[didntreadarticl]

There are two camps, evident in this thread. one camp is 'its just a statistical model, it cant possibly know these things'

The other camp (that I'm in) sees that we might be onto something. We humans are obviously just more than a statistical model, but nonetheless learning words and how they fit together is a big part of who we are. With LLMs we have our first glimpse of 'emergent' behaviour from simple systems scaled massively. Whats are we if not a simple system scaled massively.

Check these links out:

Evidence that LLMs form internal models of what they learn about: https://thegradient.pub/othello/

Evidence that training LLMs on code actually made them better at complex reasoning: https://yaofu.notion.site/How-does-GPT-Obtain-its-Ability-Tr...

John Carmack: https://dallasinnovates.com/exclusive-qa-john-carmacks-diffe... I think that, almost certainly, the tools that we’ve got from deep learning in this last decade—we’ll be able to ride those to artificial general intelligence.

A lot of the argument comes down to semantics about knowing and thinking. "An LLM can't think and a submarine cant swim"

[kilgnad]

From the two camps the one that says we "might" be onto something is the more intelligent and reasonable opinion.

First your camp doesn't deal in absolutes. It doesn't say absolutely chatGPT is sentient. It only questions the possibility and tries to explore further.

Second a skeptical outlook that doesn't deal with absolutes is 100% the more logical and intelligent perspective given the fact that we don't even know what "understanding" or "sentience" is. We can't fully define these words and we only have some fuzzy view of what they are. Given this fact, absolute statements against something we don't fully understand are fundamentally not logical.

This is a strange phenomenon how some people will vehemently deny something absolutely. During the VERY beginning of the COVID-19 pandemic the CDC incorrectly stated that masks didn't stop the spread of COVID-19 and you literally saw a lot of people parroting this statement everywhere as "arm chair" pandemic experts (including here on HN).

Despite this there were some people who thought about it logically if there's a solid object on my face, even if that object has holes in it for air to pass through, the solid parts will block other solid things (like COVID) from passing through thereby lessening the amount of viral material that I breath in. Eventually the logic won out. I think the exact same phenomenon is happening here.

Some or several ML experts tried to downplay LLMs (even though they don't completely understand the phenomenon themselves) and everyone else is just parroting them like they did with the CDC.

The fact of the matter is, nobody completely understands the internal mechanisms behind human sentience nor do they understand how or if chatGPT is actually "understanding" things. How can they when they don't even know what the words mean themselves?

[soiler]

I don't think you've represented the camps fairly (actually, I don't think there are two camps). Most people (here) are probably not arguing that AGI is impossible, but that current AI is not generally intelligent. The John Carmack quote is exactly in line with this. He says "ride those to [AGI]," meaning they are not AGI. The idea that genuine intelligence and self-awareness could emerge from increasingly powerful statistical models is in no way the kind of counter-cultural idea you seem to be presenting it as. I think almost all of us believe that.

But ChatGPT is not it.

[didntreadarticl]

Oh of course its not it. The question is how it relates to some future better thing. Is it a step on the road or a dead end.

I'm arguing against the 'its just a statistical model and its playing a clever trick on us' camp.

[strken]

I think there's more nuance. It's hard applying tests designed for humans to a model that can remember most of the useful text on the internet.

Imagine giving a human with a condition that leaves them without theory of mind weeks of role-play training about theory of mind tests, then trying to test them. What would you expect to see? For me I'd expect something similar to ChatGPT's output: success on common questions, and failures becoming more likely on tests that diverge more from the formula.