The progression of machine learning

It is becoming easier, faster, and cheaper for companies of every enterprise to implement machine learning--a data-fueled artificial intelligence technology used to detect patterns and anomalies, and make predictions. Even though industries find its capabilities appealing, most companies are not yet taking advantage of this transformative technology. As explained in "Signals for Strategists: Machine Learning and the Five Vectors of Progress," Deloitte believes that progress in five key areas can help overcome the barriers to adoption and eventually make machine learning technology mainstream. According to a 2017 survey of 3100 executives in various sized companies across 17 countries, fewer than 10 percent of companies are investing in machine learning [i], despite it being considered "one of the most powerful and versatile information technologies available today."[ii] Deloitte points out some major factors hindering the adoption of machine learning: the short supply of qualified practitioners [iii]; the immature and still-evolving tools and frameworks for doing machine learning work [iv]; and the time-consumption and costs associated with obtaining enough data sets for machine learning model development.[v]