What are neural-symbolic AI methods and why will they dominate 2020?
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What are neural-symbolic AI methods and why will they dominate 2020?
The AI commercial stage will be changed forever.
The recent commercial AI revolution has been largely driven by deep neural networks. First invented in the 1960s, deep NNs came into their own once fueled by the combination of internet-scale datasets and distributed GPU farms.
But the field of AI is much richer than just this one type of algorithm. Symbolic reasoning algorithms such as artificial logic systems, also pioneered in the '60s, may be poised to emerge into the spotlight - to some extent perhaps on their own, but also hybridized with neural networks in the form of so-called "neural-symbolic" systems.
Weaknesses of deep neural networks
Deep neural nets have done amazing things for certain tasks, such as image recognition and machine translation. However, for many more complex applications, traditional deep learning approaches cannot match the ability of hybrid architecture systems that additionally leverage other AI techniques such a