AI Trends, Growth Points, and Short-Term Prospects

 

The spread of AI is mainly because that the technology has matured enough while continuing to evolve. Most of all, we can expect a considerable increase in the production of specialized computer chips. Market leaders like NVIDIA, AMD, ARM, and Qualcomm have already begun manufacturing processors optimized for speech recognition and computer vision. According to the experts, the AI chip market will grow by 30-40 percent this year, while the research company Allied Market Research forecasts that the global market could grow to .185 billion by 2025.

 

A significant breakthrough for the industry was the emergence of a universal ecosystem for the development of algorithms for neutron networks. Other IT giants, most notably IBM, Huawei, Intel, AMD, ARM, and Qualcomm slowly joined the Open Neural Network Exchange (ONNX) project launched by Microsoft and Facebook. At the end of last year, Microsoft released the original ONNX code in open access, resulting in feedback from 500 developers and testers by March 2019. The merging of machine-learning ecosystems and the universalization of frameworks will continue.

 

At exactly the same time, AI has grown to be available not only to corporations with a huge staff of developers but also to small companies thanks to the development of automatic machine learning (AutoML). AutoML helps non-technical employees to prepare data, calibrate parameters, and find out optimal algorithms to solve specific business problems using machine learning. The most popular AutoML products among companies at the moment are Google Cloud software products, a solution by developer DataRobot and Driverless AI produced by H2O.ai.

 

Machine learning algorithms are beginning to be utilized in the industrial Internet of Things, and large companies are moving from pilot projects to steady use of the technology. In late March, it became known that the American company Uptake had signed a contract with the world’s largest copper producer, Codelco, under which the IT company will equip Codelco mines with intelligent systems for supervising the status of mining equipment and the efficiency of production operations.

 

In industry today, there is immense potential for using AI in mining processes in the oil & gas sector and the mining industry, as well as in discrete manufacturing.

 

In the oil & gas industry, Baker Hughes and NVIDIA have said an alliance to advance deep learning neural networks that will perform a variety of tasks, from seismic modeling and automated well planning to equipment failure prediction and supply chain optimization. On top of that, NVIDIA supercomputers will be installed not only in data processing centers but also on remote offshore platforms operated by the oil & gas giant.

 

ZYFRA is also interested in the oil production market and is already implementing a few of projects in the industry, including designing an electrical submersible pump (ESP) software unit to create a “smart” oil field. This helps to enhance the efficiency of oil extraction by boosting oil well production rates by 1.5 percent with no extra capital investment. It is equipped with artificial intelligence to provide recommendations based on historical Big Data analysis. The unit recommends a mode of well operation that will ensure the uppermost level of oil flow rate for a specific period of time and provide for stable operation during that period by analyzing current frequency, gauged oil flow rate, periods of intermittent pump operation and other operating parameters. The ESP software unit has now been operational for much more than three months in 500 oil wells in Western Siberia, Russia, boosting production by 1.5 percent and generating million of additional profit.   

 

The size of the artificial intelligence market in the industrial sector attained .1 billion in 2018. At the same time, depending on preliminary estimates, the AI market grew to .24 billion in the first quarter of 2019 and, given an average annual growth rate (CAGR) of 49.7 percent, may add another $ 45.5 million by April.

 

From a technological viewpoint, machine learning, context-dependent computation, natural language processing, and computer vision continue the most relevant topics.

 

In the manufacturing sector at the moment, AI is applied in production planning, service prediction and equipment inspection (preventive equipment diagnostics), quality control and industrial safety. The quality control segment is most likely to grow notably over the forecast period. The most active buyers remain companies in the energy sector, engineering, microelectronics, metallurgy, and pharmaceuticals.

 

This article is originally posted on tronserve.com