Comment by anonymous908213
21 hours ago
Are you trying to suppose that an LLM is more intelligent than a small child with simple thought processes, almost no language capability, little long-term planning, and minimal ability to form long-term memory? Even with all of those qualifiers, you'd still be wrong. The LLM is predicting what tokens come next, based on a bunch of math operations performed over a huge dataset. That, and only that. That may have more utility than a small child with [qualifiers], but it is not intelligence. There is no intent to deceive.
A small child's cognition is also "just" electrochemical signals propagating through neural tissue according to physical laws!
The "just" is doing all the lifting. You can reductively describe any information processing system in a way that makes it sound like it couldn't possibly produce the outputs it demonstrably produces. "The sun is just hydrogen atoms bumping into each other" is technically accurate and completely useless as an explanation of solar physics.
You are making a point that is in favor of my argument, not against it. I make the same argument as you do routinely against people trying to over-simplify things. LLM hypists frequently suggest that because brain activity is "just" electrochemical signals, there is no possible difference between an LLM and a human brain. This is, obviously, tremendously idiotic. I do believe it is within the realm of possibility to create machine intelligence; I don't believe in a magic soul or some other element that make humans inherently special. However, if you do not engage in overt reductionism, the mechanism by which these electrochemical signals are generated is completely and totally different from the signals involved in an LLM's processing. Human programming is substantially more complex, and it is fundamentally absurd to think that our biological programming can be reduced to conveniently be exactly equivalent to the latest fad technology and assume that we've solved the secret to programming a brain, despite the programs we've written performing exactly according to their programming and no greater.
Edit: Case in point, a mere 10 minutes later we got someone making that exact argument in a sibling comment to yours! Nature is beautiful.
> A small child's cognition is also "just" electrochemical signals propagating through neural tissue according to physical laws!
This is a thought-terminating cliche employed to avoid grappling with the overwhelming differences between a human brain and a language model.
Short term memory is the context window, and it's a relatively short hop from the current state of affairs to here's an MCP server that gives you access to a big queryable scratch space where you can note anything down that you think might be important later, similar to how current-gen chatbots take multiple iterations to produce an answer; they're clearly not just token-producing right out of the gate, but rather are using an internal notepad to iteratively work on an answer for you.
Or maybe there's even a medium term scratchpad that is managed automatically, just fed all context as it occurs, and then a parallel process mulls over that content in the background, periodically presenting chunks of it to the foreground thought process when it seems like it could be relevant.
All I'm saying is there are good reasons not to consider current LLMs to be AGI, but "doesn't have long term memory" is not a significant barrier.
Yes. I also don't think it is realistic to pretend you understand how frontier LLMs operate because you understand the basic principles of how the simple LLMs worked that weren't very good.
Its even more ridiculous than me pretending I understand how a rocket ship works because I know there is fuel in a tank and it gets lit on fire somehow and aimed with some fins on the rocket...
The frontier LLMs have the same overall architecture as earlier models. I absolutely understand how they operate. I have worked in a startup wherein we heavily finetuned Deepseek, among other smaller models, running on our own hardware. Both Deepseek's 671b model and a Mistral 7b model operate according to the exact same principles. There is no magic in the process, and there is zero reason to believe that Sonnet or Opus is on some impossible-to-understand architecture that is fundamentally alien to every other LLM's.
Deepseek and Mistral are both considerably behind Opus, and you could not make deepseek or mistral if I gave you a big gpu cluster. You have the weights but you have no idea how they work and you couldn't recreate them.
> I have worked in a startup wherein we heavily finetuned Deepseek, among other smaller models, running on our own hardware.
Are you serious with this? I could go make a lora in a few hours with a gui if I wanted to. That doesn't make me qualified to talk about top secret frontier ai model architecture.
Now you have moved on to the guy who painted his honda, swapped out some new rims, and put some lights under it. That person is not an automotive engineer.
What is the definition for intelligence?
Quoting an older comment of mine...
