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重庆市做人流的好医院天极平台

2017年12月15日 00:51:21    日报  参与评论()人

重庆西南医院查封闭抗体费用重庆爱德华治疗宫颈炎多少钱Human beings waste an awful lot of time. Consider my process for writing this article: Begin writing, check email, make coffee, write some more, drink coffee, peruse Facebook, take a meaningless personality quiz (only to confirm what I aly knew), tell myself I shouldn’t procrastinate, write more, check email again, respond to a non-pressing message, and finally, get up to make more coffee. Believe it or not, at some point I actually finished writing the thing.人类把很多时间都浪费在了无谓的事情上。拿我写这篇文章的过程为例:我先是写下几个句子,然后收收电子邮件,泡杯咖啡,再写两句,喝口咖啡,然后上上Facebook,再做一个毫无意义的个性测试(只是为了确认一些我已经知道的事实),然后我告诉自己不能再拖延了,然后再一鼓作气写下两段,然后再收收邮件,回一两条无关紧要的消息,最后起身再泡一杯咖啡。不管你信不信,我最后居然还是写完了这篇文章。I have plenty of guilt about my shameful time management skills, but I’m afraid that I may have also contributed to what Dan Ariely, the DukeUniversity professor and renowned behavioral economist, describes as his “marginal depression” about humans’ wasted potential.我对自己糟糕的时间管理能力感到非常羞愧。杜克大学(Duke University)教授、知名行为经济学家丹o艾瑞里曾经把人类浪费时间的倾向称为“边缘抑郁症”,恐怕我就是患者之一。“You think about the amount of human creativity and human ability to do good and the amount of progress we can make and you see what people end up doing,” Ariely told me in an interview last week. “They’re just kind of squandering their time in all sorts of terrible ways and not fulfilling their own happiness, not doing anything useful. The human stupidity really weighs on me. It’s like a depression—a marginal depression.”艾瑞里在上周的一次采访中对我说:“我们不妨思考一下,人类有多少创意,有多少干正经事的能力,有多少进步的空间,再看看人们最后都在做什么。他们把大量的时间花在各种糟糕的事情上,而没有去追求自己的幸福,没有去做任何有用的事。人类的愚蠢的确让我很苦恼。它就像某种抑郁症——边缘抑郁症。”Ariely, whose first book Predictably Irrational highlighted how painfully incompetent we are at making optimal financial decisions (and poked holes in the theory of supply and demand), has been researching human fallibility for years. (No wonder the guy is a little despondent.) But there is reason for a small, newfound source of hope. Along with Stanford University computer science professor Yoav Shoham and Jacob Bank, a Stanford doctoral candidate, Ariely has co-founded a company that aims to help people make better use of their most precious resource—time.艾瑞里研究人类不靠谱的行为已经很多年了(难怪这哥们儿有点郁闷)。他的第一本书《怪诞行为学》(Predictably Irrational)揭示了我们在做最佳财务决策方面是多么的无能(并且该书还找到了供需理论的漏洞)。他的研究给治愈“拖延症”带来了一丝小小的希望。他与斯坦福大学(Stanford University)计算机科学教授约阿夫o肖汉姆和斯坦福大学士研究生雅各布o班克一道,创办了一家公司,旨在让人们更好地利用他们最宝贵的资源——时间。The trio’s first product is an application called Timeful, a name that their Mountain View, Calif-based company shares. At first glance, the iOS app seems like a slightly souped-up calendar tool: it automatically synchronizes with existing calendars and has a familiar interface. But the app also instructs users to pick from a list of health- and happiness-minded tasks—running, flossing, calling Mom or Dad—in addition to the usual personal or work-related to-do list. It then incorporates all of that data, which can include sleep patterns and designated productive times of the day, to suggest time slots for everything. “Figuring out what to do with your time is a really complex computational problem,” Ariely says.这家公司的总部位于加州的山景城,它的第一款产品是一个名叫Timeful的应用,可以在iOS平台上下载。第一眼看过去,Timeful有点像一个加强版的日历应用:它可以自动与你的现有日历同步,而且界面也和普通日历应用差不多。不过这款应用会要求用户列出一张与健康或幸福有关的任务清单,比如跑步、用牙线清洁牙齿、给父母打电话等等,此外也有与工作或生活有关的日常任务清单。此外它还包含了睡眠模式,并且你还可以指定一天的某个时间段为最有效率的时间。整合了以上所有信息之后,这款应用便会提供一份日程建议表,将一天的每件事安排得井井有条。艾瑞里表示:“要确定怎样合理安排你的时间,的确是个非常复杂的计算问题。”Much to my relief, Ariely posits that we are pretty much all unable to take all factors into account when deciding what to do, and when. Faced with myriad big tasks and smaller to-do list items, plus the difficulty of estimating how long it will take us to complete something and which time of day we’re best able to focus, we often turn to the easiest task at hand: re-ing unanswered emails or updating one’s Facebook status.让我颇感安慰的是,艾瑞里指出,人们在决定何时做什么事时,很少会把所有因素都考虑进去。人们一想到前头还有千头万绪的大事小情等着自己,往往就开始头痛。再加上我们经常搞不清干完某件事需要多长时间,也不知道自己在一天的几点到几点干事最专心,因此我们往往会先做手头上最简单的任务——再看一遍还没回复的电子邮件,或是更新一下Facebook。Translation: We procrastinate. A lot.换句话说:我们都是严重的拖延症患者。Making matters worse is the fact that most of today’s digital calendars aren’t well-equipped to remind us of the kinds of things we typically do outside of work but could possibly do during work—like calling Mom and Dad, running, or even flossing. In other words, today’s calendar apps seem to lack the smarts or focus in providing a more holistic view of our day, incorporating both what we need to do (like writing an article) and what we should aspire to do (like go for a lunchtime run).有些事情我们通常放在工作以外的时间做,但忽视了它们也有在工作时间做的可能,比如给父母打电话、跑步甚至是清洁牙齿。但是如今的大多数电子日历,并没有先进到能够见缝插针地替我们把这些事安排到工作时间里。换句话说,现有的电子日历应用依然不够智能,无法通盘考虑如何利用一整天的时间,无法把我们需要做的事情(比如写材料)和我们应该想要做的事情(比如利用午休时间跑步)结合起来。“The calendar is great to represent meetings,” Ariely says. “But we want to achieve many more things in life than meetings.” And yet, when we see an open slot in our calendar, he says, we think we can fill it with yet another meeting.艾瑞利也指出:“日历应用在提醒会议安排上表现得很好,但是除了会议之外,我们在生活中想要完成的事情还有很多。”可现在我们一看到日历上有空闲的时间,首先想到的就是再安排一个会议。Teaching someone how to make better use of their time doesn’t seem to work—so if an app automatically finds time for you to do the things you need to, want to, and should do, you are more likely to actually get them done. Therein lies the behavioral science at work in Timeful, with a few technological twists to help facilitate it.教别人如何更好地利用他们的时间是不管用的。如果一款应用可以自动为你安排时间,告诉你何时去做你需要做的事、想要做的事和应该做的事,那么你真正完成这些事的可能性就会更高,这就是Timeful背后的行为科学原理。