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"AI should NOT be regulated at all!" - Prof. Pedro Domingos
Machine Learning Street Talk (MLST)
Discovering the Master Symmetry of Language
Symmetry-based learning incorporates permutation invariants, exemplified by ConvNets, which integrate translational symmetry to operate effectively with limited data. The advancement in artificial intelligence could significantly benefit from systems capable of discovering new symmetries rather than solely relying on known ones. In semantic parsing, this approach highlights how meaning remains invariant despite variable syntactic forms—such as active or passive voice. The intrinsic semantic consistency is often retained in paraphrases and synonyms, which represent the symmetries of language. Each language has its unique symmetry group, and uncovering these groups could lead to a deeper understanding of linguistic structures. Preliminary research indicates that it is possible to derive these symmetry groups from text, enhancing the performance of language models significantly.
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