Discussions of income inequality typically focus on how information technology raises the return to skilled labor, or on the rise of global trade, or perhaps on the way that politics skews power toward the rich and well-connected. But there’s another fundamental driver of income inequality: the improved measurement of worker performance. As we get better at measuring who produces what, the pay gap between those who make more and those who make less grows.
Many computer systems offer autocomplete: the ability to guess what you’re about to type based on the first few letters you’ve typed. For instance, if you open up IPython and create a string variable, you can do this:
In : s = 'foo'
In : s.s<tab>
s.split s.splitlines s.startswith s.strip s.swapcase
IF YOU WANT to drop some real DNA editing knowledge—like, I don’t know, at a party!—here’s a tip. Instead of calling the much hyped precise genome-editing tool CRISPR, call it CRISPR/Cas9. CRISPR, you see, just refers to stretches of repeating DNA that sit near the gene for Cas9, the actualprotein that does the DNA editing.
Well, at least for now. Today, gene-editing scientists dropped some curious news: They’ve found a CRISPR system involving a different protein that also edits human DNA, and, in some cases, it may work even better than Cas9.
The Unicorn club has a new member, and this time it hails from Europe.The Paris-based long-distance ride sharing company BlaBlaCar has raised $200 million in its latest Series D round, bringing its total funding to date to over $300 million. With a fresh $1.6 billion (1.4 billion euro) valuation, the nine-year-old startup has joined the ever-expanding list of billion-dollar startups and stands out as one of the most well-funded young tech companies in Europe.While often compared to Uber due of its ride-sharing concept and commission-based model, BlaBlaCar focuses on a different market. Trips on its platform average 220 miles in distance, and therefore the price per mile is about 20 to 30 times lower than a regular Uber ride, according to CEO Frédéric Mazzella. It competes more directly with regional bus lines and trains rather than taxis or other intra-city car-sharing services such as Uber, Lyft, and the like. One other key difference from Uber is that BlaBlaCar drivers don’t make a profit beyond sharing the costs, which has enabled the company to expand without running into regulatory issues in most markets.
At Google, we think that internet users’ time is valuable, and that they shouldn’t have to wait long for a web page to load. Because fast is better than slow, two years ago we published the Zopfli compression algorithm. This received such positive feedback in the industry that it has been integrated into many compression solutions, ranging from PNG optimizers to preprocessing web content. Based on its use and other modern compression needs, such as web font compression, today we are excited to announce that we have developed and open sourced a new algorithm, the Brotli compression algorithm.
While Zopfli is Deflate-compatible, Brotli is a whole new data format. This new format allows us to get 20–26% higher compression ratios over Zopfli. In our study ‘Comparison of Brotli, Deflate, Zopfli, LZMA, LZHAM and Bzip2 Compression Algorithms’ we show that Brotli is roughly as fast as zlib’s Deflate implementation. At the same time, it compresses slightly more densely than LZMA and bzip2 on the Canterbury corpus. The higher data density is achieved by a 2nd order context modeling, re-use of entropy codes, larger memory window of past data and joint distribution codes. Just like Zopfli, the new algorithm is named after Swiss bakery products. Brötli means ‘small bread’ in Swiss German.
Everyone wants it, lots of people try it, yet doing it is mostly painful and irritating. I’m speaking about free software aka open source. Today I’m going to summarize 25 years of experience in ten management-proof bullet points.
1. People Before Code
This is the Golden Rule, taught to me by Isabel Drost-Fromm. Build community, not software. Without community your code will solve the wrong problems. It will be abandoned, ignored, and will die. Collect people and give them space to work together. Give them good challenges. Stop writing code yourself.
One of the great things about R is the thousands of packages users have written to solve specific problems in various disciplines — analyzing everything from weather or financial data to the human genome — not to mention analyzing computer security-breach data.
Some tasks are common to almost all users, though, regardless of subject area: data import, data wrangling and data visualization. The table below show my favorite go-to packages for one of these three tasks (plus a few miscellaneous ones tossed in). The package names in the table are clickable if you want more information. To find out more about a package once you’ve installed it, type
help(package = "packagename")in your R console (of course substituting the actual package name ).
I’m the first to admit that the Word Judge is booooring application. Checking if the word can be used in a word game? Meh. From a programmer perspective however, there is one very interesting problem to solve – how to compress a large dictionary to reduce the size of the application package and at the same time be able to query this dictionary without using excessive amount of memory and CPU power?
First, let’s settle on what is a “large dictionary”. One of the languages supported by Word Judge is Polish, for which the valid word list has over 2 million entries and takes about 36MB after unpacking. Do we need to compress this data at all? Probably not. If you consider the average hardware spec and modern network speed, the 36MB is not much, but we can do so much better. Besides, it’s fun!