Current State of Art in Artificial Intelligence and Ubiquitous Cities

Heritage plays a fundamental role in maintaining the identity of the city. The character and historical significance of any city is portrayed through its built and cultural heritage. Heritage is the essence of society, and it should be conserved and preserved for future generations. The latest technologies in this digital era become a key governing factor for the conservation and preservation of heritage. Artificial intelligence (AI) based automation is the emerging technology for the conservation and preservation of heritage. The application of AI in heritage conservation is divided into four clusters, (i) AI for visual inspection and structural health monitoring, (ii) AI for digital modeling, (iii) AI for intangible heritage, and (iv) AI for cultural heritage. This chapter summarizes various techniques and algorithms of AI used for a variety of applications in heritage conservation. The chapter also outlines some of the notable developments of AI for heritage conservation and presents the case study of the development of automatic visual inspection system for heritage structures. Lastly, the chapter concludes with the framework of AI for heritage conservation in ubiquitous cities and the way forward.

The chapter explores specific developments and opportunities in smart technology in cities as well as the interface with the workforce. The first part of the chapter looks at technological development in smart grids and smart homes: integration of new Information and Communication Technologies (ICT) such as Internet of Things and advanced algorithms based on Artificial Intelligence (AI) to manage energy in urban areas and support user awareness or operator observation. The “smartness” of smart cities is defined. The enablers including AI and ICT, and their applications and benefits for users are discussed. The interoperability layers of smart grids architecture model are presented, and the importance of multidisciplinary ex-ante analysis of newly developed technologies and distributed intelligence is highlighted. The second part relates to the urban workforce. This is distinctive as most literature on smart cities looks at the impact on people as residents/users—not as workers. This part starts by discussing the overall impact of the development of smart grids and homes in the workforce across the sectors of the urban economy. Following, it concentrates on workers in trades related to the built environment. It argues that technological developments need a restructuring of vocational training, which may lead to a more skilled workforce with better working conditions. At the same time, it may reduce the quantity of employment, therefore part of the wealth generated by economic growth derived from technological advancements should be used to strengthen social protection. The chapter makes proposals to address current challenges, reaching a win–win situation.

According to “Huang's law”, Artificial intelligence (AI)-related hardware increases in power 4–10 times per year. AI can benefit various stages of real estate development, from planning and construction to occupation and demolition. However, Hong Kong's legal system is currently behind when it comes to technological abilities, while the field of AI safety in built environments is still in its infancy. Negligent design and production processes, irresponsible data management, questionable deployment, algorithm training, sensor design and/or manufacture, unforeseen consequences from multiple data inputs, and erroneous AI operation based on sensor or remote data can all lead to accidents. Yet, determining how legal rules should apply to liability for losses caused by AI systems takes time. Traditional product liability laws can apply for some systems, meaning that the manufacturer will bear responsibility for a malfunctioning part. That said, more complex cases will undoubtedly have to come before the courts to determine whether something unsafe should be the manufacturer's fault or the individual's fault, as well as who should receive the subsequent financial and/or non-financial compensation, etc. Since AI adoption has an inevitable relationship with safety concerns, this project intends to shed light on responsible AI development and usage, with a specific focus on AI safety laws, policies, and people's perceptions. We will conduct a systematic literature review via the PRISMA approach to study the academic perspectives of AI safety policies and laws and data-mining publicly available content on social media platforms such as Twitter, YouTube, and Reddit to study societal concerns about AI safety in built environments. We will then research court cases and laws related to AI safety in 61 jurisdictions, in addition to policies that have been implemented globally. Two case studies on AI suppliers that sell AI hardware and software to users of built environment will also be included. Another two case studies will be conducted on built environment companies (a contractor and Hong Kong International Airport) that use AI safety tools. The results obtained from social media, court cases, legislation, and policies will be discussed with local and international experts via a workshop, then released to the public to provide the international community and Hong Kong with unique policy and legal orientations.

