This week’s roundup includes several articles about the challenges companies are facing while adopting artificial intelligence (AI). We take a look at how scientists and engineers can work better together and delve into why some companies are smartly slowing their integration of AI into their businesses. We also consider the moral and privacy issues that AI can bring as well as learn how predictive analytics can improve fundraising for nonprofits.
Actionable big data: How to bridge the gap between data scientists and engineers
by Jonathan Salama, contributing writer for VentureBeat.com
In order to get actionable results from big data, it’s imperative that data scientists and engineers are working in concert with one another—and this isn’t the case in every company. Data scientists and engineers have different goals and ways of viewing the end product or result, and that can result in discord. Bringing these teams into alignment through collaboration and communication can optimize the value companies derive from big data.
Five Core Virtues For Data Science And Artificial Intelligence
by Aaron Burciaga, contributing writer for Forbes.com
Can artificial intelligence abide by a code of ethics? Forbes Technology Council member Aaron Burciaga believes so. He lays out five virtues that he argues both data scientists and the AI they create should follow. According to Burciaga, these virtues are essential to ensure a better and moral future as the world becomes more reliant on machines.
Privacy vs. Personalization: A Divide Amongst Generations
by Greg Heist, contributing writer for MarTechAdvisor.com
A recent Gongos survey found that Millennials welcome personalization in marketing and are more likely to trade their personal information for a better buying experience than their GenX and Boomer counterparts. It’s widely agreed that marketing personalization is only going to grow, but companies need to consider their target market and what types of information they are willing to trade for a customized marketing approach.
Moving AI Forward: Why You Need to Slow Down Now to Scale Later
by Anand Rao, contributing writer for informationweek.com
Artificial intelligence (AI) might be all the rage, but one survey found that many companies are actually scaling down their AI efforts to fix several common problems caused by moving too quickly. Correctly labeling data, finding and training the right workforce, and proving return-on-investment are all challenges that company leaders are working to solve before getting more ambitious about AI.
How Artificial Intelligence Improves Predictive Analytics for Nonprofit Fundraising
by France Huang, contributing writer for nonprofitpro.com
Nonprofits have a lot to gain by implementing artificial intelligence (AI) into their predictive analytics. By obtaining key insights into improving donor acquisition, engagement, and retention, nonprofits can focus on identifying donors most likely to contribute and ensure that certain donors receive customized messaging that is most likely to convert them. At least one study has shown that AI was better than humans at identifying the best donors, but there’s still much work to be done before the technology is widely adopted by nonprofits.
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