Leidos: Building trusted, secure, and safe AI systems
“Leidos’ mission is to make the world safer, healthier and more secure. We take on some of the world's most interesting, challenging and data-centric problems,” says Ron Keesing, VP of AI and Machine Learning. Formerly known as Science Applications International Corporation (SAIC), the company was officially founded in 1969. Eventually splitting into two entities in 2013, Leidos (from ‘kaleidoscope’) has subsequently taken on a host of high-profile projects for clients, including NASA’s next-gen lunar landers, “the world’s longest supply chain” with the National Science Foundation, and the entire health record system for the US Department of Defense (DoD).
Among the company’s core tech competencies is the development of artificial intelligence (AI) and machine learning, which it hopes to gradually incorporate into all of its solutions. Referring to Leidos’ participation in the DARPA ACE (Air Combat Evolution) programme, Keesing says, “We're essentially taking a technology that came from the commercial world and we're using it to transform aerial combat.” Inverting traditional battle paradigms, wherein many people would control a single platform or aircraft, the company uses AI so that one person can administrate multiple aerial assets, both manned and unmanned, during the course of a mission.
It can be easy to forget that the purpose of AI should be to augment or improve the lives and working conditions of people. However, this concept is central to Leidos’ mission, as Keesing explains: “We combine humans and machines to be able to perform these missions better and faster. Leidos’ role as an integrator of AI technology comes from many different sources and we bring them all together into solutions that the US Government can use. Currently, we're using AI to transform the processing of veterans’ health benefits to make sure they’re receiving improved healthcare through natural language processing (NLP). This will enable faster claims and benefits processing with much higher accuracy and speed than was possible before.”
Despite having devised so many cutting-edge applications, Keesing emphasises the importance of keeping up with the latest AI-based research and promoting understanding among clients regarding the best way to use it. This is a perspective shared by the DoD, as well as the USAF, which are cultivating AI-led workforces. “Many across the community are also starting to appreciate what it means for AI systems to be ethical; we wouldn't want systems making crucial mistakes that could put human safety at risk.” As such, Leidos believes in building trust between humans and AI in order to foster comprehension and encourage its wider application. This is particularly crucial in the digital era, when large volumes of Big Data can overwhelm those not equipped to manage it. In this particular arena, Keesing states, AI is significantly transformative. “AI can be a crucial tool by helping to replace highly manual processes, therefore allowing humans to focus on the most important and challenging problems that machines can't solve.”
Questions relating to the security of AI continue to be challenging for the tech sector as a whole, and Leidos, for its part, is dedicated to addressing them, particularly as AI becomes further integrated into its product portfolio. Therefore, Keesing closes by encouraging everyone from students to senior decision-makers to invest their attention in AI’s development. “This is such an exciting time for people thinking about launching careers in AI and machine learning; how people understand AI will affect their systems and platforms. Whether we want it or not, this technology is going to transform every aspect of our world and Leidos is staying ahead to make sure the systems we're building are safe, secure and can be trusted.”
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.