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.
Nvidia’s platform for AI startups passes 8,500 members
NVIDIA Inception, an acceleration platform for AI startups, has now surpassed 8,500 members. That’s about two-thirds of the total number of AI startups worldwide, as estimated by Pitchbook.
NVIDIA Inception is a programme built to accommodate every startup that is accelerating computing, at every stage in their journey. All programme benefits are free of charge and startups never have to give up equity to join.
Since Inception’s launch in 2016, it has grown more than tenfold. With total cumulative funding of over $60 billion and members in 90 countries, NVIDIA Inception is one of the largest AI startup ecosystems in the world. Growth has accelerated year over year, with membership increasing to 26% in 2020, and reaching 17% in the first half of 2021.
Data from across the world
Inception figures show the United States leads the world in terms of both the number of AI startups, representing nearly 27%, and the amount of secured funding, accounting for over $27 billion in cumulative funding. 42% of US-based startups were in California, with 29% in the San Francisco Bay Area.
Behind the US is China, in terms of both funding and company stage, with 12% of NVIDIA Inception members based there. India comes in third at 7%, with the UK right behind at 6%.
AI startups based in the US, China, India, and the UK account for just over half of all startups in NVIDIA Inception. Following in order after these are Germany, Russia, France, Sweden, Netherlands, Korea and Japan.
In terms of industries, healthcare, IT services, intelligent video analytics (IVA), media and entertainment (M&E) and robotics are the top five in NVIDIA Inception. AI startups in healthcare account for 16% of Inception members, followed by those in IT services at 15%.
More than 3,000 AI startups have joined Nvidia Inception since 2020. “Some countries are accelerating their ecosystem of AI startups by investing money and encouraging the local players to create more companies,” said Serge Lemonde, global head of Nvidia Inception, in an interview with VentureBeat.
“In our programme, what we are looking at is to help them all,” Lemonde said. “The lesson here is really having this window on the landscape and helping the startups all around the world — [this] is helping us understand the new trends. We can help more startups by developing our software and platforms for the upcoming trends.”