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.
I don’t get the impression that the goal is to apply the model to a hyperspecific domain, rather the idea seems 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. The goal here is to optimize a particular set of tasks which can be expressed as a set of logical steps.
That’s super cool, I’ve always thought that it was backwards that we’re using LLMs to add complexity to prompts. They should be used to reduce complexity by recognizing and factoring out patterns.
I don’t get the impression that the goal is to apply the model to a hyperspecific domain, rather the idea seems 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. The goal here is to optimize a particular set of tasks which can be expressed as a set of logical steps.
That’s super cool, I’ve always thought that it was backwards that we’re using LLMs to add complexity to prompts. They should be used to reduce complexity by recognizing and factoring out patterns.
yeah exactly