当然,要真正实现这些功能,也离不开技术上的一些“绝活”。“The system needs to capture all of the things that are vying for your time,” says Bank, who serves as the company’s chief executive. “And it needs to help you make time for them.” According to the company, Timeful’s scheduling suggestions are based on a so-called Intention Rank, an algorithm that uses machine learning to rank activities within time slots. Underneath it is a data model—Timeful calls it its “Intention Genome”—that breaks down intentions to basic components and classifies them.该公司的首席执行官班克表示:“首先,系统需要捕捉所有占用我们时间的事情,然后它要帮你安排做这些事的时间。”据该公司表示,Timeful的日程安排主要是根据一种叫做“意向排名”的算法计算出来的,这种算法使用了机器学习技术来在时间空窗内安排活动。“意向排名”的基础是所谓的“意向基因”,它会把我们的意向分解成基本的元素并进行分类。“With many [other] productivity tools, you work for the system rather than the system working for you,” says Shoham, who has co-founded (and sold) two previous startups. He adds that the Timeful calendar is just the first app for the company, which raised nearly million in Series A funding earlier this year. Down the road, Timeful could integrate data from wearables and other information in order to make more informed suggestions to users.公司的另一名创始人肖汉姆表示:“Timeful还包含了很多其它的效率工具,可以说是你为系统工作,而不是系统为你工作。”肖汉姆还表示,Timeful日历应用只是该公司开发的第一款应用。该公司今年年初刚从第一轮融资中筹集到700万美元。未来Timeful还可以整合可穿戴设备和其它渠道的数据,以便为用户提供更合理的建议。Like any new productivity tool, there is a bit of a time-consuming learning curve with Timeful. The app doesn’t take long to set up, but it does take a while for the user to get used to having an interface that incorporates both a traditional calendar and a suggested list of tasks, some of which don’t have a clear beginning and end (such as laundry). The more information and preferences you feed the app—which, incidentally, takes more time—the better it will theoretically make suggestions tailored to you.像其它效率工具一样,Timeful也有一条学习曲线,需要一定的时间才能得心应手地使用。这款应用安装起来并不麻烦,但是它既有传统电子日历的界面,又有一个系统建议的任务清单,有些任务并没有明确的开始和结束时间(比如洗衣),用户要想适应这款应用还是要花些时间的。你为这款应用提供的信息和参数越多(这些需要花时间),理论上讲它为你建议的任务清单就更适合你的实际情况。Here’s another downer: Timeful arrives in a category bursting with calendar and productivity apps. And really, who has time to evaluate them based on algorithmic superiority? We’re all too busy tackling much more important tasks—like which pizza topping most closely matches our personality.艾瑞里短期内可能还难以从他的“边缘抑郁症”恢复过来,他表示:“一个人要想学会管理好自己的时间,本身就需要很长时间。”说这话时,声音中还带着一丝挫败感。(虽然凭借这款应用,艾瑞里可能成功地帮助人们解决了时间管理问题,但人性中的其他弱点仍然让他整夜忧思难眠。他喜欢指出,人类有44%的死亡是由糟糕的决策直接或间接导致的。)Inexplicably, I got mushrooms—about the only food that I hate with a passion. I’m sure there’s good reason for it, but I’ve got another pot of coffee brewing, and I’m pretty sure I just saw another email come in.另外还有一点也比较令人沮丧:Timeful所在的领域早已充斥了大量的日历和效率应用。说真的,谁有时间来比较它们的算法谁好谁坏呢?我们都忙着干更重要的事——比如在心理测试中研究在匹萨饼上放哪种食材更符合我们的性格。 /201408/317878重庆哪家医院的妇科比较好 重庆医科大学附属第一医院妇科人流

重庆重医附一院治疗不孕不育好吗CUPERTINO, Calif. — In reaction to declining sales of tablet computing devices, Apple’s chief executive, Timothy D. Cook, recently remarked that tablets had hit a “speed bump” that was nothing to be concerned about.加利福尼亚州库比提诺——鉴于平板电脑设备销量下滑,苹果公司(Apple)首席执行官蒂莫西·D·库克(Timothy D. Cook)最近表示,平板电脑撞上了一个“减速带”,但这种情况无需担心。Now Apple is trying to accelerate past the bump.如今,苹果正试图加速经过这条减速带。The company on Thursday introduced new models of iPads, including a major revision for its iPad Air, the larger and more expensive model, and some improvements for its smaller sibling, the iPad Mini 3.周四,苹果推出了新款iPad,对更大更贵的iPad Air做出了重大改动,还对尺寸较小的iPad Mini 3做了一些改进。Apple said the iPad Air 2 was 18 percent thinner and 40 percent faster than the last one, a surprising change — and a bit of an engineering feat — because Apple made the previous version thinner and faster just last year. Essentially, the new iPad Air is thinner than a pencil. The new iPads will be available Oct. 24.苹果表示,iPad Air 2比上一代薄了18%,快了40%,这个改变有些出人意料,在工程上也是一大成就——因为苹果去年才刚刚让上一版iPad变得更轻更快。可以说,新款iPad Air比一铅笔还薄。它将从10月24日开始发售。“It’s unbelievably gorgeous and look how thin it is. Can you even see it?” said Mr. Cook, holding the new iPad in front of an audience of members of the news media and Apple employees at the company’s Silicon Valley headquarters.“它实在太棒了,看看它多么薄。差点都看不到了吧?”库克在苹果位于硅谷的总部拿着新款iPad,对媒体和苹果员工说道。The iPad Air 2 has an improved camera, and it has a display designed to reduce reflections. The tablet has 10 hours of battery life, same as the previous version. It has a starting price of 0. The iPad Mini 3 starts at 0 — but it’s not thinner than the last version.iPad Air 2改善了照相功能,显示屏的设计还能减少反光。与上一款一样,其电池续航时间为10小时。它的起售价格为500美元(约合3000元人民币)。iPad Mini 3的起售价格为400美元——但并不比上一款薄。Apple added its fingerprint sensor, called Touch ID, to each of the new iPads. The technology is used to log into the iPad in place of a typed passcode. It can also be used to make in-app purchases with Apple’s new mobile payments system, Apple Pay, which will be available Monday.苹果给每个新款iPad都增加了称为Touch ID的指纹传感器。用户将通过这项技术进入iPad,而无需像之前那样输入密码。指纹传感器还可以通过苹果的新移动付系统Apple Pay来在应用中进行付。Apple Pay将于周一推出。Apple said iOS 8.1, the next update for the software that runs Apple’s mobile devices, would also be available Monday.苹果宣布,周一还将推出iOS 8.1,即苹果移动设备运行软件的下一版更新。Apple also added gold as a color option for the new iPads.新款iPad还增加了金色版供用户选择。Apple has made big changes to its iPads more quickly than it has with other Apple products, like the iPhone, which in the past has been redesigned every two years.苹果对iPad做出重大修改的速度比对iPhone等其他苹果产品更快。过去,每隔两年,iPhone才会重新设计一次。Why the difference? For one, an iPad gives Apple’s engineers more physical space to tinker around. And from a business standpoint, Apple has to do more with the iPad to maintain healthy sales.