The White House today is hosting executives from 38 companies for a grueling, embattled day of trying to move through the as of yet murky waters of AI development. The meeting, which includes representatives from Microsoft, Intel, Google, Amazon, Pfizer, and Ford, among others, aims to gather thoughts and ideas on how to supercharge AI development in a sustainable, safe, and cost-effective way.

Fields such as agriculture, healthcare and transportation are being spearheaded as areas of interest (military applications, obviously, are being discussed elsewhere). The Washington Post quotes Michael Kratsios, deputy chief technology officer at the White House, as saying in a recent interview that "Whether you're a farmer in Iowa, an energy producer in Texas, a drug manufacturer in Boston, you are going to be using these techniques to drive your business going forward."The US government's own estimates point towards some $2 billion being spent in 2017 on unclassified AI development venues (classified investment is likely to be much higher). The meeting looks to advance thoughts on both short-term and long-term AI and robotization's impacts on job availability. This impact, however, is being quoted as being ill-interpreted: according to Dean Garfield, the president of the Information Technology Industry Council, Ai isn't going to be about job destruction, but about job movement - the old tasks which require intensive human labor should be eventually replaced, yes, but other job requirements will surface as infrastructure and management requirements increase.

The meeting also has some global politics and strategy involved, though - as it naturally would. The US aims to entrench itself firmly in the AI field against competitors such as China, who has been increasing spendings in the field: dominating the technology (or at least being at the forefront of it) would insulate "risks to U.S. national security (...) as competitors integrate information derived from personal and commercial sources with intelligence collection and data analytic capabilities based on artificial intelligence and machine learning."