Engineered like a computer, but works like a pencil

这次iPad Pro的最重要的创新应该是性能,其次的才是(依重要性排):USB-C插口、pencil地位的增强、全面屏和外观结构、FaceID、其它。

像苹果说的,iPad Pro用的是“比市面上92%的笔记本电脑还要高性能”的A12 Biontic chip,视觉计算比专门打游戏的Xbox One还好。

这是苹果第一次seriously把iPad看作为取代现在笔记本电脑的下一代computing platform的开端。像Luke说的”Engineered like a computer, but works like a pencil”(顶端的计算能力,以人为本的设计)应该就是他们对下一代计算平台的远景。

明天就去剁手😛

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Rams documentary screening

482D7C80-6414-4852-8568-10F8EF01AE0E2A7FD1CC-D5EE-46BD-8A25-268A302975BBDieter Rams纪录片的三藩市试映。电影票数个月前就售罄。

这86岁的德国老头住在一个50年前装修好的家里,被他50年前设计的物件包围着。而这些物件和他本人50年来的性格一模一样:长存、真诚、低调、又顽固。

或许他不知道他的物件和思想启发过多少个追梦少年,从50年前直到现在。

It is an end of an era。

YC的最好时机,也是陆奇的最好时机

陆奇加入YC的消息让我感到振奋。这种振奋,或许源于我私以为的“情怀/理想”的最终胜利的自私感受。这里贴一下刚在知乎上回答的如何看待陆奇担任 Y Combinator 中国创始人?YC 进中国将产生哪些影响?

1. YC在硅谷圈乃至美国科技圈的影响力很大

YC的孵化机制以及运作方式,是如今国内外的各式创业孵化器的模范对象。

YC孵化过的一千多个公司所形成的网络效应是巨大的。这些公司会互相帮助(像Stripe会给新加入的公司提供支付方案等等),即使大部分已经脱离了YC的公司,这些“毕业生”的网络也会维持下去。

对于被誉为“创业教父”的YC创始人Paul Graham,如有机会进入旧金山湾区的白人创业圈子的话,会很明显的体会到其“教父”地位 – 无论是Paul的创业文章(像how to startup a startup等十几年前写的文章现时还不断传颂),或是他的Hacker News,影响了西方数代创业者。下图为我的办公室(谷歌内部孵化器Area 120)以Paul Graham命名的打印机。其“先驱”地位略见一斑。

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2. YC的新掌托Sam Altman说要每年要孵化10000个公司 – 对于有这样的远景,陆奇难以不心动

Sam是个雄心壮志的鬼才,一年多前他来过我们的孵化器做过分享,被问到接手YC后他的下一个目标是什么。他说在孵化其他创业公司的同时,YC自己也是个要寻找下一步发展的别无两样的创业公司,下一年的目标是打算接收1万个公司。后来也看到类似报道了。

 

3. 世界正在饰演“去中心化”,而中国是第一个“去以硅谷为中心”的场地。

Peter Thiel说到2005年在斯坦福演讲,当时有台下学生问“下一个像Google这样的公司会在哪里?”。Peter半开玩笑的说“下一个这样的公司会诞生在离这个教室的5英内范围之内”(后来证明对了,这个公司就是后来的Facebook)。

Peter这么说是那时候他认为世界最前沿的发展都非常的集中,无论是资源、现金,或是人才。而这个中心就是硅谷。较为无稽的是,即使这个创业公司所做的服务是为了”解放地理位置的束缚,让工作可以随时随地跨地域进行“,这个团队的创业者都会从特地从美国东海岸飞去硅谷去找投资或者创办公司(这是A16Z的Mark Andreessen举的例子)。

但要是换了今天再回答这个问题,Peter会说“下一个这样的公司会在50英里之外”。得益于全球各种去中心化事件的征兆,以及区块链等技术的发展。而硅谷力量最终实现”去中心化”这个宏伟目标的第一步,中国无疑是最佳场所。

 

4. 去YC中国,很好地结合了陆奇的”西方“和”华人“的双重属性。

有些在美工作多年的人或许有这样的感受:多年在西方的”习得“ – 无论是知识、西方文化、生活习惯、或者语言,会有食之无味(因为有所谓的华人天花板)又弃之可惜(不舍完全放弃这些”优势“)的感受。特别是陆奇这种在北美华人中的凤毛麟角。理解了他要同时发挥”能玩转西方”和“能背靠中国”的协同优势,就很容易明白为什么陆奇会选择YC中国,而不是纯玩中国规则的百度。

Information diet, filtration, and inequality

One question people often ask is where does someone gets inspiration / information / news. Maybe an equally important question is how to filter the overwhelming information and get most out of it, especially in a world where you can spend all day long just following the ins and outs chasing what’s happening realtime in the political area, sports space, and the business world.

When asked about the daily information diet, Mark Andreessen mentioned he himself is running an experiment of consuming information in a polarized way – completely stop reading newspapers, magazines, and basically anything with time horizon that’s, for example, between 5 mins an 5 years. So he reads social medias (a very short timespan info) and books (written 50 or 100 years ago).

