Author Archives: Zewen Liang

About Zewen Liang

http://www.LiangBrian.com

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..

或许我们和熔岩灯没有两样

我家有盏“熔岩灯”,通电后里头的蜡滴因为底座的热量而随机浮沉和组合分离。某老黑指出这灯是70年代的美国old fashion,后来这装饰就一直在那显得有点尬。。

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今天得知有个公司Cloudflare为了产生随机密码,弄了100盏熔岩灯并装了摄像头拍照,通过对这些灯的形状组合所产生的像素来产生随机密钥。他们认为任何(人为设计的)程序所产生的“随机密码”并不随机,所以借助相对分离的“外界”来产生随机。
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M理论说有我们所在的宇宙并不特别,因为大约有10的500次方个平衡宇宙,各自被不一样的物理定律所支配。人类或许也并不比飞禽走兽花花草草更特别,因为如果这世界没有真正的随机可言,人类的行为和引以为豪的“意识”,都被某个方程所预测到了。
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或许我们人类和熔岩灯没有两样。

Getting uncomfortably excited

It is not until today that I realized we Chatbase, can be as formidable as Gmail or Slack in its early days, and can eventually end up with IPO exit if we are a real standalone company.

After almost two years of stumbling our way off finding product-market fit, we have now come to find a unique way of addressing some serious big problems that we wouldn’t imagine before. There were some gloomy days and self-doubts along the way, but now I am very confident of where we are going. Super excited about what’s ahead for us.

2B和2C的产品没有区别

某程度上,做2B和2C的产品没有根本上的区别。很多时候对公司来说,只是盈利模式上的一种选择。

假设你们公司在某方面有突破的自然语义分析的技术,你们可以选择去开发一个“Siri”去接触终端用户,也可以把同样的技术打包成API或某种企业服务再卖给正在开发各式各样的“Siri”的第三方企业。可能由于市场风向的飘忽,你们公司做着做着“Siri”发现一直摸索不出盈利模式,为了趋吉避凶和提早养活员工,你们瞄向了企业市场。后端不变,前端转个急弯,换个面目又成一条活路,而且活的比预想中好很多。

这样的例子并不鲜见。苹果电脑在80年代时只想做企业市场取代IBM但现在却是消费者市场王者,Dropbox多年来在消费者和企业市场中摇摆不定自我消耗,Gmail脱下数年的消费者外衣摇身一变成为企业主打,Google Glass完全落败于消费者市场后在企业市场重拾点自信。

回到Siri的例子。你或许说,要开发Siri的技术和条件有很多,自然语义只是其中一个,不足以搭建一个完整的能面市的Siri。这种想法在如今蓬勃发展的企业市场或开发者市场里也越来越落伍了。如今各类产品的身上都插着数不清的api和各种第三方服务的输液管。你的产品若有一个特长,已然是建立一个新的商品的充分条件。

当然了当你拉近缩小从其他方面看,做Enterprise和Consumer是有各样不同 – 越靠近水面的部分(泛泛意义上的前端),差别越大。

这同时也说明了为什么很多做后端的人不会太care做2B或2C。因为他们站在锚的这一端,而舞动得热烈的是那一端水面上的船。