neural-symbolic integration

Neural-symbolic integration combines two approaches: neural networks, which learn patterns from data, and symbolic systems, which rely on clear rules and logic. This integration aims to create AI systems that can both learn from experience and reason based on structured knowledge. By doing so, AI can better understand complex concepts, explain its reasoning, and adapt to new information, much like how humans balance intuitive learning with logical thinking. This approach seeks to develop more versatile, transparent, and intelligent systems capable of handling a wide range of tasks.