  Recent advances in artificial intelligence (AI) and machine learning have
created a general perception that AI could be used to solve complex problems,
and in some situations over-hyped as a tool that can be so easily used.
Unfortunately, the barrier to realization of mass adoption of AI on various
business domains is too high because most domain experts have no background in
AI. Developing AI applications involves multiple phases, namely data
preparation, application modeling, and product deployment. The effort of AI
research has been spent mostly on new AI models (in the model training stage)
to improve the performance of benchmark tasks such as image recognition. Many
other factors such as usability, efficiency and security of AI have not been
well addressed, and therefore form a barrier to democratizing AI. Further, for
many real world applications such as healthcare and autonomous driving,
learning via huge amounts of possibility exploration is not feasible since
humans are involved. In many complex applications such as healthcare, subject
matter experts (e.g. Clinicians) are the ones who appreciate the importance of
features that affect health, and their knowledge together with existing
knowledge bases are critical to the end results. In this paper, we take a new
perspective on developing AI solutions, and present a solution for making AI
usable. We hope that this resolution will enable all subject matter experts
(eg. Clinicians) to exploit AI like data scientists.