为何会有这种差别?首先,iPad给了苹果工程师更多进行改进的实际空间。从商业角度来看,苹果需要对iPad投入更多,才能维持良好的销售业绩。In the second quarter, Apple’s iPad sales declined 9.3 percent compared with the same period a year ago, according to the industry analysis firm IDC. And the worldwide market for tablet sales is starting to cool. While shipments of tablets exploded from 18 million in 2010 to 207 million last year, they are expected to increase just 11 percent this year, according to another research firm, Gartner. Last year, shipments had increased 55 percent.据科技研究公司IDC透露,二季度苹果iPad的销售量同比下降了9.3%,全球市场的平板电脑销售也开始降温。据另一家调研公司高德纳(Gartner)透露,去年,平板电脑的出货量从2010年的1800万台飙升至2.07亿台,今年全球平板电脑的出货量只会增加11%,而去年的增幅达到了55%。But the iPad is still Apple’s second-biggest moneymaker, accounting for about 10 percent of its profit. That is a long way from the iPhone, which accounts for about 70 percent of its profit, but still important.但iPad仍是苹果的第二大收入来源,产生的利润在苹果总利润中占10%。尽管这距离iPhone产生的利润——70%——还有很大的差距,但它仍是一款重要的产品。Also at the event on the company’s campus, Apple released its new Macintosh operating system, OS X Yosemite, which it introduced this year. The software system, which is a free download, has a new design with new icons and more vibrant colors.在苹果总部举行的发布会上,苹果还推出了新的麦金塔(Macintosh)操作系统OS X Yosemite,苹果公司于今年发布了该系统。新版系统有新设计、新图标,色更加鲜明。A key feature is called Continuity, which makes it easier to juggle content across different Apple devices. For example, a user can be making a presentation on a Mac, and then swipe up from the corner of an iPad to resume working on the same presentation.一种称为Continuity的关键功能,使在不同苹果设备中进行同步操作变得更容易。例如,用户可以用Mac做演示,然后滑动iPad屏幕,继续刚才的演示。In addition, the company said WatchKit, a tool kit for software makers to use in developing apps for its coming smart watch, would be released next month. The Apple Watch, which Apple demonstrated last month, is still on track for a release early next year, according to Mr. Cook.除此之外,该公司还表示将于下月发布软件开发者用来为苹果的智能手表开发应用时所需的工具WatchKit。苹果上个月展示了Apple Watch,但据库克透露,按照计划,这款产品将于明年早些时候推出。Apple on Thursday also released a new iMac, a desktop computer with a high-resolution, 27-inch screen. Apple said the display has seven times more pixels than a high-definition television. It costs ,500.周四,苹果还发布了新版iMac,这款台式电脑拥有27寸的高分辨率屏幕。该公司表示,其显示屏的分辨率是高清电视的7倍。这款电脑售价2500美元。In addition, the company released a new upgrade for its Mac Mini, the smaller desktop computer, with a faster processor. It costs 0.苹果还推出了新款Mac Mini,这款配有更快处理器的小台式电脑售价500美元。 /201410/336839重庆治疗精子活力 A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence. This was the home of Watson, the electronic genius that conquered Jeopardy! in 2011. The original Watson is still here—it#39;s about the size of a bedroom, with 10 upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines#39; backs. It is surprisingly warm inside, as if the cluster were alive.数月前,我长途跋涉来到位于纽约州约克城高地的IBM研究实验室的林间园区,为的就是能早早一窥那近在眼前却让人期待许久的人工智能的未来。这儿是超级电脑“沃森”(Watson)的研发地,而沃森在2011年就在“危险边缘”(Jeopardy!)节目的比赛里拔得头筹。最初的沃森电脑仍留于此处——它是一个体积约与一个卧室相当,由10台直立的冷柜式机器围成四面墙的计算机系统。技术人员可以通过系统内部的小细孔把各种线缆接到机器背部。系统内部温度高得出奇,仿佛这个计算机集群是活生生的一般。Today#39;s Watson is very different. It no longer exists solely within a wall of cabinets but is sp across a cloud of open-standard servers that run several hundred “instances” of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be immediately transferred to the others. And instead of one single program, it#39;s an aggregation of diverse software engines—its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations—all cleverly integrated into a unified stream of intelligence.如今的沃森系统与之前相比有了显著差异。它不再仅仅存在于一排机柜之中,而是通过大量对用户免费开放的务器传播,这些务器能够即时运行上百种人工智能的“情况”。同所有云端化的事物一样,沃森系统为世界各地同时使用的客户务,他们能够用手机、台式机以及他们自己的数据务器连上该系统。这类人工智能可以根据需求按比例增加或减少。鉴于人工智能会随人们的使用而逐步改进,沃森将始终变得愈发聪明;它在任何一次情况中所获悉的改进点都会立即传送至其他情况中。并且,它也不是一个单一的程序,而是各种软件引擎的集合——其逻辑演绎引擎和语言引擎可以在不同的代码、芯片以及位置上运行——所有这些智慧的因素都汇集成了一个统一的智能流。Consumers can tap into that always-on intelligence directly, but also through third-party apps that harness the power of this AI cloud. Like many parents of a bright mind, IBM would like Watson to pursue a medical career, so it should come as no surprise that one of the apps under development is a medical-diagnosis tool. Most of the previous attempts to make a diagnostic AI have been pathetic failures, but Watson really works. When, in plain English, I give it the symptoms of a disease I once contracted in India, it gives me a list of hunches, ranked from most to least probable. The most likely cause, it declares, is Giardia—the correct answer. This expertise isn#39;t yet available to patients directly; IBM provides access to Watson#39;s intelligence to partners, helping them develop user-friendly interfaces for subscribing doctors and hospitals. “I believe something like Watson will soon be the world#39;s best diagnostician—whether machine or human,” says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trek medical tricorder and powered by a cloud AI. “At the rate AI technology is improving, a kid born today will rarely need to see a doctor to get a diagnosis by the time they are an adult.”用户可以直接接入这一永久连接(always-on)的智能系统,也可以通过使用这一人工智能云务的第三方应用程序接入。正如许多高瞻远瞩的父母一样,IBM想让沃森电脑从事医学工作,因此他们正在开发一款医疗诊断工具的应用程序,这倒也不足为奇。之前,诊疗方面的人工智能尝试大多以惨败告终,但沃森却卓有成效。简单地说,当我输入我曾经在印度感染上的某种疾病症状时,它会给我一个疑似病症的清单,上面一一列明了可能性从高到低的疾病。它认为我最可能感染了贾第鞭毛虫病(Giardia)——说的一点儿也没错。这一技术尚未直接对患者开放;IBM将沃森电脑的智能提供给合作伙伴接入使用,以帮助他们开发出用户友好界面为预约医生及医院方面务。“我相信类似沃森这种——无论它是机器还是人——都将很快成为世界上最好的诊疗医生”,创业公司Scanadu的首席医疗官艾伦·格林(Alan Greene)说道,该公司受到电影《星际迷航》中医用三录仪的启发,正在利用云人工智能技术制造一种诊疗设备。“从人工智能技术改进的速率来看,现在出生的孩子长大后,很可能不太需要通过看医生来得知诊疗情况了。”