Remote working boosts demand for Aware’s AI solutions
US-based Aware, the AI solution for governance, risk, compliance (GRC) and insights, has announced a US$12 million Series B equity investment led by Spring Mountain Capital – alongside Blue Heron Capital and Allos Ventures.
Aware's platform provides AI-enhanced controls and governance capabilities for the conversation data set within remorse working tools including Slack, Microsoft Teams and Workplace from Facebook.
The conversation data is enriched with proprietary natural language processing (NLP) and AI models, providing organizational value and insights such as ongoing employee sentiment.
The Columbus, Ohio company says the investment will accelerate the company's go-to-market efforts, increase partnerships, grow Aware's integration ecosystem and support ongoing product research and development.
"We're excited to partner with investors who truly understand the opportunity that lives within this unique space,” said Jeff Schumann, CEO and Co-Founder of Aware. “The conversation data found in technologies like Slack and Teams is different than anything IT teams have faced before – they include comments, replies, emojis, @ mentions, images, attachments and so much more.
“Legacy systems just aren't set up to properly handle this unstructured data set. With the use of these tools only increasing due to an influx in remote work, we see an opportunity to not only provide the necessary controls specific to this data set, but also leverage our proprietary AI to derive a wealth of knowledge and insights that leaders will find invaluable as they embrace a new way of working."
Jamie Weston, Managing Director of Spring Mountain Capital, added that Aware is uniquely positioned to tackle today's remote-work-first world.
“They solve an immediate risk and compliance need that many companies didn't realize they had until the pandemic hit,” said Weston. “Meanwhile, Aware continues to push the envelope by using this data set and technology to add immense value back to the organization."
Founded in 2017, Aware quickly secured high-profile customers including Wipro, AstraZeneca, Sun Life Financial and British Telecom.
Aware is a Microsoft Gold Partner, Slack compliance partner and a Workplace by Facebook integration partner.
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.