Calendar Template Excel 2019
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How much can we trust AI? How to build confidence before a large-scale deployment How much can we trust AI? How to build confidence before a large-scale deployment Organizations must still build trust in AI before they deploy it throughout the organization. Here are some simple steps to make AI more dependable and ethical. Organizations must still build trust in AI before they deploy it throughout the organization. Here are some simple steps to make AI more dependable and ethical. Image: iStock/metamorworks Image: iStock/metamorworks In 2019, Amazon’s facial-recognition technology erroneously identified Duron Harmon of the New England Patriots, Brad Marchand of the Boston Bruins and 25 other New England athletes as criminals when it mistakenly matched the athletes to a database of mugshots. In 2019, Amazon’s facial-recognition technology erroneously identified Duron Harmon of the New England Patriots, Brad Marchand of the Boston Bruins and 25 other New England athletes as criminals when it mistakenly matched the athletes to a database of mugshots. SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium) SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium) How can artificial intelligence be better, and when will companies and their customers be able to trust it? How can artificial intelligence be better, and when will companies and their customers be able to trust it? “The issue of mistrust in AI systems was a major theme at IBM’s annual customer and developer conference this year,” said Ron Poznansky, who works in IBM design productivity. “To put it bluntly, most people don’t trust AI—at least, not enough to put it into production. A 2018 study conducted by The Economist found that 94% of business executives believe that adopting AI is important to solving strategic challenges; however, the MIT Sloan Management Review found in 2018 that only 18% of organizations are true AI ‘pioneers,’ having extensively adopted AI into their offerings and processes. This gap illustrates a very real usability problem that we have in the AI community: People want our technology, but it isn’t working for them in its current state.” “The issue of mistrust in AI systems was a major theme at IBM’s annual customer and developer conference this year,” said Ron Poznansky, who works in IBM design productivity. “To put it bluntly, most people don’t trust AI—at least, not enough to put it into production. A 2018 study conducted by The Economist found that 94% of business executives believe that adopting AI is important to solving strategic challenges; however, the MIT Sloan Management Review found in 2018 that only 18% of organizations are true AI ‘pioneers,’ having extensively adopted AI into their offerings and processes. This gap illustrates a very real usability problem that we have in the AI community: People want our technology, but it isn’t working for them in its current state.” Poznansky feels that lack of trust is a major issue. Poznansky feels that lack of trust is a major issue. “There are some very good reasons why people don’t trust AI tools just yet,” he said. “For starters, there’s the hot-button issue of bias. Recent high-profile incidents have justifiably garnered significant media attention, helping to give the concept of machine learning bias a household name. Organizations are justifiably hesitant to implement systems that might end up producing racist, sexist or otherwise biased outputs down the line.” “There are some very good reasons why people don’t trust AI tools just yet,” he said. “For starters, there’s the hot-button issue of bias. Recent high-profile incidents have justifiably garnered significant media attention, helping to give the concept of machine learning bias a household name. Organizations are justifiably hesitant to implement systems that might end up producing racist, sexist or otherwise biased outputs down the line.” SEE: Metaverse cheat sheet: Everything you need to know (free PDF) (TechRepublic) Understand AI bias SEE: Metaverse cheat sheet: Everything you need to know (free PDF) (TechRepublic) Understand AI bias
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