>Intelligence is the ability to reason about logic. If 1 + 1 is 2, and 1 + 2 is 3, then 1 + 3 must be 4. This is deterministic, and it is why LLMs are not intelligent and can never be intelligent no matter how much better they get at superficially copying the form of output of intelligence.
This is not even wrong.
>Probabilistic prediction is inherently incompatible with deterministic deduction.
And his is just begging the question again.
Probabilistic prediction could very well be how we do deterministic deduction - e.g. about how strong the weights and how hot the probability path for those deduction steps are, so that it's followed every time, even if the overall process is probabilistic.
Probabilistic doesn't mean completely random.
1 reply →
>Intelligence is the ability to reason about logic. If 1 + 1 is 2, and 1 + 2 is 3, then 1 + 3 must be 4.
Human Intelligence is clearly not logic based so I'm not sure why you have such a definition.
>and yet LLMs, as expected, still cannot do basic arithmetic that a child could do without being special-cased to invoke a tool call.
One of the most irritating things about these discussions is proclamations that make it pretty clear you've not used these tools in a while or ever. Really, when was the last time you had LLMs try long multi-digit arithmetic on random numbers ? Because your comment is just wrong.
>What if 1 + 2 is 2 and 1 + 3 is 3? Then we can reason that under these constraints we just made up, 1 + 4 is 4, without ever having been programmed to consider these rules.
Good thing LLMs can handle this just fine I guess.
Your entire comment perfectly encapsulates why symbolic AI failed to go anywhere past the initial years. You have a class of people that really think they know how intelligence works, but build it that way and it fails completely.
3 replies →
>The LLM is predicting what tokens come next, based on a bunch of math operations performed over a huge dataset.
Whereas the child does what exactly, in your opinion?
You know the child can just as well to be said to "just do chemical and electrical exchanges" right?
Okay but chemical and electrical exchanges in an body with a drive to not die is so vastly different than a matrix multiplication routine on a flat plane of silicon
The comparison is therefore annoying
>Okay but chemical and electrical exchanges in an body with a drive to not die is so vastly different than a matrix multiplication routine on a flat plane of silicon
I see your "flat plane of silicon" and raise you "a mush of tissue, water, fat, and blood". The substrate being a "mere" dumb soul-less material doesn't say much.
And the idea is that what matters is the processing - not the material it happens on, or the particular way it is.
Air molecules hitting a wall and coming back to us at various intervals are also "vastly different" to a " matrix multiplication routine on a flat plane of silicon".
But a matrix multiplication can nonetheless replicate the air-molecules-hitting-wall audio effect of reverbation on 0s and 1s representing the audio. We can even hook the result to a movable membrane controlled by electricity (what pros call "a speaker") to hear it.
The inability to see that the point of the comparison is that an algorithmic modelling of a physical (or biological, same thing) process can still replicate, even if much simpler, some of its qualities in a different domain (0s and 1s in silicon and electric signals vs some material molecules interacting) is therefore annoying.
Intelligence does not require "chemical and electrical exchanges in an body". Are you attempting to axiomatically claim that only biological beings can be intelligent (in which case, that's not a useful definition for the purposes of this discussion)? If not, then that's a red herring.
"Annoying" does not mean "false".
2 replies →
At least read the other replies that pre-emptively refuted this drivel before spamming it.
At least don't be rude. They refuted nothing of the short. Just banged the same circular logic drum.
6 replies →
Intelligence is about acquiring and utilizing knowledge. Reasoning is about making sense of things. Words are concatenations of letters that form meaning. Inference is tightly coupled with meaning which is coupled with reasoning and thus, intelligence. People are paying for these monthly subscriptions to outsource reasoning, because it works. Half-assedly and with unnerving failure modes, but it works.
What you probably mean is that it is not a mind in the sense that it is not conscious. It won't cringe or be embarrassed like you do, it costs nothing for an LLM to be awkward, it doesn't feel weird, or get bored of you. Its curiosity is a mere autocomplete. But a child will feel all that, and learn all that and be a social animal.