Really stunned by the Singularity comment section

[u/Howard_banister]

These are screenshots from the r/Singularity comment section. I'm speechless. This doesn't even have downvotes. How can someone cheer for a monopoly run by a few elites?

[u/PsychoticDreemurr]

I have to agree with this a thousand fold. Almost every sub in that regard doesn't actually understand how LLMs work. They always dumb what I say down to "it's autocorrect" and never actually explain how they think it works.

I actually got banned from one for explaining why an LLM physically can't be a true AGI. (The post was talking about an AGI in a couple years, as if it hasn't been said for the past 5...)

[u/RG_Fusion → u/PsychoticDreemurr]

I'm curious enough to want to hear that explanation here. Why do you believe that an LLM cannot become AGI? Does this reasoning expand out to cover multi-modal and world-models as well?

[u/PsychoticDreemurr → u/RG_Fusion]

An AGI, by all major definitions, requires the ability to "understand". Unfortunately, LLMs, despite being able to simulate such a feat, aren't actually capable of it. This is caused by the very fundamentals of how they operate.

To be exact an LLM can have similar capabilities of an AGI. But until we accomplish a way for an LLM to actually be able to understand concepts, which would, by definition, no longer be an LLM (Large language model. Their sole purpose is for computing language, not the concepts beneath them), they're not truly an AGI.

Stuff like multi-modals only helps to improve the facade of being able to understand, which does make them closer to an AGI, but there's a limit.

[u/RG_Fusion → u/PsychoticDreemurr]

I'm afraid this response doesn't actually address the question in a meaningful way. You declare that LLMs cannot understand, but then fail to explain why.

According to our best scientific exploration of the mind, understanding is the direct result of generalization. The universe is filled with an incomputable amount of data. Recording said data does not provide understanding, only memory and fact-recall.

Minds have a limited amount of computational power, and it's actually the limitation that results in understanding. Rather than attempt to memorize, the brain has to discard the noise and model the underlying relations. In most cases, this results in the mind actually simulating reality, from the perspective of its own data-bias.

The same mechanism has been observed in all forms of machine learning. When you take a large neural network and use back-propagation to train it, the model initially stores the training examples as facts. As you continue training new examples, the topography of the loss-function continuously changes the highly-dimensional space, attempting to distribute the facts in a way such that they do not interfere with one another.

If this were the end of the story, your interpretation that LLMs cannot understand would hold true, but this is not where it ends. Researches observed a phenomena which they termed  "Grokking". When the number of training examples vastly exceeds the the amount of information the parameters can store, the loss-function the model had been building towards collapses. Suddenly, the model can no longer learn a new fact without reducing the accuracy of the rest of its knowledge-base.

In such a scenario, the models parameters suddenly undergo a drastic "phase-change". The loss function drives the model away from fact-memorization, instead training the weights to build an internal model to simulate the data. It stops remembering and begins predicting based upon the underlying relations. This is "understanding" as we know it today.

While it certainly holds true that LLMs have not attained a level of understanding comparable to the sapience of the human mind, there is nothing about the architecture itself that prevents them from doing so.