Bionics (the imitation or abstraction of the “inventions of nature) and, to an even greater extent, synthetic biology, will be as relevant to engineering development and industry as the silicon chip was over the last 50 years. Chemical industries already use so-called “white biotechnology” for new processes, new raw materials, and more sustainable use of resources. Synthetic biology is also used for the development of second-generation biofuels and for harvesting the sun's energy with the help of tailor-made microorganisms or biometrically designed catalysts. The market potential for bionics in medicine, engineering processes, and DNA storage is huge. “Moonshot” projects are already aggressively focusing on diseases and new materials, and a US-led competition is currently underway with the aim of creating a thousand new molecules. This article describes a timeline that starts with current projects and then moves on to code engineering projects and their implications, artificial DNA, signaling molecules, and biological circuitry. Beyond these projects, one of the next frontiers in bionics is the design of synthetic metabolisms that include artificial food chains and foods, and the bioengineering of raw materials; all of which will lead to new insights into biological principles. Bioengineering will be an innovation motor just as digitalization is today. This article discusses pertinent examples of bioengineering, particularly the use of alternative carbon-based biofuels and the techniques and perils of cell modification. Big data, analytics, and massive storage are important factors in this next frontier. Although synthetic biology will be as pervasive and transformative in the next 50 years as digitization and the Internet are today, its applications and impacts are still in nascent stages. This article provides a general taxonomy in which the development of bioengineering is classified in five stages (DNA analysis, bio-circuits, minimal genomes, protocells, xenobiology) from the familiar to the unknown, with implications for safety and security, industrial development, and the development of bioengineering and biotechnology as an interdisciplinary field. Ethical issues and the importance of a public debate about the consequences of bionics and synthetic biology are discussed.

While recommendation plays an increasingly critical role in our living, study, work, and entertainment, the recommendations we receive are often for irrelevant, duplicate, or uninteresting products and services. A critical reason for such bad recommendations lies in the intrinsic assumption that recommended users and items are independent and identically distributed (IID) in existing theories and systems. Another phenomenon is that, while tremendous efforts have been made to model specific aspects of users or items, the overall user and item characteristics and their non-IIDness have been overlooked. In this paper, the non-IID nature and characteristics of recommendation are discussed, followed by the non-IID theoretical framework in order to build a deep and comprehensive understanding of the intrinsic nature of recommendation problems, from the perspective of both couplings and heterogeneity. This non-IID recommendation research triggers the paradigm shift from IID to non-IID recommendation research and can hopefully deliver informed, relevant, personalized, and actionable recommendations. It creates exciting new directions and fundamental solutions to address various complexities including cold-start, sparse data-based, cross-domain, group-based, and shilling attack-related issues.

After a systematic review of 38 current intelligent city evaluation systems (ICESs) from around the world, this research analyzes the secondary and tertiary indicators of these 38 ICESs from the perspectives of scale structuring, approaches and indicator selection, and determines their common base. From this base, the fundamentals of the City Intelligence Quotient (City IQ) Evaluation System are developed and five dimensions are selected after a clustering analysis. The basic version, City IQ Evaluation System 1.0, involves 275 experts from 14 high-end research institutions, which include the Chinese Academy of Engineering, the National Academy of Science and Engineering (Germany), the Royal Swedish Academy of Engineering Sciences, the Planning Management Center of the Ministry of Housing and Urban-Rural Development of China, and the Development Research Center of the State Council of China. City IQ Evaluation System 2.0 is further developed, with improvements in its universality, openness, and dynamic adjustment capability. After employing deviation evaluation methods in the IQ assessment, City IQ Evaluation System 3.0 was conceived. The research team has conducted a repeated assessment of 41 intelligent cities around the world using City IQ Evaluation System 3.0. The results have proved that the City IQ Evaluation System, developed on the basis of intelligent life, features more rational indicators selected from data sources that can offer better universality, openness, and dynamics, and is more sensitive and precise.

The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on a distributed cluster with tens to thousands of machines, it is often the case that significant engineering efforts are required—and one might fairly ask whether such engineering truly falls within the domain of ML research. Taking the view that “big” ML systems can benefit greatly from ML-rooted statistical and algorithmic insights—and that ML researchers should therefore not shy away from such systems design—we discuss a series of principles and strategies distilled from our recent efforts on industrial-scale ML solutions. These principles and strategies span a continuum from application, to engineering, and to theoretical research and development of big ML systems and architectures, with the goal of understanding how to make them efficient, generally applicable, and supported with convergence and scaling guarantees. They concern four key questions that traditionally receive little attention in ML research: How can an ML program be distributed over a cluster? How can ML computation be bridged with inter-machine communication? How can such communication be performed? What should be communicated between machines? By exposing underlying statistical and algorithmic characteristics unique to ML programs but not typically seen in traditional computer programs, and by dissecting successful cases to reveal how we have harnessed these principles to design and develop both high-performance distributed ML software as well as general-purpose ML frameworks, we present opportunities for ML researchers and practitioners to further shape and enlarge the area that lies between ML and systems.

The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.

The emerging prototype for a Smart City is one of an urban environment with a new generation of innovative services for transportation, energy distribution, healthcare, environmental monitoring, business, commerce, emergency response, and social activities. Enabling the technology for such a setting requires a viewpoint of Smart Cities as cyber-physical systems (CPSs) that include new software platforms and strict requirements for mobility, security, safety, privacy, and the processing of massive amounts of information. This paper identifies some key defining characteristics of a Smart City, discusses some lessons learned from viewing them as CPSs, and outlines some fundamental research issues that remain largely open.

This article provides in-depth insights into the necessary technologies for automated driving in future cities. State of science is reflected from different perspectives such as in-car computing and data management, road side infrastructure, and cloud solutions. Especially the challenges for the application of HD maps as core technology for automated driving are depicted in this article.

This study provides a definition for urban big data while exploring its features and applications of China’s city intelligence. The differences between city intelligence in China and the “smart city” concept in other countries are compared to highlight and contrast the unique definition and model for China’s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intelligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China’s urban big data. Considering the supporting and core role that urban big data plays in city intelligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China’s urban development. In conclusion, it is imperative that China make full use of its unique advantages—including using the nation’s current state of development and resources, geographical advantages, and good human relations—in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.