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Neurosymbolic AI: Merging Intelligence with Logic
Think of neurosymbolic AI as the combination of two types of intelligence. Here you have neural networks. They provide powerful pattern recognition for messy, unstructured data from the real world including image, voice, and sensor data. Here you have symbolic reasoning, a powerful way to apply rules, logic, and structured knowledge to formal problem solving.
How may we combine both of these approaches? Each approach is great on its own. Today’s AI can very well detect a cat in an image and very well solve a logic puzzle, but it cannot do both together. Neurosymbolic AI makes this possible. It can:
1. Reason and explain its decisions—not just give answers but explain why those answers are valid
2. Learn quickly—as it encounters new patterns, it can not only rely on the new knowledge but also relate what it has already learned, instead of having to start with zero application and comprehension.
3. Recognize and account for uncertainty better. Neurosymbolic AI can apply logic when data is articulated clearly, and learn when it is messy.
In the next technological wave, we may see AI reading complex legal contracts, teasing out the author’s intent, and reasoning toward implications. Or we may see medical AI that integrates lab tests and established care guidelines toward timely and safe diagnoses.
Neurosymbolic AI provides an AI with something resembling an “intuition”