I’m the Founder & CEO of Fast Forward Labs, a machine intelligence research company, and the Data Scientist in Residence at Accel Partners. I co-founded HackNY, and I’m a member of NYC Resistor. More about Hilary… I speak to audiences around the world about data, machine learning, artificial intelligence, and how to build real, functional, and robust products. I’m optimistic (because this stuff is
家電からエネルギーまで多角的な事業を掲げるGEを引っ張るイメルトCEO(最高経営責任者)は最近、メディア事業を完全売却したことに加えて、金融事業の分離構想も明かした。 GEを航空エンジンや医療機器といったインフラと各種のエネルギーの会社に転換させる狙いである。さらに春の株主総会では「分析とソフトウエアに大きく投資する」と“分析会社”への転換も宣言している。 イメルトCEOは「話すより聞こう」とか「個人よりチーム」と、わかりやすい言葉で経営方針を示す。インフラ整備のためには、売り込みより相手のニーズを「聞き」、整備から保守まで「チーム」で動くことが大切というわけだ。 そのイメルトCEOが分析とソフトウエアにこだわるのは顧客の問題解決に欠かせないとの認識からだ。エネルギーやインフラは安定運用が欠かせない。「故障する前にそれをデータで予見できるといい」。それがイメルトCEOの考えだ。 すでに昨年
Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus—as long as the right policies and enablers are in place. The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer s
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, Apa
Data Jujitsu: The art of turning data into product Smart data scientists can make big problems small. Having worked in academia, government and industry, I’ve had a unique opportunity to build products in each sector. Much of this product development has been around building data products. Just as methods for general product development have steadily improved, so have the ideas for developing data
The evolution of data products The data that drives products is shifting from overt to covert. In “What is Data Science?,” I started to talk about the nature of data products. Since then, we’ve seen a lot of exciting new products, most of which involve data analysis to an extent that we couldn’t have imagined a few years ago. But that begs some important questions: What happens when data becomes a
The classic book on statistical graphics, charts, tables. Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relat
In this post I lay out a number of tips, tricks and suggestions for working with data, from collection to storage to analysis. The main idea is to help students and other younger analysts and data scientists. However, they apply to all ages and experiences. Indeed, while many of the following ideas may sound obvious and trivial, it is surprising how often I see the counter-examples and anti-patter
How Billions of Trivial Data Points can Lead to Understanding Peter Norvig (Director of Research, Google) presents as part of the UBC Department of Computer Science's Distinguished Lecture Series, September 23, 2010. In decades past, models of human language were wrought from the sweat and pencils of linguists. In the modern day, it is more common to think of language modeling as an exercise
If you asked me to describe the rising philosophy of the day, I’d say it is data-ism. We now have the ability to gather huge amounts of data. This ability seems to carry with it certain cultural assumptions — that everything that can be measured should be measured; that data is a transparent and reliable lens that allows us to filter out emotionalism and ideology; that data will help us do remarka
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