As AIs develop, we might have to engineer ways to prevent consciousness in them—our most premium AI services will be advertised as consciousness-free.随着人工智能发展,我们可能要设计出一些阻止它们拥有意识的方式——我们所宣称的最优质的人工智能务将是无意识务。Medicine is only the beginning. All the major cloud companies, plus dozens of startups, are in a mad rush to launch a Watson-like cognitive service. According to quantitative analysis firm Quid, AI has attracted more than billion in investments since 2009. Last year alone more than billion was invested in 322 companies with AI-like technology. Facebook and Google have recruited researchers to join their in-house AI research teams. Yahoo, Intel, Dropbox, LinkedIn, Pinterest, and Twitter have all purchased AI companies since last year. Private investment in the AI sector has been expanding 62 percent a year on average for the past four years, a rate that is expected to continue.医学仅仅只是一个开始。所有主流的云计算公司,加上数十家创业公司都在争先恐后地开展类似沃森电脑的认知务。根据量化分析公司Quid的数据,自2009年以来,人工智能已经吸引了超过170亿美元的投资。仅去年一年,就有322家拥有类似人工智能技术的公司获得了超过20亿美元的投资。Facebook和谷歌也为其公司内部的人工智能研究小组招聘了研究员。自去年以来,雅虎、英特尔、Dropbox、LinkedIn、Pinterest以及推特也都收购了人工智能公司。过去四年间,人工智能领域的民间投资以平均每年62%的增长速率增加,这一速率预计还会持续下去。Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence. The AI on the horizon looks more like Amazon Web Services—cheap, reliable, industrial-grade digital smartness running behind everything, and almost invisible except when it blinks off. This common utility will serve you as much IQ as you want but no more than you need. Like all utilities, AI will be supremely boring, even as it transforms the Internet, the global economy, and civilization. It will enliven inert objects, much as electricity did more than a century ago. Everything that we formerly electrified we will now cognitize. This new utilitarian AI will also augment us individually as people (deepening our memory, speeding our recognition) and collectively as a species. There is almost nothing we can think of that cannot be made new, different, or interesting by infusing it with some extra IQ. In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and now it#39;s here.纵观所有这些活动,人工智能的未来正进入我们的视野之中,它既非如那种哈尔9000(HAL 9000)(译者注:小说及电影《2001:太空漫游》中的超级电脑)——一台拥有超凡(但有潜在嗜杀倾向)的类人意识并依靠此运行的独立机器那般——也非让奇点论者心醉神迷的超级智能。即将到来的人工智能颇似亚马逊的网络务——廉价、可靠、工业级的数字智慧在一切事物的背后运行,偶尔在你的眼前闪烁几下,其他时候近乎无形。这一通用设施将提供你所需要的人工智能而不超出你的需要。和所有设施一样,即使人工智能改变了互联网、全球经济以及文明,它也将变得令人厌倦。正如一个多世纪以前电力所做的那样,它会让无生命的物体活跃起来。之前我们电气化的所有东西,现在我们都将使之认知化。而实用化的新型人工智能也会增强人类个体(加深我们的记忆、加速我们的认知)以及人类群体的生活。通过加入一些额外的智能因素,我们想不到有什么东西不能变得新奇、不同且有趣。实际上,我们能轻易地预测到接下来的一万家创业公司的商业计划:“做某项事业,并加入人工智能”。兹事体大,近在眼前。Around 2002 I attended a small party for Google—before its IPO, when it only focused on search. I struck up a conversation with Larry Page, Google#39;s brilliant cofounder, who became the company#39;s CEO in 2011. “Larry, I still don#39;t get it. There are so many search companies. Web search, for free? Where does that get you?” My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defense this was before Google had ramped up its ad-auction scheme to generate real income, long before YouTube or any other major acquisitions. I was not the only avid user of its search site who thought it would not last long. But Page#39;s reply has always stuck with me: “Oh, we#39;re really making an AI.”大约在2002年时,我参加了谷歌的一个小型聚会——彼时谷歌尚未IPO,还在一心一意地做网络搜索。我与谷歌杰出的联合创始人、2011年成为谷歌CEO的拉里·佩奇(Larry Page)随意攀谈起来。“拉里,我还是搞不懂,现在有这么多搜索公司,你们为什么要做免费的网络搜索?你是怎么想到这个主意的?”我那缺乏想象力的无知着实明了我们很难去做预测,尤其是对于未来的预测。但我要辩解的是,在谷歌增强其广告拍卖方案并使之形成实际收益,以及进行对YouTube的并购或其他重要并购之前,预测未来是很难的。我并不是唯一一个一边狂热地用着谷歌的搜索引擎一边认为它撑不了多久的用户。但佩奇的回答让我一直难以忘怀:“哦,我们实际上是在做人工智能。”I#39;ve thought a lot about that conversation over the past few years as Google has bought 14 AI and robotics companies. At first glance, you might think that Google is beefing up its AI portfolio to improve its search capabilities, since search contributes 80 percent of its revenue. But I think that#39;s backward. Rather than use AI to make its search better, Google is using search to make its AI better. Every time you type a query, click on a search-generated link, or create a link on the web, you are training the Google AI. When you type “Easter Bunny” into the image search bar and then click on the most Easter Bunny-looking image, you are teaching the AI what an Easter bunny looks like. Each of the 12.1 billion queries that Google#39;s 1.2 billion searchers conduct each day tutor the deep-learning AI over and over again. With another 10 years of steady improvements to its AI algorithms, plus a thousand-fold more data and 100 times more computing resources, Google will have an unrivaled AI. My prediction: By 2024, Google#39;s main product will not be search but AI.过去数年间,关于那次谈话我想了很多,谷歌也收购了14家人工智能以及机器人方面的公司。鉴于搜索业务为谷歌贡献了80%的收入,因此乍一看去,你可能会觉得谷歌正在扩充其人工智能方面的投资组合以改善搜索能力。但是我认为正好相反。谷歌正在用搜索技术来改善人工智能,而非使用人工智能来改进搜索技术。每当你输入一个查询词,点击搜索引擎生成的链接,或者在网页上创造一个链接,你都是在训练谷歌的人工智能技术。当你在图片搜索栏中输入“复活节兔子”(Easter Bunny)并点击看起来最像复活节兔子的那张图片时,你都是在告诉人工智能,复活节兔子是长成什么样的。谷歌每天拥有12亿搜索用户,产生1210亿搜索关键词,每一个关键词都是在一次又一次地辅导人工智能进行深度学习。如果再对人工智能的算法进行为之10年的稳固改进,加之一千倍以上的数据以及一百倍以上的计算资源,谷歌将会开发出一款无与伦比的人工智能产品。我的预言是:到2024年,谷歌的主营产品将不再是搜索引擎,而是人工智能产品。This is the point where it is entirely appropriate to be skeptical. For almost 60 years, AI researchers have predicted that AI is right around the corner, yet until a few years ago it seemed as stuck in the future as ever. There was even a term coined to describe this era of meager results and even more meager research funding: the AI winter. Has anything really changed?这个观点自然也会招来怀疑的声音。近60年来,人工智能的研究者都预测说人工智能时代即将到来,但是直到几年前,人工智能好像还是遥不可及。人们甚至发明了一个词来描述这个研究结果匮乏、研究基金更加匮乏的时代:人工智能之冬。那么事情真的有变化吗?Yes. Three recent breakthroughs have unleashed the long-awaited arrival of artificial intelligence:是的。