Beyond Limits’ artificial intelligence for industry use
The company says its approach combines existing human knowledge with data sources, so that its systems can adapt in situations where data is missing.
Beyond limits also says its Cognitive AI technology involves the use of both conventional techniques such as machine learning, neural networks and deep learning alongside knowledge-based reasoning to make solutions with audit trails that can explain the reasoning behind autonomous decisions - with crucial industries such as healthcare, that accountability being an ethical necessity.
In , AJ Abdallat, CEO and Founder of Beyond Limits, said: ““Today we are seeing unprecedented, world-wide demand for systems that go beyond the limitations of conventional AI. Our cognitive software has the ability to understand situations and place problems in real-world contexts as well as to learn over time. We’re excited to help more customers by applying our unique and powerful AI approach to solve some of the toughest problems facing industries and the world today.”
The company said it would use the funding for research and development, and to expand in the United States and Globally, with a launch in Asia planned.
“bp ventures was established to identify and invest in high-potential, game-changing technology companies that can help us reimagine our global energy system,” said Morag Watson, Senior Vice President, Digital Science and Engineering at bp. “With this additional investment, we believe that Beyond Limits’ Cognitive AI could help create a more intelligent and sustainable future for the energy sector and indeed across industry as a whole.”
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.