"A software that beats most humans on most cognitive tasks".
People make it harder than it needs to he IMO
Even search was considered an AI before Google, but today is just a thing computers are and always have been faster than humans at. Even before ChatGPT, lawyers relied upon computers to double-check citations.
Even in programming, I expect that compilers and IDEs check for the most common errors.
We knew 30 years ago that computer can beat human in the field it was engineered for.
But AGI is good at anything you throw at it, including building a better AI. Now give it agency and that's a recipe for a disaster.
Because the RL agent should happily use the theorem prover as a "calculator", and only get "charged" for how much the ML inference and the actual cheap calculator execution really cost. Exploration in-context is what humans also do, and immensely powerful. Similar to, when programming, testing some expressions to figure out edge-cases before incorporating them into a large function/algorithm that has poor observability.
So far, machines still mostly do the task of "computing" in theorem proving for us, rather than show some proper intelligence. It's damming that LeanDojo got so high marks without reinforcement learning. They pretty much just dumped prover context state during a run through the entire matlib "standard" library and handed it to two transformers, one for embedding premises and one encoder-decoder fed with the current state and as many premises as the encoder context fits after the state, generating/sampling a few tactics to apply. No freedom of where in the proof tree to apply the next tactic, no awareness of any such side-branches.
If and when there exists software close to this capability, I think all reasonable people would agree that AGI has been accomplished.
That said, I think ChatGPT and LLMs trick us into seeing intelligence where there is none because we are evolutionarily wired to look for signs of intelligence as a potential threat. Our defense mechanisms are asymmetrical as they have to err on the side of being overly cautious: you may mistake a rock for a bear but you won't mistake a bear for a rock.
So it is with ChatGPT: its conversation style triggers a false alarm deep in us that there is intelligence there and we can't shake that warning off, but this software design is just exploiting the flaw (of sorts) in our wiring.
We'll know we have discovered AGI when it gives us a prompt instead of us providing one.
The basic description is "a computer system that can do all relevant things a human being can do". And sure, we don't have a very codified definition of what those things are but we, human beings, exist and so even if we can't define our own capacities, we have an strong idea they exist.
The implicit argument is that one can't engineer something one doesn't understand. I tend to agree in the case of full capacities but LLMs seem to have some capacities that their creator don't understand, so the claim doesn't seem a "slam dunk" anymore.
That's what machine learning is. We build these systems we don't fully understand, and set them on solving problems we don't understand. It's revolutionary because it lets us solve problems we don't understand. We don't really fully understand what they do or how, but it does still work.
Technically that's what deep learning accepts. Other forms of machine learning have greater or lesser degrees of explicit statistical basis.
It's revolutionary because it lets us solve problems we don't understand
It's certainly a change! I would submit that an approach like that also is often limited in it's understanding of to what degree it has solved a given problem. That's not too bad when you have human verifying the end result but can problematic if one turns a machine of this sort loose to make it's own decisions.
We can and do explicitly explain their inner workings in math.
I certainly don't! I don't know of anyone who understands how and why they work. Granted there are many who write code that "works" but where's the proof that the code actually "works properly"? And if you don't know where you are then it is difficult to determine a path to a better place.
I see few attempts to explicitly explain their inner workings in math. I do see lots of programs/code, however but that isn't math.
Similar dramatic moments have likely occurred before many times. The bare facts lie before us but we do not understand. I'm thinking of late 1800s-1900 just before Einstein published his works on special relativity. We knew Maxwell's equations and their relationship to the speed of light. We even had Lorentz's equations but we did not understand what they meant. Einstein interpreted the math and gave us special relativity.
Perhaps there is an Einstein (or two, or three, ...) working with LLMs now but they are quiet so far.
We do, of course, understand ML models at a pretty deep level. What you can't do is identify the weight values that encode all information about squirrels. You can't point to a particular neuron and say this is why it hallucinates. We do not grok these models. I severely doubt that it's even possible for a human to grok a 13B parameter LLM
Isn't that easy to debunk?
I'd be hard pressed to implement an algorithm that can control a double-jointed inverted pendulum without reading up on control theory again.
But I can instead read up on neural networks and implement one that can be trained to control the pendulum.
For a more advanced example, I would never be able implement speech recognition explicitly by writing a regular program. But I could probably implement a passable one using neural networks and lots of training material.
How is this not "engineer something one doesn't understand"?
Not so much unlike the time travel and rayguns, isn't it?
That's not the (only) problem, it's that no one can describe what AGI will really look like outside of vague and circular definitions. People have an idea of what they would like it to be, but there's no evidence any of it can be built, particularly human like intelligence, which may be only possible in the human host.
> "If we can make something that appears to be able to mimic human responses, isn't that AGI in practice even if it's not in principle?"
That's another problem, people's credulity when it comes to AI performance. ELIZA was pretty convincing in its time. ChatGPT is convincing a lot of people. But mimicking human responses turns out to be relatively easy, especially when models are trained on human output, but that doesn't move us very far along the path to AGI.