AI chipset market projected to grow at 40% per year
The artificial intelligence (AI) chipset market is set to grow at more than 40 percent per year from 2020 to 2026, according to a new report.
The Artificial Intelligence (Chipsets) Market Global Forecast from Research and Markets anticipates a short term inter ruption as supply chains are disrupted by Covid-19, but still reckons the market will grow at a compound annual growth rate (CAGR) of 40.1 percent.
Growing adoption of neural networks
It cites increasingly large and complicated datasets, implementation in consumer services, the need to reduce operating costs, improving computing power, more AI applications and a growing adoption of deep learning and neural networks as factors driving the boom.
But it urges short-term caution as supply chains are devastated by the Covid-19 pandemic, and potentially longer term restraints related to a local of skilled workers. The report also references a low return on investment for AI chipmakers in a market which is hobbled by its own relative infancy.
AI growth powered by industry and cybersec
Instead, growth is being fuelled by machine learning applications, particularly in data handling, speech translation, autonomous robots and facial recognition. Much of the short-term growth is likely to come from predictive maintenance and machinery inspection, particularly the use of inspection cameras and Internet of Things (IoT) connected to industrial hardware.
While industrial applications that support operational cost reduction were seen as strong contenders for growth, they were eclipsed by the cybersecurity industry, which held the lion’s share of the AI chipsets market in 2019. Researchers pointed to the increase of personal devices that widen the range of attack surfaces for cybercriminals to exploit, and the role of AI in tackling emerging strains of cybercrime.
Major AI chipset manufacturers include Nvidia, Intel, Xilinx, Samsung, Micron Technology, Qualcomm, IBM, Google, Microsoft, AWS, AMD, General Vision and Huawei.
Google launches Visual Inspection AI tool for manufacturers
Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped.
Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale.
How does it work?
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI.
- Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor.
- Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps.
- Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers.
- Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
- Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.
Industry use cases
The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing.
Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”
Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture.