become available for LLMs to learn from. Beyond that, even the tacit aspects of that experience, the things that can't be put down in writing, will still leave an imprint on anything I do and write from that point on. Those patterns may be more or less subtle, but they are there, and could be picked up at scale.
I believe LLM training is happening at a scale great enough for models to start picking up on those patterns. Whether or not this can ever be equivalent to living through the experience personally, or at least asymptomatically approach it, I don't know. At the limit, this is basically asking about the nature of qualia. What I do believe is that continued development of LLMs and similar general-purpose AI systems will shed a lot of light on this topic, and eventually help answer many of the long-standing questions about the nature of conscious experience.
> will shed a lot of light on this topic, and eventually help answer
I dunno. I figure it's more likely we keep emulating behaviors without actually gaining any insight into the relevant philosophical questions. I mean what has learning that a supposed stochastic parrot is capable of interacting at the skill levels presently displayed actually taught us about any of the abstract questions?
> I mean what has learning that a supposed stochastic parrot is capable of interacting at the skill levels presently displayed actually taught us about any of the abstract questions?
IMHO a lot. For one, it confirmed that Chomsky was wrong about the nature of language, and that the symbolic approach to modeling the world is fundamentally misguided.
It confirmed the intuition I developed of the years of thinking deeply about these problems[0], that the meaning of words and concepts is not an intrinsic property, but is derived entirely from relationships between concepts. The way this is confirmed, is because the LLM as a computational artifact is a reification of meaning, a data structure that maps token sequences to points in a stupidly high-dimensional space, encoding semantics through spatial adjacency.
We knew for many years that high-dimensional spaces are weird and surprisingly good at encoding semi-dependent information, but knowing the theory is one thing, seeing an actual implementation casually pass the Turing test and threaten to upend all white-collar work, is another thing.
--
I realize my perspective - particularly my belief that this informs the study of human mind in any way - might look to some as making some unfounded assumptions or leaps in logic, so let me spell out two insights that makes me believe LLMs and human brains share fundamentals:
1) The general optimization function of LLM training is "produce output that makes sense to humans, in fully general meaning of that statement". We're not training these models to be good at specific skills, but to always respond to any arbitrary input - even beyond natural language - in a way we consider reasonable. I.e. we're effectively brute-forcing a bag of floats into emulating the human mind.
Now that alone doesn't guarantee the outcome will be anything like our minds, but consider the second insight:
2) Evolution is a dumb, greedy optimizer. Complex biology, including animal and human brains, evolved incrementally - and most importantly, every step taken had to provide a net fitness advantage[1], or else it would've been selected out[2]. From this follows that the basic principles that make a human mind work - including all intelligence and learning capabilities we have - must be fundamentally simple enough that a dumb, blind, greedy random optimizer can grope its way to them in incremental steps in relatively short time span[3].
2.1) Corollary: our brains are basically the dumbest possible solution evolution could find that can host general intelligence. It didn't have time to iterate on the brain design further, before human technological civilization took off in the blink of an eye.
So, my thinking basically is: 2) implies that the fundamentals behind human cognition are easily reachable in space of possible mind designs, so if process described in 1) is going to lead towards a working general intelligence, there's a good chance it'll stumble on the same architecture evolution did.
--
[0] - I imagine there are multiple branches of philosophy, linguistics and cognitive sciences that studied this perspective in detail, but unfortunately I don't know what they are.
[1] - At the point of being taken. Over time, a particular characteristic can become a fitness drag, but persist indefinitely as long as more recent evolutionary steps provide enough advantage that on the net, the fitness increases. So it's possible for evolution to accumulate building blocks that may become useful again later, but only if they were also useful initially.
[2] - Also on average, law of big numbers, yadda yadda. It's fortunate that life started with lots of tiny things with very short life spans.
[3] - It took evolution some 3 billion years to get from bacteria to first multi-cellular life, some extra 60 million years to develop a nervous system and eventually a kind of proto-brain, and then an extra 500 million years iterating on it to arrive at a human brain.
whereof one cannot speak, thereof one must remain silent.