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?
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?