The advocates of machine learning are known to be a fiercely contentious lot, each asserting that its own approach is superior to all others, and that any evidence adduced to the contrary is propaganda, fake news of the worst sort, stemming from jealous advocates of inferior approaches. The closest approximation to the internecine warfare of the machine learning field is the human learning field, in which advocates of public, government-run and union-staffed schools exchange harsh words with advocates of charter schools, with a level of invective and passion that indicates that someone is strongly in favor of hopelessly uneducated machines and/or humans.
As research director for global marketing intelligence firm IDC, Zhang studies how commercial robotics is likely to shape tomorrow's workforce. According to Zhang, the field of robotics actually favors what Trump pledged to do on the campaign trail – bring manufacturing back to the US. In an increasingly unstable employment market, developed nations desperately need more science, technology, engineering and math – commonly abbreviated as Stem – graduates to remain competitive. Private schools such as Carnegie Mellon University, for example, may be able to offer state-of-the-art robotics laboratories to students, but the same cannot be said for community colleges and vocational schools that offer the kind of training programs that workers displaced by robots would be forced to rely upon.
This involves programming software that learns from experience (machine learning or ML), understands human language including common usage and context (natural language processing or NLP), and has the ability to figure out what to do in the future based on the past (predictive analytics). If teachers, students, and parents learn to ask the right questions, A.I. It can also notice patterns and anomalies that may go unnoticed by teachers, allowing them to make critical changes to improve student outcomes. In the future, teachers will be able to narrow down quality resources faster, figure out which students are at risk of failing a test before they take it, and adapt instruction to match every learner without spending a ridiculous amount of time planning instruction.