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The claim of “not reading newspapers and magazines” is funny though because they have a great potions overlapped – You will inevitably consume a lot of news when you are on social media from people you follow. So you are not actually missing out too much if not seeing the headlines of newspapers.

The real difference is the 2000 inbound startups Mark Andreessen reviewed each year from some of the by definition the smartest people from the domains they operate in. This is the most inclusive information that only he and his handful co-workers have access to. And it is hard to pick up a magazine to find similar interesting topics because things will only come out months or years after.

While the invention of internet breakthroughs the isolations and make information available and accessible to everyone, people’s ability of filtering those information isn’t equal. Those who come from upper classes or having different social capitals essentially still have a better filtration system that will keep them better off. The other side of the crowd might still be suffering by circling around low quality information and knowledge resulted from bad filtration capability.

谷歌的盈利模式和AdWords

业界仍有某部分人有这样的小白认识:谷歌的大部分产品只负责烧钱,而AdWords*这个产品挣了谷歌接近90%以上的钱。更可怕的是这种认识,延伸到不少谷歌的同事。甚至是我一些AdWords的同事,也私以为然。

广告是谷歌最主要的盈利模式是没错(其中搜索广告最大),年赚千亿美元。但谷歌的大量“不赚钱”的产品像安卓、地图、Chrome、Gmail、YouTube等等所建立的交叉生态及产品价值所吸引的大量用户以及随之而来的商家,使广告盈利变成了可能。而AdWords是这一连串funnel里面的最后一步,一个承载着精妙广告机制设计的vehicle,但并不该承担所有或唯一的归因。

若分拆计算的话,Google其他各种“不赚钱”的产品都应该各自有相当可观的imputed valuation。而AdWords自身并不比某些产品的价值高。

*注:AdWords最近改名为Google Ads

Ways to think about machine learning by Benedict Evan

Benedict Evan’s article about Ways to Think About Machine Learning is so spot on that I need to quote:

What, then, are the washing machines of machine learning, for real companies? I think there are two sets of tools for thinking about this. The first is to think in terms of a procession of types of data and types of question:

  1. Machine learning may well deliver better results for questions you’re already asking about data you already have, simply as an analytic or optimization technique. For example, our portfolio company Instacart built a system to optimize the routing of its personal shoppers through grocery stores that delivered a 50% improvement (this was built by just three engineers, using Google’s open-source tools Keras and Tensorflow).
  2. Machine learning lets you ask new questions of the data you already have. For example, a lawyer doing discovery might search for ‘angry’ emails, or ‘anxious’ or anomalous threads or clusters of documents, as well as doing keyword searches,
  3. Third, machine learning opens up new data types to analysis – computers could not really read audio, images or video before and now, increasingly, that will be possible.

Five years ago, if you gave a computer a pile of photos, it couldn’t do much more than sort them by size. A ten year old could sort them into men and women, a fifteen year old into cool and uncool and an intern could say ‘this one’s really interesting’. Today, with ML, the computer will match the ten year old and perhaps the fifteen year old. It might never get to the intern. But what would you do if you had a million fifteen year olds to look at your data? What calls would you listen to, what images would you look at, and what file transfers or credit card payments would you inspect?

Indeed, I think one could propose a whole list of unhelpful ways of talking about current developments in machine learning. For example:

  • Data is the new oil
  • Google and China (or Facebook, or Amazon, or BAT) have all the data
  • AI will take all the jobs
  • And, of course, saying AI itself.

More useful things to talk about, perhaps, might be:

  • Automation
  • Enabling technology layers
  • Relational databases.

Google ‘has all the data’, or that IBM has an actual thing called ‘Watson’. Really, this is always the mistake in looking at automation: with each wave of automation, we imagine we’re creating something anthropomorphic or something with general intelligence.

By the 1990s, pretty much all enterprise software was a relational database – PeopleSoft and CRM and SuccessFactors and dozens more all ran on relational databases. No-one looked at SuccessFactors or Salesforce and said “that will never work because Oracle has all the database” – rather, this technology became an enabling layer that was part of everything.

So, this is a good grounding way to think about ML today – it’s a step change in what we can do with computers, and that will be part of many different products for many different companies. Eventually, pretty much everything will have ML somewhere inside and no-one will care.

 

 

 

 

Xiaomi opens for trading tomorrow

Year’s largest IPO – Xiaomi, starts trading publicly tomorrow. Though its valuation has been “downgraded” to $48B from what Lei Jun claimed to be $200B, I doubt how far this copycat can go, with its confusing business modal – sales of mobile phone holds 80% of company revenue yet counts only 2.7% profit.

I like to make jokes about Xiaomi’s micro-imitation of Apple inside out – from product design to human behaviors to promotion techniques. For example, Lei Jun’s choreography and speech given on stage, Xiaomi router cloning Apple’s Magic Trackpad, invitation and promotions design, and even how a hardware should be grasped or arranged in a photography..