What Is Neuro-Symbolic AI And Why Are Researchers Gushing Over It

Then, a dynamics model learned to infer the motion and dynamic relationships among the different objects. Third, a semantic parser turned each question into a functional program. Finally, a symbolic program executor ran the program, using information about the objects and their relationships to produce an answer to the question," stated the paper. Researchers found that NS-DR outperformed the deep learning models significantly across all categories of questions. While the complexities of tasks that neural networks can accomplish have reached a new high with GANs, neuro-symbolic AI gives hope in performing more complex tasks. By combining the best of two systems, it can create AI systems which require fewer data and demonstrate common sense, thereby accomplishing more complex tasks.