At one time it was thought that software that could beat a human at chess would be, in your lingo, "a shade of AGI." And for the same reason you're listing your milestones - because it sounded extremely difficult and complex. Of course now we realize that was quite silly. You can develop software that can crush even the strongest humans through relatively simple algorithmic processes.

And I think this is the trap we need to avoid falling into. Complexity and intelligence are not inherently linked in any way. Primitive humans did not solve complex problems, yet obviously were highly intelligent. And so, to me, the great milestones are not some complex problem or another, but instead achieving success in things that have no clear path towards them. For instance, many (if not most) primitive tribes today don't even have the concept of numbers. Instead they rely on, if anything, broad concepts like a few, a lot, and more than a lot.

Think about what an unprecedented and giant leap is to go from that to actually quantifying things and imagining relationships and operations. If somebody did try to do this, he would initially just look like a fool. Yes here is one rock, and here is another. Yes you have "two" now. So what? That's a leap that has no clear guidance or path towards it. All of the problems that mathematics solve don't even exist until you discover it! So you're left with something that is not just a recombination or stair step from where you currently are, but something entirely outside what you know. That we are not only capable of such achievements, but repeatedly achieve such is, to me, perhaps the purest benchmark for general intelligence.

So if we were actually interested in pursuing AGI, it would seem that such achievements would also be dramatically easier (and cheaper) to test for. Because you need not train on petabytes of data, because the quantifiable knowledge of these peoples is nowhere even remotely close to that. And the goal is to create systems that get from that extremely limited domain of input, to what comes next, without expressly being directed to do so.

I agree that general, open ended problem solving is a necessary condition for General intelligence. However I differ in that I believe that such open ended problem solving can be demonstrated via current chat interfaces involving asking questions with text and images.

It’s hard for people to define AGI because Earth only has one generally intelligent family: Homo. So there is a tendency to identify Human intelligence or capabilities with General intelligence.

Imagine if dolphins were much more intelligent and could write research-level mathematics papers on par with humans, communicating with clicks. Even though dolphins can’t play the cello or do origami, lacking the requisite digits, UCLA still has a dolphin tank to house some of their mathematics professors, who work hand-in-flipper with their human counterparts. That’s General intelligence.

Artificial General Intelligence is the same but with a computer instead of a dolphin.

The term "AGI" has been loosely used for so many years that it doesn't mean anything very specific. The meaning of words derives from their usage.

To me Shane Legg's (DeepMind) definition of AGI meaning human level across full spectrum of abilities makes sense.

Being human or super-human level at a small number of specialized things like math is the definition of narrow AI - the opposite of general/broad AI.

As long as the only form of AI we have is pre-trained transformers, then any notion of rapid self-improvement is not possible (the model can't just commandeer $1B of compute for a 3-month self-improvement run!). Self-improvement would only seem possible if we have an AI that is algorithmically limited and does not depend on slow/expensive pre-training.

What if it sleeps for 8 hours every 16 hours and during that sleep period, it updates its weights with whatever knowledge it learned that day? Then it doesn't need $1B of compute every 3 months, it would use the $1B of compute for 8 hours every day. Now extrapolate the compute required for this into the future and the costs will come down. I don't know where I was going with that...

These current LLMs are purely pre-trained - there is no way to do incremental learning (other than a small amount of fine-tuning) without disrupting what they were pre-trained on. In any case, even if someone solves incremental learning, this is just a way of growing the dataset, which is happening anyway, and under the much more controlled/curated way needed to see much benefit.

There is very much a recipe (10% if this, 20% of that, curriculum learning, mix of modalities, etc) for the type of curated dataset creation and training schedule needed to advance model capabilities. There have even been some recent signs of "inverse scaling" where a smaller model performs better in some areas than a larger one due to getting this mix wrong. Throwing more random data at them isn't what is needed.

I assume we will eventually move beyond pre-trained transformers to better architectures where maybe architectural advances and learning algorithms do have more potential for AI-designed improvement, but it seems the best role for AI currently is synthetic data generation, and developer tools.