Education is an indispensable part of everyone's life. It lays the ground for the future generation and is a prerequisite for an independent life. It also determines the future of a country and a nation, and affects a country’s development. With the advancement of technology, the world has tremendous changes. People have changed their lifestyles and use new technologies for connecting, interacting, reading, and obtaining information. Hence education needs to adapt to the new era and changes in social customs. This research throws light on the impact of artificial intelligence robots on the education sector. We initially found one hundred thirteen articles published between 1999 and 2019 and only 18 articles meeting the criteria. We then categorise these studies into the following topics: (1) the impact of artificial intelligence educational robots on the creativity and motivation of students’ learning, and (2) the impact of artificial intelligence robots in education. This chapter comprises of six parts: the first part is the introduction, the second part summarises the purpose and research questions of this chapter, the third part is a literature review of educational robots concluding that robotics has a unique role in education, the fourth part discusses the research methods and survey results of this study, the fifth part provides a summary and discovery, and the last part is the conclusion.

Construction accidents often lead to injuries, pain, loss of future earnings and even deaths. One way to lower the likelihood of accidents is to levy high compensation on wrongdoers, including main contractors, subcontractors and even the workers who fail to take safety measures or are careless. Under the common law system, precedents are part of the legal system. Hong Kong is no exception. Therefore, construction companies and legal firms are interested to know the possible amount of compensation. FastText-based classification, a kind of Computer-based automated text classification, classifies documents into predefined categories according to the content of the papers, is proposed in this book chapter for accident compensation in courts. We utilised 3000 sentences in court cases in Hong Kong. 90% of the data was used for training, and 10% was used for testing. The results show that the system’s precision for classifying construction accident cases into successfully or unsuccessfully obtained compensation was 95.7%. This demonstrates that the fastText-based classification can successfully classify papers with a high level of accuracy. This pilot research provides a practical example to showcase the possibility of utilising artificial intelligence for predicting the likelihood of obtaining construction accident compensation. This approach could offer a rough estimation of the chance of getting compensation, save human resources, and allow non-specialists without much legal knowledge to have a quick reference on the likelihood of obtaining compensation for accidents. The results can also be generalised to other types of accidents and regions operated under the common law system.

The Chief Executive's Policy addresses direct Hong Kong moving towards a smart city. Although the smart home is one of the major features in smart city, understanding on smart home is limited among lay people in Hong Kong. Elderly and disabled persons are no exception. The smart home gadgets are useful among these two special groups. For example, a water sensor installed in the washing basin can inform the elderly who forget to turn off the water closet, carbon monoxide sensor alerts the resident if there is a gas leakage problem. Bed sensor can send messages to care takers' apps when the elderly is suspected to be fainted in the bed. The smart home robots companion lonely elderly who may not have companions with them. Video recording system can inform the caretakers in case of fall incidents so that appropriate and timely action can be taken. On the other hand, a voice controllable system can help disabled persons control the electrical appliances such as television, and play music in the whole room by using their voice rather than their hand. It helps the blind people and those who cannot move around the room easily or have limited hand function. Robotic arms on wheelchairs help disabled persons with limited hand function to grab things on the floor, eat and drink. Facial recognition systems prevent strangers enter the home via facial recognition systems. This research agenda proposes to (1) build a sample smart home in a University to demonstrate the possible future direction in smart home. It incorporates three generations of the smart home gadgets (a) the smart home gadgets which are connected by a network system; (b) smart gadgets which have a certain level of artificial intelligence which can reply to our questions; (c) smart home robots which can move around the home to move around the home quickily on behalf of the elderly and disabled persons. (2) Install smart home gadgets at an affordable price for 200 households with elderly/disabled persons to inform the elderly and disabled persons about the possibility of these tools helping their daily lives. Practically speaking, the project can provide valuable insights to policy makers when they implement smart city, elderly, and disabled persons’ policies. Caretakers can know more about the current-state-of-art in smart home gadgets, which may reduce their everyday work. This can also raise the awareness of the elderly and disabled persons’ needs in Hong Kong. Academically speaking, the smart home gadgets installed in the smart home laboratory shall provide important information to university researchers to study people’s behaviour/reaction to the smart home. Whilst we can find many of the smart home laboratories in overseas universities such as the University of West Florida and Iowa State University, we cannot find any smart home laboratory designed for elderly and disabled persons. This research agenda will fill the research gap.

