AGI ≠ a lot of AI. They are fundamentally different things.
The first computer was designed in 1837, long before a computer was ever built. We know how fusion reactions work, now we’re tweaking the engineering to harness it in a reactor.
We don’t know how human intelligence works. We don’t have designs or even a philosophy for AGI. Yet, the prevailing view is that our greatest invention will just suddenly “emerge.”
No other field I’m aware of so strongly purports it will reach its ultimate breakthrough without having a clue of the fundamentals of that breakthrough.
It’s like nuclear scientists saying “if we just do a lot of fission, we think fusion will just happen.”
your hypothesis is that there is a qualitatively different property to agi than whatever we have now. Most people here are just saying "if chatgpt is smarter that would be agi".
Going by the differences between gpt3.5 and gpt4 is really interesting. It is better able to reason in basically any problem I throw at it. Personally I think that a hypothetical system that is able to generate a sufficiently good response for ANY text input is AGI.
There aren't really any "gotcha" cases with this technology that I'm aware of where it just can't ever respond appropriately. Most clear failings of existing systems involve ever more contrived logic puzzles, which each successive generation is able to solve, and eventually at some point the required logic puzzle will be so dense few humans can solve it.
This isn't a case a case of "studying for the test" of popular internet examples either. I encourage you to try and invent your own gotchas for earlier versions then try them on newer models. Change the wording and order of logic puzzles, or encase them within scenarios to ensure its not responding to the format of the prompt
There are absolubtely cases of people overhypting it, or it overfitting to training data (see the debacle about it passing whatever bar exam, university test etc.). But despite the hype there is an underlying level of intelligence that is building and I use it to solve problems pretty much every day. I think of it atm as like a 4 year old that has inexplicably read every book ever written
In many ways, AGI and AI are opposites. We don’t actually “train” humans. We present them information. And a human can choose to ingest that information or - just as importantly - choose not to.
A 3yo learns English not through being force fed, but through their own curiosity and motivation to learn.
GPT and the like are proving to be amazing tools. The compute model of probabilistic algorithms (“plain” AI) is going to transform industries.
But the more sophisticated these tools get, the further from AGI they will become.
When we create AGI, it will begin with a blank slate, not the whole of human knowledge. It will study some topics deeply and be uninterested in others. It might draw its own conclusions instead of just taking the conclusions presented to it. Or it may decide not to study at all, preferring to instead become a hermit.
again, I believe the opposite to you. I literally think a smarter chatGPT would be AGI. However I agree, it wouldn't be the thing you describe. But recognise that your disagreement is not because of a misunderstanding of the existing technology, but over what an AGI is.
I think some people explitly dont want AGI to simulate emotion or self-defined goals because that would be little more than an artistic curiousity. Why intentionally make a tool less useful? Less willing to do the things you want it to do? Perhaps you hold some belief that simulating such things is necessary for some goal you have in mind, but personally I don't think that's true, for literally any goal
Sorry, I must have missed the rest of your prior comment (or posted before you made an edit to it?)
LLMs are not able to create new knowledge, only organize existing knowledge. Critically, we don't use induction to create new knowledge - but induction is all these LLMs can do. (e.g. To explain the origin of the universe, there is nothing we can induce, as the big bang is not observable.)
I don't see how training these AI models more will cause this property to emerge. But knowledge creation is the thing that we want when we say we want AGI.
The reason I describe AGI as such is because we only have one example of what an AGI is, namely humans. A popular idea is that machine AGI will look very different from human AGI (e.g. no consciousness or intrinsic motivation or qualia).
But this is a bold claim. It contends that there are multiple kinds of general intelligences vs some universal kind. Other universal concepts don't look like this. Consider the universality of computation: a computer is either Turing-complete or it isn't. There is no other kind of general purpose computer.
Once we created a Turing-complete computer, it could perform all conceivable computation. This is the power of universality. The first Turing-complete computer could technically play Call of Duty (though no one would have had the patience to play it).
It's not like there is some mode of ChatGPT that will produce AGI-level responses if given months or years of time to compute it.
Fundamentally, its ability to do whatever it is that AGIs do (AGI's version of Turing computation) is missing.
what would you consider a test of the ability to create new knowledge? I don't think there exists a meaningful distinction here. If I give an AI the same information astronomers had to figure out the big bang conclusion, it would probably be able to figure it out. They "induced" from a bunch of data points that there was one likely explanation.
Similarly if I gave it a phenomena I wanted to know about it could propose experiements to run to understand that thing.
taking independant action in the world is indeed beyond the limits of chatgpt on it's own, but its not hard to build systems relying heavily on chatgpt to do that, a simple loop calling it with information from sensors and a prompt to experiment with outputs is enough to get some kind of agent. It can process any information, and given more power it can draw ever-higher inferences from that information. This is analogous to a turing machine, something that can eventually get anywhere, but it will take an arbitrarily long time to do it.
I think of it kinda like the CPU in a Von Neumann architecture. A cpu on it's own doesn't do much of anything unless you tell it to. We have that part solved, we just need to settle on a good structure for repeatedly querying it.
AGI ≠ a lot of AI. They are fundamentally different things.
The first computer was designed in 1837, long before a computer was ever built. We know how fusion reactions work, now we’re tweaking the engineering to harness it in a reactor.
We don’t know how human intelligence works. We don’t have designs or even a philosophy for AGI. Yet, the prevailing view is that our greatest invention will just suddenly “emerge.”
No other field I’m aware of so strongly purports it will reach its ultimate breakthrough without having a clue of the fundamentals of that breakthrough.
It’s like nuclear scientists saying “if we just do a lot of fission, we think fusion will just happen.”