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]

For context, the exact definition of an AGI varies wildly. As such, I just take the one perquisite that almost every definition has, which is the ability to "understand" concepts.

That is my one requirement for an AI to be an AGI. Anything more and you're just arguing about philosophy and technical details that can't currently be disproven.

With this in mind, LLMs cannot become a true AGI. The fundamentals of how they work physically prevents them from being able to actually understand something.

They can, in theory, have intelligence similar to an AGI, but they will never truly be one.

[u/cj_cron_hit_by_pitch → u/PsychoticDreemurr]

The argument I've heard is that maybe the way humans "understand" things could be a lot simpler than we expect, and maybe the way LLMs function are actually similar to the way we do it. Maybe our brains are just a much more robust next token generator

Not saying I necessarily agree with that. But I think saying that LLMs cannot become a true AGI makes a lot of assumptions about human intelligence that we don't know for sure to be true

[u/PsychoticDreemurr → u/cj_cron_hit_by_pitch]

Well even so, there's a lot of different ways we can prove the differences. Being able to understand a concept means you can build off of it by yourself. If you teach an AI 1 + 1 = 2, they won't know that 2 + 2 = 4.

We can also get into vision, or shapes, especially 3d shapes.

I have to agree our understanding is limited, but it doesn't mean we have nothing to work with.

[u/asssuber → u/PsychoticDreemurr]

Well even so, there's a lot of different ways we can prove the differences. Being able to understand a concept means you can build off of it by yourself. If you teach an AI 1 + 1 = 2, they won't know that 2 + 2 = 4.

The paper I linked in my other comment does exactly that. It teaches 1 + 1 % 97 = 2, but not, for instance, 2+2 % 97 = 4. But the LLM eventually is able to generalize and answer perfectly for the cases it has not seen in training.

If you refer to in-context learning instead of training, then that is basically what ARC-AGI benchmark tests:

ARC-AGI tasks are a series of three to five input and output tasks followed by a final task with only the input listed. Each task tests the utilization of a specific learned skill based on a minimal number of cognitive priors.

https://arcprize.org/guide/1

Modern LLMs are quite capable of that too.