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Comment by dr_dshiv | Hacker News Reader

https://www.noemamag.com/artificial-general-intelligence-is-...

Here is a mainstream opinion about why AGI is already here. Written by one of the authors the most widely read AI textbook: Artificial Intelligence: A Modern Approach https://en.wikipedia.org/wiki/Artificial_Intelligence:_A_Mod...

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Why does the Author choose to ignore the "General" in AGI?

Can ChatGPT drive a car? No, we have specialized models for driving vs generating text vs image vs video etc etc. Maybe ChatGPT could pass a high school chemistry test but it certainly couldn't complete the lab exercises. What we've built is a really cool "Algorithm for indexing generalized data", so you can train that Driving model very similarly to how you train the Text model without needing to understand the underlying data that well.

The author asserts that because ChatGPT can generate text about so many topics that it's general, but it's really only doing 1 thing and that's not very general.

There are people who can’t drive cars. Are they not general intelligence?

I think we need to separate the thinking part of intelligence from tool usage. Not everyone can use every tool at a high level of expertise.

Generally speaking, anyone can learn to use any tool. This isn't true of generative AI systems which can only learn through specialized training with meticulously curated data sets.

People physically unable to use the tool can't learn to use it. This isn't necessarily my view, but one could make a pretty easy argument that the LLMs we have today can't drive a car only because they aren't physically able to control the car.

> but one could make a pretty easy argument that the LLMs we have today can't drive a car only because they aren't physically able to control the car.

Of course they can. We already have computer controlled car systems, the reason LLMs aren't used to drive them is because AI systems that specialize in text are a poor choice for driving - specialized driving models will always outperform them for a variety of technical reasons.

We have compute controlled automobiles, not LLM controlled automobiles.

That was my whole point. Maybe in theory an LLM could learn to drive a car, but they can't today because they don't physically have access to cars they could try to drive just like a person who can't learn to use a tool because they're physically limited from using it.

It doesn't make sense to connect a LLM to a car, that could never work because they are trained offline using curated data sets.

>can only learn through specialized training with meticulously curated data sets.

but so do I!

This isn't true. A curated data set can greatly increase learning efficiency in some cases, but it's not strictly necessary and represents only a fraction of how people learn. Additionally, all curated data sets were created by humans in the first place, a feat that language models could never achieve if we did not program them to do so.

Generality is a continuous value, not a boolean; turned out that "AGI" was poorly defined, and because of that most people were putting the cut-off threshold in different places.

Likewise for "intelligent", and even "artificial".

So no, ChatGPT can't drive a car*. But it knows more about car repairs, defensive driving, global road features (geoguesser), road signs in every language, and how to design safe roads, than I'm ever likely to.

* It can also run python scripts with machine vision stuff, but sadly that's still not sufficient to drive a car… well, to drive one safety, anyway.

Text can be a carrier for any type of signal. The problem gets reduced to that of an interface definition. It’s probably not going to be ideal for driving cars, but if the latency, signal quality, and accuracy is within acceptable constraints, what else is stopping it?

This doesn’t imply that it’s ideal for driving cars, but to say that it’s not capable of driving general intelligence is incorrect in my view.

You can literally today prompt ChatGPT with API instructions to drive a car, then feed it images of a car's window outlooks and have it generate commands for the car (JSON schema restricted structured commands if you like). Text can represent any data thus yes, it is general.

> JSON schema restricted structured commands if you like

How about we have ChatGPT start with a simple task like reliably generating JSON schema when asked to.

Hint: it will fail.

ChatGPT can write a working Python script to generate the Json. It can call a library to do that.

But it cannot think on it's own! Billions of years of evolution couldn't bring human level 'AGI' to many many species, and we think a mere LLM company could do so. AGI isn't just a language model, there's tons of things baked into dna(the way brain functions, it's structure when it grows etc). It's not simply neuron interactions as well. The complexity is mind boggling

Humans and other primates are only a million years apart. Animals are quite intelligent.

The latest models are natively multimodal. Gemini, GPT-4o, Llama 4.

Same model trained on audio, video, images, text - not separate specialized components stitched together.

> AGI is already here

Last time I checked, in an Anthropic paper, they asked the model to count something. They examined the logits and a graph showing how it arrived at the answer. Then they asked the model to explain its reasoning, and it gave a completely different explanation, because that was the most statistically probable response to the question. Does that seem like AGI to you?

That's exactly what I would expect from a lot of people. Post factum rationalization is a thing.

Exactly. A lot of these arguments end up dehumanizing people because our own intelligence doesn’t hit the definition

There is no post factum rationalization here. If you ask a human to think about how they do something before they do it, there's no post factum rationalization. If you ask an LLM to do the same, it will give you a different answer. So, there is a difference. It's all about having knowledge of your internal state and being conscious of your actions and how you perform them, so you can learn from that knowledge. Without that, there is no real intelligence, just statistics.

If you ask a human to think about how to do a thing, before they do it, then you will also get a different answer.

There’s a good reason why schools spend so much time training that skill!

Yes, humans can post rationalize. But an LLM do nothing but post rationalize, as you yourself admitted humans can think it through beforehand and then actually do what they planned, while an LLM wont follow that plan mentally.

It is easy to see why, since the LLM doesn't communicate what it thinks it communicates what it thinks a human would communicate. A human would explain their inner process, and then go through that inner process. An LLM would explain a humans inner process, and then generate a response using a totally different process.

So while its true that humans doesn't have perfect introspection, the fact that we have introspection about our own thoughts at all is extremely impressive. An LLM has no part that analyzes its own thoughts the way humans do, meaning it has no clue how it thinks.

I have no idea how you would even build introspection into an AI, like how are we able to analyze our own thoughts? What is even a thought? What would this introspection part of an LLM do, what would it look like, would it identify thoughts and talk about them the way we do? That would be so cool, but that is not even on the horizon, I doubt we will ever see that in our lifetime, it would need some massive insight changing the AI landscape at its core to get there.

But, once you have that introspection I think AGI will happen almost instantly. Currently we use dumb math to train the model, that introspection will let the model train itself in an intelligent way, just like humans do. I also think it will never fully replace humans without introspection, intelligent introspection seems like a fundamental part to general intelligence and learning from chaos.

I would argue that this is a fringe opinion that has been adopted by a mainstream scholar, not a mainstream opinion. That or, based on my reading of the article, this person is using a definition of AGI that is very different than the one that most people use when they say AGI.

"AGI is already here, just wait 30 more years". Not very convincing.

... that was written in mid-2023. So that opinion piece is trying to redefine 2 year old LLMs like GPT-4 (pre-4o) as AGI. Which can only be described as an absolutely herculean movement of goalposts.

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