As the market for artificial intelligence matures, it is helping to drive accelerated growth in computing technologies to support highly parallel workloads, artificial intelligence (AI) and machine learning (ML). This new paradigm in computing is opening up the benefits of GPU or accelerated computing to a broader audience – far beyond the traditional users of supercomputing.
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The growth in AI and machine learning has been dramatic. In April this year, market research firm IDC predicted that western European revenues for cognitive and AI systems would reach $1.5 billion in 2017. IDC predicts this rise will continue in the coming years as the company forecasts a growth rate of 42.5 per cent through 2020 when revenues will exceed $4.3 billion. Much of this growth comes from comes from three key industries which were early adopters of AI and cognitive systems – banking, retail, and discrete manufacturing, although the IDC report does note that cross-industry applications have the largest share across all industries.
Three key early adaptors
The report states that by 2020 these industries – including cross-industry applications – will account for almost half of all IT spending on cognitive and artificial intelligence systems. ‘IDC is seeing huge interest in cognitive applications and AI across Europe right now, from different industry sectors, healthcare, and government,’ said Philip Carnelley, research director for Enterprise Software at IDC Europe, and leader of IDC’s European AI Practice.
Although only a minority of European organisations have deployed AI solutions today, a large majority are either planning to deploy or evaluating its potential‘Although only a minority of European organisations have deployed AI solutions today, a large majority are either planning to deploy or evaluating its potential. They are looking at use cases with clear ROI, such as predictive maintenance, fraud prevention, customer service, and sales recommendation,’ Carnelley added.
The report also notes that from a technology perspective, the most significant area of spending in western Europe in 2017 will be cognitive applications at approximately $516 million. This includes ‘cognitively-enabled’ process and industry applications that automatically learn, discover, and make recommendations or predictions. […]
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As the market for artificial intelligence matures, it is helping to drive accelerated growth in computing technologies to support highly parallel workloads, artificial intelligence (AI) and machine learning (ML). This new paradigm in computing is opening up the benefits of GPU or accelerated computing to a broader audience – far beyond the traditional users of supercomputing.
copyright by www.scientific-computing.com
Three key early adaptors
The report states that by 2020 these industries – including cross-industry applications – will account for almost half of all IT spending on cognitive and artificial intelligence systems. ‘IDC is seeing huge interest in cognitive applications and AI across Europe right now, from different industry sectors, healthcare, and government,’ said Philip Carnelley, research director for Enterprise Software at IDC Europe, and leader of IDC’s European AI Practice.
Although only a minority of European organisations have deployed AI solutions today, a large majority are either planning to deploy or evaluating its potential‘Although only a minority of European organisations have deployed AI solutions today, a large majority are either planning to deploy or evaluating its potential. They are looking at use cases with clear ROI, such as predictive maintenance, fraud prevention, customer service, and sales recommendation,’ Carnelley added.
The report also notes that from a technology perspective, the most significant area of spending in western Europe in 2017 will be cognitive applications at approximately $516 million. This includes ‘cognitively-enabled’ process and industry applications that automatically learn, discover, and make recommendations or predictions. […]
read more – copyright by www.scientific-computing.com
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