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Today's Large Language Models are Essentially BS Machines
(quandyfactory.com)
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this post was submitted on 12 Sep 2023
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You seem unfamiliar with the concept of consciousness as an emergent property.
What if we dramatically reduce the cost of training - what if we add realtime feedback mechanisms as part of a perpetual model refinement process?
As far as I'm aware, we don't know.
How are you so confident that your feelings are not simply a consequence of complexity?
Even if they are a result of complexity, that still doesn't change the fact that LLMs will never be complex in that manner.
Again, LLMs have no self-awareness. They are not designed to have self-awareness. They do not have feelings or emotions or thoughts; they cannot have those things because all they do is generate words in response to queries. Unless their design fundamentally changes, they are incompatible with consciousness. They are, as I've said before, complicated autosuggestion algorithms.
Suggesting that throwing enough hardware at them will change their design is absurd. It's like saying if you throw enough hardware at a calculator, it will develop sentience. But a calculator will not do that because all it's programmed to do is add numbers together. There's no hidden ability to think or feel lurking in its design. So too LLMs.
I believe I understand everything you are saying and why you are saying it. I think you are completely missing the point, though. LLMs already do quite a few things they were not designed to do. Also, your idea of sentience seems very limited. Yes, with our biological computers we have some degree of presence over "time", but is that critical - or is it just critical for us due to our limited faculties.
What if "the internet" developed some form of self-awareness - would we know? Our entire society could be subtly manipulated through carefully placed latency spikes, for example. I'm not saying this is happening, just that I think you are incredibly overconfident because you have an understanding of LLMs current lack of state etc.
If we added a direct feedback mechanism - realtime or otherwise - we could start seeing more compelling emergent properties develop. What about feedback and ability to self-modify?
These systems are processing information on a level we cannot even pretend to comprehend. How can you be so certain that a single training refinement couldn't result in some sort of spark - curiosity, desire to be introspective, whatever.
Perhaps Hofstadter is losing his mind - but I think we should at least consider the possibility that his concern is warranted. We are not special.
No; they do exactly what they were designed to do, which is convert words to vectors, do math with them, and convert it back again. That we've find more utility in this use does not change their design.
Uh what? Like how would it? This is just technomystical garbage. Enough data in one place and enough CPU in one place doesn't magically make that place sentient. I love it as a book idea, but this is real life.
This would be a significant design divergence from what LLMs are, so I'd call those things something different.
But in any event that still would not actually give LLMs anything approaching: thoughts, feelings, or rationality. Or even the capability to understand what they were operating on. Again, they have none of those things and they aren't close to them. They are word completion algorithms.
Humans are not word completion algorithms. We have an internal existence and thought process that LLMs do not have and will never have.
Perhaps at some point we will have true artificial intelligence. But LLMs are not that, and they are not close.
Are we arguing semantics here?
https://www.jasonwei.net/blog/emergence https://arxiv.org/pdf/2206.07682.pdf https://arxiv.org/pdf/2304.15004.pdf
I could be wrong, obviously, but I don't think this is as straightforward or settled as you are suggesting.
Lol... come on. Your second source disagrees with your assertion:
You are wrong and it is quite settled. Read more, including the very sources you're trying to recommend others read.
You apply a reductionist view to LLMs that you do not apply to humans.
LLMs receive words and produce the next word. Humans receive stimulus from their senses and produce muscle movements.
LLMs are in their infancy, but I'm not convinced their "core loop", so to speak, is any more basic than our own.
In the world of text: text in -> word out
In the physical word: sense stimulation in -> muscle movement out
There's nothing more to it than that, right?
Well, actually there is more to it than that, we have to look at these things on a higher level. If we believe that humans are more than sense stimulation and muscle movements, then we should also be willing to believe that LLMs are more than just a loop producing one word at a time. We need to assess both at the same level of abstraction.
They have no core loop. You are anthropomorphizing them. They are literally no more self-directed than a calculator, and have no more of a "core loop" than a calculator does.
Do you believe humans are simply very advanced and very complicated calculators? I think most people would say "no." While humans can do mathematics, we are different entirely to calculators. We experience sentience; thoughts, feelings, emotions, rationality. None of the devices we've ever built, no matter how clever, has any of those things: and neither do LLMs.
If you do think humans are as deterministic as a calculator then I guess I don't know what to tell you other than I disagree. Other people actually exist and have internal realities. LLMs don't. That's the difference.
As a programmer I can confirm that LLMs definitely have loops. Look at the code, look at the algorithms, you will see the loops. The "core loop" in the LLM algorithm is "read the context, produce the next work, read the context, produce the next word".
The core loop in animals is "receive stimulus using senses, move muscles, receive stimulus using senses, move muscles". That's all humans do, that's all animals do.
I think there's a possibility that humans are simply very advance machines. Look at the debate over whether humans have free will, it's an interesting question and the important take away is that we still have a lot to learn about our brains and physics. I don't want to get into that though.
You've ignored my main complaint. I said that you treat LLMs and humans at different levels of abstraction:
It's not fair to say that LLMs simply predict the next word and humans have feelings and reason.
It would be fair though, to say that LLMs simply predict the next word and humans simply bounce electric-chemical signals between neurons and move muscles.
I don't think that way about people or LLMs though. I think people have feeling and reason, and I think LLMs reason too. LLMs aren't the same as people and aren't as good though. But LLMs are good enough to say that they can "reason" in my experience[0].
[0]: I formed this opinion when learning linear algebra from GPT4. It was quite a good teacher. The textbook I'm using made a mistake that GPT4 caught. I encountered a proof that GPT4 wasn't aware of, and GPT4 wouldn't agree with me that C(A) = C(AA^T) until I explained the proof, and then GPT4 could finally reason for itself and see for itself that C(A) = C(AA^T). As an experiment, I started a new GPT4 session and repeated the experiment using a faulty proof, but I wasn't able to convince GPT4 with a faulty proof, it was able to reason through the math concepts well enough to recognize when a mathematical proof was faulty and could not be convinced by a faulty proof. I tried this experiment 4 or 5 times. To be clear, what happened here is that GPT4 was able to learn a near math concept in one shot (within a single context window), but only if accompanied by a proper mathematical proof, and was smart enough to recognize faulty proofs as being faulty. To me, that rises to the level of "reason".
The two types of loops you equivocate are totally different; saying that a computer executing a program, and an animal living, are actually the same, is very silly indeed. Like, air currents have a "core loop" of blowing around a lot but no one says that they're intelligent or that they're like computer programs or humans.
No; you are analogizing them but losing sense of their differences in the process. I am not abstracting LLMs. That is all they do. That is what they were designed to do and what they accomplish.
You are drawing a comparison between a process humans have that generates consciousness, and literally the entirety of an LLM's existence. There is nothing else to an LLM. Whereas if you say "well a human is basically just bouncing electro-chemical signals between neurons and moving muscles" people (like me) would rightly say you were missing the forest for the trees.
The "trees" for an LLM are their neural networks and word vectors. The forest is a word prediction algorithm. There is no higher level to what they do.
At what level do LLMs teach? Something was teaching me linear algebra and I thought it was the GPT4. When GPT4 was able to recognize a valid mathematical proof that was previously unknown to it, what level was it operating at?
LLMs do not "teach," and that is why learning from them is dangerous. They synthesize words and return other words, but they do not understand the content presented to them in any sense. Because of this, there is the chance that they are simply spouting bullshit.
Learn from them if you like, but remember they are absolutely no substitute for a human, and basically everything they tell you must be checked for correctness.
GPT4 did teach me. I say this as the one who learned, whatever that's worth.