In this case, AI and its branch technology machine learning (ML) can suggest with high accuracy the size of the workload (CPU, memory, replicas, …

Construction professionals are turning to AI to add efficiency and accuracy to projects, monitor and track equipment usage and location, and a long list of other AI-driven applications. Research and Markets reports that construction-related activities bring in over $10 trillion every year, a figure expected to grow by 4.2% through 2023. AI is helping to grow the industry. In its article, "Artificial intelligence: Construction technology's next frontier," McKinsey & Company identifies AI as a critical component of modern engineering and construction approaches. AI technologies, the firm reports, help these industries solve some of their greatest challenges, including cost and schedule overruns and safety concerns.

The term artificial intelligence (AI) refers to computing systems that perform tasks normally considered within the realm of human decision making. These software-driven systems and intelligent agents incorporate advanced data analytics and Big Data applications. AI systems leverage this knowledge repository to make decisions and take actions that approximate cognitive functions, including learning and problem solving. AI, which was introduced as an area of science in the mid 1950s, has evolved rapidly in recent years. It has become a valuable and essential tool for orchestrating digital technologies and managing business operations.

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it's no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

For the past decade, many IT departments have been on the defensive trying to keep pace with escalating end-user demands and competitive pressures. The emergence of'shadow IT' as a major force within many enterprises raised questions about the role of IT in a cloud-first world. Now, enlightened CIOs are exploring ways to employ artificial intelligence (AI) and machine learning (ML) to actively engage their IT teams as key players in the rapidly evolving digital transformation efforts within their organizations. A growing number of IT departments have recognized that they have been unable to satisfy the rising expectations of end-users and corporate executives because they failed to successfully adopt IT service management (ITSM) tools and best practices in the past. This failure not only resulted in inconsistent processes, but also the inability to collect and act on timely data in an effective fashion.

Artificial intelligence (AI) is in the midst of an undeniable surge in popularity, and enterprises are becoming particularly interested in a form of AI known as deep learning. According to Gartner, AI will likely generate $1.2 trillion in business value for enterprises in 2018, 70 percent more than last year. "AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity and variety of data, as well as advances in deep neural networks (DNNs)," said John-David Lovelock, research vice president at Gartner. Those deep neural networks are used for deep learning, which most enterprises believe will be important for their organizations. A 2018 O'Reilly report titled How Companies Are Putting AI to Work through Deep Learning found that only 28 percent of enterprises surveyed were already using deep learning.

Is Big Data vs. artificial intelligence even a fair comparison? To some degree it is, but first let's cut through the confusion. Those are two buzzwords you are hearing an awful lot lately, perhaps to the point of confusion. What are the similarities and differences between artificial intelligence and Big Data? Do they have anything in common?

Someday, artificial intelligence (AI) will advance to the point where it can analyze all the data about big data and come up with its own predictions about the market. However, until that day comes (and it may be sooner than you expect), human research analysts probably offer the best forecasts about the future of the market. The market is clearly growing rapidly, and enterprises are investing in some different types of technologies than they have in the past. Integrating some of those new technologies into business processes is presenting challenges for organizations, and many are likely to experience some failures. In addition, people and organizational challenges continue to hamper organizational efforts to become more data-driven.

Many businesses are deploying Big Data applications for competitive advantage, yet many of these businesses are "learning on the job," using trial and error to do the best they can, with mixed results. To provide guidance, I spoke with two leading practitioners of data science discuss how this rapidly evolving technology is used in business today. S. Clark: I think one of the biggest challenges toward that cultural gap is just around trust. Human beings trust each other, and developing trust between two people has a sort of defined process. Usually, it just takes time. It takes many examples of someone saying they're going to do something and then doing it.

Machine learning (ML) is a sector that is both growing exponentially right along with ML's twin, artificial intelligence (AI). IDC predicts that AI and ML spending will explode in the coming years, from $8 billion in 2016 to $47 billion by 2020. While the two terms are used interchangeably, and often together, there is a difference between the two. AI is a large umbrella of automation, while machine learning is a subset of AI that involves a program or application gaining better knowledge or understanding of the task it is performing, based on data, without requiring it to be reprogrammed. Both emerging technologies have spawned new business ventures.