LLMs are so incredibly useful and powerful but they will NEVER be AGI. I actually wonder if the success of (and subsequent obsession with) LLMs is putting true AGI further out of reach. All that these AI companies see are the $$$. When the biggest "AI Research Labs" like OpenAI shifted to product-izing their LLM offerings I think the writing was on the wall that they don't actually care about finding AGI.
It seems like even if it's possible to achieve GI, artificial or otherwise, you'd never be able to know for sure that thats what you've done. It's not exactly "useful benchmark" material.
LLMs/GPTs are essentially "just" statistical models. At this point the argument becomes more about philosophy than science. What is "intelligence?"
If an LLM can do something truly novel with no human prompting, with no directive other than something it has created for itself - then I guess we can call that intelligence.
Words mean what they're defined to mean. Talking about "general intelligence" without a clear definition is just woo, muddy thinking that achieves nothing. A fundamental tenet of the scientific method is that only testable claims are meaningful claims.
OpenAI used to define it as "a highly autonomous system that outperforms humans at most economically valuable work."
Now they used a Level 1-5 scale: https://briansolis.com/2024/08/ainsights-openai-defines-five...
So we can say AGI is "AI that can do the work of Organizations":
> These “Organizations” can manage and execute all functions of a business, surpassing traditional human-based operations in terms of efficiency and productivity. This stage represents the pinnacle of AI development, where AI can autonomously run complex organizational structures.
definition: new or unusual in an interesting way.
ChatGPT can create new things, sure, but it does so at your directive. It doesn't do that because it wants to which gets back to the other part of my answer.
When an LLM can create something without human prompting or directive, then we can call that intelligence.
I guess if we exclude those, then it just means the computer is really good at doing the kind of things which humans do by thinking. Or maybe it's when the computer is better at it than humans and merely being as good as the average human isn't enough (implying that average humans don't have natural general intelligence? Seems weird.)
Look, your argument ultimately reduces down to goalpost-moving what "novel" means, and you can position those goalposts anywhere you want depending on whether you want to push a pro-AI or anti-AI narrative. Is writing a paragraph that no one has ever written before "truly novel"? I can do that. AI can do that. Is inventing a new atomic element "truly novel"? I can't do that. Humans have done that. AI can't do that. See?
This is somewhat defensible, because what the non-AI-researcher means by AI - which may be AGI - is something more than expert systems by themselves can deliver. It is possible that "real AI" will be the combination of multiple approaches, but so far all the reductionist approaches (that expert systems, say, are all that it takes to be an AI) have proven to be inadequate compared to what the expectations are.
The GP may have been riffing off of this "that's not AI" issue that goes way back.
I was a little bored of the novel I have been reading so I sat down with Gemini and we collaboratively wrote a terrible novel together.
At the start I was promoting it a lot about the characters and the plot, but eventually it starting writing longer and longer chapters by itself. Characters were being killed off left right and center.
It was hilariously bad, but it was creative and it was fun.
[0] https://gizmodo.com/leaked-documents-show-openai-has-a-very-...
That's the opposite of generality. It may well be the opposite of intelligence.
An intelligent system/individual reliably and efficiently produces competent, desirable, novel outcomes in some domain, avoiding failures that are incompetent, non-novel, and self-harming.
Traditional computing is very good at this for a tiny range of problems. You get efficient, very fast, accurate, repeatable automation for a certain small set of operation types. You don't get invention or novelty.
AGI will scale this reliably across all domains - business, law, politics, the arts, philosophy, economics, all kinds of engineering, human relationships. And others. With novelty.
LLMs are clearly a long way from this. They're unreliable, they're not good at novelty, and a lot of what they do isn't desirable.
They're barely in sight of human levels of achievement - not a high bar.
The current state of LLMs tells us more about how little we expect from human intelligence than about what AGI could be capable of.
That's general intelligence - the ability to explore a system you know nothing about (in our case, physics, chemistry and biology) and then interrogate and exploit it for your own purposes.
LLMs are an incredible human invention, but they aren't anything like what we are. They are born as the most knowledgeable things ever, but they die no smarter.
An LLM cannot be placed in a simulated universe, with an internally consistent physics system of which it knows nothing, and go from its initial state to a world-spanning civilization that understands and exploits a significant amount of the physics available to it.
I know that is true because if you place an LLM in such a universe, it's just a gigantic matrix of numbers that doesn't do anything. It's no more or less intelligent than the number 3 I just wrote on a piece of paper.
You can go further than that and provide the LLM with the ability to request sensory input from its universe and it's still not intelligent because it won't do that, it will just be a gigantic matrix of numbers that doesn't do anything.
To make it do anything in that universe you would have to provide it with intrinsic motivations and a continuous run loop, but that's not really enough because it's still a static system.
To really bootstrap it into intelligence you'd need to have it start with a very basic set of motivations that it's allowed to modify, and show that it can take that starting condition and grow beyond them.
You will almost immediately run into the problem that LLMs can't learn beyond their context window, because they're not intelligent. Every time they run a "thought" they have to be reminded of every piece of information they previously read/wrote since their training data was fixed in a matrix.
I don't mean to downplay the incredible human achievement of reaching a point in computing where we can take the sum total of human knowledge and process it into a set of probabilities that can regurgitate the most likely response to a given input, but it's not intelligence. Us going from flint tools to semiconductors, vaccines and spaceships, is intelligence. The current architectures of LLMs are fundamentally incapable of that sort of thing. They're a useful substitute for intelligence in a growing number of situations, but they don't fundamentally solve problems, they just produce whatever their matrix determines is the most probable response to a given input.
Generality is about differences in kind. Like how my drill press can do things that an M4 can't. How could you ever know that your kinds of intelligence are all of them?