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There's an idea that passes in and out of vogue[0] that metaphor is the foundation of semantics in general. It's not been popular lately, but I get the feeling

There's an idea that passes in and out of vogue[0] that metaphor is the foundation of semantics in general. It's not been popular lately, but I get the feeling it will come back the more we bump up against the hard limits of tabula rasa-style learning. I happen to be rather fond of it myself, and agree completely with you here. You can have all sorts of fun building models based around 'metaphoric delta', the bridge that must be crossed between the set of metaphors I understand, and the set you understand. And of course, when they are misinterpreted, or understood differently, or leak, as all abstractions do. You get people talking with the same words but not understanding eachother. --- [0] most lately, Lakoff. See The Metaphors We Live By

carlmr 9y ago
>It's not been popular lately, but I get the feeling it will come back the more we bump up against the hard limits of tabula rasa-style learning. Most of Neural Networks ideas exist since the 60s/70s, right now we're just converging to a point where tabula-rasa style learning of smaller neural networks has become feasible with our computing power. It's not that we've made such huge strides in theory, it's more that we can now engineer systems that actually do something useful with NNs. For generalized AI and metaphor based learning, we'll need much much more powerful computers that can stitch together multiple prelearned NNs and modify tweak them just slightly on new data and construct connected multi-domain NNs. This will have the complexity resembling some higher animal brains. Metaphors is kind of another name for using a similar pre built NN as a start for another Neural Network and connecting these in a supernetwork.