For an intelligence which is capable of continual addition of new domains of knowledge, generating its own schema, and making accurate predictions out-of-distribution? 5 to 10 years.
Why: AlphaGo was only six years ago. The global effort per year toward development is still increasing rapidly; and chips are not a bottleneck. We know for sure that human brains, with our 100T synapses, do not need to be replicated in every detail, before comparable performance is reached. As proof: Google’s RETRO language-model out-performs the average human, while using only one 13,000th of a human brain’s synapses. Our Broca’s Area alone is many times larger; our brains are clearly bloat-ware.
So, AI performance will not be hindered by hardware… the limitation is its capacity to learn from few examples, and generalize. Looking at how image recognition once required 10 Million examples to learn a cat, and comparing that to the ten photos needed to construct a high-res 3D render, now, I don’t see ‘few examples’ being a problem. Generalization is more difficult, because we must expand the search of architectures to include the more-neglected and less-performant models, like Numenta’s. I still don’t expect that stumbling-block to take long, and once a model can generalize out-of-distribution from conceptualizations even a little bit, then we have a matter of months, a year at best, before it’s overwhelmingly good.
When FOOM? We’re late to that party: it’s already happening. If we had told any futurist in 2010 that we were getting 10x, 25x, and more, regularly, across numerous performance metrics, for various applications… they would have called that pace a FOOM. It just doesn’t need artificial general intelligence to happen… human + machine, and AutoML seem to be plenty for self-acceleration. By the time AGI is entrusted with anything, we’re likely to have only a few multiples of algorithmic performance remaining! Most of the FOOM, on log-chart, will happen before AGI—and that’s a very specific prediction.
For an intelligence which is capable of continual addition of new domains of knowledge, generating its own schema, and making accurate predictions out-of-distribution? 5 to 10 years.
Why: AlphaGo was only six years ago. The global effort per year toward development is still increasing rapidly; and chips are not a bottleneck. We know for sure that human brains, with our 100T synapses, do not need to be replicated in every detail, before comparable performance is reached. As proof: Google’s RETRO language-model out-performs the average human, while using only one 13,000th of a human brain’s synapses. Our Broca’s Area alone is many times larger; our brains are clearly bloat-ware.
So, AI performance will not be hindered by hardware… the limitation is its capacity to learn from few examples, and generalize. Looking at how image recognition once required 10 Million examples to learn a cat, and comparing that to the ten photos needed to construct a high-res 3D render, now, I don’t see ‘few examples’ being a problem. Generalization is more difficult, because we must expand the search of architectures to include the more-neglected and less-performant models, like Numenta’s. I still don’t expect that stumbling-block to take long, and once a model can generalize out-of-distribution from conceptualizations even a little bit, then we have a matter of months, a year at best, before it’s overwhelmingly good.
When FOOM? We’re late to that party: it’s already happening. If we had told any futurist in 2010 that we were getting 10x, 25x, and more, regularly, across numerous performance metrics, for various applications… they would have called that pace a FOOM. It just doesn’t need artificial general intelligence to happen… human + machine, and AutoML seem to be plenty for self-acceleration. By the time AGI is entrusted with anything, we’re likely to have only a few multiples of algorithmic performance remaining! Most of the FOOM, on log-chart, will happen before AGI—and that’s a very specific prediction.