Artificial intelligence (AI) and robotics research and implementation emerged in the architecture, engineering, and construction (AEC) industry to positively impact project efficiency and effectiveness concerns such as safety, productivity, and quality. This shift, however, warrants the need for ethical considerations of AI and robotics adoption due to its potential negative impacts on aspects such as job security, safety, and privacy. Nevertheless, this did not receive sufficient attention, particularly within the academic community. This research systematically reviews AI and robotics research through the lens of ethics in the AEC community for the past five years. It identifies nine key ethical issues namely job loss, data privacy, data security, data transparency, decision-making conflict, acceptance and trust, reliability and safety, fear of surveillance, and liability, by summarizing existing literature and filtering it further based on its AEC relevance. Furthermore, thirteen research topics along the process were identified based on existing AEC studies that had direct relevance to the theme of ethics in general and their parallels are further discussed. Finally, the current challenges and knowledge gaps are discussed and seven specific future research directions are recommended. This study not only signifies more stakeholder awareness of this important topic but also provides imminent steps towards safer and more efficient realization.

Recent developments in Artificial Intelligence (AI) provide unprecedented automation opportunities in the Architecture, Engineering, and Construction (AEC) industry. However, despite the enthusiasm regarding the use of AI, 85% of current big data projects fail. One of the main reasons for AI project failures in the AEC industry is the disconnect between those who plan or decide to use AI and those who implement it. AEC practitioners often lack a clear understanding of the capabilities and limitations of AI, leading to a failure to distinguish between what AI should solve, what it can solve, and what it will solve, treating these categories as if they are interchangeable. This lack of understanding results in the disconnect between AI planning and implementation because the planning is based on a vision of what AI should solve without considering if it can or will solve it. To address this challenge, this work introduces the LeanAI method. The method has been developed using data from several ongoing longitudinal studies analyzing AI implementations in the AEC industry, which involved 50+ hours of interview data. The LeanAI method delineates what AI should solve, what it can solve, and what it will solve, forcing practitioners to clearly articulate these components early in the planning process itself by involving the relevant stakeholders. By utilizing the method, practitioners can effectively plan AI implementations, thus increasing the likelihood of success and ultimately speeding up the adoption of AI. A case example illustrates the usefulness of the method.

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.

The high interest towards the first truly global Artificial Intelligence conference in Architecture, Engineering and Construction industry in 2021 gave the spark for the annual conferences. In 2021 event, there were 235 registered participants from 30 countries, who graded the overall conference experience over 4 on a scale 1-5. The 2021 conference was already successful, but in 2022 we are heading to have even bigger and more international event. How society and economy are changing through digital disruption is one of the defining issues of our time. Throughout the history of the mankind, we have had a quest for seeking solutions for better human life.

The latest annual average anomaly is 0.8 C. (source: NASA/GISS, Global Climate Change) By 2050, two out of every three people are likely to be living in cities or other urban areas -- highlighting the need for more sustainable urban planning and public services. Most of the increase is expected to be highly-concentrated in just a handful of countries. The AEC industry is not moving fast enough. Design are often developed manually without taking advantage the wealth of environmental and contextual information and computing power available online. Even the simple task of properly situating buildings within the environment to take advantage of wind, sun, and land is often ignored.

In debates about the future of work, technology is often portrayed as the villain. One recent study calculated that 38 percent of jobs in the United States were at a "high risk" of being automated during the next decade. In the construction industry, predictions are especially dire: estimates of robot-fueled joblessness range from 24 percent in Britain to 41percent in Germany. Borja García de Soto is an Assistant Professor of Civil Engineering at New York University Abu Dhabi (NYUAD), a Global Network Assistant Professor of Civil and Urban Engineering at the NYU Tandon School of Engineering, and Director of NYUAD's S.M.A.R.T. Construction Research Group. There is no question that automation will change the way people work, but for some sectors of the economy, change is long overdue.

There is an unprecedented wave of speculation and investment in Artificial intelligence (AI) sweeping the industry with ideas like machine learning (ML) appearing as a magic wand of business innovation. Beneath the seemingly boundless hype of AI, however, there is something quite simple at play -- the idea of finding ways to perform well-understood, common tasks more efficiently, according to leading data and analytics company GlobalData. An analysis of GlobalData's Disruptor Tech Database reveals that enterprise AI practitioners are primarily seeing very pointed benefits within problems that directly impact the bottom line. For instance, one of the compelling areas where businesses have been using AI is generating leads. Harley-Davidson in New York chose AI to automatically craft the most appropriate digital marketing and advertising campaign on a customer-by-customer basis.

Skylogic presented its new "Quick Start Guide to Drones in Construction" that is extremely designed to help the UAS service providers and business users familiarize with the changes, innovations, growth it would bring to the architecture, engineering and construction (AEC) industry. The report authored by Colin Snow, founder and CEO of Skylogic, concentrates the Skylogic's insights into the challenges and solutions drones add to a work site. Plus, the report also covers the SWOT analysis for evaluating and designing a drone program. About DEEPAERO DEEP AERO is a global leader in drone technology innovation. At DEEP AERO, we are building an autonomous drone economy powered by AI & Blockchain.