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[[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.
[[Artur Garcez|Garcez]] and Lamb describe research in this area as being ongoing for at least the past twenty years,{{sfn|Garcez|Lamb|2020|p=2}} dating from their 2002 book on neurosymbolic learning systems.{{sfn|Garcez|Broda|Gabbay|Gabbay|2002}} A series of workshops on neuro-symbolic reasoning has been held every year since 2005
In their 2015 paper, Neural-Symbolic Learning and Reasoning: Contributions and Challenges, Garcez et al. argue that:
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