What I was saying is that one is based on the other

Not in any direct way, no. At least not in any way more rigorous than handwavey analogies.

Because Blue Steel.

People that can not do Matrix multiplication do not possess the basic concepts of intelligence now?

As a mathematician (at least by education), I think that's a great definition, yes.

You're definitely overselling how AI works and underselling how human brains work here, but there is a kernel of truth to what you're saying.

Neural networks are a biomimicry technology. They explicitly work by mimicking how our own neurons work, and surprise surprise, they create eerily humanlike responses.

The thing is, LLMs don't have anything close to reasoning the way human brains reason. We are actually capable of understanding and creating meaning, LLMs are not.

So how are they human-like? Our brains are made up of many subsystems, each doing extremely focussed, specific tasks.

We have so many, including sound recognition, speech recognition, language recognition. Then on the flipside we have language planning, then speech planning and motor centres dedicated to creating the speech sounds we've planned to make. The first three get sound into your brain and turn it into ideas, the last three take ideas and turn them into speech.

We have made neural network versions of each of these systems, and even tied them together. An LLM is analogous to our brain's language planning centre. That's the part that decides how to put words in sequence.

That's why LLMs sound like us, they sequence words in a very similar way.

However, each of these subsystems in our brains can loop-back on themselves to check the output. I can get my language planner to say "mary sat on the hill", then loop that through my language recognition centre to see how my conscious brain likes it. My consciousness might notice that "the hill" is wrong, and request new words until it gets "a hill" which it believes is more fitting. It might even notice that "mary" is the wrong name, and look for others, it might cycle through martha, marge, maths, maple, may, yes, that one. Okay, "may sat on a hill", then send that to the speech planning centres to eventually come out of my mouth.

Your brain does this so much you generally don't notice it happening.

In the 80s there was a craze around so called "automatic writing", which was essentially zoning out and just writing whatever popped into your head without editing. You'd get fragments of ideas and really strange things, often very emotionally charged, they seemed like they were coming from some mysterious place, maybe ghosts, demons, past lives, who knows? It was just our internal LLM being given free rein, but people got spooked into believing it was a real person, just like people think LLMs are people today.

In reality we have no idea how to even start constructing a consciousness. It's such a complex task and requires so much more linking and understanding than just a probabilistic connection between words. I wouldn't be surprised if we were more than a century away from AGI.

Maybe I am over selling current AI and underselling our brains. But the way I see it is that the exact mechanism that allowed intelligence to flourish within ourselves exists with current nural networks. They are nowhere near being AGI or UGI yet but I think these tools alone are all that are required.

The way I see it is, if we rewound the clock far enough we would see primitive life with very basic nural networks beginning to develop in existing multicellular life (something like jellyfish possibly). These nural networks made from neurons neurotransmitters and synapses or possibly something more primitive would begin forming the most basic of logic over centuries of evolution. But it wouldn't reassemble anything close to reason or intelligence, it wouldn't have eyes, ears or any need for language. At first it would probably spend its first million years just trying to control movement.

We know that this process would have started from nothing, nural networks with no training data, just a free world to explore. And yet over 500 million years later here we are.

My argument is that modern nural networks work the same way that biological brains do, at least the mechanism does. The only technical difference is with neurotransmitters and the various dampening and signal boosting that can happen along with nuromodulation. Given enough time and enough training, I firmly believe nural networks could develop reason. And given external sensors it could develop thought from these input signals.

I don't think we would need to develop a consciousness for it but that it would develop one itself given enough time to train on its own.

A large hurdle that might arguably be a good thing, is that we are largely in control of the training. When AI is used it does not learn and alter itself, only memorising things currently. But I do remember a time when various AI researchers allowed earlier models to self learn, however the internet being the internet, it developed some wildly bad habits.

Formatting might be off on some of these, had to convert some papers to text as some were only scanned and I couldn't be bothered writing it all out by hand:

"The PDP models are inspired by the structure and function of the brain. In particular, they are based on networks of neuron-like units whose interactions resemble those among neurons in the cerebral cortex."
Parallel Distributed Processing: Explorations in the Microstructure of Cognition McClelland, Rumelhart, & the PDP Research Group

"The design of artificial neural networks was inspired by knowledge of the brain, in particular the way biological neurons are interconnected and the way they communicate through synapses."
Deep Learning LeCun, Bengio, Hinton

"The design of deep learning architectures owes much to our understanding of the hierarchical structure of the visual cortex, particularly as revealed by Hubel and Wiesel’s work on simple and complex cells."
Neuroscience-Inspired Artificial Intelligence Hassabis et al.

