I think we are past the "just predicting the next token" stage. GPT and it's various incarnations do exhibit behaviour that most people will describe as thinking
Just because GPT exhibits a behavior does not mean it performs that behavior. You are using those weasel words for a very good reason!
Language is a symbolic representation of behavior.
GPT takes a corpus of example text, tokenizes it, and models the tokens. The model isn't based on any rules: it's entirely implicit. There are no subjects and no logic involved.
Any "understanding" that GPT exhibits was present in the text itself, not GPT's model of that text. The reason GPT can find text that "makes sense", instead of text that "didn't make sense", is that GPT's model is a close match for grammar. When people wrote the text in GPT's corpus, they correctly organized "stuff that makes sense" into a string of letters.
The person used grammar, symbols, and familiar phrases to model ideas into text. GPT used nothing but the text itself to model the text. GPT organized all the patterns that were present in the corpus text, without ever knowing why those patterns were used.
In what sense is your "experience" (mediated through your senses) more valid than a language model's "experience" of being fed tokens? Token input is just a type of sense, surely?
It's not that I think multimodal input is important. It's that I think goals and experimentation are important. GPT does not try to do things, observe what happened, and draw inferences about how the world works.
I would say it's not a question of validity, but of the additional immediate, unambiguous, and visceral (multi sensory) feedback mechanisms to draw from.
If someone is starving and hunting for food, they will learn fast to associate cause and effect of certain actions/situations.
A language model that only works with text may yet have an unambiguous overall loss function to minimize, but as it is a simple scalar, the way it minimizes this loss may be such that it works for the large majority of the training corpus, but falls apart in ambiguous/tricky scenarios.
This may be why LLMs have difficulty in spatial reasoning/navigation for example.
Whatever "reasoning ability" that emerged may have learned _some_ aspects to physicality that it can understand some of these puzzles, but the fact it still makes obvious mistakes sometimes is a curious failure condition.
So it may be that having "more" senses would allow for an LLM to build better models of reality.
For instance, perhaps the LLM has reached a local minima with the probabilistic modelling of text, which is why it still fails probabilistically in answering these sorts of questions.
Introducing unambiguous physical feedback into its "world model" maybe would provide the necessary feedback it needs to help it anchor its reasoning abilities, and stop failing in a probabilistic way LLMs tend to currently do.
You used evolution, too. The structure of your brain growth is the result of complex DNA instructions that have been mutated and those mutations filtered over billions of iterations of competition.
There are some patterns of thought that are inherent to that structure, and not the result of your own lived experience.
For example, you would probably dislike pain with similar responses to your original pain experience; and also similar to my lived pain experiences. Surely, there are some foundational patterns that define our interactions with language.
> The model isn't based on any rules: it's entirely implicit. There are no subjects and no logic involved.
In theory a LLM could learn any model at all, including models and combinations of models that used logical reasoning. How much logical reasoning (if any) GPT-4 has encoded is debatable, but don’t mistake GTP’s practical limitations for theoretical limitations.
> In theory a LLM could learn any model at all, including models and combinations of models that used logical reasoning.
Yes.
But that is not the same as GPT having it's own logical reasoning.
An LLM that creates its own behavior would be a fundamentally different thing than what "LLM" is defined to be here in this conversation.
This is not a theoretical limitation: it is a literal description. An LLM "exhibits" whatever behavior it can find in the content it modeled. That is fundamentally the only behavior an LLM does.
thats because people anthropormophize literally anything, and many treat some animals as if they have the same intelligence as humans. GPT has always been just a charade that people mistake for intelligence. Its a glorified text prediction engine with some basic pattern matching.
"Descartes denied that animals had reason or intelligence. He argued that animals did not lack sensations or perceptions, but these could be explained mechanistically. Whereas humans had a soul, or mind, and were able to feel pain and anxiety, animals by virtue of not having a soul could not feel pain or anxiety. If animals showed signs of distress then this was to protect the body from damage, but the innate state needed for them to suffer was absent."
Your comment brings up the challenge of defining intelligence and sentience, especially with these new LLMs shaking things up, even for HN commenters.
It's tough to define these terms in a way that includes only humans and excludes other life forms or even LLMs. This might mean we either made up these concepts, or we're not alone in having these traits.
Without a solid definition, how can we say LLMs aren't intelligent? If we make a definition that includes both us and LLMs, would we accept them as intelligent? And could we even exclude ourselves?
We need clear definitions to talk about the intelligence and sentience of LLMs, AI, or any life forms. But finding those definitions is hard, and it might clash with our human ego. Discussing these terms without definitions feels like a waste of time.
Still, your Descartes reference reminds us that our understanding of human experiences keeps changing, and our current definitions might not be spot-on.
It's a charade, it mimics intelligence. Let's take it ine step further... Suppose it mimics it so well that it becomes indistinguishable for any human from being intelligent. Then still it would not be intelligent, one could argue. But in that case you could also argue that no person is intelligent. The point being, intelligence cannot be defined. And, just maybe, that is the case because intelligence is not a reality, just something we made up.
Yeah, calling AI a "token predictor" is like dismissing human cognition dumb "piles of electrical signal transmitters." We don't even understand our minds, let alone what constitutes any mind, be it alien or far simpler than ours.
Simple != thoughtless. Different != thoughtless. Less capable != thoughtless. A human black box categorically dismissing all qualia or cognition from another remarkable black box feels so wildly arrogant and anthropocentric. Which, I suppose, is the most historically on-brand behavior for our species.
It might be a black box to you, but it’s not in the same way the human brain is to researchers. We essentially understand how LLMs work. No, we may not reason about individual weights. But in general it is assigning probabilities to different possible next tokens based on their occurrences in the training set and then choosing sometimes the most likely, sometimes a random one, and often one based on additional training from human input (e.g. instruct). It’s not using its neurons to do fundamental logic as the earlier posts in the thread point out.
"But at least as of now we don’t have a way to 'give a narrative description' of what the network is doing. And maybe that’s because it truly is computationally irreducible, and there’s no general way to find what it does except by explicitly tracing each step. Or maybe it’s just that we haven’t 'figured out the science', and identified the 'natural laws' that allow us to summarize what’s going on."
Anyway, I don't see why you think that the brain is more logical than statistical. Most people fail basic logic questions, as in the famous Linda problem.[1]
the words "based on" are doing a lot of work here. No, we don't know what sort of stuff it learns from its training data nor do we know what sorts of reasoning it does, and the link you sent doesn't disagree.
We know that the relative location of the tokens in the training data influences the relative locations of the predicted tokens. Yes the specifics of any given related tokens are a black box because we're not going to go analyze billions of weights for every token we're interested in. But it's a statistical model, not a logic model.
