key: cord-0798424-fvkxvvyv authors: Yigitcanlar, Tan; Butler, Luke; Windle, Emily; Desouza, Kevin C.; Mehmood, Rashid; Corchado, Juan M. title: Can Building “Artificially Intelligent Cities” Safeguard Humanity from Natural Disasters, Pandemics, and Other Catastrophes? An Urban Scholar’s Perspective date: 2020-05-25 journal: Sensors (Basel) DOI: 10.3390/s20102988 sha: 0383cfb624c940e03e84da5590e778956af62fca doc_id: 798424 cord_uid: fvkxvvyv In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand—where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development. What Is an Artificially Intelligent City? During the current Anthropocene era-the geological epoch which has had significant human impact on Earth's geology and ecosystems-we have developed technological capabilities that have AI is one of the most disruptive technologies of our time and its capabilities have progressed rapidly [26] . The uptake of AI in organizations is on the rise. For instance, between 2018 and 2019, the number of organizations that deployed AI grew from 4% to 14%, and among the AI applications, conversational AI is at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and Apple's Siri [27] . Gartner [28] provides insights into the hype cycle for AI applications, which reflects the growing popularity of machine learning, intelligent applications, and AI-as-a-Service (AIaaS) or AI-Platform-as-a-Service (AI-PaaS) ( Figure 2 ). In recent years, governments around the world have started to see AI as a nation-defining and global economic competitiveness-increasing capability [29] . In recognition of the increasing importance of AI, as of February 2020, 50 countries have already developed specific national AI strategies-where these countries represent 90% of global gross domestic product (GDP). Figure 3 illustrates the location of these countries; a brief further info on each country's national strategy is provided in Appendix A ( Figure A1 ). AI-driven computational techniques are diverse and wide-ranging. For example, AI has been in use for quite some time in the tasks that are risky or cause harm to humans. This includes the use of automated robots for bomb detection or combat of unmanned aerial vehicles, and the use of autonomous trucks in the mining industry or mobile reconnaissance units for space exploration [30] . AI-enabled applications include robotic processes [31] for automating public sector tasks, and autonomous delivery bots [32] and chatbots [33] for enhancing business intelligence, stakeholder engagement experience, and customer service quality. Today, AI is rapidly changing the nature of jobs. Many of the services that have been offered by human workers are now being revolutionized by technology. For example, chatbots automate the work of information technology (IT) professionals [34] and human resource (HR) departments [35] , so that they can focus on higher value tasks. Autonomous vehicles and driverless shuttle buses are being trialed worldwide. Driverless shuttle bus services are expected to start carrying fare-paying customers in Scotland later in 2020 [36] . Likewise, robot police services are planned to be launched in Houston, Texas to curb petty crime and free up law enforcement resources in 2020 [37] . AI-based systems are providing various solutions. These solutions facilitate the creation of new products and services in many different fields. Particularly, sensor networks are undergoing great expansion and development and the combination of both AI and sensor networks has now become a reality to change our lives and our cities. The integration of these two prominent technologiesincluding AIoT (AI-of-Things)-also benefits other areas such as Industry 4.0, Internet-of-Things (IoT), demotic systems, and so on [38, 39] . AI is being employed to model the spread of COVID-19 to assist decision makers in understanding the future implications of the virus and the measures that should be taken to limit its spread [40] . For instance, in China, AI is being used to minimize the spread of COVID-19 by mobilizing robots that do cleaning and food preparation tasks [41] . Moreover, the European Union [42] launched the EU vs. Virus challenge via a Pan-European hackathon to find ways to tackle COVID-19 via AI and other applications. AI also has the potential to help in addressing some of the planetary challenges ( Table 1) . The World Economic Forum [43] underlines the following eight AI applications as "game changers": (a) autonomous and connected electric vehicles; (b) distributed energy grids; (c) smart agriculture and food systems; (d) next-generation weather and climate prediction; (e) smart disaster response; (f) AIdesigned intelligent, connected, and livable cities; (g) a transparent digital earth; and (h) reinforcement learning for earth sciences breakthroughs. In the previous section, we have provided some examples of the use of AI in cities. Here in this section, we share a few more examples