"The relationship between biological and artificial neural networks has now become a central issue in both neuroscience and AI. Deep networks trained with backpropagation may offer a plausible model of some aspects of human cognition."
Cognitive computational neuroscience Kriegeskorte & Douglas (2018)

"Goal-driven deep learning models, when trained to solve behavioral tasks, can develop internal representations that match those found in the brain."
Using goal-driven deep learning models to understand sensory cortex Yamins & DiCarlo

And longer excepts on the similarities of AI neural networks to biological brains, more specifically human children, in the pursuit of study with improving learning and education development. Super interesting papers that are easily accessible to anyone:

"Humans are imperfect reasoners. We reason most effectively about entities and situations that are consistent with our understanding of the world. Our experiments show that language models mirror these patterns of behavior. Language models perform imperfectly on logical reasoning tasks, but this performance depends on content and context. Most notably, such models often fail in situations where humans fail — when stimuli become too abstract or conflict with prior understanding of the world. Beyond these parallels, we also observed reasoning effects in language models that to our knowledge have not been previously investigated in the human literature. For example, the patterns of errors on the ‘violate realistic’ rules, or the relative ease of ‘shuffled realistic’ rules in the Wason tasks. Likewise, language model performance on the Wason tasks increases most when they are demonstrated with realistic examples; benefits of concrete examples have been found in cognitive and educational contexts (Sweller et al., 1998; Fyfe et al., 2014), but remain to be explored in the Wason problems. Investigating whether humans show similar effects is a promising direction for future research."
5.9-10 Language models show human-like content effects on reasoning
Ishita Dasgupta*,1, Andrew K. Lampinen*,1, Stephanie C. Y. Chan1, Antonia Creswell1, Dharshan Kumaran1, James L. McClelland1,2 and Felix Hill1 *Equal contributions, listed alphabetically, 1DeepMind, 2Stanford University

"In this article we will point out several characteristics of human cognitive processes that conventional computer architectures do not capture well. Then we will note that connectionist models {neural networks} are much better able to capture these aspects of human processing. After that we will mention three recent applications in connectionist artificial intelligence which exploit these characteristics. Thus, we shall see that connectionist models offer hope of overcoming the limitations of conventional AI. The paper ends with an example illustrating how connectionist models can change our basic conceptions of the nature of intelligent processing."

"The framework for building connectionist models is laid out in detail in Rumelhart, McClelland and the PDP Group (1986), and many examples of models constructed in that framework are described.
Two examples of connectionist models of human processing abilities that capture these characteristics are the interactive activation model of visual word recognition from McClelland and Rumelhart (1981), and the model of past tense learning from Rumelhart and McClelland (1986). These models were motivated by psychological experiments, and were constructed to capture the data found in these studies. We describe them here to illustrate some of the roots of the connectionist approach in an attempt to understand detailed aspects of human cognition."

"The models just reviewed capture important aspects of data from psychological experiments, and illustrate how the characteristics of human processing capabilities enumerated above can be captured in an explicit comptutational framework. Recently connectionist models that capture these same characteristics have begun to give rise to a new kind of Artificial Intelligence, which we will call connectionist AI. Connectionist AI is beginning to address several topics that have not been easily solved using other approaches. We will consider three cases of this. In each case we will describe recent progress that illustrates the ability of connectionist networks to capture the characteristics of human performance mentioned above."

"This paper began with the idea that humans exploit graded information, and that computational mechanisms that aim to emulate the natural processing capabilities of humans should exploit this kind of information as well. Connectionist models do exploit graded information, and this gives them many of their attractive characteristics."
Parallel Distributed Processing: Bridging the Gap Between Human and Machine Intelligence
James L. McClelland, Axel Cleeremans, and David Servan-Schreiber Carnegie Mellon University

"Artificial neural networks have come and gone and come again- and there are several good reasons to think that this time they will be around for quite a while. Cheng and Titterington have done an excellent job describing that nature of neural network models and their relations to statistical methods, and they have overviewed several applications. They have also suggested why neuroscientists interested in modeling the human brain are interested in such models. In this note, I will point out some additional motivations for the investigation of neural networks. These are motivations arising from the effort to capture key aspects of human cognition and learning that have thus far eluded cognitive science. A central goal of congnitive science is to understand the full range of human cognitive function"..."{there are} good reasons for thinking that artificial neural networks, or at least computationally explicit models that capture key properties of such networks, will play an important role in the effort to capture some of the aspects of human cognitive function that have eluded symbolic approaches."
Neural Networks: A Review from Statistical Perspective]: Comment: Neural Networks and Cognitive Science: Motivations and Applications
James L. McClelland Statistical Science, Vol. 9, No. 1. (Feb., 1994), pp. 42-45.

