Comment by bloaf

Are you a stream of words or are your words the “simplistic” projection of your abstract thoughts? I don’t at all discount the importance of language in so many things, but the question that matters is whether statistical models of language can ever “learn” abstract thought, or become part of a system which uses them as a tool.

My personal assessment is that LLMs can do neither.

> Everyone is out here acting like "predicting the next thing" is somehow fundamentally irrelevant to "human thinking" and it is simply not the case.

Nobody is. What people are doing is claiming that "predicting the next thing" does not define the entirety of human thinking, and something that is ONLY predicting the next thing is not, fundamentally, thinking.

A motorcycle is not "sprinting" and an LLM is not "thinking". Everyone would agree that a motorcycle is not running but the same dumb shit is posted over and over and over on here that somehow the LLM is "thinking".

AI has made me question what it is to be a human.

I am not having some existential crisis, but if we get to a point where X% of humans cannot outperform “AI” on any task that humans deem “useful”, for some nontrivial value of X, then many assumptions that culture has inculcated into me about humanity are no longer valid.

What is the role of humans then?

Can it be said that humans “think” if they can’t think a thought that a non thinking AI cannot also think?

It may be doing the "thinking" and could reach AGI. But we don't want it. We don't want to take a fork lift to the gym. We don't want plastic aliens showing off their AGI and asking humanity to outsource human thinking and decision-making to them.

Predicting the next token is not at all the same thing as predicting the next action in a causal chain of actions. Not even close. One is model of language tokens, the other is a model of the physical world. You can come up with all sorts of predictions that can't be expressed cleanly in natural language. And plenty of things that parse cleanly from a language perspective but are unhinged in their description of empirical reality.

When you have a thought, are you "predicting the next thing"—can you confidently classify all mental activity that you experience as "predicting the next thing"?

Language and society constrains the way we use words, but when you speak, are you "predicting"? Science allows human beings to predict various outcomes with varying degrees of success, but much of our experience of the world does not entail predicting things.

How confident are you that the abstractions "search" and "thinking" as applied to the neurological biological machine called the human brain, nervous system, and sensorium and the machine called an LLM are really equatable? On what do you base your confidence in their equivalence?

Does an equivalence of observable behavior imply an ontological equivalence? How does Heisenberg's famous principle complicate this when we consider the role observer's play in founding their own observations? How much of your confidence is based on biased notions rather than direct evidence?

The critics are right to raise these arguments. Companies with a tremendous amount of power are claiming these tools do more than they are actually capable of and they actively mislead consumers in this manner.

Yes, personally I'm completely fine with the fact that LLMs don't actually think. I don't care that they're not AGI, though the hysterics about "AGI is so close now" seems silly to me. Fusion reactors and self-driving cars are just around the same corner.

They prove to have some useful utility to me regardless.

I'm an LLMs are being used in workflows they don't make sense in-sayer. And while yes, I can believe that LLMs can be part of a system that actually does think, I believe that to achieve true "thinking", it would likely be a system that is more deterministic in its approach rather than probabilistic.

Especially when modeling acting with intent. The ability to measure against past results and think of new innovative approaches seems like it may come from a system that may model first and then use LLM output. Basically something that has a foundation of tools rather than an LLM using MCP. Perhaps using LLMs to generate a response that humans like to read, but not in them coming up with the answer.

Either way, yes, its possible for a thinking system to use LLMs (and potentially humans piece together sentences in a similar way), but its also possible LLMs will be cast aside and a new approach will be used to create an AGI.

So for me: even if you are an AI-yeasayer, you can still believe that they won't be a component in an AGI.

A good heuristic is that if an argument resorts to "actually not doing <something complex sounding>" or "just doing <something simple sounding>" etc, it is not a rigorous argument.

The issue is that prediction is "part" of the human thought process, it's not the full story...

It's fascinating when you look at each technical component of cognition in human brains and contrast against LLMs. In humans, we have all sorts of parallel asynchronous processes running, with prediction of columnar activations seemingly the fundamental local function, with tens of thousands of mini columns and regions in the brain corresponding to millions of networked neurons using the "predict which column fires next" objective to increment or decrement the relative contribution of any functional unit.

In the case of LLMs you run into similarities, but they're much more monolithic networks, so the aggregate activations are going to scan across billions of neurons each pass. The sub-networks you can select each pass by looking at a threshold of activations resemble the diverse set of semantic clusters in bio brains - there's a convergent mechanism in how LLMs structure their model of the world and how brains model the world.

This shouldn't be surprising - transformer networks are designed to learn the complex representations of the underlying causes that bring about things like human generated text, audio, and video.

If you modeled a star with a large transformer model, you would end up with semantic structures and representations that correlate to complex dynamic systems within the star. If you model slug cellular growth, you'll get structure and semantics corresponding to slug DNA. Transformers aren't the end-all solution - the paradigm is missing a level of abstraction that fully generalizes across all domains, but it's a really good way to elicit complex functions from sophisticated systems, and by contrasting the way in which those models fail against the way natural systems operate, we'll find better, more general methods and architectures, until we cross the threshold of fully general algorithms.

