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.
Basically, the idea is to use a symbolic logic engine within a dynamic context created by the LLM. Traditionally, the problem with symbolic AI has been with creating the ontologies. You obviously can’t have a comprehensive ontology of the world because it’s inherently context dependent, and you have an infinite number of ways you can contextualize things. What neurosymbolics does is use LLMs for what they are good at, which is classifying noisy data from the outside world, and building a dynamic context. Once that’s done, it’s perfectly possible to use a logic engine to solve problems within that context.
So for peasants running Chairman Xi’s LLMs on local GPUs, we could try the largest model we can run and have it generate scripts to run instead of having the model do the actual processing of bulk data, to get more out of it.
Basically, the idea is to use a symbolic logic engine within a dynamic context created by the LLM. Traditionally, the problem with symbolic AI has been with creating the ontologies. You obviously can’t have a comprehensive ontology of the world because it’s inherently context dependent, and you have an infinite number of ways you can contextualize things. What neurosymbolics does is use LLMs for what they are good at, which is classifying noisy data from the outside world, and building a dynamic context. Once that’s done, it’s perfectly possible to use a logic engine to solve problems within that context.
So for peasants running Chairman Xi’s LLMs on local GPUs, we could try the largest model we can run and have it generate scripts to run instead of having the model do the actual processing of bulk data, to get more out of it.
kind of yeah, incidentally I experimented with a similar idea in a more restricted domain and it works pretty well https://lemmy.ml/post/41786590