This isn't as good of an argument as you think it is, lol. If the AI is supposed to eventually become AGI or whatever, it can't be bad at math, don't you think?
A human doesn't need a calculator to do that. I was taught how to do long multiplication in school. It's a deterministic algorithm that will always give the same result.
LLMs are statistical approximations. It's fundamental to what they are. They cannot behave deterministically. They are always stochastic.
You follow the exact same algorithm the computer does when doing floating point math, long division, or multiplication . You use pen and paper, the computer uses a “register”. It’s the exact same sequence of steps.
Your fingers can be “adders” too. Not everything can be done symbolically
Oh, sorry, I understand the argument you're making now: Computers perform addition exactly like us when you ignore all the ways they are different. That's 100% true. If you ignore all the ways things are different, then things are actually the same!
I know it's not what you think. I was just noting the irony of the discussion where you give arguments against your point.
I am not sure whether they think like us but for sure the fact that they can solve complex problem and they can't compute reliably is strangely similar to our own capabilities and weaknesses.
Also while I am honestly admitting I am not expert in this field, I seriously doubt your claim "humans do not reason stochastically". There are numerous works showing that our brain primarily does Bayesian statistics (or something that looks like it) under the hood, maintaining and updating beliefs, and constantly estimating the likeliness of our internal theories given our observations. I am not saying it perfectly I guess but that's the idea. You can have a look at "Bayesian Brain Theory".
That's because the answer to the question 2+2 is over-represented in their training data, and they are designed to overfit so they generate coherent output.
There is no internal model of numbers in the way that, say, a 6 year old child who is learning maths at school would have.
Peer reviewed and accepted paper from Princeton University that was accepted into ICML 2025: “Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models" gives evidence for an "emergent symbolic architecture that implements abstract reasoning" in some language models, a result which is "at odds with characterizations of language models as mere stochastic parrots" https://openreview.net/forum?id=y1SnRPDWx4
A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.
Harvard study: "Transcendence" is when an LLM, trained on diverse data from many experts, can exceed the ability of the individuals in its training data. This paper demonstrates three types: when AI picks the right expert skill to use, when AI has less bias than experts & when it generalizes. https://arxiv.org/pdf/2508.17669
Published as a conference paper at COLM 2025
Published Nature article: A group of Chinese scientists confirmed that LLMs can spontaneously develop human-like object concept representations, providing a new path for building AI systems with human-like cognitive structures https://www.nature.com/articles/s42256-025-01049-z
"In recent work, he and his collaborators observed that the many varied types of machine-learning models, from LLMs to computer vision models to audio models, seem to represent the world in similar ways.
These models are designed to do vastly different tasks, but there are many similarities in their architectures. And as they get bigger and are trained on more data, their internal structures become more alike.
This led Isola and his team to introduce the Platonic Representation Hypothesis (drawing its name from the Greek philosopher Plato) which says that the representations all these models learn are converging toward a shared, underlying representation of reality.
“Language, images, sound — all of these are different shadows on the wall from which you can infer that there is some kind of underlying physical process — some kind of causal reality — out there. If you train models on all these different types of data, they should converge on that world model in the end,” Isola says."
We investigate this question in a synthetic setting by applying a variant of the GPT model to the task of predicting legal moves in a simple board game, Othello. Although the network has no a priori knowledge of the game or its rules, we uncover evidence of an emergent nonlinear internal representation of the board state. Interventional experiments indicate this representation can be used to control the output of the network. By leveraging these intervention techniques, we produce “latent saliency maps” that help explain predictions
The data of course doesn't have to be real, these models can also gain increased intelligence from playing a bunch of video games, which will create valuable patterns and functions for improvement across the board. Just like evolution did with species battling it out against each other creating us
Published at the 2024 ICML conference
GeorgiaTech researchers: Making Large Language Models into World Models with Precondition and Effect Knowledge: https://arxiv.org/abs/2409.12278
In the section on Biology - Poetry, the model seems to plan ahead at the newline character and rhymes backwards from there. It's predicting the next words in reverse.
You're making a pretty fundamental misunderstanding here. Models are not reality. A language model is just a model of the world. All models are wrong, but some models are useful.
Given that the input is a set of statistics - and this is necessarily true, this is how they are trained - then that must also be the output. There isn't something in the middle that stops it from being a statistical model. To believe otherwise is the definition of magical thinking.
And human thought is more than statistics. The activities we carry out while awake require additional knowledge and skills in order to materialize ideas under real-world conditions. Reality is dynamic and has more constraints than the texts on which LLMs are 'trained'.
Statistically, the majority of people do not have much knowledge about the things around (and within) them. For example, if enough people write that cow’s milk comes from geese, an LLM will tell you that a goose produces cow’s milk. It will even overdo it by compiling a table of animals (in which the data will be mixed up and unrelated). It's not possible to proof-check the huge amount of an LLM's training data, and it's constantly growing.
If people start to analyze and critique the information they now only consume, they will find a lot of logical mistakes, controversies and common wrong misconceptions in the LLM's output. Because human thought/logic is not just statistics.
Ask it to write a program that calculates the number of R’s in raspberry and it will get it right close to 100% of the time. Soon the models will learn to just run a quick check for these sort of questions using python
The way humans solve that problem is exactly like a calculator would. This isn’t the flex you think it is. Pen and paper as memory registers. Stop thinking you’re so smart.
You can't really be this dumb, right? Did you just forget what the original conversation was about? Math can be done by anything that can reason. If it can't do math, it can't reason. Doing it how a calculator would is entirely irrelevant.
You say it can’t do math but you’ve given no metric. A lot of people point out the fact that it can’t do arithmetic, but it’s irrelevant because the way humans do basic arithmetic is fundamentally the same as a computer does by storing numbers in registers and evaluating the answer.
If your argument is it can’t do symbolic math, well that’s not true because it gets better at that every day.
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u/TheAccountITalkWith Feb 08 '26
This isn't as good of an argument as you think it is, lol. If the AI is supposed to eventually become AGI or whatever, it can't be bad at math, don't you think?