Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life. As computational capacities grow, the way we digitize and process our analog reality can also expand, until we are juggling billion-parameter tensors instead of seven-character strings. Insofar as computers suffered from the same chokepoints, their builders relied on all-too-human hacks like symbols to sidestep the limits to processing, storage and I/O. 5 You could say that they are necessary to overcome biological chokepoints in throughput. 4 Symbols compress sensory data in a way that enables humans, large primates of limited bandwidth, to share information with each other. The signifier indicates the signified, like a finger pointing at the moon. It could be the variable x, pointing at an unknown quantity, or it could be the word rose, which is pointing at the red, curling petals layered one over the other in a tight spiral at the end of a stalk of thorns. For our purposes, the sign or symbol is a visual pattern, say a character or string of characters, in which meaning is embedded, and that sign or symbol is pointing at something else. That something else could be a physical object, an idea, an event, you name it. The words sign and symbol derive from Latin and Greek words, respectively, that mean mark or token, as in “take this rose as a token of my esteem.” Both words mean “to stand for something else” or “to represent something else”. 2 Recent work by MIT, DeepMind and IBM has shown the power of combining connectionist techniques like deep neural networks with symbolic reasoning.Īpply Reinforcement Learning to Simulations » Signs, Symbols, Signifiers and Signifieds Research into so-called one-shot learning may address deep learning’s data hunger, while deep symbolic learning, or enabling deep neural networks to manipulate, generate and otherwise cohabitate with concepts expressed in strings of characters, could help solve explainability, because, after all, humans communicate with signs and symbols, and that is what we desire from machines. Geoff Hinton himself has expressed scepticism about whether backpropagation, the workhorse of deep neural nets, will be the way forward for AI. why did my model make that prediction?) and the large amount of data that deep neural networks require in order to learn. The two biggest flaws of deep learning are its lack of model interpretability (i.e. Symbolic reasoning is one of those branches. Symbolic Reasoning (Symbolic AI) and Machine Learningĭeep learning has its discontents, and many of them look to other branches of AI when they hope for the future.
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