  The development of smart cities and their fast-paced deployment is resulting
in the generation of large quantities of data at unprecedented rates.
Unfortunately, most of the generated data is wasted without extracting
potentially useful information and knowledge because of the lack of established
mechanisms and standards that benefit from the availability of such data.
Moreover, the high dynamical nature of smart cities calls for new generation of
machine learning approaches that are flexible and adaptable to cope with the
dynamicity of data to perform analytics and learn from real-time data. In this
article, we shed the light on the challenge of under utilizing the big data
generated by smart cities from a machine learning perspective. Especially, we
present the phenomenon of wasting unlabeled data. We argue that
semi-supervision is a must for smart city to address this challenge. We also
propose a three-level learning framework for smart cities that matches the
hierarchical nature of big data generated by smart cities with a goal of
providing different levels of knowledge abstractions. The proposed framework is
scalable to meet the needs of smart city services. Fundamentally, the framework
benefits from semi-supervised deep reinforcement learning where a small amount
of data that has users' feedback serves as labeled data while a larger amount
is without such users' feedback serves as unlabeled data. This paper also
explores how deep reinforcement learning and its shift toward semi-supervision
can handle the cognitive side of smart city services and improve their
performance by providing several use cases spanning the different domains of
smart cities. We also highlight several challenges as well as promising future
research directions for incorporating machine learning and high-level
intelligence into smart city services.
