Illumina acquires ML-powered cancer detection firm Grail
Biotechnology giant Illumina has announced it is to acquire early cancer detection firm Grail.
The Silicon Valley-based company, founded in 2016, focuses on developing tests to detect multiple different types of cancer at an early stage - when successful treatment is much more likely.
The test involves taking a blood sample, which is then analysed via machine learning and data science techniques, alongside a database of cancer signatures. Grail’s Galleri platform is expected to launch commercially in 2021, with prior versions of the technology capable of detecting over 50 types of cancer and identify where in the body they are located
In , Hans Bishop, Chief Executive Officer of Grail, said: “Cancer is one of society’s most significant challenges, with most cancer being detected too late. We believe multi-cancer early detection technology could address a tremendous unmet need and reduce the cancer burden worldwide. Combining forces with Illumina enables broader and faster adoption of Grail’s innovative, multi-cancer early detection blood test, enhancing patient access and expanding global reach.”
Grail was spun-out of Illumina, and went on to attract of investment across four funding rounds before being brought back into Illumina’s orbit. The , which has entered into a definitive agreement, involves cash and stock considerations totalling $8bn.
Illumina gave its rationale for the purchase as tapping into the growing oncology market, as well as dovetailing with Illumina’s global scale to allow delivery of cancer tests to more patients, more quickly.
Francis deSouza, Illumina’s President and Chief Executive Officer, said: “Over the last four years, Grail’s talented team has made exceptional progress in developing the technology and clinical data required to launch the Galleri multi-cancer screening test. Galleri is among the most promising new tools in the fight against cancer, and we are thrilled to welcome Grail back to Illumina to help transform cancer care using genomics and our NGS platform.”
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.