The advantage of neural networks is that they can deal with messy and unstructured data. Instead of manually laboring through the rules of detecting cat pixels, you can train a deep learning algorithm on many pictures of cats. When you provide it with a new image, it will return the probability that it contains a cat. Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside. If we are working towards AGI this would not help since an ideal AGI would be expected to come up with its own line of reasoning .I’m really surprised this article only describes symbolic AI based on the 1950s to 1990s descriptions when...

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