近期的三大突破让人们期待已久的人工智能近在眼前:1. Cheap parallel computation1. 成本低廉的并行计算Thinking is an inherently parallel process, billions of neurons firing simultaneously to create synchronous waves of cortical computation. To build a neural network—the primary architecture of AI software—also requires many different processes to take place simultaneously. Each node of a neural network loosely imitates a neuron in the brain—mutually interacting with its neighbors to make sense of the signals it receives. To recognize a spoken word, a program must be able to hear all the phonemes in relation to one another; to identify an image, it needs to see every pixel in the context of the pixels around it—both deeply parallel tasks. But until recently, the typical computer processor could only ping one thing at a time.思考是一种人类固有的并行过程,数以亿计的神经元同时放电以创造出大脑皮层用于计算的同步脑电波。搭建一个神经网络——即人工智能软件的主要结构——也需要许多不同的进程同时运行。神经网络的每一个节点都大致模拟了大脑中的一个神经元——其与相邻的节点互相作用,以明确所接收的信号。一项程序要理解某个口语单词,就必须能够听清(不同音节)彼此之间的所有音素;要识别出某幅图片,就需要看到其周围像素环境内的所有像素——二者都是深层次的并行任务。但直到最近,标准的计算机处理器也仅仅能一次处理一项任务。That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual—and parallel—demands of games, in which millions of pixels had to be recalculated many times a second. That required a specialized parallel computing chip, which was added as a supplement to the PC motherboard. The parallel graphical chips worked, and gaming soared. By 2005, GPUs were being produced in such quantities that they became much cheaper. In 2009, Andrew Ng and a team at Stanford realized that GPU chips could run neural networks in parallel.事情在十多年前就已经开始发生变化,彼时出现了一种被称为图形处理单元(graphics processing unit -GPU)的新型芯片,它能够满足可视游戏中高密度的视觉以及并行需求,在这一过程中,每秒钟都有上百万像素被多次重新计算。这一过程需要一种专门的并行计算芯片,该芯片添加至电脑主板上,作为对其的补充。并行图形芯片作用明显,游戏可玩性也大幅上升。到2005年,GPU芯片产量颇高,其价格便降了下来。2009年,吴恩达(Andrew Ng)(译者注:华裔计算机科学家)以及斯坦福大学的一个研究小组意识到,GPU芯片可以并行运行神经网络。That discovery unlocked new possibilities for neural networks, which can include hundreds of millions of connections between their nodes. Traditional processors required several weeks to calculate all the cascading possibilities in a 100 million-parameter neural net. Ng found that a cluster of GPUs could accomplish the same thing in a day. Today neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or, in the case of Netflix, to make reliable recommendations for its more than 50 million subscribers.这一发现开启了神经网络新的可能性,使得神经网络能容纳上亿个节点间的连接。传统的处理器需要数周才能计算出拥有1亿节点的神经网的级联可能性。而吴恩达发现,一个GPU集群在一天内就可完成同一任务。现在,一些应用云计算的公司通常都会使用GPU来运行神经网络,例如,Facebook会籍此技术来识别用户照片中的好友,Netfilx也会依其来给5000万订阅用户提供靠谱的推荐内容。2. Big Data2. 大数据Every intelligence has to be taught. A human brain, which is genetically primed to categorize things, still needs to see a dozen examples before it can distinguish between cats and dogs. That#39;s even more true for artificial minds. Even the best-programmed computer has to play at least a thousand games of chess before it gets good. Part of the AI breakthrough lies in the incredible avalanche of collected data about our world, which provides the schooling that AIs need. Massive databases, self-tracking, web cookies, online footprints, terabytes of storage, decades of search results, Wikipedia, and the entire digital universe became the teachers making AI smart.每一种智能都需要被训练。哪怕是天生能够给事物分类的人脑,也仍然需要看过十几个例子后才能够区分猫和。人工思维则更是如此。即使是(国际象棋)程序编的最好的电脑,也得在至少对弈一千局之后才能有良好表现。人工智能获得突破的部分原因在于,我们收集到来自全球的海量数据,以给人工智能提供了其所需的训练。巨型数据库、自动跟踪(self-tracking)、网页cookie、线上足迹、兆兆字节级存储、数十年的搜索结果、维基百科以及整个数字世界都成了老师,是它们让人工智能变得更加聪明。3. Better algorithms3. 更优的算法Digital neural nets were invented in the 1950s, but it took decades for computer scientists to learn how to tame the astronomically huge combinatorial relationships between a million—or 100 million—neurons. The key was to organize neural nets into stacked layers. Take the relatively simple task of recognizing that a face is a face. When a group of bits in a neural net are found to trigger a pattern—the image of an eye, for instance—that result is moved up to another level in the neural net for further parsing. The next level might group two eyes together and pass that meaningful chunk onto another level of hierarchical structure that associates it with the pattern of a nose. It can take many millions of these nodes (each one producing a calculation feeding others around it), stacked up to 15 levels high, to recognize a human face. In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers. Deep-learning algorithms accelerated enormously a few years later when they were ported to GPUs. The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBM#39;s Watson, Google#39;s search engine, and Facebook#39;s algorithms.20世纪50年代,数字神经网络就被发明了出来,但计算机科学家花费了数十年来研究如何驾驭百万乃至亿级神经元之间那庞大到如天文数字一般的组合关系。这一过程的关键是要将神经网络组织成为堆叠层(stacked layer)。一个相对来说比较简单的任务就是人脸识别。当某神经网络中的一组比特被发现能够形成某种图案——例如,一只眼睛的图像——这一结果就会被向上转移至该神经网络的另一层以做进一步分析。接下来的这一层可能会将两只眼睛拼在一起,将这一有意义的数据块传递到层级结构的第三层,该层可以将眼睛和鼻子的图像结合到一起(来进行分析)。识别一张人脸可能需要数百万个这种节点(每个节点都会生成一个计算结果以供周围节点使用),并需要堆叠高达15个层级。2006年,当时就职于多伦多大学的杰夫·辛顿(Geoff Hinton)对这一方法进行了一次关键改进,并将其称之为“深度学习”。他能够从数学层面上优化每一层的结果从而使神经网络在形成堆叠层时加快学习速度。数年后,当深度学习算法被移植到GPU集群中后,其速度有了显著提高。仅靠深度学习的代码并不足以能产生复杂的逻辑思维,但是它是包括IBM的沃森电脑、谷歌搜索引擎以及Facebook算法在内,当下所有人工智能产品的主要组成部分。This perfect storm of parallel computation, bigger data, and deeper algorithms generated the 60-years-in-the-making overnight success of AI. And this convergence suggests that as long as these technological trends continue—and there#39;s no reason to think they won#39;t—AI will keep improving.这一由并行计算、大数据和更深层次算法组成的完美风暴使得持续耕耘了60年的人工智能一鸣惊人。而这一聚合也表明,只要这些技术趋势继续下去——它们也没有理由不延续——人工智能将精益求精。