Semantic Intelligence

We have seen in the past decade that statistical models have already proved themselves thereby revolutionizing the world but with a major drawback of explainability and interpretability by human. The statistical models are very good at learning in a static world and executing low-level patterns, provided they are fed with a lot of data. More deep, more intelligent, and of course more black. The traditional symbolic approaches (the so-called Good Old Fashioned AI) manage structured knowledge very well and result in a pool of machine-interpretable, linked, open-access, and interoperable knowledge over a wide range of domains; but have a limited scope because of many reasons like the lack of extensibility. The symbolist approach is nowadays manifested as a knowledge graph.

The question is that “Is Artificial Intelligence (AI) of today Artificial Super Intelligence (ASI) / Artificial General Intelligence (AGI) / Artificial Narrow Intelligence (ANI)? Is AI of today the AI that we are craving for?” In fact, today’s artificial intelligence is weak AI, which is very good at doing specialized tasks. Symbolic AI and statistical AI have to go together to achieve contextual computing. The Hybrid Model combines machine intelligence with human intelligence to reach conclusions faster than possible by humans alone along with the explanations needed for understandability and trust in the decisions and results; while requiring far fewer data samples for training and conversing in natural language. The Hybrid Model is able to generalize and is excellent at perceiving, learning, and reasoning with minimal supervision.