The rapid development of big data techniques has offered great opportunities to develop smart city services in public safety, transportation management, city planning, etc. Meanwhile, the smart city development levels of different cities are still unbalanced. For a large of number of cities which just start development, the governments will face a critical cold-start problem, 'how to develop a new smart city service suffering from data scarcity?'. To address this problem, transfer learning is recently leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm. This article investigates the common process of urban transfer learning, aiming to provide city governors and relevant practitioners with guidelines of applying this novel learning paradigm. Our guidelines include common transfer strategies to take, general steps to follow, and case studies to refer. We also summarize a few future research opportunities in urban transfer learning, and expect this article can attract more researchers into this promising area.
The Future of AI: Blockchain and Deep Learning First point: considering blockchain and deep learning together suggests the emergence of a new class of global network computing system. These systems are self-operating computation graphs that make probabilistic guesses about reality states of the world. Second point: blockchain and deep learning are facilitating each other's development. This includes using deep learning algorithms for setting fees and detecting fraudulent activity, and using blockchains for secure registry, tracking, and remuneration of deep learning nets as they go onto the open Internet (in autonomous driving applications for example). Blockchain peer-to-peer nodes might provide deep learning services as they already provide transaction hosting and confirmation, news hosting, and banking (payment, credit flow-through) services.
Artificial intelligence (AI) is a powerful technology paradigm. Machine learning, deep learning and other AI-related technologies offer potentially vast business value. But powerful tech often requires powerful computing infrastructure, so AI is usually deployed on centralized servers and mainframes. To get maximum value from this technology, organizations will increasingly need to deploy AI in low-power scenarios--on mobile phones, tablets and especially across the billions of smart devices that make up the Internet of Things (IoT). If you follow tech trends, you'll be only too well aware of the big, big future predicted for IoT.