to cover some of the other aspects of AI for cities. In particular, the AI solutions implemented in Australia have been taken as an example. Like many other advanced knowledge and innovation economies, AI is a rapidly growing field in Australia. Furthermore, the country has been an early adopter of smart technologies [44] , particularly for targeting industrial and urban sustainability outcomes [45] [46] [47] . Some of the existing AI applications and experienced challenges in the country are discussed as follows: Autonomous vehicles and driverless shuttle buses are being trialed throughout Australia, in all capital cities and some regional centers [48] . Nevertheless, the regulation efforts of autonomous vehicles are yet to follow the autonomous driving trials and developments. State of NSW police have been using AI systems to identify drivers illegally using mobile phones [49] . These systems review images, detect offences, and then exclude non-offenders. Nonetheless, images are then authorized following a review by human operators. The importance of review of AI outputs by human operators was highlighted by the Australian federal government's incorrect use of AI for automatic detection of Centrelink debt and issuing of infringement notices without human input [50] . The process resulted in some individuals receiving notices incorrectly and placed the onus of proof onto the accused. Other issues have resulted from facial recognition software used in surveillance and crime prevention, which may have unfairly discriminated against Aboriginal and Torres Strait Islanders [51] . Despite these issues, development of AI continues in a variety of fields in Australia, and has been investigated for its use in product/goods delivery [52] , environmental and transport monitoring [53] , disaster prediction [54] , healthcare [55] , infrastructure [56] , data privacy [57] , and agriculture [58] . Just to provide some examples, AI's contributions to healthcare practice are listed in Table 2 . Additionally, AI applications have been used in big data analytics, such as its use in social media analytics to aid natural disaster management. Figure 4 is an example of the disaster severity map generated for the 2010-2011 Queensland Floods with the help of machine learning technology [59] . Table 2 . AI applications and motivation for adoption in healthcare practice, derived from Park [60] . Robot-assisted surgery Technological advances in robotic solutions for more types of surgery Virtual nursing assistants Increasing pressure caused by medical labor shortage Administrative workflow Easier integration with existing technology infrastructure Fraud detection Need to address complex service and payment fraud attempts Dosage error reduction Prevalence of medical errors, which leads to tangible penalties Connected machines Proliferation of connected machines and devices Clinical trial participation Client cliff, plethora of data, outcomes-driven approach Preliminary diagnosis Interoperability and data architecture to enhance accuracy Automated image diagnosis Storage capacity, greater trust in AI technology Cybersecurity Increase in breaches, pressure to protect health data In terms of strategizing AI, there have been some promising developments in Australia. The most notable one is the AI roadmap, codeveloped by CSIRO's Data61 and the Australian Government Department of Industry, Innovation and Science. The roadmap identifies strategies to help develop a national AI capability to boost the productivity of Australian industry, create jobs and economic growth, and improve the quality of life for current and future generations. The roadmap emphasizes the need to concentrate on the three key domains: (a) natural resources and the environment; (b) health, aging, and disability; and (c) cities, towns, and infrastructure [61] . Table 3 below elaborates the objectives of these AI domains. Additionally, OECD's [62] AI policy observatory provides a useful repository of AI in Australia. Table 3 . Priority AI specialization domains and their objectives, derived from Data61 [61] . Natural resources and the environment Developing AI solutions for enhanced natural resource management to reduce the costs and improve the productivity of agriculture, mining, fisheries, forestry, and environmental management Health, aging, and disability Developing AI solutions for health, aging, and disability support to reduce costs, improve wellbeing, and make quality care accessible for all Australians Cities, towns, and infrastructure Developing AI solutions for better towns, cities, and infrastructure to improve the safety, efficiency, cost-effectiveness, and quality of the built environment 3. Discussion Cities are complex organisms and their complexity increases exponentially as they continue to grow [63] . With computational abilities vastly superior to humans, when it comes to ingesting large swaths of data, AI systems are among the core