Very important breakthroughs in data-centric deep-learning algorithms led to impressive performance in ‘transactional’ point applications of Artificial Intelligence (AI) such as Face Recognition, or EKG classification. With all due appreciation, however, ‘knowledge-blind’ data-only machine learning algorithms have severe limitations for non-transactional AI applications, such as medical diagnosis beyond the EKG results. Such applications require deeper and broader knowledge in their problem-solving capabilities, e.g. integrating anatomy and physiology knowledge with EKG results and other patient’s findings. Following a review and illustrations of such limitations for several real-life AI applications, we point at ways to overcome them.

The proposed Wikipedia for Smart Machines initiative aims at building repositories of software structures that represent humanity’s science & technology knowledge in various parts of life; knowledge that we all learn in schools, universities and during our professional life. Target readers for these repositories are smart machines; not human. AI software developers will have these Reusable Knowledge structures readily available, hence, the proposed name ReKopedia. Big Data is by now a mature technology, it is time to focus on ‘Big Knowledge’. Some will be derived from data, some will be obtained from mankind’s gigantic repository of knowledge.
Wikipedia for smart machines along with the new Double Deep Learning approach offer a paradigm for integrating data-centric deep learning algorithms with algorithms that leverage deep knowledge, e.g. evidential reasoning and causality reasoning. For illustration, a project is described to produce ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders. Data is important, but knowledge- deep, basic, and commonsense- is equally important.