Here is a link to my forecast
And here are the rough justifications for this distribution:
I don’t have much else to add beyond what others have posted, though it’s in part influenced by an AIRCS event I attended in the past. Though I do remember being laughed at for suggesting GPT-2 represented a very big advance toward AGI.
I’ve also never really understood the resistance to why current models of AI are incapable of AGI. Sure, we don’t have AGI with current models, but how do we know it isn’t a question of scale? Our brains are quite efficient, but the total energy consumption is comparable to that of a light bulb. I find it very hard to believe that a server farm in an Amazon, Microsoft, or Google Datacenter would be incapable of running the final AGI algorithm. And for all the talk of the complexity in the brain, each neuron is agonizingly slow (200-300Hz).
That’s also to say nothing of the fact that the vast majority of brain matter is devoted to sensory processing. Advances in autonomous vehicles are already proving that isn’t an insurmountable challenge.
Current AI models are performing very well at pattern recognition. Isn’t that most of what our brains do anyway?
Self attended recurrent transformer networks with some improvements to memory (attention context) access and recall to me look very similar to our own brain. What am I missing?
First, you ask why it isn’t a question of scale. But then you seem to wonder why we need any more scaling? This seems to mix up two questions: can current hardware support AGI for some learning paradigm, and can it support AGI for the deep learning paradigm?
My point here was that even if the deep learning paradigm is not anywhere close to as efficient as the brain, it has a reasonable chance of getting to AGI anyway since the brain does not use all that much energy. The biggest models from GPT-3 can run on a fraction of what a datacenter can supply, hence the original question, how do we know AGI isn’t just a question of scale in the current deep learning paradigm.