elements of most smart city projects [64] . Other smart technologies such as internet-of-things (IoT) [65] , autonomous vehicles (AV) [66, 67] , big data [68] , 5G wireless communication [69] , robotics [70] , blockchain [71] , cloud computing [72] , 3D printing [73] , virtual reality (VR) [74] , augmented reality (AR) [75] , digital twins [76] , and so on are also transforming our cities [77] . For instance, it is increasingly common to combine machine learning with other emerging technologies to generate advanced urban solutions. Examples include: the use of deep learning and high-performance computing (HPC) for traffic predictions using sensor data [78] , incident prediction [79] , disaster management [80] , and rapid transit systems designed to optimize urban mobility systems [81] . Machine learning has also been used with big data technologies and social media for logistics and urban planning [82, 83] , event detection for urban governance [84] , disease detection [85] , and identifying the sources of noise pollution at the city scale [86] . Additionally, machine learning has been applied along with distributed computing to improve basic scientific computing operations that are fundamental to urban design modeling methodologies [87] . Moreover, machine learning is paired with IoT for human activity recognition [88] , smart farming [89] , and developing next-generation distance learning systems [90] . Furthermore, machine learning benefits from data fusion in ubiquitous IoT environments [91] , where this creates a potential to significantly enhance AV decision capabilities [92] . Figure 5 lists AI capabilities and their use by domains. Nevertheless, it is when AI is combined with these technologies that we can really see its big potential to address complex challenges and harness opportunities within our urban environmentsgiven that some ethical issues are adequately addressed. Despite the AI and ethics issue being discussed in academic and government circles, so far only limited guiding principles have been produced and legislated [94] . In that regard, the European Parliament's [95] initiative on guidelines for the European Union (EU) on ethics in AI is a commendable but limited attempt, as ethical rules on AI are so far essentially of a self-regulatory nature, and there is growing demand for more government oversight. A recent study [96] that evaluated the levels of smartness of Australian local government areas advocated for the importance of integrating urban technologies, including AI, into local service delivery and governance, for instance, the use of AI in tasks that enhance environmental sustainability, such as sorting waste for recycling [97] . Additionally, the practice review conducted by McKinsey Global Research Institute [93] discloses projects from across the globe where AI is utilized for achieving UN's sustainable development goals (SDG) ( Figure 6 ). Nevertheless, before AI is implemented on a wider scale, it is important to understand how this technology can contribute to making our cities (and the planet) smarter. Conversely, understanding the pitfalls of AI will enable us to ensure AI delivers the desired outcomes in urban areas and beyond. With the above-mentioned issue in mind, our team in another study [22] evaluated the promises and pitfalls of AI for cities according to the main smart city dimensions of economy, society, environment, and governance [98] . Table 4 below summarizes the key findings of the study. The biggest pitfalls of AI-enabled solutions are that they may aggravate the existing socioeconomic disparity [99] and have privacy [100] (for example, increased government surveillance during COVID-19) and cybersecurity [101] issues. Most of our cities are already fragile and inattention to how local governments maintain social compacts will only increase their fragility [102] . It is imperative that technological progress does not accelerate the widening of existing fractures, or incubate new sources of fractures, in our cities [103] . Given the fast-paced implementation of AI, it is important that we act now and find ways to minimize the pitfalls of AI while maximizing its promises [104] . Some of the useful actions are presented below. The first step should be to engage multiple stakeholders [105] . Active collaboration among people from a wide range of industries and backgrounds can help highlight the promises of AI technology, identify pitfalls, and improve trust. This will also contribute to humanizing AI. Secondly, paramount to developing trust is demonstrating the ability of AI technology to ensure data security and reduce vulnerabilities [106] , including hacking and misinformation. Thirdly, AI technology should be agile, so that it can cope with uncertainty [107] . It must also be frugal so it can be implemented in a way that does not lead to wasting public resources on failed attempts and does not become obsolete. Additionally, regulation is crucial