No-code, low-code (horizontal) machine learning platforms are useful at scaling data science in an enterprise. Still, as many organizations are now finding out, there are so many ways that data science can go wrong in solving new problems. Zillow experienced billions of dollars in losses buying houses using a flawed data-driven home valuation model. Data-driven human resources technology, especially when based off facial recognition software, has been shown to bias hiring decisions against protected classes. While automation is a great tool to have in your arsenal, you need to consider the challenges before utilizing a horizontal ML platform.

Automation may cast a dark shadow over low-skill workers, but a new report suggests there's still hope for humans. The robots aren't taking over, yet: At least, that's the finding of a report by the World Bank that looked at data on global industrial jobs. The World Development Report 2019 says advanced economies have shed industrial jobs, but the rise of the industrial sector in East Asia has more than compensated for this loss, meaning overall numbers haven't changed. Jobs are relocating, not disappearing. The skills trends: What that does mean is that while the jobs may not be going away, if you don't live in the right place or have the right abilities, you could still be out of work.

All the talk about artificial intelligence and machine learning may make it seem these methods are easy to use. But good artificial intelligence(AI) is based on building up knowledge over time by practicing with data to learn what people do, and how torespond. The same is true for people – learning takes place over time and with a lot of practice. Data scientists start out by working with businesses to identify problems that computers can learn to solve. Data scientists determine which algorithms are good starting points for machine learning.

Carnegie Mellon University is revamping the way it teaches artificial intelligence. The university's computer science department debuted Tuesday its CMU AI initiative intended to better prepare students for entering the workforce. The goal is to train students to build complex software systems or powerful robots that utilize multiple different AI technologies, whether it be machine learning tech to help those systems learn from data or technology that helps robots see and perceive the world similar to humans. Get Data Sheet, Fortune's technology newsletter. "There is a real science to building these things," said CMU dean of computer science Andrew Moore.