For years, the hedonic regression model has dominated housing price research worldwide. However, the hedonic regression model suffered from the problem of over-simplification and heterogeneity. Machine learning has become a hot method in housing price prediction in recent years. The machine learning method in predicting housing prices is more accurate and precise than the traditional methods. This paper introduced three regression methods in housing price prediction: the traditional hedonic regression model, Google AutoML and Microsoft AutoML. It reviewed the factors that affected housing prices in literature and used the dataset of the housing price in Beijing in Kaggle to study the factors affected the housing price in Beijing. The results showed that Google AutoML had the best performance in predicting housing prices in Beijing. It had the highest R square (0.820) and the least RMSE and MAE. The average housing price in a community was the most important feature that impacted housing price prediction. Number of days open for sale and geographical location ranked the second and the third most important features in predicting the housing price.

Construction practitioners make decisions about safety risks that can be subjective and prone to error. The trained computer object detection provides a standardised method to deal with this issue, but it typically relies on many photos to train one object, which is costly and time-consuming. This project proposes a new algorithm to train a computer to identify construction safety risks with fewer photos. In addition, holographic hybrid reality will be developed for safety training in the construction industry; Mercedes-Benz has used a similar approach to inform employees about collision zones. We will use the trained images to develop HoloLens hybrid reality to share construction site safety knowledge via wearable HoloLens for on-site safety risk detection. Lastly, although decision-making in various areas has been studied using neuroscience, how an individual’s brain makes decisions when different construction safety risks are perceived and the impact of holographic safety training on brain reaction and activities remains unknown. These issues will be studied via haemodynamic response and neuroimaging. In this research agenda, we plan to construct a photo library of 10,000 high-quality photos of various construction risks with different shading, size, and orientation by collecting Creative Commons construction photos and turning existing online Creative Commons videos into photos. To achieve this goal, we will input the online common creative photos into our image-based CAPTCHA system (similar to Google’s ReCaptcha). Each group will include 16 photos to allow construction practitioners to identify and click on those that include a safety risk. The identified photos with safety risks will be saved, and specific categories of safety risks will be uploaded to social media and sent via email. The trained figures will be deployed to the HoloLens in our laboratory. About 20 safety experts, including safety managers and trainers, will be invited to use the HoloLens for detecting hazards on-site and provide comments for improvement. Holographic hybrid reality will be built with Unity C# and HoloToolkit. The object detection results obtained will also be used so the research participants will see the real scene with not only hazards labelled by AI, but also some high-risk elements that cannot be included in ordinary safety training, such as open taps with water running into the ground and blasting. Four holographic hybrid reality training scenarios will be generated: a general construction site, and three specially designed scenarios for refurbishment, new building, and road construction settings. In the last stage, we will use functional near-infrared spectroscopy (fNIRS) to study construction practitioners’ brain responses when they see and identify various kinds of hazards. The first group will be exposed to holographic hybrid reality with some safety risks on the first day, and they will be asked on the tenth day to identify the safety risks. The control group will receive no safety training but will be asked to identify risks. All research participants will be monitored by fNIRS when they attempt to identify the safety risks. Haemodynamic response and neuroimaging tests will be used to study the effectiveness of the safety training.

