AI Projects Stalled by Messy Data 

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Tens of billions will be spent this year on AI development, but those efforts continue to be stymied by ratty data that has undermined model training efforts and burned through project budgets.

That’s the sobering conclusion of a vendor survey of data scientists, AI technologist and business executives that uncovered widespread problems with data quality, specifically data labeling required to train AI models. The result is that most AI projects are stalled, with little to show for early and substantial investments.

The survey released Thursday (May 23) by AI training data specialist Alegion found that despite heavy investment in focused AI and machine learning projects (most respondents said they have four or fewer projects in development), 78 percent of those projects have slowed at some stage before deployment.

The primary reason is data quality and labeling challenges, prompting many early movers to either develop an in-house solution or outsource data labeling needed to transition machine learning projects to production.

Read the full story here at sister web site Datanami.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).