当机构投资亚博赞助欧冠者于1967年4月发表了第一个问题时，杰拉德·蔡·蔡司毫无疑问是世界上最具影响力的投资经理。
1968年新闻周刊故事，“Tsai和Go-go基金”宣称：“没有人在基金世界中的影响力比杰拉尔德·蔡Jr。”Gil Kaplan, II’s founder, had lavished similar praise on Tsai at II’s first investor conference (which attracted 1,521 money managers), in January 1968, describing him as “a man who bears the same relationship to performance funds that Joe Namath does to the American Football League, the man who broke the price ceiling on talent.”
The cause of this notoriety? Tsai pioneered “performance funds” — what we would today call aggressive growth funds — that used momentum instead of the de rigueur dividend discount model as the source of his investment decisions. His “go-go” approach revealed an entirely new way to invest, and, according to Edward C. Johnson II, the founder of Fidelity, it was “a beautiful thing to watch his reactions. What grace, what timing — glorious!”
As this is my final column for the final print edition of the magazine (but fear not! I’ll be writing just as much online), I decided to put down a marker and predict who in the next five years will similarly disrupt asset management. It’s not going to be Elon Musk or Jeff Bezos or even Richard Craib. It will be Kai-Fu Lee.
如果你问自己，“谁是Kai-Fu Lee？，那么你就可以被扰乱了。
简而言之，李是中国华化企业的创始人兼首席执行官和华化企业的人工智能研究所总裁。但他比这更大。
李是学术创新者，资本市场投资者，技术企业家和名人的一种不寻常的组合，以及杰克特雷尼尔，悬崖仿佛，史蒂夫乔布斯和凯蒂佩里的汞合金。
Lee的整个职业生涯 - 在技术突破和商业部署的Nexus下放置了Lee - 为他准备了破坏者的作用。他是人工智能的创造者之一，发展了世界上第一个独立的讲话，连续的演讲识别系统作为他的博士学位。thesis at Carnegie Mellon in 1988. He was an executive at Apple and Silicon Graphics, then founded Microsoft’s Beijing research lab — which in 2004 MIT Technology Review called “the world’s hottest computer lab” — in 1998 and Google China in 2005. Now, as the founder of Beijing-based Sinovation, Lee is investing in early-stage AI companies.
His successful tech career and ongoing mentorship of young Chinese entrepreneurs and scholars have earned Lee star status, attracting more than 50 million followers on the Chinese microblogging platform Sina Weibo. (Ray Dalio, by contrast, has about 129,000 Twitter followers.)
危险地，由于李经常指出，李的三雷罗尔是中国 - 和中国，拥有三种必要的成分，可为AI开发和商业化：科学人才，数据和政府支持。
一，人才。中国拥有一支越来越多的天赋工程和数学研究生。These students, along with world-class talent at China’s tech giants (e.g., Alibaba, Baidu, Tencent), start-ups, and universities, have been prolific in their R&D, filing more than 8,000 AI patents in the five years to 2015 — a 190 percent growth rate that outpaces other leading markets significantly — and publishing more deep learning–related papers in journals than researchers from any other country.
第二,数据。数据的关键成分uilding transformative AI technology, and, according to The Economist, China is “the Saudi Arabia of data.” Science magazine reports that China now has more than 750 million people online, and more than 95 percent of them access the internet using mobile devices: “In 2016, Chinese mobile-payment transactions totaled $5.5 trillion, about 50 times more than in the United States that
年。。。。即使在面部扫描后，即使在公共厕所的卫生间也在有限的情况下分配有限。“
同样重要的是，经济学家指出“中国人似乎并不是非常关注的隐私，这使得收集数据更容易。”
第三，政府支持。2017年，中国中央政府提出了下一代人工智能发展计划，该计划要求中国成为AI的世界领导者到2030年。该计划包括中央和区域政府数十亿美元的资金。Cynics might scoff at this as another futile command-and-control five-year plan, but many experts agree with Andrew Ng, the former chief scientist at China’s largest online search firm, Baidu, that “when the Chinese government announces a plan like this, it has significant implications for the country and the economy. It’s a very strong signal to everyone that things will happen.”
Lee alone has the scientific knowledge, investment expertise, mercantile bent, personal and professional credibility, and outsider’s mentality to catalyze the current technological and social AI phenomena in China into a disruptive investment juggernaut.
Although he has not explicitly stated such an intention, he has offered telling clues. In his 2017 commencement address at Columbia University, he told graduates, “In the next ten years, all financial companies will be turned upside down, with AI replacing traders, bankers, accountants, and research analysts.” He added, “In 2016 my personal investment algorithm returned eight times more than my private banker.” The absolute clincher is his view that “our brains are never intended to make smart investment decisions. Machines are the ones who should do this for us.”
More generally, he has regularly said it is an engineer’s responsibility to make the world a better place. What more fitting application of AI than to help beneficiaries achieve a more secure financial future?