for controlled implementation [108] -standards and ethical frameworks help ensure AI is deployed responsibly and in keeping with public values. Furthermore, more research and development (R&D) is required to ensure the cascading effects of AI, across the various levels of a city (local, neighborhood, city, and the larger regional ecosystem) and society. Deploying AI systems calls for an assessment of their impact on a system of systems [109] . Next, it is critical to develop AI solutions with a public research consortium to ensure that technology is not solely used as a means of gaining profit. Finally, it is also important to consider the intended, as well as the unintended, consequences of AI [110] that will arise not only within one system (e.g., economic) but across the collection of interrelated systems (e.g., interaction between economic, social, and physical infrastructures). According to Andrew Ng, cofounder of Google Brain, "AI is the new electricity. Just as 100 years ago electricity transformed industry after industry, AI will now do the same." The impact of AI will go beyond the industry; it is set to change the world [111] . An internationally conducted survey [112] highlighted that "the prospect of an AI future both excites and concerns people around the globe. Nonetheless, fears around the drawbacks of AI are offset by the benefits, and the net result is positive. AI will likely to change society for the better." AI applications have also significant potential to transform our cities. This may lead to the next-generation smart cities [113] being coined as "artificially intelligent cities". Building artificially intelligent cities may save our civilization from the earlier mentioned catastrophes, but it all depends on how we design and use AI, and on who will profit from it [114] . The risk here is for AI to become a vehicle for increasing the wealth of the top 1% of income earners (i.e., top 10 wealthiest people in the world and monopolistic multinational corporations) and the power of biased and unethical politicians [115] . Time will tell if AI systems make our cities "smart enough" to provide better living conditions for all (i.e., people, flora, and fauna coexisting in urban ecosystems). To date, while there have been significant technological advances, these have not been matched with innovations in governance mechanisms. In addition, the policy apparatuses of most local governments need significant modernization to take full advantage of technology affordances in an agile manner [116] . If there is one thing that cities and local governments have learned from the ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus, is that when they are willing, they can respond in a proactive and agile manner to the changing environmental conditions. We hope that cities will keep on this track after the current crises pass, modernizing their governance mechanisms and policy frameworks to take full advantage of emerging technologies-particularly AI. The COVID-19 pandemic has also demonstrated that local governments need to seriously consider their digital infrastructure capabilities and capacities. For example, when the Queensland state government (in Australia) decided to make education available online, its infrastructure failed to deliver the public service (i.e., provision of online education) due to significant web traffic. A few schools had backups in place with paper resources but the problem highlighted significant issues with existing networks [117] . Online education has also highlighted social equity issues associated with the digital divide with some lower income students struggling to meet the required technology capabilities [118] , and special needs students, including those who speak a language other than English, struggling to receive the required one-on-one assistance [119] . In this paper, we mainly focused on the artificial narrow intelligence level of AI. Nevertheless, if somehow one day we manage to build artificial general intelligence or artificial superintelligence (these two AI levels also correspond to singularity, that is, in simple terms, the intelligence explosion), we need to do all it takes for it to be, as Tegmark [120] calls it, a "Friendly-AI" (a superintelligence whose goals are aligned with ours). Speculation on how to build artificial general intelligence or artificial superintelligence or singularity that would reshape our cities, societies, and civilization is beyond the scope of this viewpoint. There are some important issues, in the context of AI and cities, that prospective research must address in order to provide our cities and societies with the best technological outcomes. We strongly believe that further investigating some of the critical issues in prospective research projects by scholars of this highly interdisciplinary field will shed light on the better conceptualization and practice of AI (artificial narrow intelligence level) in