《The Man Who Solved The Market》中文版翻译连载56

文艺复兴的两个核心均出自IBM:语音识别与金融投资存在异曲同工之妙

《The Man Who Solved The Market》中文版翻译连载56
解决市场的人

he Man Who Solved The Market (56)

As a teenager, Peter Brown watched his father deal with a serious of daunting business challenges. In 1972, when Peter was seventeen, Henry Brown and a partner came up with the idea of cobbling together investments from individual investors to buy relatively safe, yet higher-yielding debt, introducing the world’s first money-market mutual fund. Henry’s fund offered higher rates than those available in bank saving accounts, but few investors had even a passing interest. Peter would help his father stuff envelopes and mail letters to hundreds of potencial customers, hoping to elicit interest in the new fund. Henry worked every day that year except Christmas, resorting to eating peanut-butter sandwiches and taking out a second mortgage to fund his business, as his wife, Betsey, worked as a family therapist.

在青少年时期,彼得·布朗看着他的父亲处理一系列令人生畏的商业挑战。1972年,当彼得17岁的时候,亨利·布朗和一个合伙人想出了一个主意:将个人投资者的投资拼凑在一起,购买相对安全但收益较高的债券,从而推出了世界上第一支货币市场共同基金。亨利的基金提供比银行储蓄账户更高的利率,但是几乎没有投资者感兴趣。彼得帮他的父亲装好信封,给几百个潜在客户寄信,希望能引起人们对新基金的兴趣。那一年亨利除了圣诞节每天都在工作。由于妻子贝齐是一名家庭治疗师,所以亨利只能靠吃花生酱三明治和申请第二笔抵押贷款来维持生计。

“A combination of starvation and pure greed drove us,” Henry explained to the Wall Street Journal.

“饥饿和贪婪驱动着我们,”亨利对《华尔街日报》解释称。

His lucky break came the next year in the form of a New York Times article about the fledgling fund. Clients began calling, and soon Henry and his partner were managing $100 million in their Reserve Primary Fund. The fund grew, reaching billions of dollars, but Henry resigned, in 1985, to move with Betsey to the Brown family’s farm in a Virginia hamlet, where he raised cattle on five hundred acres. Henry also competed in trebuchet, a kind of mechanical catapult, wining competitions with a contraption that sent an eight-pound pumpkin over one thousand feet. In their new neighborhood, Betsey became a civic activist and local Democratic politician.

第二年,《纽约时报》刊登了一篇关于这只羽翼未丰的基金的文章,让亨利迎来了好运。客户们开始打电话,很快亨利和合伙人的这只Reserve Primary Fund的管理规模就超过了1亿美元。基金规模不断增长,达到数亿美元,但是亨利在1985年辞职了,和贝齐一起搬到布朗家在弗吉尼亚州的一个小村庄的农场,在那里他有500英亩土地养牛。亨利还参加了投石机比赛,这是一种机械弹射器,他凭借一个精巧的装置赢得了比赛,这个装置能把一个8磅重的南瓜发射到1000英尺以外。在新社区,贝齐成为一名公民活动家和当地的民主党政治家。

Henry’s business still dominated his thoughts, though. For more than a decade, he squabbled with his former partner, Bruce Bent, whom Henry accused of reneging on an agreement to buy his half-interest in the company. Henry eventually filed a lawsuit, claiming Bent was rewarding himself excessively while running the fund, before the men finally worked out a deal for Brown to sell his half-ownership to Brent in 1999. ( In 2008, the fund would lose so much money from the debt of investment bank Lehman Brothers, among other things, that its troubles would sow fear throughout the financial system.)

然而,亨利的生意仍然占据着他的头脑。十多年来,他和前合伙人布鲁斯·本特争吵不休,亨利指责本特违背了购买他一半公司股份的协议。亨利最终起诉了本特,称他在运营基金的时候过度奖励了自己,直到1999年二人终于达成协议,布朗将把一半的股份卖给布伦特。(2008年,这只基金因为在雷曼兄弟银行的债券上损失惨重,而这个问题在整个金融系统中播下恐惧的种子。)

While his family had wealth, friends say Peter sometimes expressed anxiety about his finances, perhaps due to his father’s early challenges or his extended battle with his partner. Peter reserved his own ambitious for science and math. After graduating from Harvard University with an undergraduate degree in mathematics, Brown joined a unit of Exxon that was developing ways to translate spoken language into computer text, an early form of speech-recognition technology. Later, he’d earn a PhD incomputer science from Carnegie Mellon University in Pittsburgh.

虽然他的家庭很富裕,但朋友们都说彼得有时候还会对自己的财务状况感到焦虑,这可能归因于因为他父亲早年遇到的困难,也可能是他与合伙人长期的斗争造成的。彼得把自己的雄心壮志留给了科学和数学。他从哈佛大学取得了数学学士学位后,彼得加入了埃克森美孚的一个小组,该小组正在研究将口语翻译成计算机文本的方法,这也是语音识别技术的早期形式。后来,彼得在匹兹堡的卡内基梅隆大学获得了计算机科学的博士学位。

In 1984, at the age of twenty-nine, Brown joined IBM’s speech group, where Mercer and others had been working to develop computer software to transcribe spoken text. Conventional wisdom in the decades-old field was that only linguist and phoneticians, teaching computers rules of syntax and grammar, and a chance at getting computers to recognize language.