"The idea has arisen that as the scale of experience and computation begins to approach the scale of experience and computation available to a young child—who sees millions of images and hears millions of words per year, and whose brain contains 10–100 billion neuron-like processing units each updating their state on a time scale of milliseconds—the full power and utility of neural networks to capture natural computation is finally beginning to become a reality, allowing artificially intelligent systems to capture more fully the capabilities of the natural intelligence present in real biological networks in the brain."

"One major development in the last 25 years has been the explosive growth of computational cognitive neuroscience. The idea that computer simulations of neural mechanisms might yield insight into cognitive phenomena no longer requires, at least in most quarters, vigorous defense—there now exist whole fields, journals, and conferences dedicated to this pursuit. One consequence is the elaboration of a variety of different computationally rigorous approaches to neuroscience and cognition that capture neural information processing mechanisms at varying degrees of abstraction and complexity. These include the dynamic field theory, in which the core representational elements are fields of neurons whose activity and interactions can be expressed as a series of coupled equations (Johnson, Spencer, & Sch€oner, 2008); the neural engineering framework, which seeks to understand how spiking neurons might implement tensor-product approaches to symbolic representations (Eliasmith & Anderson, 2003; Rasmussen & Eliasmith, 2011); and approaches to neural representation based on ideal-observer models and probabilistic inference (Deneve, Latham, & Pouget, 1999; Knill & Pouget, 2004). Though these perspectives differ from PDP in many respects, all of these efforts share the idea that cognition emerges from interactions among populations of neurons whose function can be studied in simplified, abstract form."
Parallel Distributed Processing at 25: Further Explorations in the Microstructure of Cognition T. T. Rogers, J. L. McClelland / Cognitive Science 38 (2014) p1062-1063

If all you're saying is that neural networks could develop consciousness one day, sure, and nothing I said contradicts that. Our brains are neural networks, so it stands to reason they could do what our brains can do. But the technical hurdles are huge.

You need at least two things to get there:

1. Enough computing power to support it.
2. Insight into how consciousness is structured.

1 is hard because a single brain alone is about as powerful as a significant chunk of worldwide computing, the gulf between our current power and what we would need is about... 100% of what we would need. We are so woefully under resourced for that. You also need to solve how to power the computers without cooking the planet, which is not something we're even close to solving currently.

2 means that we can't just throw more power or training at the problem. Modern NN modules have an underlying theory that makes them work. They're essentially statistical curve-fitting machines. We don't currently have a good theoretical model that would allow us to structure the NN to create a consciousness. It's not even on the horizon yet.

Those are two enormous hurdles. I think saying modern NN design can create consciousness is like Jules Verne in 1867 saying we can get to the Moon with a cannon because of "what progress artillery science has made in the last few years".

Moon rockets are essentially artillery science in many ways, yes, but Jules Verne was still a century away in terms of supporting technologies, raw power, and essential insights into how to do it.

We're on the same page about consciousness then. My original comment only pointed out that current AI have problems that we have because they replicate how we work and it seems that people don't like recognising that very obvious fact that we have the exact problems that LLMs have. LLMs aren't rational because we inherently are not rational. That was the only point I was originally trying to make.

For AGI or UGI to exist, massive hurdles will need to be made, likely an entire restructuring of it. I think LLMs will continue to get smarter and likely exceed us but it will not be perfect without a massive rework.

Personally and this is pure speculation, I wouldn't be surprised if AGI or UGI is only possible with the help of a highly advanced AI. Similar to how microbiologist are only now starting to unravel protein synthesis with the help of AI. I think the shear volume of data that needs processing requires something like a highly evolved AI to understand, and that current technology is purely a stepping stone for something more.

We don't have the same problems LLMs have.

LLMs have zero fidelity. They have no - none - zero - model of the world to compare their output to.

Humans have biases and problems in our thinking, sure, but we're capable of at least making corrections and working with meaning in context. We can recognise our model of the world and how it relates to the things we are saying.

LLMs cannot do that job, at all, and they won't be able to until they have a model of the world. A model of the world would necessarily include themselves, which is self-awareness, which is AGI. That's a meaning-understander. Developing a world model is the same problem as consciousness.

What I'm saying is that you cannot develop fidelity at all without AGI, so no, LLMs don't have the same problems we do. That is an entirely different class of problem.

