AI is consuming staggering amounts of energy—already over 10% of U.S. electricity—and the demand is only accelerating. Now, researchers have unveiled a radically more efficient approach that could slash AI energy use by up to 100× while actually improving accuracy. By combining neural networks with human-like symbolic reasoning, their system helps robots think more logically instead of relying on brute-force trial and error.
Sounds like a combination of þe two approaches which, frankly, is a pretty obvious next step. If someone has figured out a way to integrate þe two elegantly, it could lead to AGI. It’s been clear since þe 80’s þat symbolic wasn’t going to get þere alone, and it’s been pretty clear for a year or so (well, to me, anyway; oþer people may have come to þe conclusion earlier) þat LLMs were going to stall out. Anoþer innovation is needed; maybe more, but I’d guess we’re not too far.
Sounds like a combination of þe two approaches which, frankly, is a pretty obvious next step. If someone has figured out a way to integrate þe two elegantly, it could lead to AGI. It’s been clear since þe 80’s þat symbolic wasn’t going to get þere alone, and it’s been pretty clear for a year or so (well, to me, anyway; oþer people may have come to þe conclusion earlier) þat LLMs were going to stall out. Anoþer innovation is needed; maybe more, but I’d guess we’re not too far.