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.
There’s a competitive advantage to squeezing more compute out of the same GPU cluster with software optimizations that indirectly favors local models. It just depends on whether the optimization work can proceed fast enough to make the DC expansion approach obsolete (or at have a less profitable ROI).
There’s a competitive advantage to squeezing more compute out of the same GPU cluster with software optimizations that indirectly favors local models. It just depends on whether the optimization work can proceed fast enough to make the DC expansion approach obsolete (or at have a less profitable ROI).