At supermarkets like Giant Eagle, robots are freeing up employees who previously spent time taking inventory to focus on disinfecting and sanitizing surfaces and processing deliveries to keep shelves stocked. Retailers insist the robots are augmenting the work of employees, not replacing them. But as the panic buying ebbs and sales decline in the recession that is expected to follow, companies that reassigned workers during the crisis may no longer have a need for them. The role of a cashier is also changing. For many years, retailers have provided self-checkout kiosks.

For our February AI Ethics Twitter Chat, we invited expert guest, Dr. Brandie Nonnecke, Founding Director, Citris Policy Lab at UC Berkeley to discuss "Deconstructing Deepfakes". Mia Dand: Dr. Nonnecke, Let's start off with the basics, what are deepfakes? Dr. Brandie Nonnecke: "Deepfakes" are deceptive audio or visual media created with AI to depict real people saying or doing things they did not. The term "deepfake" is a portmanteau of "deep learning" (a type of machine learning) & "fake". Don't confuse deepfakes w/ "cheap fakes" or "shallow fakes", which are created w/out AI.

Researchers behind an AI app that detects coronavirus in your voice have asked for volunteers to help by uploading audio of them coughing, breathing, and talking. Scientists from Cambridge University will use the data to develop machine learning algorithms that analyze a voice for symptoms of COVID-19. The COVID-19 Sounds App joins a growing list of tools using voice analysis to diagnose the coronavirus. The method remains unproven, but the team believes the sounds made by COVD-19 patients are so specific that they can reveal who has the disease. "Having spoken to doctors, one of the most common things they have noticed about patients with the virus is the way they catch their breath when they're speaking, as well as a dry cough, and the intervals of their breathing patterns," said Cambridge University Professor Cecilia Mascolo, who led the development of the app.

Insurance is an industry that thrives on predictability. The more certain the outcome, the more insurance firms can be sure to offer fair rates and generate value for customers and shareholders alike. As such, it's an industry that has been slow to adopt new technologies and adapt to global change. Today, however, change is here, and more is on the way. Global megatrends, from the imminent arrival of the self-driving car to accelerating climate change, threaten to disrupt the insurance sector in a way that's never been seen before.

Working at Royal Dutch Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.

Like many agencies, the Census Bureau looks for reductions in expenses and workloads when it makes decisions about machine learning. But the agency has discovered another advantage in the technology: It can find data that employees never knew they needed. More than 100 different surveys are handled by siloed programs within the Census Bureau, and the capture, instrumentation, processing and summation of the resulting data is "really hard to manage," said Zachary Whitman, chief data officer, at an AFCEA Bethesda event Wednesday, The bureau's dissemination branch exports data in a consolidated system where discovery and preparation is "difficult" for employees, Whitman said. So the agency is piloting ML that flags valuable information employees may not have even been searching for originally. "How do you get people to translate into information they might not know about but would be very valuable to them?" Whitman said.

Artificial intelligence's emergence into the mainstream of enterprise computing raises significant issues -- strategic, cultural, and operational -- for businesses everywhere. What's clear is that enterprises have crossed a tipping point in their adoption of AI. A recent O'Reilly survey shows that AI is well on the road to ubiquity in businesses throughout the world. The key finding from the study was that there are now more AI-using enterprises -- in other words, those that have AI in production, revenue-generating apps -- than organizations that are simply evaluating AI. Taken together, organizations that have AI in production or in evaluation constitute 85% of companies surveyed. This represents a significant uptick in AI adoption from the prior year's O'Reilly survey, which found that just 27% of organizations were in the in-production adoption phase while twice as many -- 54% -- were still evaluating AI.

However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.

As the novel coronavirus (COVID-19) continues to spread across the world, governments and hospitals are being overwhelmed with an influx of patients. Under such circumstances, one of the key challenges they must address is managing their resources and developing care and hospitalization strategies that can prioritize the riskiest patients. This is one area where artificial intelligence can help, experts at Jvion believe. The company, which specializes in clinical AI, is undertaking a data analysis project that will inform COVID-19 readiness strategies and help hospitals take a proactive approach to manage patient populations in the inpatient and outpatient settings. Jvion is using machine learning algorithms to determine the social risk factors that make people more likely to contract and spread the virus or acquire an infection that requires hospitalization.

Artificial intelligence (AI) is already deeply embedded in so many areas of our lives. Society's reliance on AI is set to increase at a pace that is hard to comprehend. AI isn't the kind of technology that is confined to futuristic science fiction movies – the robots you've seen on the big screen that learn how to think, feel, fall in love, and subsequently take over humanity. No, AI right now is much less dramatic and often much harder to identify. Artificial intelligence is simply machine learning.