Neuro-symbolic AI seen as evolution of artificial intelligence IAM Network

Symbolic AI algorithms have played an important role in AI's history, but they face challenges in learning on their own. After IBM Watson used symbolic reasoning to beat Brad Rutter and Ken Jennings at Jeopardy in 2011, the technology has been eclipsed by neural networks trained by deep learning. The power of neural networks is that they help automate the process of generating models of the world. This has led to several significant milestones in artificial intelligence, giving rise to deep learning models that, for example, could beat humans in progressively complex games, including Go and StarCraft. But it can be challenging to reuse these deep learning models or extend them to new domains. Now researchers and enterprises are looking for ways to bring neural networks and symbolic AI techniques together.