the context of cities and societies. These issues are listed below: How can AI systems be developed for cities that are robust, less hackable, and are not used to manipulate and control populations (e.g., voting for a politician/political party)? How can we best tackle the AI pitfalls to assure positive outcomes for cities and societies (e.g., security, privacy, regulations, and inequality)? How can we avoid heavy reliance on automated decision-making systems, making the society passive or inactive in determining its goals? How can AI-induced decisions or solutions in cities be more participatory, democratic, and transparent? How can AI be utilized best in cities to achieve desired urban outcomes for all (i.e., human and non-human)? How can we determine the best possible scenarios and factors of success and failure in implementing AI in cities? How can we determine the best approach to start building artificially intelligent cities (e.g., from scratch, retrofitting, or a combination of both)? How can the uniqueness, image, or character of each city and society be maintained given AI is in the play and there might be one best solution? How can we design AI systems for cities that preserve, and even promote, societal values and cultural heritage and historic artifacts (e.g., embrace legacy), while simultaneously exploiting emerging technologies and contemporary platforms? How can we form the AI commons and ensure that AI can achieve its potential for social good? How can local governments meet the need for rich, real-time, location-and context-specific data and preserve privacy and security, while designing AI systems? How can the negative environmental externalities of large AI technology and systems be minimized? How can the blueprints be developed for the next global transformation of cities to create carbon-free and adaptive futures for humanity? Considering AI's current ability to ingest big data for exploratory studies and real-time decisionmaking, it would be worthwhile to address the following research question (in addition to the above list): How can AI be used to find what we may have missed in terms of developing better (e.g., fairer and more productive) social structures, social geography, social good, political structures, economic structures, energy sources, modes of transportation, design of living structures and spaces (i.e., in normal and disaster times, such as those that COVID-19 and similar pandemics could bring on us), and so on? The concept of AI advising us on human sociology and similar matters may sound very offensive to some, but when properly done, AI is merely a tool that can be used by humans for their advantage (in the sense of artificial narrow intelligence). Humans tend to learn and incrementally apply the acquired knowledge into practice. AI can analyze ideas for us faster and more in depth, and together with other developments in technologies (e.g., AR, high-performance computing, IoT, and big data), it could allow us to study and predict the potential harms and benefits of alternative ideologies, and develop better futures. Moreover, AI, in the context of artificially intelligent cities, can also help us transform our cities into smarter and more prosperous and creative ones [121] [122] [123] . Lastly, we conclude the paper by elaborating on the question we raised in the title of this paper-Can building artificially intelligent cities safeguard humanity from natural disasters, pandemics, and other catastrophes? The existing AI literature reviewed in this paper, unfortunately, does not allow us to answer it with a confident "yes". The answer to this question depends on the findings of the studies focusing on the above-listed critical questions. While we continue to have hope that AI technology will help fix or at least ease the problems created by us, perhaps another important issue is whether we will be able to use AI for the common good of all-rather than the so-called 1% [124] that is already in control of the world economy. On that very point, at his Turing Lecture on deep learning for AI, Yoshua Bengio [125] highlighted the critical importance of using AI for social good and introduced two actionable items: (a) favoring machine learning applications to help the poorest countries fight climate change, improve healthcare and education, and so on; and (b) forming the concept of AI commons and coordinate, prioritize, and channel funding for the use of AI for social good. As stated by Hager et al. [126] , "AI can be a major force for social good; but it depends on how we shape this new technology, and the questions we use to inspire young researchers." Author Contributions: T.Y. designed and supervised the study, and finalized the manuscript. K.C.D., R.M. and J.M.C. contributed to the write-up of the manuscript, and improved the rigor, relevance, and reach of the study. L.B. and E.W. prepared the first draft of the manuscript and assisted in data collection. