symbolic-mathematics-finally-yields-to-neural-networks-20200520
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summary
This article discusses how researchers have made progress in using neural networks to perform symbolic mathematics, a field that has historically been resistant to machine learning techniques. Symbolic mathematics involves manipulating abstract mathematical expressions symbolically rather than computing numerical values. The article explains the challenges faced in applying neural networks to symbolic mathematics and highlights recent breakthroughs that have enabled neural networks to solve problems involving symbolic reasoning. It also explores the potential applications of this research, such as improving automated theorem proving and advancing artificial intelligence in scientific domains. Overall, the article demonstrates the promising advancements in using neural networks to tackle symbolic mathematical problems.