this post was submitted on 07 Aug 2024
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And how does reasoning work exactly in the human body? Isn’t it LLM/LAM working together with hormones? How do you know that humans aren’t just doing something similar? Your mind tricks you about a lot of things you experience, how can you be sure, your "reasoning” is just sorta LLM in disguise?
The "how do you know humans don't work the way machine learning does" is the wrong side of the argument. You should be explaining why you think LLMs work like humans.
Even as LLMs solve thinking problems, there is little evidence they do so the same way humans do, as they can't seem to solve issues that aren't included in their training data
Humans absolutely can and do solve new and novel problems without prior experience of the logic involved. LLMs can't seem to pull that off.
I think LLM is a part of the human mind very similar to the one we have on PCs but there are other parts as well where the brain can simulate objects and landscape with nearly perfect physical forces, it can do logical detection on an other place and a lot more. A LLM is just the speaking module, and others we already have like the logical math part and the 3D physics engine and 2D picture generator. Let’s connect all of them and see what happens 🤷🏻♀️
You think? So you base this on no studies or evidence?
Yes
It's good that you know you base your claims on literally nothing, but you should really look into how this stuff actually works right now before you start publicly speculating on what you misguidedly think it might be able to achieve.
Sometimes you have to ask strange questions in order to get people to explain to you what they think to know 😇
Why? Because someone on the internet said it does?
Because someone on the internet said it does, I know that I don’t have a basis on that what I philosophically talking? 🤔don’t get what you want to tell me..
Nope. Autists reason entirely without words/language *. Neurotypicals are capable of that too, but it's generally more convenient for them to bridge over words in conscious reasoning. They are basically fooled into thinking, 'thinking' is based on language.
* have to "translate" thoughts for conversation, which is exhausting
Well, maybe I should have written "neural network” instead of LLM/LAM.. Our brains, like LLM work by hardening paths which the data goes through the nodes. In LLM we simulate the chemical properties of the neurones using math. And we have already prototype of chips that work with lab grown brain tissue that show very efficient training capabilities in machine learning (it already plays pong) 🤷🏻♀️ think about that how you want, we will see anyway
PS: 😁 I am most likely neurodivergent as well 🙌🏻
No, we don't. A machine learning node accepts inputs, which it processes into one or multiple outputs. But literally no part of how the virtual neuron functions is based on or limited to what we THINK human neurons do.
Using actual biological neurons for computing is a completely separate field of study with almost no overlap with machine learning.
Stop pulling shit out your ass.
Well😆that made me laugh, sorry
Chips with actual biological neurons are in no way equivalent to the neural networks constructed for machine learning applications.
Do not confuse the two.
So how are they different, since you seem to know..
Are you serious? Start looking this stuff up instead of smugly acting like you can't possibly have guessed wrong.
One is literal living neurons, activated and read by electrodes. What exactly happens in the neurons is a complete mystery. I don't know, because NO ONE KNOWS. Neurons use so much more than simple on/off states, sending different electric and chemical signals with different lag-times with who-knows-what signaling purpose. Their structure is completely random with connections going around with seemingly no rime or reason, and we certainly don't control how exactly they grow.
Machine learning neurons are literally just arbitrary input-output nodes. How exactly they accept input and transform it can be coded to work however you like. And is. They don't simulate shit because we don't know exactly how biological neurons work. We run them using parallel processors like GPUs, but that still doesn't let us do something like whatever neurotransmitters do in a brain.
Additionally, they get arranged in sequential arrays of layers, where the overrall structure of the model is pre-determined in order to optimize for a given task before it even starts training. Brains don't do that. They just work. Somehow. The inter-connections in a brain are orders of magnitude more complex and they form on their own.
The way they learn is completely different. Machine learning models are trained by "evolving" them, creating a thousand mutations, then seeing which one works best, then repeating that for that model while deleting the rest.
With neurons, it just works. You don't need a million iterations to get ONE that works. And we don't know HOW brains do that.
Also, fuck you, I'm blocking you now. Go learn this stuff properly before you open your mouth again. You're a misinformed fool. Stop being one.
LoL
Language models are literally incapable of reasoning beyond what is present in the dataset or the prompt. Try giving it a known riddle and change it so it becomes trivial, for example "With a boat, how can a man and a goat get across the river?", despite it being a one step solution, it'll still try to shove in the original answer and often enough not even solve it. Best part, if you then ask it to explain its reasoning (not tell it what it did wrong, that's new information you provide, ask it why it did what it did), it'll completely shit it self hallucinating more bullshit for the bullshit solution. There's no evidence at all they have any cognitive capacity.
I even managed to break it once through normal conversation, something happened in my life that was unique enough for the dataset and thus was incomprehensible to the AI. It just wasn't able to follow the events, no matter how many times I explained.
Maybe the grown up human LLM that keeps learning 24/7 and is evolved in thousands of years to make the learning part as efficient as possible is just a little bit better than those max 5year old baby LLM with brut force learning techniques?
