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Neuro-Symbolic AI
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With AI gaining more traction, in August of 2019, strong research efforts began to enable common-sense and reasoning abilities in AI systems by reverse engineering the brain of human babies. As the name implies, the recipe of neuro-symbolic programming involves two main ingredients: NNs and symbolic programming. We will explore these two ingredients using the Compositional Language and Elementary Visual Reasoning (CLEVR) example case. CLEVR is a dataset of 100,000 computer-generated scenes portraying 3D shapes (https://cs.stanford.edu/people/jcjohns/clevr/). The objective of this dataset is for AI to reason about these images and be able to answer questions regarding the said images—for example: How many spheres are in the image?
Motivated by their observations, the researchers highlighted one key aspect of the reasoning abilities of humans (and other organisms, for that matter): world knowledge. We can reason about...