As it does, this cloud-based AI will become an increasingly ingrained part of our everyday life. But it will come at a price. Cloud computing obeys the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger. The bigger the network, the more attractive it is to new users, which makes it even bigger, and thus more attractive, and so on. A cloud that serves AI will obey the same law. The more people who use an AI, the smarter it gets. The smarter it gets, the more people use it. The more people that use it, the smarter it gets. Once a company enters this virtuous cycle, it tends to grow so big, so fast, that it overwhelms any upstart competitors. As a result, our AI future is likely to be ruled by an oligarchy of two or three large, general-purpose cloud-based commercial intelligences.随着这一趋势的持续,这种基于云技术的人工智能将愈发成为我们日常生活中不可分割的一部分。但天上没有掉馅饼的事。云计算遵循收益递增(increasing returns)法则,这一法则有时也被称为网络效应(network effect),即随着网络发展壮大,网络价值也会以更快的速度增加。网络(规模)越大,对于新用户的吸引力越强,这又让网络变得更大,又进一步增强了吸引力,如此往复。为人工智能务的云技术也遵循这一法则。越多人使用人工智能产品,它就会变得越聪明;它变得越聪明,就有越多人来使用它;然后它变得更聪明,进一步就有更多人使用它。一旦有公司迈进了这个良性循环中,其规模会变大、发展会加快,以至于没有任何新兴对手能望其项背。因此,人工智能的未来将有两到三家寡头公司统治,它们会开发出大规模基于云技术的多用途商业智能产品。In 1997, Watson#39;s precursor, IBM#39;s Deep Blue, beat the reigning chess grand master Garry Kasparov in a famous man-versus-machine match. After machines repeated their victories in a few more matches, humans largely lost interest in such contests. You might think that was the end of the story (if not the end of human history), but Kasparov realized that he could have performed better against Deep Blue if he#39;d had the same instant access to a massive database of all previous chess moves that Deep Blue had. If this database tool was fair for an AI, why not for a human? To pursue this idea, Kasparov pioneered the concept of man-plus-machine matches, in which AI augments human chess players rather than competes against them.1997年,沃森电脑的前辈、IBM公司的深蓝电脑在一场著名的人机大赛中击败了当时的国际象棋大师加里·卡斯帕罗夫(Garry Kasparov)。在电脑又赢了几场比赛之后,人们基本上失去了对这类比赛的兴趣。你可能会认为故事到此就结束了,但卡斯帕罗夫意识到,如果他也能像深蓝一样立即访问包括以前所有棋局棋路变化在内的巨型数据库的话,他在对弈中能表现得更好。如果这一数据库工具对于人工智能设备来说是公平的话,为什么人类不能使用它呢?为了探究这一想法,卡斯帕罗夫率先提出了“人加机器”(man-plus-machine)比赛的概念,即用人工智能增强国际象棋选手水平,而非让人与机器之间对抗。Now called freestyle chess matches, these are like mixed martial arts fights, where players use whatever combat techniques they want. You can play as your unassisted human self, or you can act as the hand for your supersmart chess computer, merely moving its board pieces, or you can play as a “centaur,” which is the human/AI cyborg that Kasparov advocated. A centaur player will listen to the moves whispered by the AI but will occasionally override them—much the way we use GPS navigation in our cars. In the championship Freestyle Battle in 2014, open to all modes of players, pure chess AI engines won 42 games, but centaurs won 53 games. Today the best chess player alive is a centaur: Intagrand, a team of humans and several different chess programs.这种比赛如今被称为自由式国际象棋比赛,它有点儿像混合武术对抗赛,选手们可以使用任何他们想要用的作战技巧。你可以单打独斗;也可以接受你那装有超级聪明的国际象棋软件的电脑给出的帮助,你要做的仅仅是按照它的建议来移动棋子;或者你可以当一个卡斯帕罗夫所提倡的那种“半人半机”的选手。半人半机选手会听取人工智能设备在其耳边提出的棋路建议,但是也间或不会采用这些建议——颇似我们开车时候用的GPS导航一般。在接受任何模式选手参赛的2014年自由式国际象棋对抗锦标赛上,纯人工智能的国际象棋引擎赢得了42场比赛,而半人半机选手则赢得了53场。当今世上最优秀的国际象棋选手就是半人半机选手Intagrand,它是一个由多人以及数个不同国际象棋程序所组成的小组。But here#39;s the even more surprising part: The advent of AI didn#39;t diminish the performance of purely human chess players. Quite the opposite. Cheap, supersmart chess programs inspired more people than ever to play chess, at more tournaments than ever, and the players got better than ever. There are more than twice as many grand masters now as there were when Deep Blue first beat Kasparov. The top-ranked human chess player today, Magnus Carlsen, trained with AIs and has been deemed the most computer-like of all human chess players. He also has the highest human grand master rating of all time.但最令人惊讶的是:人工智能的出现并未让纯人类的国际象棋棋手的水平下降。恰恰相反,廉价、超级智能的国际象棋软件吸引了更多人来下国际象棋,比赛比以前增多了,棋手的水平也比以前上升了。现在的国际象棋大师(译者注:国际象棋界的一种等级)人数是深蓝战胜卡斯帕罗夫那时候的两倍多。现在的排名第一的人类国际象棋棋手马格努斯·卡尔森(Magnus Carlsen)就曾接受人工智能的训练,他被认为是所有人类国际象棋棋手中最接近电脑的棋手,同时也是有史以来积分最高的人类国际象棋大师。If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers. Most of the commercial work completed by AI will be done by special-purpose, narrowly focused software brains that can, for example, translate any language into any other language, but do little else. Drive a car, but not converse. Or recall every pixel of every on YouTube but not anticipate your work routines. In the next 10 years, 99 percent of the artificial intelligence that you will interact with, directly or indirectly, will be nerdily autistic, supersmart specialists.如果人工智能能帮助人类成为更优秀的国际象棋棋手,那么它也能帮助我们成为更为优秀的飞行员、医生、法官以及教师。大多数由人工智能完成的商业工作都将是有专门目的的工作,严格限制在智能软件能做到的工作之内,比如,(人工智能产品)把某种语言翻译成另一种语言,但却不能翻译成第三种语言。再比如,它们可以开车,但却不能与人交谈。或者是能回忆起YouTube上每个视频的每个像素,却无法预测你的日常工作。在未来十年,你与之直接或者间接互动的人工智能产品,有99%都将是高度专一、极为聪明的“专家”。In fact, this won#39;t really be intelligence, at least not as we#39;ve come to think of it. Indeed, intelligence may be a liability—especially if by “intelligence” we mean our peculiar self-awareness, all our frantic loops of introspection and messy currents of self-consciousness. We want our self-driving car to be inhumanly focused on the road, not obsessing over an argument it had with the garage. The synthetic Dr. Watson at our hospital should be maniacal in its work, never wondering whether it should have majored in English instead. As AIs develop, we might have to engineer ways to prevent consciousness in them—and our most premium AI services will likely be advertised as consciousness-free.实际上,这并非真正的智能,至少不是我们细细想来的那种智能。