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。
It is an end of an era。
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？
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。
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。
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。
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).
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.
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:
- 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).
- 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,
- 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:
- 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.
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..