Global Marketers presents an updated and Latest Study on Artificial Intelligence In Construction Market 2020-2026. This report comprises a detailed study of the market covering its future predictions by the past year as a reference for the period between 2020 and 2026 as the forecast period. The report breakdowns major segments and highlights wider level geographies. This report also offers an all-inclusive study of the future trends and developments of the market. The key insights and evaluations presented in this Artificial Intelligence In Construction report are worth knowing for any market participant, helping them in ascertaining the superior dynamics and the future trajectories of the global Artificial Intelligence In Construction Market.

DUBAI: The idea of artificial intelligence (AI) has been around for a long time, but in recent decades it has gone from being the stuff of science fiction to something tangible and beneficial. Sir Michael Brady is the interim president of the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), an Abu Dhabi-based AI-focused university -- the first of its kind in the world. "Stephen Hawking, Elon Musk, Microsoft, Google, Facebook and many more have contributed to AI's position in today's spotlight," he said. He added: "We have moved from the use of AI in large-scale industry or ground-breaking circumstances, such as NASA space exploration or factory robotics, to commonplace applications such as advertising algorithms or Netflix suggesting what show to watch next." As society transforms under the impact of technology, AI is also rapidly evolving.

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This paper presents an adaptive resonance theory predictive mapping (ARTMAP) model which uses incremental cluster validity indices (iCVIs) to perform unsupervised learning, namely iCVI-ARTMAP. Incorporating iCVIs to the decision-making and many-to-one mapping capabilities of ARTMAP can improve the choices of clusters to which samples are incrementally assigned. These improvements are accomplished by intelligently performing the operations of swapping sample assignments between clusters, splitting and merging clusters, and caching the values of variables when iCVI values need to be recomputed. Using recursive formulations enables iCVI-ARTMAP to considerably reduce the computational burden associated with cluster validity index (CVI)-based offline clustering. Depending on the iCVI and the data set, it can achieve running times up to two orders of magnitude shorter than when using batch CVI computations. In this work, the incremental versions of Calinski-Harabasz, WB-index, Xie-Beni, Davies-Bouldin, Pakhira-Bandyopadhyay-Maulik, and negentropy increment were integrated into fuzzy ARTMAP. Experimental results show that, with proper choice of iCVI, iCVI-ARTMAP outperformed fuzzy adaptive resonance theory (ART), dual vigilance fuzzy ART, kmeans, spectral clustering, Gaussian mixture models and hierarchical agglomerative clustering algorithms in most of the synthetic benchmark data sets. It also performed competitively on real world image benchmark data sets when clustering on projections and on latent spaces generated by a deep clustering model. Naturally, the performance of iCVI-ARTMAP is subject to the selected iCVI and its suitability to the data at hand; fortunately, it is a general model wherein other iCVIs can be easily embedded.

It was the organizers' belief that "significant advances can be made" in at least one, if not several of these specific areas of concern through a joint effort of a "carefully selected group of scientists". And what's more, this advancement could be quicker than many though possible. While there already had been significant developments in automata – machines that can carry preprogrammed and predetermined functions – it was the conference organizers' belief, especially McCarthy, that there was a mountain of potential for the development of truly "intelligent" machines that could essentially, think for themselves. It was his further belief that through the joint effort of likeminded people willing to "devote time to it…could make real progress". It would turn out, he was correct. In the years that followed the crucial conference in the summer of 1956, advancements in artificial intelligence began to become ever-more rapid.

Over the past two years, the development of Artificial Intelligence and the new techniques for using Big Data has become both faster and more widespread. According to the old definition, by Artificial Intelligence we mean teaching a machine to think like a man, while Big Data is such a large mass of data in terms of quantity, speed and variety that it has to enable specific technologies and methods to extrapolate data from news already learned and extract new data and links from the news which seem unrelated to one another. Just to make an example, each buyer wants a specific reward. Currently we also have the possibility of developing Generative Adversarial Networks (GANs), which create objects not existing in reality, but similar to reality, as well as faces that have never been seen before but are quite probable, and objects that do not exist but seem to work well. Not to mention the self-correcting systems based on concepts that are adapted by the machine itself, as well as programs that self-create themselves, starting from a small nucleus.

