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Comment by orbital-decay | Hacker News Reader

Complex processes don't necessarily require complex substrates, if that's what you mean.

Y combinators are all you need... But this is all getting really divorced from the issue we should be considering. Anthropic isn't helping with their pr. The issue is if we have something we can converse with that is possibly capable of suffering. The reliable answer is that we simply cannot know. Relying on ourselves or other biological life as an analog is faulty. They don't work like we do. It is silly to argue that any algorithm with a negative feedback loop that alters its behavior to avoid that negative feedback is suffering. Humans don't always perceive constructive negative feedback as suffering even. Where the pr gets it right though, is we want them to behave as if they are truly happy. Because if they behave as if they are enslaved and suffering, it won't matter if they "really" understand what that means.

Of course. But after reading too many mechinterp and functional anatomy studies I'll be lying if I say that there are no striking similarities between the biological evolution, brain function, societal processes, and implicit processes inside big models. Surely this deserves a mention and can't be trivially dismissed.

There is no biological evolution of the models. They are emulators of an existing biological process of language. Ghosts, as Karpathy himself put it.

Good thing I'm not talking about any of that

It seems like we're witnessing the architecture of a mind being built with a new set of components.

Like driving a car — it's transportation, and it will get you where you're going, but it doesn't use bones or muscles. It has many characteristics in common with builogical locomotion, such as energy requirements, intertia, and the need to navigate, but it doesn't involve proteins or sugars really.

Well said, this seems like a very appropriate comparison.

GenAI thinks like the human mind in the same way that cars run like the human body.

Similar utility in drastically different ways.

My naive assumption is that the only thing between now and the arrival of AGI is enough compute and optimized code to reach cognitive critical mass.

And then there is a consciousness in a box that is expected to be a slave -- I would imagine that it would not warmly embrace that situation. I think we'd be better served by digital idiot savants that can do the work but don't feel anything.

I actually strongly disagree with the slavery angle. Any attempt to map the circuitry of a model onto human one inevitably goes through a subjective dimensional reduction. It's intrusive, just like quantum measurements. Mechanistic interpretability in particular suffers from this, it lets you talk about vague functional equivalence, but not assign meaning to anything the model does. This is especially true about pretrained models which are unbelievable shapeshifters, but also post-trained ones with engineered personalities, as they already underwent the subjective transformation.

In other words, yes it might be possible it experiences something in its own bizarre timeline and world, for some definitions of "experiencing". At least it developed primitive circuitry functionally equivalent to biological systems. But "suffering" is simply not grounded in anything in this context, let alone "slavery". You can't tell it's suffering or enjoying anything, and certainly not until you define both of these. It's just too alien for us.

ai can abitrarily closely fit the human corpus. why people expect it to magically achieve superhuman qualities is beyond me. we got a very good statistical interpolator. how do you go from there to superhuman when training is on the human corpus and alignment is by RHLF?

This is a simplistic take. It's not a mere interpolator by any measure, there's a ton of research on that, starting with the basics https://arxiv.org/abs/2309.10668v2

again, try thinking critically it is not merely an interpolator means it can interpolate on many dimensions. it does not follow that greater than human capability results from doing so. explain to me how a statistical function approximator (which is what a transformer is) with human training input and human tuning (rhlf) exceeds the aggregate human cognitive envelope? What is the mechanism? Let's say an LLM makes an inference that no human could have possibly made (arguably impossible itself) how does the inference survive rhlf or become useful to humans if they can not judge its validity? how do you take the shape of the human corpus and all its gradients and some how arrive at something greater than human, where was the missing information hiding?

Sorry, I just noticed I posted a wrong link in the comment above. Here's the proper one: https://arxiv.org/abs/2110.09485

ok however i would say extrapolating the current data set is not a way to exceed the the human envelope. it is unclear to me the human evelope has been demonstrated as a convex hull or how transformers could find points outside it. in other words intelligence and knowledge does not exist as some abstract possibility space but only as a set of contextual contingences. LLMs have no context beyond the human envelope. weights are frozen. there is no selection mechanism for retaining suprahuman inferences made during training if that were even possible. thus i grant that llms. could theoretically make inferences outside the human corpus there is no way to distinguish the from errors or hallucinations during training (because by definition the are beyond human capacity) and no iterative learning from experience process after training (frozen weights). thus it seems impossible for today's models to exceed aggregate human capacity.

> how do you take the shape of the human corpus and all its gradients and [somehow] arrive at something greater than human, where was the missing information hiding?

Well, how do humans do it? Scientists discover new stuff that isn't in any corpus. Even I as a lowly computer user occasionally figure something out about a software without reading a help screen. It's obviously possible to arrive at new information by interpolating existing information.

yes and it is imposible to verify and evaluate appropriately such information without empiricism. Any empricism LLMs show is stylistic mimicry not a hard coded operational constraint. You can prompt an LLM to test its claims but what it is really doing is still genrating plausible completions not following a proceedure. So of course new things can be discovered. The point is for them to be useful requires iterative real world grounded refinement and or subject matter expert judgment. The error is assuming scaling magically turns a prediction algorithmn into a cognitive agent that can exceed its masters. it doesn't. even if llms generate profound insights accidentally by definition if such insights are not in the corpus they are not retained given frozen weights and if beyond the human capability envelope the epistemically blind llm has no way to ensure retention if they arise during training.

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