You have completely misunderstood what I was saying so badly I can't even formulate a response other than to suggest you read my reply again. I was not suggesting that LLMs are programmed with print statements, for fuck's sake.
This perspective that consciousness cannot be programmed can only make sense if you're a dualist. We don't know how consciousness arises. If you're a naturalist it can't be ruled out based on the simplicity of the algorithm.
> When you write a program to predict tokens based on context, seeding its context with something that makes it predict "self-reflecting" text is trivial. Program does what it is programmed to do. Would observing the output of the following program inspire doubt as to its sentience?
Then you follow it up with print statements as if that is a good analogy.
As I said, they are not programmed, so your question above is not relevant to your argument.
You say they're programs that are stochastically jiggled, but that's simply not accurate either. All LLM abilities are emergent, even when the training corpus is well defined.
I didn't think you literally thought they were made of print statements, but you are implying they're software that's been "fuzzed". Hopefully you don't literally that either and you're just using it as a bad analogy.
You could have argued from the stance of neural networks being universal functions, which might at least be closer to the truth, but instead your example is print statements!
I get you're trying to say that something trained to say a thing doesn't mean it has arrived at the thing like a mind would, and perhaps that would have been closer for GPT 2.
These days though, we just have so much more awareness of what they're actually doing internally that it's bizarre to even compare them to stochastic parrots of the training corpus, if that is closer to what you're implying.
First you run a program (training framework) to generate a database of values.
Then you run a program (inference engine) which performs calculations against the database of values.
To put it in ELI5 terms: run a program against a book, counting how many times "I love " appears in the book. Note "dogs" 4 times, "cats" 5 times, "you" 1 time into a database. Then run a program against that database. When inputting "I love" as the preceding text, the second program determines the most likely result is "cats" and returns "I love cats" (or returns "I love cats" 50% of the time, or dogs 40% of the time, or you 10% of the time, or some variation by different methods of weighting).
Yes, this is an extreme simplification. Yes, the model is not technically a database either. But this is fundamentally the process followed. You would consider it a single program if the training framework and inference engine were part of the same software and stored the computed training values to memory instead of disk, taking an input dataset and an input context as params and returning "I love cats" as the output. There's all kinds of incredibly sophisticated techniques applied on top of this foundation to vastly improve the statistical modeling and efficiency, but the underlying basics have not fundamentally changed.
> Then you follow it up with print statements as if that is a good analogy.
The print statements were not an analogy. They were pointing out the ridiculousness of doubting whether software is conscious because it generated self-referential text. Gettting software to generate self-referential text is as easy as `print(self_referential_text)`. So the only question is how the self-referential text is generated. For self-referential text generation to be more interesting than passing it as a literal print value, there would have to be some really wondrous "how" going on. But, it turns out, the "how" of an inference engine isn't that much more interesting than literally doing a `print`.