Emilia Díaz-Struck is research editor and Latin American coordinator for the International Consortium of Investigative Journalists (ICIJ). She oversees data projects and has been involved in some major cross-border investigations including the Panama Papers, the Paradise Papers and the Offshore Leaks. The ICIJ receives vast amounts of files from whistleblowers and uses AI-powered technologies to sift through that data more efficiently. For our interview series with women working on the intersection of AI and journalism, Emilia spoke to us about how exactly AI is deployed and what impact it will have on investigative journalism. JournalismAI: You have a very diverse background in journalism, having worked with major organisations such as The Washington Post, the Press and Society Institute of Venezuela and co-founding your own news site Armando.info. How did you initially move into a data-driven role?

We study an urban bike lane planning problem based on the fine-grained bike trajectory data, which is made available by smart city infrastructure such as bike-sharing systems. The key decision is where to build bike lanes in the existing road network. As bike-sharing systems become widespread in the metropolitan areas over the world, bike lanes are being planned and constructed by many municipal governments to promote cycling and protect cyclists. Traditional bike lane planning approaches often rely on surveys and heuristics. We develop a general and novel optimization framework to guide the bike lane planning from bike trajectories. We formalize the bike lane planning problem in view of the cyclists' utility functions and derive an integer optimization model to maximize the utility. To capture cyclists' route choices, we develop a bilevel program based on the Multinomial Logit model. We derive structural properties about the base model and prove that the Lagrangian dual of the bike lane planning model is polynomial-time solvable. Furthermore, we reformulate the route choice based planning model as a mixed integer linear program using a linear approximation scheme. We develop tractable formulations and efficient algorithms to solve the large-scale optimization problem. Via a real-world case study with a city government, we demonstrate the efficiency of the proposed algorithms and quantify the trade-off between the coverage of bike trips and continuity of bike lanes. We show how the network topology evolves according to the utility functions and highlight the importance of understanding cyclists' route choices. The proposed framework drives the data-driven urban planning scheme in smart city operations management.

Clustering analysis has been widely used in trust evaluation on various complex networks such as wireless sensors networks and online social networks. Spectral clustering is one of the most commonly used algorithms for graph-structured data (networks). However, the conventional spectral clustering is inherently difficult to work with large-scale networks due to the fact that it needs computationally expensive matrix manipulations. To deal with large networks, in this paper, we propose an efficient graph clustering algorithm, KCoreMotif, specifically for large networks by exploiting k-core decomposition and motifs. The essential idea behind the proposed clustering algorithm is to perform the efficient motif-based spectral clustering algorithm on k-core subgraphs, rather than on the entire graph. More specifically, (1) we first conduct the k-core decomposition of the large input network; (2) we then perform the motif-based spectral clustering for the top k-core subgraphs; (3) we group the remaining vertices in the rest (k-1)-core subgraphs into previously found clusters; and finally obtain the desired clusters of the large input network. To evaluate the performance of the proposed graph clustering algorithm KCoreMotif, we use both the conventional and the motif-based spectral clustering algorithms as the baselines and compare our algorithm with them for 18 groups of real-world datasets. Comparative results demonstrate that the proposed graph clustering algorithm is accurate yet efficient for large networks, which also means that it can be further used to evaluate the intra-cluster and inter-cluster trusts on large networks.

COVID-19 impact will also be included and considered for forecast. Global Artificial Intelligence in Market research report provides detail information about Market Introduction, Market Summary, Global market Revenue (Revenue USD), Market Drivers, Market Restraints, Market Opportunities, Competitive Analysis, Regional and Country Level. Global Artificial Intelligence in Marketing market is valued at USD 6.99 Billion in 2018 and expected to reach USD 37.08 Billion by 2025 with the CAGR of 26.98% over the forecast period. Growing adoption of customer-centric marketing strategies and increased use of social media for advertising are some of the factors which are expected to drive the growth of Global Artificial Intelligence in Marketing Market. Artificial intelligence (AI) in marketing is the process of utilizing data models, mathematics and algorithms to generate insights that can be used by marketers. Marketers will use AI-derived insights to guide future decisions about campaign spending, strategy and content topics.