The neuro-symbolic concept learner designed by the researchers at MIT and IBM combines elements of symbolic AI and deep learning. The idea is to build a strong AI model that can combine the reasoning power of rule-based software and the learning capabilities of neural networks.
In the hybrid AI model, the symbolic component takes advantage of the neural network’s ability to process and analyze unstructured data. Meanwhile, the neural network also benefits from the reasoning power of the rule-based AI system, which enables it to learn new things with much less data.
“Neural networks and symbolic ideas are really wonderfully complementary to each other,” Cox said. “Because neural networks give you the answers for getting from the messiness of the real world to a symbolic representation of the world, finding all the correlations within images. Once you’ve got that symbolic representation, you can do some pretty magical things in terms of reasoning.”
For instance a neuro-symbolic system would use a neural network’s pattern recognition capabilities to identify objects. Then it would rely on symbolic A.I. to apply logic and semantic reasoning to uncover new relationships
Developing a notebook that trains a model is only a small fraction of any serious ML project. Data collection before that, and deployment after, are usually more difficult and time-consuming.