1984年,29岁的布朗加入了IBM的语音识别小组,在这里,默瑟和其他人正在研发计算机软件转录口语文本,该领域过去几十年的传统观点是,只有语言学家和语音学家才能教计算机语法和语法规则,使得计算机有机会识别语言。

Brown, Mercer, and their fellow mathematicians and scientists, including the group’s hard-driving leader, Fred Jelinek, viewed language very differently from the traditionalists. To them, language could be modeled like a game of chance. At any point in a sentence, there exists a certain probability of what might come next, which can be estimated based on past, common usage. “Pie” is more likely to follow the word “apple” in a sentence than words like “him” or “the”, for example. Similar probabilities also exist for pronunciation, the IBM crew argued.

布朗、默瑟和其他同行的数学家、科学家,包括小组的强硬派领导人——弗雷德·贾里尼克,对语言的看法与传统学者们非常不同。对他们来说,语言可以被模仿成一个几率游戏。在句子里的任何一个点,对于接下来的内容都存在一个特定的可能性,这些内容可以基于以往常见的使用用法来预估。例如,“派”通常会跟随“苹果”这个词在句子里出现,而不是“他”或“这”。IBM小组认为对于发音来说也有相似的可能性存在。

Their goal was to feed their computers with enough data of recorded speech and written text to develop a probabilistic, statistical model capable of predicting likely would sequences based on sequences of sounds. Their computer code wouldn’t necessarily understand what it was transcribing, but it would learn to transcribe language, nonetheless.

他们的目标是给计算机输入足够多的语音记录和文本信息,从而开发一个概率统计模型,能够基于声音序列预测可能出现的排列。他们的计算机代码不需要明白它在转录什么,但它可以学会转录语言。

In mathematical terms, Brown, Mercer, and the rest of Jelinek’s team viewed sounds as the output of a sequence in which each step along the way is random, yet dependent on the previous step — a hidden Markov model. A speech-recognition system’s job was to take a set of observed sounds, crunch the probabilities, and make the best possible guess about the “hidden” sequences of words that could have generated those sounds. To do that, the IBM researchers employed the Baum-Welch algorithm — codeveloped by Jim Simons’s early trading partner Lenny Baum — to zero in on the various language probabilities. Rather than manually programming in static knowledge about how language worked, they created a program that learned from data.

在数学术语里,布朗、默瑟和贾里尼克团队的其他人把声音看做一个序列的输出,在这个序列中每一步都是随机的,且仅依赖上一步——一种隐马尔可夫模型。一个语音识别系统的工作就是获取一组观察到的声音,快速分析概率,根据可能产生那些声音的“隐藏”的单词序列做出最佳的猜测。为了做到这一点,IBM的研究员们采用了鲍姆-韦尔奇算法——这是由吉姆·西蒙斯早期的交易伙伴莱尼·鲍姆联合发明的——来锁定各种语言的可能性。他们创造了一个可以从数据中学习的程序,而不是在静态知识下手工编程语言是如何工作的。

Brown, Mercer, and the others relied upon Bayesian mathematics, which had emerged from the statistical rule proposed by Reverend Thomas Bayes in the eighteenth-century. Bayesians will attach a degree of probability to every guess and update their best estimates as they receive new information. The genius of Bayesian statistics is that it continuously narrows a range of possibilities. Think, for example, of a spam filter, which doesn’t know with certainly if an email is malicious, but can be effective by assigning odds to each one received by constantly learning from emails previously classified as “junk”. ( This approach wasn’t as strange as it might seem. According to linguists, people in conversation unconsciously guess the next words that will be spoken, updating their expectations along the way.)

布朗、默瑟和其他人依靠贝叶斯数学——这是18世纪托马斯·贝叶斯牧师提出的一种从统计规则中衍生出来的数学。贝叶斯定理就是给每个猜测都加上一定程度的概率,在接受新的信息的同时更新最佳猜测。贝叶斯定理统计的精妙就是可以不断缩小可能性范围。以垃圾邮件过滤器为例,它不能确定一封电子邮件是否是恶意的,但是通过不断从以前归类为“垃圾”的电子邮件中了解情况,为收到的每封邮件分配概率从而变得有效。(这种方法并不像看起来的那么奇怪。根据语言学家的说法,人们在交谈中会无意识地猜测下一个要说的词,从而在交谈中更新他们的预期。)

(免责声明:仅供个人阅读学习及翻译参考。如有不准之处,请留言帮助改进)

发表评论

您的电子邮箱地址不会被公开。 必填项已用*标注

分享本页
返回顶部