Biological brains are a computational substrate - we exist as brains in bone vats, connected to a wonderfully complex and sophisticated sensor suite and mobility platform that feeds electrically activated sensory streams into our brains, which get processed into a synthetic construct we experience as reality.

Part of the underlying basic functioning of our brains is each individual column performing the task of predicting which of any of the columns it's connected to will fire next. The better a column is at predicting, the better the brain gets at understanding the world, and biological brains are recursively granular across arbitrary degrees of abstraction.

LLMs aren't inherently incapable of fully emulating human cognition, but the differences they exhibit are expensive. It's going to be far more efficient to modify the architecture, and this may diverge enough that whatever the solution ends up being, it won't reasonably be called an LLM. Or it might not, and there's some clever tweak to things that will push LLMs over the threshold.

most humans in any percentile act towards the thing of someone else. most of these things are a lot worse than what the human "would originally intend". this behavior stems from 100s and thousands of nudges since childhood.

the issue with AI and AI-naysayers is, by analogy, this: cars were build to drive from A to Z. people picked up tastes and some people started building really cool looking cars. the same happens on the engineering side. then portfolio communists came with their fake capitalism and now cars are build to drive over people but don't really work because people, thankfully, are overwhelming still fighting to attempt to act towards their own intents.

Exactly. Our base learning is by example, which is very much learning to predict.

Predict the right words, predict the answer, predict when the ball bounces, etc. Then reversing predictions that we have learned. I.e. choosing the action with the highest prediction of the outcome we want. Whether that is one step, or a series of predicted best steps.

Also, people confuse different levels of algorithm.

There are at least 4 levels of algorithm:

• 1 - The architecture.

This input-output calculation for pre-trained models are very well understood. We put together a model consisting of matrix/tensor operations and few other simple functions, and that is the model. Just a normal but high parameter calculation.

• 2 - The training algorithm.

These are completely understood.

There are certainly lots of questions about what is most efficient, alternatives, etc. But training algorithms harnessing gradients and similar feedback are very clearly defined.

• 3 - The type of problem a model is trained on.

Many basic problem forms are well understood. For instance, for prediction we have an ordered series of information, with later information to be predicted from earlier information. It could simply be an input and response that is learned. Or a long series of information.

• 4 - The solution learned to solve (3) the outer problem, using (2) the training algorithm on (1) the model architecture.

People keep confusing (4) with (1), (2) or (3). But it is very different.

For starters, in the general case, and for most any challenging problem, we never understand their solution. Someday it might be routine, but today we don't even know how to approach that for any significant problem.

Secondly, even with (1), (2), and (3) exactly the same, (4) is going to be wildly different based on the data characterizing the specific problem to solve. For complex problems, like language, layers and layers of sub-solutions to sub-problems have to be solved, and since models are not infinite in size, ways to repurpose sub-solutions, and weave together sub-solutions to address all the ways different sub-problems do and don't share commonalities.

Yes, prediction is the outer form of their solution. But to do that they have to learn all the relationships in the data. And there is no limit to how complex relationships in data can be. So there is no limit on the depths or complexity of the solutions found by successfully trained models.

Any argument they don't reason, based on the fact that they are being trained to predict, confuses at least (3) and (4). That is a category error.

It is true, they reason a lot more like our "fast thinking", intuitive responses, than our careful deep and reflective reasoning. And they are missing important functions, like a sense of what they know or don't. They don't continuously learn while inferencing. Or experience meta-learning, where they improve on their own reasoning abilities with reflection, like we do. And notoriously, by design, they don't "see" the letters that spell words in any normal sense. They see tokens.

Those reasoning limitations can be irritating or humorous. Like when a model seems to clearly recognize a failure you point out, but then replicates the same error over and over. No ability to learn on the spot. But they do reason.

Today, despite many successful models, nobody understands how models are able to reason like they do. There is shallow analysis. The weights are there to experiment with. But nobody can walk away from the model and training process, and build a language model directly themselves. We have no idea how to independently replicate what they have learned, despite having their solution right in front of us. Other than going through the whole process of retraining another one.

This is the "but LLMs will get better, trust me" thread?

LLMs merely interpolate between the feeble artifacts of thought we call language.

The illusion wears off after about half an hour for even the most casual users. That's better than the old chatbots, but they're still chatbots.

Did anyone ever seriously buy the whole "it's thinking" BS when it was Markov chains? What makes you believe today's LLMs are meaningfully different?

I suspect that people instinctively believe they have free will, both because it feels like we do, and because society requires us to behave that way even when we don't.

The truth is that the evidence says we don't. See the Libet experiment and its many replications.

Your decisions can be predicted from brain scans up to 10 seconds before you make them, which means they are as deterministic as an LLM's. Sorry, I guess.