In recent years, automation via deep learning/machine learning has flourished. As such, automated valuation will likely become a major task in valuation over the coming years. While most studies in industry/academia utilise multivariate regression for housing price predictions, we aim to introduce explainable AutoML models (H₂O AutoML and Google Cloud Platform (GCP AutoML Tables) for housing price predictions, which will be the first of its kind. The objectives are as three folds: 1. predict housing prices based on the following factors: (i) sentiment, as reflected in (ia) animal spirits (market sentiment) interpreted and constructed using social listening tools with artificial intelligence, and (ib) a sentiment index constructed based on Google searches; (ii) health-related factors, including COVID-19 and radio base stations; (iii) sustainability factors, i.e., the urban heat island effect and polychlorinated dibenzo-p-dioxins (PCDD); (iv) the number of years left on a land lease; and (v) the impact of precarious social events on housing prices. 2. Study the impact of these factors and compare their relative importance on housing price predictions. 3. Predict housing prices via (i) H₂O Auto ML and (ii) GCP AutoML Tables and compare their results. The first stage includes a systematic literature review and content analysis of big data housing price research for the past ten years in Science Direct, Emerald, Sage and Taylor, and Francis, to uncover the current state-of-the-art research in housing prices and revise/update the variables to be included in our prediction model. Fifty-five highest housing price transaction records in Hong Kong will be used for the housing price predictions. Housing estate name, price, date of transaction, gross floor area, building completion date, and floor level will be downloaded from the Economic Property Research Center (EPRC). We will then reformat the data so that it can be fitted to AutoML. Second, we will obtain the sentiment of 55 housing estates and the “Hong Kong’s housing market”, as reflected in social media, by using keywords to search on a Natural Language Processing platform. Third, PCDD will be obtained from the Environmental Protection Department, while radio base station locations will be obtained from the Office of the Communications Authority. The number of years left on the housing estates’ land lease will be obtained from the Land Registry Department, with dummy variables being assigned in the prediction models to represent the precarious protest period and the number of COVID-19 cases obtained from Statista to study their impact on housing prices. Moreover, the remote sensing data of housing estates obtained from Landsat 8 (an American Earth observation satellite) can be downloaded through the United States Geological Survey (USGS). The land surface temperature, computed by ENVI 5.3 software, will be compared with nearby areas of vegetation to compute the extra heat caused by the “urban heat island effect”. Big data collection will allow us to utilise H₂O AutoML and GCP Cloud AutoML for housing price predictions. We will apply H₂O AutoML to select the best machine learning algorithms. Unlike traditional machine learning algorithms, AutoML minimises parameter adjustments and automatically tunes all parameters according to embedded algorithms. The results will then be compared with the results from GCP Cloud AutoML. We will also select the model with the highest efficacy, which will reveal the relative importance of factors and show the coefficients of the factors on housing price predictions.

A smart home provides residents with medical care, life assistance, security, and environmental sustainability. At the same time, automation technology has become the core part of innovative home technology that includes the Internet of Things, artificial intelligence and blockchain. This research uses a big data approach to extract and analyze the keywords related to the smart homes on various websites such as Baidu, TMall, Know the Topic and understands the smart home market in China. The results reveal that Beijing and Shenzhen have the most significant number of searches for the smart homes. The top three most popular smart appliances include smart toilets, speakers, and television. People aged 30–39 are the group with the largest number of individuals searching for smart homes.

AI development has affected many industries over the last decade. For example, EmBot uses AI to search legal precedents in patent disputes and identify cancer in magnetic resonance images. In addition to AI, there is rapid development in ubiquitous technology for u-city. Ubiquitous refers to the ability to exist everywhere and be everywhere simultaneously. It has been used in computer sciences in ‘ubiquitous computing’ and elaborated with ‘ubiquitous community’ as the human presence and activity in physical and digital spaces. Given the rapid development in AI and u-city, we invited top professors in these two areas to share their thoughts and this chapter summarises their viewpoints: (1) keep abreast of the latest development trends, prospects and challenges in AI and u-cities; (2) identify various AI and ubiquitous tools that can be used in research projects; and (3) review the current state of art applications of AI for u-city.