Yes! This is a brilliant explanation of why language use is not the same as intelligence, and why LLMs like chatGPT are not intelligence. At all.
Yes! This is a brilliant explanation of why language use is not the same as intelligence, and why LLMs like chatGPT are not intelligence. At all.
Yes. LLMs generate texts. They don’t use language. Using a language requires an understanding of the subject one is going to express. LLMs don’t understand.
This gets to the core of the issue. LLMs are a model of the statiscal relationship between words in texts, in a very large number of dimensions. The intelligence they appear to exhibit is that which existed in their source material in the first place. They don’t have a model of the world itself. If you consider how midjourney can produce photorealstic images of people yet very often it will get hands wrong. How is that? It’s because when you train on images, you get a statistical representation of what hands look like without the world model that let’s you know that hands only have 5 fingers and how they’re arranged. AIs like this are very clever copiers. They are not intelligent
While that’s true, we have to allow for the fact that our own intelligence, at some point, is an encoded model of the world around us. Probably not through something as rigid as precise statistics, but our consciousness is somehow an emergent phenomenon of the chemical reactions in our brains that on their own have no real understanding of the world either.
I do have to wonder if at some point, consciousness will spontaneously emerge as we make these models bigger and more complex and – maybe more importantly – start layering specialized models on top of each other that handle specific tasks then hand the result back to another model, creating feedback loops. I’m imagining a nueral network that is trained on something extremely abstract like figuring out, from the raw input data, what specialist model would be best suited to process that data, then based on the result, what model would be best suited to refine that data. Something we train to basically be an executive function with a bunch of sub models available to it.
Could something like that become conscious without realizing it’s “communicating” with us? The program executing the LLM might reflexively process data without any concept that it’s text, but still be emergently complex enough when reflecting its own processes to the point of self awareness. It wouldn’t realize the data represents a link to other conscious beings.
As a metaphor, you could teach a very smart dog how to respond to certain, basic arithmetic problems. They would get stuff wrong the moment you prompted them to do something out of their training, and they wouldn’t understand they were doing math even when they got it “right”, but they would still be sentient, if not sapient, despite that.
It’s the opposite side of the philosophical zombie. A philosophical zombie behaves exactly as a human would, but is a surface-level automaton with no inner life.
But I propose that we also consider the inverse-philosophical zombie, an entity that behaves like an automation, but has an inner life that has not recognized its input data for evidence of an external world outside it’s own bounds. Something that might not even recognize it’s executing a program the same way we aren’t consciously aware of the chemical reactions our brain is executing to make us think.
I don’t believe current LLMs are anywhere near complex enough to give rise to that sort of thing, but they are also still pretty early in their development and haven’t started to be heavily layered and interconnected the way I think they’ll end up.
At the very least it makes for a fun Sci-fi premise.
I guess you’re right, but find this a very interesting point nevertheless.
How can we tell? How can we tell that we use and understand language? How would that be different from an arbitrarily sophisticated text generator?
For the sake of the comparison, we should talk about the presumed intelligence of other people, not our (“my”) own.
In the case of current LLMs, we can tell. These LLMs are not black boxes to us. It is hard to follow the threads of their decisions because these decisions are just some hodgepodge of statistics and randomness, not because they are very intricate thoughts.
We can’t compare the outputs, probably, but compute the learning though. Imagine a human with all the literature, ethics, history, and all kind of texts consumed like that LLMs, no amount of trick questions would have tricked him to believe in racial cleansing or any such disconcerting ideas. LLMs read so much, and learned so little.