I'm surprised that any of these companies consider what they are working on to be Artificial General Intelligences. I'm probably wrong, but my impression was AGI meant the AI is self aware like a human. An LLM hardly seems like something that will lead to self-awareness.
For example, in this article it says it can't do coding exercises outside the training set. That would definitely be on the "AGI checklist". Basically doing anything that is outside of the training set would be on that list.
I will get excited for/scared of LLMs when they can tackle this kind of problem. But I don't believe they can because of the fundamental nature of their design, which is both backward looking (thus not better than the human state of the art) and lacks human intuition and self awareness. Or perhaps rather I believe that the prompt that would be required to get an LLM to produce such a program is a problem of at least equivalent complexity to implementing the program without an LLM.
That’s possible for a highly intelligent, extensively trained, very small subset of humans.
That also ignores the fact that the small set of humans capable of building programming languages and compilers is a consequence of specialization and lack of interest. There are plenty of humans that are capable of learning how to do it. LLMs, on the other hand, are both specialized for the task and aren't lazy or uninterested.
Things like drive a car, fold laundry, run an errand, do some basic math.
You'll notice that two of those require some form of robot or mobility. I think that is key -- you can't have AGI without the ability to interact with the world in a way similar to most humans.
I've personally had some mild success getting these UTM variants to output their own children in a meta programming arrangement. The base program only has access to the valid instruction set of ~12 instructions per byte, while the task program has access to the full range of instructions and data per byte (256). By only training the base program, we reduce the search space by a very substantial factor. I think this would be similar to the idea of a self-hosted compiler, etc. I don't think there would be too much of a stretch to give it access to x86 instructions and a full VM once a certain amount of bootstrapping has been achieved.
[0]: https://arxiv.org/abs/2406.19108
There, you don't need to invoke Turing or compiler bootstrapping. You just need one example of a use case where the accuracy of responses is mission critical
https://chatgpt.com/share/67373737-04a8-800d-bc57-de74a415e2...
I think the parent comment's challenge is more appropriate.
Oh, you just asked it to make a trivia app that feeds on JSON. Cute, but that's not what I meant. The web is full of tutorials for basic stuff like that.
To be clear I meant that LLMs can't write trivia questions and answers, thus proving that they can't produce trustworthy outputs.
And a trivia app is a toy (one might even say... a trivial example), but it's a useful demonstration of why you wouldn't put an LLM into a system on which lives depend on, let alone invest billions on it.
If you don't trust my words just go back to fiddling with your models and ask them to write a trivia quiz about a topic that you know very well. Like a TV show.