The 5 year old baby LLM can't learn shit and lacks the ability to understand new information. You're assuming that we and LLMs "learn" in the same way. Our brains can reason and remember information, detect new patterns and build on them. An LLM is quite literally incapable of learning a brand new pattern, let alone reason and build on it. Until we have an AI that can accept new information without being tolled what is and isn't important to remember and how to work with that information, we're not even a single step closer to AGI. Just because LLMs are impressive, doesn't mean they posses any cognition. The only way AIs "learn" is by countless people constantly telling it what is and isn't important or even correct. The second you remove that part, it stops working and turns to shit real quick. More "training" time isn't going to solve the fact that without human input and human defined limits, it can't do a single thing. AI cannot learn form it self without human input either, there are countless studies that show how it degrades, and it degrades quickly, like literally just one generation down the line is absolute trash.
A human not trained by other humans also just dies…
Nope, people are quite resilient. As long as it's not a literal new born, the chance of survival isn't THAT low. Once you get past 4 years and up, a human can manage quite well.
Also dying because no one takes care of you and you fail to aquire food and dying of a stroke/seizure are 2 very different things.
This is because of semi hardcoded stuff using the mechanics of hormones that interact with the neurons in the brain, I would say. They are hardcoded by the instructions provided by the DNA, I believe.
About the learning differences between human and LLM, there I believe that a sub-“module" of the brain functions very similar to how the LLMs work with just a way better/efficient learning algorithm that is helped by the other modules in the brain like the part that can simulate 3D space and interpret other sensory data like feeling touch, vision, smell etc
Current LLM models are being used in static manner without ability to learn in real time, so of course it can not do anything it has not learned yet.
It is just a theory and it can not be proven wrong since the understanding of neurons is not advanced yet.
Well, or at least, I did not hear a good argument that proves that theory 100% wrong.
You can think of the brain as a set of modules, but sensors and the ability to adhere to a predefined grammar aren't what define AGI if you ask me. We're missing the most important module. AGI requires cognition, the ability to acquire knowledge and understanding. Such an ability would make larger language models completely redundant as it could just learn langue or even come up with one all on its own, like kids in isolation for example.
What I was trying to point out is that "neural networks" don't actually learn in the way we do, using the world "learn" is a bit misleading, because it implies cognition. A neural network in the computer science sense is just a bunch of random operations in sequence. In goes a number, out goes a number. We then collect a bunch of input output pairs, the dataset, and semi randomly adjust these operations until they happen to somewhat match this collection. The reasoning is done by the humans assembling the input output pairs. That step is implicitly skipped for the AI. It doesn't know why they belong together and it isn't allowed to reason about why, because the second it spits out something else, that is an error and this whole process breaks. That's why LLMs hallucinate with perfect confidence and why they'll never gain cognition, because the second you remove the human assembling the dataset, you're quite literally left with nothing but semi random numbers, and that's why they degrade so fast when learning from themselves.
This technology is very impressive and quite useful, and demonstrates how powerful of a tool language alone is, but it doesn't get us any closer to AGI.
Do you know if current LLM models use static neural networks (like where each node is connected with each of the next layers)or if they can rearrange their connections into other layers?
They are indeed. It has multiple steps, but pretty much. 3Blue1Brown has a nice visual explanation: https://www.youtube.com/watch?v=wjZofJX0v4M
So if you want it more like a brain, you would have to have nodes that are able to form the connections while learning letting each node decide in what "direction” it wants to grow it’s connection in some sort, rather than having fixed connections where you only adjust the correlation of the nodes. And you would need multiple transformer (and most likely some hard logic algorithms as well) for different inputs as well as a main "thinker” that decides through which transformer (or algorithm) a input has to go and if the output of that transformer needs to be feeded in again as no input.
In theory. Then comes the question of how exactly are you gonna teach/train it. I feel our current approach is too strict for proper intelligence to emerge, but what do I know. I honestly have no clue how such a model could be trained. I guess it would be similar to how people train actual braincells? Tho that field is very immature atm... The neat thing about the human brain is, that it's already preconfigured for self learning, tho it does come with its own bias on what to learn due to its unique needs and desires.
😁🥳 I’d say, you would then need something that takes the role of hormones in that system (like hardcoded reactions to events in and outside of the AI brain/body(so called emotions I would say)) that trigger the connections to grow, shrink, get their values adjusted etc.
At least that would be my approach.
Calling the reward system hormones, doesn't really change the fact that we have no clue where to even start. What is a good reward for general intelligence? Solving problems? That's our current approach, which has the issue of the AI not actually understanding the problems and just ending up remembering question answer pairs (patterns). We need to figure out what defines inteligence and "understanding" in an easily measurable way. Which is something people knew almost a hundred years ago when we came up with the idea of neural networks, and why I say we didn't get any closer to AGI with LLMs.