The AI Incident Database wants to improve the safety of machine learning


The AI Incident Database documents failures of AI systems in the real world, revealing past failures to avoid repeating them.Read More

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A closer look at the AI Incident Database of machine learning failures

The failures of artificial intelligent systems have become a recurring theme in technology news. Credit scoring algorithms that discriminate against women. Computer vision systems that misclassify dark-skinned people. Recommendation systems that promote violent content. Trending algorithms that amplify fake news. Most complex software systems fail at some point and need to be updated regularly. We have procedures and tools that help us find and fix these errors. But current AI systems, mostly dominated by machine learning algorithms, are different from traditional software. We are still exploring the implications of applying them to different applications, and protecting them against failure needs new… This story continues at The Next Web

AI imaging database for COVID-19 diagnosis provided to UK hospitals

An AI imaging database for COVID-19 diagnosis has been provided to British hospitals and universities. The National COVID-19 Chest Imaging Database (NCCID) is comprised of more than 40,000 CT scans, MRIs, and X-rays taken from more than 10,000 UK patients since the start of the pandemic. Clinicians are already using the images to track patterns and markers of illness. These insights could help speed up diagnosis, inform treatment plans, and predict whether a patient will end up in a critical condition. The database was developed by NHSX, a digital unit of the UK’s National Health Service. “We are applying the power of artificial… This story continues at The Next Web

Your company’s AI strategy is failing — here are 3 reasons why

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20% of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. There’s no questioning the promises of machine learning in nearly every sector. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is… This story continues at The Next Web

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