的确,智能可能是一种倾向——尤其是如果我们眼中的智能意味着我们那特有的自我意识、一切我们所有的那种狂乱的自省循环以及凌乱的自我意识流的话。我们希望无人驾驶汽车能一心一意在路上行驶,而不是纠结于之前和车库的争吵。医院中的综合医生“沃森”能专心工作,不要去想自己是不是应该专攻英语。随着人工智能的发展,我们可能要设计出一些阻止它们拥有意识的方式——我们所宣称的最优质的人工智能务将是无意识务。What we want instead of intelligence is artificial smartness. Unlike general intelligence, smartness is focused, measurable, specific. It also can think in ways completely different from human cognition. A cute example of this nonhuman thinking is a cool stunt that was performed at the South by Southwest festival in Austin, Texas, in March of this year. IBM researchers overlaid Watson with a culinary database comprising online recipes, USDA nutritional facts, and flavor research on what makes compounds taste pleasant. From this pile of data, Watson dreamed up novel dishes based on flavor profiles and patterns from existing dishes, and willing human chefs cooked them. One crowd favorite generated from Watson#39;s mind was a tasty version of fish and chips using ceviche and fried plantains. For lunch at the IBM labs in Yorktown Heights I slurped down that one and another tasty Watson invention: Swiss/Thai asparagus quiche. Not bad! It#39;s unlikely that either one would ever have occurred to humans.我们想要的不是智能,而是人工智慧。与一般的智能不同,智慧(产品)具有专心、可衡量、种类特定的特点。它也能够以完全异于人类认知的方式来思考。这儿有一个关于非人类思考的一个很好的例子,今年三月在德克萨斯州奥斯汀举行的西南偏南音乐节(South by Southwest festival)上,沃森电脑就上演了一幕厉害的绝技:IBM的研究员给沃森添加了由在线菜谱、美国农业部(USDA)出具的营养表以及让饭菜更美味的味道研究报告组成的数据库。凭借这些数据,沃森依靠味道配置资料和现有菜色模型创造出了新式的菜肴。其中一款由沃森创造出的受人追捧的菜肴是美味版本的“炸鱼和炸薯条”(fish and chips),它是用酸橘汁腌鱼和油炸芭蕉制成。在约克城高地的IBM实验室里,我享用了这道菜,也吃了另一款由沃森创造出的美味菜肴:瑞士/泰式芦笋乳蛋饼。味道挺不错!Nonhuman intelligence is not a bug, it#39;s a feature. The chief virtue of AIs will be their alien intelligence. An AI will think about food differently than any chef, allowing us to think about food differently. Or to think about manufacturing materials differently. Or clothes. Or financial derivatives. Or any branch of science and art. The alienness of artificial intelligence will become more valuable to us than its speed or power.非人类的智能不是错误,而是一种特征。人工智能的主要优点就是它们的“相异智能”(alien intelligence)。一种人工智能产品在思考食物方面与任何的大厨都不相同,这也能让我们以不同的方式看待食物,或者是以不同的方式来考虑制造物料、衣、金融衍生工具或是任意门类的科学和艺术。相较于人工智能的速度或者力量来说,它的相异性对我们更有价值。As it does, it will help us better understand what we mean by intelligence in the first place. In the past, we would have said only a superintelligent AI could drive a car, or beat a human at Jeopardy! or chess. But once AI did each of those things, we considered that achievement obviously mechanical and hardly worth the label of true intelligence. Every success in AI redefines it.实际上,人工智能将帮助我们更好地理解我们起初所说的智能的意思。过去,我们可能会说只有那种超级聪明的人工智能产品才能开车,或是在“危险边缘”节目以及国际象棋大赛中战胜人类。而一旦人工智能做到了那些事情,我们就会觉得这些成就明显机械又刻板,并不能够被称为真正意义上的智能。人工智能的每次成功,都是在重新定义自己。But we haven#39;t just been redefining what we mean by AI—we#39;ve been redefining what it means to be human. Over the past 60 years, as mechanical processes have replicated behaviors and talents we thought were unique to humans, we#39;ve had to change our minds about what sets us apart. As we invent more species of AI, we will be forced to surrender more of what is supposedly unique about humans. We#39;ll spend the next decade—indeed, perhaps the next century—in a permanent identity crisis, constantly asking ourselves what humans are for. In the grandest irony of all, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science—although all those will happen. The greatest benefit of the arrival of artificial intelligence is that AIs will help define humanity. We need AIs to tell us who we are.但我们不仅仅是在一直重新定义人工智能的意义——也是在重新定义人类的意义。过去60年间,机械加工复制了我们曾认为是人类所独有的行为和才能,我们不得不改变关于人机之间区别的观点。随着我们发明出越来越多种类的人工智能产品,我们将不得不放弃更多被视为人类所独有能力的观点。在接下来的十年里——甚至,在接下来的一个世纪里——我们将处于一场旷日持久的身份危机(identity crisis)中,并不断扪心自问人类的意义。在这之中最为讽刺的是,我们每日接触的实用性人工智能产品所带来的最大益处,不在于提高产能、扩充经济或是带来一种新的科研方式——尽管这些都会发生。人工智能的最大益处在于,它将帮助我们定义人类。我们需要人工智能来告诉我们,我们究竟是谁。 /201411/340814重庆妇科医院治疗不孕不育多少钱

彭水苗族土家族自治县产检哪家医院最好的It#39;s been more than a year and a half since I left Bessemer Venture Partners to join Pinterest. Since then, I#39;ve taken quite a few meetings and phone calls from junior VCs or MBAs asking about my transition from VC to operating. By far, the most common question I get from this bunch is something along the lines of, ;Did you learn anything actually useful in VC?;大约一年半前,我从柏尚风险投资公司(Bessemer Venture Partners)辞职加入了Pinterest。自那以来,我遇到过、也接到过一些年轻风投人或MBA们的电话,询问我是从风投转型到营运的心得。迄今为止,我遇到过的最常见的问题大概就是“你从风投行业学到了什么真正有用的东西?”Yes.是的,我们今天就来聊聊这个话题。1. I learned how to ask the right questions. Anyone can ask questions. But learning how to ask the right questions -- to use questions as a mechanism to uncover the hidden truth in a company#39;s business model, or the tradeoffs in an engineer#39;s architecture, is something that comes with training. VCs spend a huge amount of their time asking questions, and thus learn the craft of asking the right ones. This skill has been enormously valuable to me as I transitioned to Pinterest.1.我学会了提问。任何人都可以提问。但如何提问,并藉此发现一家公司商业模式的真相或者一位工程师架构的妥协,是需要一些训练的。风投资本家花大量的时间提问,因此懂得如何问正确的问题。我转型至Pinterest时,这项技能对我来说极其重要。2. I learned how to people. In my first performance review at Bessemer, people judgment was one of my weaknesses. I#39;d now say it#39;s one of my strengths. As a VC, you#39;re constantly meeting founders and building your pattern recognition for ing people. This skillset is particularly useful when you#39;re in a business or corporate development role but, as with asking the right questions, it#39;s one of those horizontal skills that will serve you anywhere.2.我学会了识人。我在柏尚做第一次绩效评估时,识人是我的一个弱点。现在,我可以说这是我的强项。风投资本家需要经常与创业者会面,需要建立自己的识人模式。