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors thank the managing editor and three anonymous referees for their invaluable comments on an earlier version of the manuscript. The authors declare no conflict of interest. A micro-level indexing model for assessing urban ecosystem sustainability Towards post-anthropocentric cities: Reconceptualizing smart cities to evade urban ecocide The Bridge at the Edge of the World: Capitalism, the Environment, and Crossing from Crisis to Sustainability Knowledge-Based Development for Cities and Societies: Integrated Multi-Level Approaches: Integrated Multi-Level Approaches Public perceptions and governance of controversial technologies to tackle climate change: Nuclear power, carbon capture and storage, wind, and geoengineering Global assessment of technological innovation for climate change adaptation and mitigation in developing world Machine learning and artificial intelligence to aid climate change research and preparedness A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring Honey bees inspired learning algorithm: Nature intelligence can predict natural disaster The role of artificial intelligence in achieving the sustainable development goals Artificial intelligence (AI) applications for COVID-19 pandemic Artificial Intelligence: An Engineering Approach How to Classify AI Technologies Not deep learning but autonomous learning of open innovation for sustainable artificial intelligence On big data, artificial intelligence and smart cities Governing in the Age of the Artificially Intelligent City Artificial intelligence and smart cities Dubai Offers Lessons for Using Artificial Intelligence in Local Government Artificial intelligence and the future of smart cities Artificial intelligence, transport and the smart city: Definitions and dimensions of a new mobility era Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature Technology, innovation, employment and power: Does robotics and artificial intelligence really mean social transformation? Understanding the Four Types of AI, from Reactive Robots to Self-Aware Beings Types of Artificial Intelligence Artificial intelligence, cybercities and technosocieties What's New in Gartner's Hype Cycle for AI 2019?utm_campaign=RM_NA_2019_SWG_NL_NL38_IT&utm_medium=email&utm_ source=Eloqua&cm_mmc=Eloqua-_-Email-_-LM_RM_NA_2019_SWG_NL_NL38_IT-_-0000 50 National Artificial Intelligence Strategies Shaping the Future of Humanity Artificial Intelligence: What Everyone Needs to Know The New Machinery of Government: Robotic Process Automation in the Public Sector Starship's Obliging Robots Extend Their Delivery Area to Bring Lunch or Dinner to More People in Milton Keynes Emotionless Chatbots are Taking over Customer Service and It is Bad News for Consumers Chatbots Debut in North Carolina, Allow IT Personnel to Focus on Strategic Tasks Chatbots are the New HR Managers: Want to Use Chatbots to Automate the Majority of HR Services? Driverless Buses Can Help End the Suburbs' Public Transport Woes Robot Police Coming to Houston Transit Center, Rail Platform, Park and Ride Lot Intrusion detection systems based on artificial intelligence techniques in wireless sensor networks Artificial agent: The fusion of artificial intelligence and a mobile agent for energy-efficient traffic control in wireless sensor networks COVID-19 Death Toll Estimated to Reach 3,900 by Next Friday, According to AI Modelling These Robots are Fighting the Coronavirus in China by Disinfecting Hospitals, Taking Temperatures, and Preparing Meals EuvsVirus Challenge: Pan-European Hackathon World Economic Forum. 8 Ways AI Can Help Save the Planet Australian Local Governments' Practice and Prospects with Online Planning Towards a sustainable industrial ecology: Implementation of a novel approach in the performance evaluation of Italian regions How can life cycle thinking support sustainability of buildings? Investigating life cycle assessment applications for energy efficiency and environmental performance Stimulating technological innovation through incentives: Perceptions of Australian and Brazilian firms Autonomous Vehicle Trials in Australia Mobile Phone Detection Cameras: Cameras Targeting Illegal Phone Use across NSW Robodebt Failed Its Day in Court, What Now? Technology's Potential to Help or Harm 'Almost Limitless', Human Rights Commission Warns Experiments in Robotics Could Help Amazon Beat Australia's Slow Delivery Problem A Solution to Cut Extreme Heat by up to 6 Degrees is in Our Own Backyards Data61. Spark: Predicting Bushfire Spread. 2020. Available online Assessing Breast Density Automatically Water Pipe Failure Prediction Artificial Intelligence and Machine Learning How engaging are disaster management related social media channels? The case of Australian state emergency organisations Top 10 AI Applications for Healthcare in 2020: Accenture Report. 