AI adoption may be steadily rising, but a closer examination shows that most enterprise companies may not be quite ready for the big time when it comes to artificial intelligence. Recent data from Palo Alto, California-based AI unicorn SambaNova Systems, for example, shows that more than two-thirds of organizations think using artificial intelligence (AI) will cut costs by automating processes and using employees more efficiently. But only 18% are rolling out large-scale, enterprise-class AI initiatives. The rest are introducing AI individually across multiple programs, rather than risking an investment in big-picture, large-scale adoption. That will create an increasing amount of distance between companies that are AI leaders and innovators and those that fall behind, said Marshall Choy, senior vice president of product at SambaNova, which offers custom-built dataflow-as-a-service (and won VentureBeat's AI Innovation Award for Edge AI in 2021). Companies that are more mature in AI and able to invest in large-scale adoption will reap the rewards, he told VentureBeat, while the ones introducing AI across multiple programs will suffer from information and insight silos.

Computers and robots can be biased too and it likely stems from the majority-focused photos used to train them. Researchers at the Massachusetts Institute of Technology are helping to pinpoint why facial recognition software is not accurate across all races -- and the issue likely stems from both recycled code and a Caucasian-dominated computer engineering field. Joichi Ito, MIT's Media Lab director, said during an artificial intelligence panel held this week at the World Economic Forum Annual Meeting that the software's apparent trouble with recognizing diversity is likely because the engineers, and the faces used to train the software, are mostly white. More: Google Photos' 'racist' error highlights facial recognition's limits The issue goes back to the basics of artificial intelligence. Machine learning programs are based on teaching a computer with a set of data.