Читать книгу The Innovation Ultimatum - Steve Brown - Страница 48

Causal AIs understand cause and effect, while deep learning systems work by finding correlations inside data. To reason, deep learning AIs find complex associations within data and assess the probabilities of association. Reasoning by association has proven adequate for today's simple AI solutions, but correlation does not imply causation. To create an AI with human-level intelligence, researchers will need far more capable machines. Some AI researchers, most notably Dr. Judea Pearl, believe that the best path forward for AI development is to design AIs that understand cause and effect. This would allow AIs to reason based on an understanding of causation. A deep learning AI associates events (A and B happen together) while a causal AI understands that one event caused the another (A caused B), and not the other way around (B caused A). The sophisticated machines needed to solve big problems like climate change will need to understand all of the causational relationships involved in highly complex systems. Causal AI will rely on the commonsense knowledge mentioned in the previous section to give it the vital context it needs for sound reasoning.