2020. Available online Artificial Intelligence Roadmap Calibration of cellular automata by using neural networks for the simulation of complex urban systems Can cities become smart without being sustainable? A systematic review of the literature An information framework for creating a smart city through internet of things Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy Disruptive impacts of automated driving systems on the built environment and land use: An urban planner's perspective Applications of big data to smart cities Technol Mag IoT-aided robotics applications: Technological implications, target domains and open issues Future living framework: Is blockchain the next enabling network? A unified urban mobile cloud computing offloading mechanism for smart cities Van Wijk, I. 3D Printing with Biomaterials: Towards a Sustainable and Circular Economy To go where no man has gone before: Virtual reality in architecture, landscape architecture and environmental planning Markerless vision-based augmented reality for urban planning Urban digital twins for smart cities and citizens: The case study of Herrenberg How are the smart city concepts and technologies perceived and utilized? A systematic geo-twitter analysis of smart cities in Australia Smarter traffic prediction using big data, in-memory computing, deep learning and GPUs Katib, I. In-memory deep learning computations on GPUs for prediction of road traffic incidents using big data fusion A smart disaster management system for future cities using deep learning, GPUs, and in-memory computing Rapid transit systems: Smarter urban planning using big data, in-memory computing, deep learning, and GPUs Enabling next generation logistics and planning for smarter societies Knowledge Generation for Smart Cities Road traffic event detection using twitter data, machine learning, and apache spark A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning Sonyc: A system for monitoring, analyzing, and mitigating urban noise pollution ZAKI+: A machine learning based process mapping tool for SpMV computations on distributed memory architectures Analysis of eight data mining algorithms for smarter Internet of Things (IoT) Towards a semantically enriched computational intelligence (SECI) framework for smart farming UtiLearn: A sersonalized ubiquitous teaching and learning system for smart societies Data fusion and IoT for smart ubiquitous environments: A survey TAAWUN: A decision fusion and feature specific road detection approach for connected autonomous vehicles Applying Artificial Intelligence for Social Good Artificial Intelligence: Australia's Ethics Framework-A Discussion Paper EU guidelines on Ethics in Artificial Intelligence: Context and Implementation Smart Cities down under: Performance of the Australian Local Government Areas Oscar' the Garbage bot Sorts Waste at YVR Using Artificial Intelligence Understanding 'smart cities': Intertwining development drivers with desired outcomes in a multidimensional framework The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms Advances in Artificial Intelligence for Privacy Protection and Security Cybersecurity and its discontents: Artificial intelligence, the internet of things, and digital misinformation Fragile cities in the developed world: A conceptual framework How Technological Progress Can Cause Urban Fragility To Protect Us from the Risks of Advanced Artificial Intelligence, We Need to Act Now Hope and Fear Surround Emerging Technologies, But All of Us Must Contribute to Stronger Governance Research on information security situation awareness system based on big data and artificial intelligence technology Using fuzzy measures for modeling human perception of uncertainty in artificial intelligence Artificial intelligence as a challenge for law and regulation Cities as systems within systems of cities Confronting the Risks of Artificial Intelligence Artificial Intelligence: A Starter Guide to the Future of Business The ARM 2020 Global AI Survey. 2020. Available online The making of smart cities: Are Songdo Disruptive technology: Economic consequences of artificial intelligence and the robotics revolution Who are the 99 Percent? Under the hood: A look at techno-centric smart city development Home Schooling Website Glitch Blocks Students on First Day of Online Learning Three Kids and no Computer: The Families Hit Hardest by Australia's School Closures Migrant Parents in Australia Face Challenges Posed by Home Learning Model Amid Coronavirus Pandemic Life 3.0: Being Human in the Age of Knowledge-based, smart and sustainable cities: A provocation for a conceptual framework Building Prosperous Knowledge Cities: Policies, Plans and Metrics Creative Urban Regions: Harnessing Urban Technologies to Support Knowledge City Initiatives Are 26 Billionaires Worth More Than Half the Planet? The Debate, Explained Deep Learning for AI Artificial Intelligence for Social Good Appendix A Figure A1 . Global landscape of national artificial intelligence strategies, derived from Holon IQ [29] .