Nowadays, the extensive exploitation of Deep Neural Networks (DNNs) in safety-critical applications raises new reliability concerns. In practice, methods for fault injection by emulation in hardware are efficient and widely used to study the resilience of DNN architectures for mitigating reliability issues already at the early design stages. However, the state-of-the-art methods for fault injection by emulation incur a spectrum of time-, design- and control-complexity problems. To overcome these issues, a novel resiliency assessment method called APPRAISER is proposed that applies functional approximation for a non-conventional purpose and employs approximate computing errors for its interest. By adopting this concept in the resiliency assessment domain, APPRAISER provides thousands of times speed-up in the assessment process, while keeping high accuracy of the analysis. In this paper, APPRAISER is validated by comparing it with state-of-the-art approaches for fault injection by emulation in FPGA. By this, the feasibility of the idea is demonstrated, and a new perspective in resiliency evaluation for DNNs is opened.

Computer engineers and radiologists at Duke University have developed an artificial intelligence platform to analyze potentially cancerous lesions in mammography scans to determine if a patient should receive an invasive biopsy. But unlike its many predecessors, this algorithm is interpretable, meaning it shows physicians exactly how it came to its conclusions. The researchers trained the AI to locate and evaluate lesions just like an actual radiologist would be trained, rather than allowing it to freely develop its own procedures, giving it several advantages over its "black box" counterparts. It could make for a useful training platform to teach students how to read mammography images. It could also help physicians in sparsely populated regions around the world who do not regularly read mammography scans make better health care decisions.

Computer engineers and radiologists at Duke University have developed an artificial intelligence platform to analyze potentially cancerous lesions in mammography scans to determine if a patient should receive an invasive biopsy. But unlike its many predecessors, this algorithm is interpretable, meaning it shows physicians exactly how it came to its conclusions. The researchers trained the AI to locate and evaluate lesions just like an actual radiologist would be trained, rather than allowing it to freely develop its own procedures, giving it several advantages over its "black box" counterparts. It could make for a useful training platform to teach students how to read mammography images. It could also help physicians in sparsely populated regions around the world who do not regularly read mammography scans make better health care decisions.

McNee is the president of Ultimate Collision Repair, an auto repair shop in Edison, New Jersey. From his perspective, appraisers and claims adjusters, paid by insurance companies, generally want to pay less for repairs than he thinks his shop deserves. Since Covid-19 swept the globe last year, McNee sees far fewer appraisers. Instead, insurers are deploying technology, including photo-based estimates and artificial intelligence. McNee kind of misses his old adversaries.

If you're trying to stay up to date on customer experience and customer service best practices and innovation, it helps to closely follow USAA, the giant in insurance, banking, financial services, car retailing, and other disciplines (all of which are services that USAA offers to its member base worldwide, which consists primarily U.S. servicepeople and their families). I've made a point of doing that myself, as a customer service consultant and author. There's anextensive discussion of USAA's customer experience and customer service-related innovation and the culture of customer service excellence that powers them in my Forbes article here and in my upcoming book, Ignore Your Customers (And They'll Go Away), which HarperCollins Leadership is on the cusp of releasing. USAA's leadership in innovation related to customer service and customer experience is unparalleled. It has over 900 patents based on innovations suggested by employees, most often, incredibly, by employees who are not in technical positions.

Another Example: Real Estate Appraisal Consider a real estate appraiser whose job is to predict the sale price of residential houses. As with the Bank Loans example, the input pattern consists of a group of numbers. This problem is similar to the Bank Loans example, because it has many non-linearities, and is subject to millions of possible inputs patterns. The difference here is that the output prediction will consist of a calculated value -- the selling price of the house. It is possible to train the neural network to simulate the opinion of an expert appraiser, or to predict the actual selling price.