“So it was surprising to read an entire chapter about this dilemma comparing China and the U.S. in Kai-Fu Lee’s book AI Superpowers
While the book is about AI, Lee is trying to undo American conceptions of Chinese innovation early on in the text. Yes, the country was once a copycat haven, but that has changed as the learnings of copying have led to originality:
Lee’s ultimate point is that by focusing on markets instead of mission, Chinese startups move far faster and more aggressively to seize opportunities. But that also means that there are can be thousands of startups all targeting the same market at the same time, which forces outside-the-box (read: quite possibly unethical or illegal) behavior in order to compete. ‘For these gladiators, no dirty trick or underhanded maneuver was out of bounds. They deployed tactics that would make Uber founder Travis Kalanick blush.'”
“President Trump signed an executive order Monday meant to spur the development and regulation of artificial intelligence, technology that many experts believe will define the future of everything from consumer products to health care to warfare.
A.I. experts across industry, academia and government have long called on the Trump administration to make the development of artificial intelligence a major priority. Last spring, worried that the United States was not keeping pace with China and other countries, Jim Mattis, then the defense secretary, sent a memo to the White House imploring the president to create a national strategy on A.I.
In July 2017, Chinese unveiled a plan to become the world leader in A.I., aiming to create an industry worth $150 billion to its economy by 2030, and two Chinese cities promised to invest $7 billion in the effort. Other governments, too, began making large investments, including South Korea, Britain, France and Canada.”
“Tencent’s new “2017 Global AI Talent White Paper” suggests the bottleneck here is education. It estimates that 200,000 of the 300,000 active researchers are already employed in various industries (not just tech), while the remaining 100,000 are still studying. Attendance in machine learning and AI courses has skyrocketed in recent years, as has enrollment in online courses, but there is obviously a lag as individuals complete their education.”