处在商业或企业拓展职位时,这项技能特别有用。但正如提问一样,这类横向技能不管走到哪里都能用得上。3. I learned how to learn. In VC, you#39;re constantly ramping up in a new area. Each company you evaluate comes with its own ecosystem that needs to be understood. Similarly, trends in the tech ecosystem turn over so quickly that, if you ever stop adapting and learning, you#39;ll quickly become a dinosaur and won#39;t know a Snapchat when you meet one. That drive to constantly learn will help you adapt to new environments and challenges.3.我学会了学习。风投行业的人经常要硬着头皮了解新的领域。你评估的每一家公司都有独特的生态系统,需要你去了解。类似的,科技生态系统的趋势变化这么快,一旦停止适应和学习,人很快就会变成老古董,连什么是Snapchat都不知道。这种持续学习的动力能帮助你适应新的环境和挑战。There#39;s a flipside to these three though:但这三点也存在另一面:1. In startups, you#39;ve got to answer the questions. One thing I learned early on at Pinterest is that my muscle for asking questions was a lot stronger than my muscle for answering them. As with asking questions, there#39;s an art to answering questions well. It#39;s been good to exercise this skill.1.在初创公司中,你必须回答问题。我在Pinterest很早就了解到的一件事是,我的提问能力远超回答能力。和提问一样,回答也是一门艺术。锻炼这项技能很重要。2. I didn#39;t learn how to an organization. VC firms tend to be smaller partnerships. Although Bessemer was about 45 people when I left, I was never in an office with more than 10 people. As Pinterest has grown from 30-odd people when I joined to more than 200, I#39;ve had to learn how to navigate a company. People who have come from larger companies definitely have a leg up in this regard.2.我不知道如何解读一个组织。风投公司往往是规模较小的合伙企业。虽然在我离开时,柏尚已经有了大约45人,但每次我在办公室里时,办公室里从没超过10个人。随着Pinterest从我加入时的30多人发展到了如今的200多人,我必须学会如何理清公司的结构。来自大公司的人们在这方面当然比我强很多。3. I#39;m not specialized. VCs rarely specialize. Sure -- I knew the e-commerce ecosystem cold, met with countless consumer companies, and quite a few adtech companies, but that doesn#39;t compare to spending several years working at Google. But you#39;ve got to start somewhere ...3.我没有专长的领域。风投资本家很少专长某一领域。当然 -- 我很了解电子商务生态系统,我见识过大量的消费公司和广告科技公司,但这都不比不上在谷歌(Google)工作几年。但凡事总有个开始...Good luck!祝你好运! /201312/268293 Two years ago, Stephen Elop likened Nokia to a burning oil platform, referring to the intense competition from Apple#39;s iPhone and Google#39;s Android operating system. There is little in the Finnish handset maker#39;s full-year earnings to suggest the CEO has since located the fire extinguisher.两年前,埃洛普(Stephen Elop)将诺基亚(Nokia)比作是正在燃烧的石油平台,意指诺基亚面临苹果(Apple)iPhone和谷歌(Google)安卓(Android)操作系统的激烈竞争。而从这家芬兰手机制造商的全年收益来看,其首席执行长埃洛普在打了那个比方之后没能找到相应的灭火器。One of Nokia#39;s self-inflicted problems has been that it dumped its in-house operating system, Symbian, in favor of Microsoft#39;s Windows Phone in February 2011, but took 10 months to get a product running on the platform, the Lumia, to market. In a slow-moving business, it might have gotten away with it. But Samsung Electronics was just powering up. Nokia#39;s share of global smartphone volumes touched 4% in the third quarter of last year, down from 23% in the first quarter of 2011, notes Strategy Analytics. Samsung#39;s share has almost tripled to 34%.其实诺基亚曾几次玩火自焚,其中一次是2011年2月放弃了内部操作系统塞班(Symbian),改用微软(Microsoft) Windows Phone系统,而将运行于Windows Phone的产品Lumia推向市场就花了10个月的时间。本来,若是在一个进程缓慢的行业里,诺基亚可能会借助Lumia侥幸取得成功。但偏偏三星电子(Samsung Electronics)那时又卯足了劲。据研究公司Strategy Analytics统计,诺基亚智能手机去年第三季度的全球市场份额为4%,远低于2011年第一季度的23%。三星电子智能手机的市场份额几乎增加了两倍,至34%。Nokia is optimistic that Lumia sales will take off. It says that features like its camera and maps set the latest versions of the high-end smartphone launched last November apart and that wireless operators are supportive of a third ecosystem. But despite a fourth-quarter improvement in gross margins and lower operating expenses, Nokia still needs to ship double the number of phones just to break even on smartphones, estimates Barclays.诺基亚对于Lumia销售额将迅速增长表示乐观。该公司说,这款去年11月发布的高端智能手机的最新版与众不同的是摄像头和地图等功能,而且无线运营商持第三个生态系统。但巴克莱(Barclays)估计,尽管诺基亚去年第四季度的毛利增加且营业费用减少,但其智能手机业务要想达到收平衡,诺基亚仍需将手机出货量增加一倍。There is also a risk of the fire sping. Nokia#39;s regular mobile-phone business, which accounts for more than half of group operating profit, is under pressure from cheaper upstarts based on Android. Sales fell by a fifth last year, while the average selling price of its phones dropped 11% to 31 (.30). Last year#39;s results were also flattered by a turnaround at its telecom-equipment joint venture Nokia Siemens Networks. But NSN#39;s operating-profit margins are forecast to drop back to around 3% in the first quarter, from 14.4% in the fourth quarter.此外,这股正在燃烧的火势还有蔓延的危险。诺基亚常规手机业务目前也感受到了那些价格更便宜的基于安卓的后起之秀带来的压力。诺基亚的营业利润有超过一半来自常规手机业务。去年,诺基亚常规手机的销量减少了五分之一,而平均售价下降了11%,至31欧元(41.30美元)。去年的业绩其实还受到电信设备合资企业诺基亚西门子通信公司(Nokia Siemens Networks)扭亏为盈的“粉饰”。但预计今年第一季度诺基亚西门子通信的营业利润率将从去年第四季度的14.4%重新降至3%左右。Nokia appears to be through the worst of its cash burn, but it may not be over. Net cash on its balance sheet fell by 1.2 billion to 4.4 billion last year. Nokia has proposed not to pay a dividend this year. But it also expects another 750 million in restructuring related cash outflows, the same as last year#39;s dividend payout.诺基亚似乎已经挺过了最烧钱的阶段,但整体烧钱趋势可能还没结束。该公司资产负债表上的现金净额去年减少了12亿欧元,至44亿欧元。诺基亚已提议今年不付股息。但它预计在重组现金流出方面将再花费7.5亿欧元,与去年股息付额相当。The shares have more than halved since Mr. Elop joined in September 2010, and the company now has an enterprise value equivalent to 25 times forecast 2014 operating profit, based on Barclays estimates. Investors may be overestimating Mr. Elop#39;s fire-fighting skills.自2010年9月埃洛普加入诺基亚以来,该公司股票已下跌了一半有余。据巴克莱估计,基于2014年预期营业利润计算的诺基亚预期市盈率为25倍。投资者可能高估了埃洛普的救火能力。 /201301/222682重庆市爱德华医院打掉孩子多少钱重庆市爱德华医院男科

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