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Recent years have witnessed the increasing popularity of mobile devices (suchas iphone) due to the convenience that it brings to human lives. On one hand,rich user profiling and behavior data (including per-app level, app-interactionlevel and system-interaction level) from heterogeneous information sources makeit possible to provide much better services (such as recommendation,advertisement targeting) to customers, which further drives revenue fromunderstanding users' behaviors and improving user' engagement. In order todelight the customers, intelligent personal assistants (such as Amazon Alexa,Google Home and Google Now) are highly desirable to provide real-time audio,video and image recognition, natural language understanding, comfortable userinteraction interface, satisfactory recommendation and effective advertisementtargeting.  This paper presents the research efforts we have conducted on mobile deviceswhich aim to provide much smarter and more convenient services by leveragingstatistics and big data science, machine learning and deep learning, usermodeling and marketing techniques to bring in significant user growth and userengagement and satisfactions (and happiness) on mobile devices. The developednew features are built at either cloud side or device side, harmonicallyworking together to enhance the current service with the purpose of increasingusers' happiness. We illustrate how we design these new features from systemand algorithm perspective using different case studies, through which one caneasily understand how science driven innovations help to provide much betterservice in technology and bring more revenue liftup in business. In themeantime, these research efforts have clear scientific contributions andpublished in top venues, which are playing more and more important roles formobile AI products.

Science Driven Innovations Powering Mobile Product: Cloud AI vs. Device AI Solutions on Smart Device