AI in a crisis and privacy management
Privacy regulations changed dramatically after the September 11th attacks in 2001. Anti-terrorist policies were introduced that allowed governments to collect massive amounts of data, and trawl through it, legally, in the name of security and protection.
Today, the global pandemic has taken data trawling to a heightened level, as track and trace systems are put into place to increase workplace safety, and governments employ facial recognition and app-based tracking to identify people who might be carrying the virus. Making the workforce feel safe is a vital component to getting the economy back into recovery. But the use of AI to collect data on citizens without their permission, even when it is with the best of intentions, carries with it, big questions.
Privacy vs protection
The misapplication of tracking technology is a major concern. Employees are justified in asking questions regarding how data will be used and could it be applied to track other behaviours – not just record likelihood of infection. There is the concern that such powerful surveillance results in employees having no privacy rights within the workspace, thus ruining trust between employer and worker. Here are three ways companies can apply AI legitimately and effectively, without violating or antagonizing a workforce that values privacy.
- Set privacy parameters
Make sure your company data collection policies are ethical – and adapt them to set a new standard if they are lacking. Follow best practices. Be transparent about what will be done with the data and why.
- Choose your AI system with care
Don’t panic buy. It is important to choose the best system for your purposes. Only collect the data your company needs. Potential future uses are not your concern. Stick to the script. This will help you set fair limits are generate trust with your workforce.
- Set boundaries
The standards that you set for your own employees and practices must extend to your vendors. Urgency to adopt a new technical system that is being rushed in elsewhere, can result in poor decisions being made. Be methodical and don’t hurry the process.
Less haste more speed
Ultimately, choosing the right system for your corporation is essential. The pandemic has hit the workforce from two directions; that which directly impacts health, and that which directly impacts privacy. An unhappy workforce is one that fails to thrive – and clarity and transparency is paramount when questions of privacy and trust arise. The advance of technology has proven to be an extraordinary tool when a disaster strikes. But maintaining privacy in the wake of such events must be considered a top priority too.
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.