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Author: Tronserve admin

Wednesday 28th July 2021 11:08 AM

AI Trends, Growth Points, and Short-Term Prospects


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In accordance with last year’s forecasts, the artificial intelligence (AI) market stays to grow considerably, and together with qualitative improvement in technologies, there is a further expansion of the areas in which artificial intelligence is going to be implemented, including such traditional industries as engineering, mining, and agriculture.


The spread of AI is because of the fact that the technology has matured enough while continuing to evolve. Most of all, we can expect a substantial increase in the production of specialized computer chips. Market leaders like NVIDIA, AMD, ARM, and Qualcomm have already initiated manufacturing processors optimized for speech recognition and computer vision. According to the experts, the AI chip market will expand by 30-40 percent this year, while the research company Allied Market Research forecasts that the global market could grow to $91.185 billion by 2025.


A crucial 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 but surely 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 is now 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 decide 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 starting to be utilised in the industrial Internet of Things, and large companies are moving from pilot projects to persistent 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 crucial 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 a partnership to create 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. Meanwhile, 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 likewise 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 boost 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 particular 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 a little over three months in 500 oil wells in Western Siberia, Russia, boosting production by 1.5 percent and generating $2 million of additional profit.   


The size of the artificial intelligence market in the industrial sector reached $1.1 billion in 2018. At the same time, reported by introductory estimates, the AI market grew to $1.24 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 opinion, machine learning, context-dependent computation, natural language processing, and computer vision continue the most relevant topics.


In the manufacturing sector these days, AI is applied in production planning, service prediction and equipment inspection (preventive equipment diagnostics), quality control and industrial safety. The quality control segment is very likely to grow considerably 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 manufacturing.net


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Posted on : Wednesday 28th July 2021 11:08 AM

AI Trends, Growth Points, and Short-Term Prospects


none
Posted by  Tronserve admin
image cap

In accordance with last year’s forecasts, the artificial intelligence (AI) market stays to grow considerably, and together with qualitative improvement in technologies, there is a further expansion of the areas in which artificial intelligence is going to be implemented, including such traditional industries as engineering, mining, and agriculture.


The spread of AI is because of the fact that the technology has matured enough while continuing to evolve. Most of all, we can expect a substantial increase in the production of specialized computer chips. Market leaders like NVIDIA, AMD, ARM, and Qualcomm have already initiated manufacturing processors optimized for speech recognition and computer vision. According to the experts, the AI chip market will expand by 30-40 percent this year, while the research company Allied Market Research forecasts that the global market could grow to $91.185 billion by 2025.


A crucial 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 but surely 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 is now 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 decide 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 starting to be utilised in the industrial Internet of Things, and large companies are moving from pilot projects to persistent 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 crucial 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 a partnership to create 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. Meanwhile, 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 likewise 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 boost 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 particular 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 a little over three months in 500 oil wells in Western Siberia, Russia, boosting production by 1.5 percent and generating $2 million of additional profit.   


The size of the artificial intelligence market in the industrial sector reached $1.1 billion in 2018. At the same time, reported by introductory estimates, the AI market grew to $1.24 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 opinion, machine learning, context-dependent computation, natural language processing, and computer vision continue the most relevant topics.


In the manufacturing sector these days, AI is applied in production planning, service prediction and equipment inspection (preventive equipment diagnostics), quality control and industrial safety. The quality control segment is very likely to grow considerably 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 manufacturing.net

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artificial interlligence arm qualcomm intel open neural network exchange onnx automatic machine learning automl datarobot driverless ai codelco