  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 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 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 gigantic repository of knowledge.
Wikipedia for smart machines along with the new Double Deep Learning approach
offer a paradigm for integrating datacentric 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.