Some moon rockets fail, but they don't have that in common with moon cannons. One of those can in theory achieve a moon landing and the other cannot, ever, in any iteration.

I was channeling the Interstellar docking computer (“improper contact” in such a sassy voice)

There is a distinction between data and an action you perform on data (matrix maths, codec algorithm, etc.). It’s literally completely different.

An audio codec (not a pipeline) is just actually doing math - just like the workings of an LLM. There’s plenty of work to be done after the audio codec decodes the m4a to get to tunes in your ears. Same for an LLM, sandwiching those matrix multiplications that make the magic happen are layers that crunch the prompts and assemble the tokens you see it spit out.

LLMs can’t think, that’s just the fact of how they work. The problem is that AI companies are happy to describe them in terms that make you think they can think to sell their product! I literally cannot be wrong that LLMs cannot think or reason, there’s no room for debate, it’s settled long ago. AI companies will string the LLMs together and let them chew for a while to try make themselves catch when they’re dropping bullshit. It’s still not thinking and reasoning though. They can be useful tools, but LLMs are just tools not sentient or verging on sentient

They do not, and I, a simple skin-bag of chemicals (mostly water tho) do say

So close, LLMs work via matrix multiplication, which is well understood by many meat bags and matrix math can’t think. If a meat bag can’t do matrix math, that’s ok, because the meat bag doesn’t work via matrix multiplication. lol imagine forgetting how to do matrix multiplication and disappearing into a singularity or something

No, they’re right. The “research” is biased by the company that sells the product and wants to hype it. Many layers don’t make think or reason, but they’re glad to put them in quotes that they hope peeps will forget were there.

Well, on the other hand. Meat bags can't really do neuron stuff either, despite that is essential for any meat bag operation. Humans are still here though and so are dogs.

You can, but the stuff that’s really useful (very competent code completion) needs gigantic context lengths that even rich peeps with $2k GPUs can’t do. And that’s ignoring the training power and hardware costs to get the models.

Techbros chasing VC funding are pushing LLMs to the physical limit of what humanity can provide power and hardware-wise. Way less hype and letting them come to market organically in 5/10 years would give the LLMs a lot more power efficiency at the current context and depth limits. But that ain’t this timeline, we just got VC money looking to buy nuclear plants and fascists trying to subdue the US for the techbro oligarchs womp womp

There is a distinction between data and an action you perform on data (matrix maths, codec algorithm, etc.). It’s literally completely different.

Incorrect. You might want to take an information theory class before speaking on subjects like this.

I literally cannot be wrong that LLMs cannot think or reason, there’s no room for debate, it’s settled long ago.

Lmao yup totally, it's not like this type of research currently gets huge funding at universities and institutions or anything like that it's a dead research field because it's already "settled". (You're wrong 🤭)

LLMs are just tools not sentient or verging on sentient

Correct. No one claimed they are "sentient" (you actually mean "sapient", not "sentient", but it's fine because people commonly mix these terms up. Sentience is about the physical senses. If you can respond to stimuli from your environment, you're sentient, if you can "I think, therefore I am", you're sapient). And no, LLMs are not sapient either, and sapience has nothing to do with neural networks' ability to mathematically reason or use logic, you're just moving the goalpost. But at least you moved it far enough to be actually correct?

It’s wild, we’re just completely talking past each other at this point! I don’t think I’ve ever gotten to a point where I’m like “it’s blue” and someone’s like “it’s gold” so clearly. And like I know enough to know what I’m talking about and that I’m not wrong (unis are not getting tons of grants to see “if AI can think”, no one but fart sniffing AI bros would fund that (see OP’s requested source is from an AI company about their own model), research funding goes towards making useful things not if ChatGPT is really going through it like the rest of us), but you are very confident in yourself as well. Your mention of information theory leads me to believe you’ve got a degree in the computer science field. The basis of machine learning is not in computer science but in stats (math). So I won’t change my understanding based on your claims since I don’t think you deeply know the basis just the application. The focus on using the “right words” as a gotchya bolsters that vibe. I know you won’t change your thoughts based on my input, so we’re at the age-old internet stalemate! Anyway, just wanted you to know why I decided not to entertain what you’ve been saying - I’m sure I’m in the same boat from your perspective

loses the argument "we’re at the age-old internet stalemate!" LMAO

Indeed I did not, we’re at a stalemate because you and I do not believe what the other is saying! So we can’t move anywhere since it’s two walls. Buuuut Tim Apple got my back for once, just saw this now!: https://lemmy.blahaj.zone/post/27197259

I’ll leave it at that, as thanks to that white paper I win! Yay internet points!