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Neuro-symbolic AI attempts to integrate neural and symbolic architectures in a manner that addresses strengths and weaknesses of each, in a complementary fashion, in order to support robust AI capable of reasoning, learning, and cognitive modeling. As argued by [[Leslie Valiant|Valiant]]{{sfn|Valiant|2008}} and many others,{{sfn|Garcez|Besold|De Raedt|Földiák|2015}} the effective construction of rich computational [[cognitive model]]s demands the combination of sound symbolic reasoning and efficient (machine) learning models. [[Gary Marcus]], similarly, argues that: "We cannot construct rich cognitive models in an adequate, automated way without the triumvirate of hybrid architecture, rich prior knowledge, and sophisticated techniques for reasoning.",{{sfn|Marcus|2020|p=44}} and in particular:
"To build a robust, knowledge-driven approach to AI we must have the machinery of symbol-manipulation in our toolkit. Too much of useful knowledge is abstract to make do without tools that represent and manipulate abstraction, and to date, the only machinery that we know of that can manipulate such abstract knowledge reliably is the apparatus of symbol
[[Henry Kautz]],{{sfn|Kautz|2020}} [[Francesca Rossi]],{{sfn|Rossi|2022}} and [[Bart Selman]]{{sfn|Selman|2022}} have also argued for a synthesis. Their arguments are based on a need to address the two kinds of thinking discussed in [[Daniel Kahneman]]'s book, ''[[Thinking, Fast and Slow]]''. Kahneman describes human thinking as having two components, [[Thinking, Fast and Slow#Two systems|System 1 and System 2]]. System 1 is fast, automatic, intuitive and unconscious. System 2 is slower, step-by-step, and explicit. System 1 is the kind used for pattern recognition while System 2 is far better suited for planning, deduction, and deliberative thinking. In this view, deep learning best models the first kind of thinking while symbolic reasoning best models the second kind and both are needed.
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