Source: Multi Scale Modeling of Chromatin and Nucleosomes

Science has never been more powerful, but it is under attack.

If this place has done its job—and I suspect it has—you’re all scientists now. Sorry, English and history graduates, even you are, too. Science is not a major or a career. It is a commitment to a systematic way of thinking, an allegiance to a way of building knowledge and explaining the universe through testing and factual observation. The thing is, that isn’t a normal way of thinking. It is unnatural and counterintuitive. It has to be learned. Scientific explanation stands in contrast to the wisdom of divinity and experience and common sense. Common sense once told us that the sun moves across the sky and that being out in the cold produced colds. But a scientific mind recognized that these intuitions were only hypotheses. They had to be tested.

When I came to college from my Ohio home town, the most intellectually unnerving thing I discovered was how wrong many of my assumptions were about how the world works—whether the natural or the human-made world. I looked to my professors and fellow-students to supply my replacement ideas. Then I returned home with some of those ideas and told my parents everything they’d got wrong (which they just loved). But, even then, I was just replacing one set of received beliefs for another. It took me a long time to recognize the particular mind-set that scientists have. The great physicist Edwin Hubble, speaking at Caltech’s commencement in 1938, said a scientist has “a healthy skepticism, suspended judgement, and disciplined imagination”—not only about other people’s ideas but also about his or her own. The scientist has an experimental mind, not a litigious one.

As a student, this seemed to me more than a way of thinking. It was a way of being—a weird way of being. You are supposed to have skepticism and imagination, but not too much. You are supposed to suspend judgment, yet exercise it. Ultimately, you hope to observe the world with an open mind, gathering facts and testing your predictions and expectations against them. Then you make up your mind and either affirm or reject the ideas at hand. But you also hope to accept that nothing is ever completely settled, that all knowledge is just probable knowledge. A contradictory piece of evidence can always emerge. Hubble said it best when he said, “The scientist explains the world by successive approximations.”

Source: The Mistrust of Science – The New Yorker

https://vimeo.com/170189199

“One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in the Urban Challenge, the winning entries went 60 miles under city-like constraints.

Things are clearly progressing rapidly when it comes to machine intelligence. But how did we get here, after not one but multiple “A.I. winters”? What’s the breakthrough? And why is Silicon Valley buzzing about artificial intelligence again?

From types of machine intelligence to a tour of algorithms, a16z Deal and Research team head Frank Chen walks us through the basics (and beyond) of AI and deep learning in this slide presentation.

Source: AI, Deep Learning, and Machine Learning: A Primer – Andreessen Horowitz

Big themes and trends for DevTools in 2016 and beyond.

Google I/O 2016 is a wrap. DevTools had a strong presence at I/O, including a talk by Paul Bakaus, Paul Irish, and Seth Thompson outlining the future of DevTools. Check out the video below or read on to learn more about where DevTools is headed in 2016 and beyond.

Source: DevTools Digest: DevTools in 2016 and Beyond | Web Updates – Google Developers

We paid for the research with taxes, and Internet sharing is easy. What’s the hold-up?

In 1836, Anthony Panizzi, who later became principal librarian of the British Museum, gave evidence before a parliamentary select committee. At that time, he was only first assistant librarian, but even then he had an ambitious vision for what would one day became the British Library. He told the committee:

I want a poor student to have the same means of indulging his learned curiosity, of following his rational pursuits, of consulting the same authorities, of fathoming the most intricate inquiry as the richest man in the kingdom, as far as books go, and I contend that the government is bound to give him the most liberal and unlimited assistance in this respect.

He went some way to achieving that goal of providing general access to human knowledge. In 1856, after 20 years of labour as Keeper of Printed Books, he had helped boost the British Museum’s collection to over half a million books, making it the largest library in the world at the time. But there was a serious problem: to enjoy the benefits of those volumes, visitors needed to go to the British Museum in London.

Imagine, for a moment, if it were possible to provide access not just to those books, but to all knowledge for everyone, everywhere—the ultimate realisation of Panizzi’s dream. In fact, we don’t have to imagine: it is possible today, thanks to the combined technologies of digital texts and the Internet. The former means that we can make as many copies of a work as we want, for vanishingly small cost; the latter provides a way to provide those copies to anyone with an Internet connection. The global rise of low-cost smartphones means that group will soon include even the poorest members of society in every country.

That is to say, we have the technical means to share all knowledge, and yet we are nowhere near providing everyone with the ability to indulge their learned curiosity as Panizzi wanted it.

What’s stopping us? That’s the central question that the “open access” movement has been asking, and trying to answer, for the last two decades. Although tremendous progress has been made, with more knowledge freely available now than ever before, there are signs that open access is at a critical point in its development, which could determine whether it will ever succeed in realising Panizzi’s plan.

Table of Contents

• The arcana of academic publishing
• What about us?
• In the beginning was arXiv
• Scholarly skywriting
• Opening up the Americas
• Public Library of Science
• Open access is born
• CERN’s SCOAP
• PLoS ONE
• Gold open access
• Hybrid problems
• Green open access
• The empire strikes back
• Diamond open access
• From Aaron Swartz…
• …to Sci-Hub

Source: Open access: All human knowledge is there—so why can’t everybody access it? | Ars Technica UK

• Scientific Python Cheatsheet
  • Pure Python
    • Types
    • Lists
    • Dictionaries
    • Strings
    • Operators
    • Control Flow
    • Functions, Classes, Generators, Decorators
  • IPython
    • Python Console
    • Debugger commands
  • NumPy
    • array initialization
    • reading/ writing files
    • array properties and operations
    • indexing
    • boolean arrays
    • elementwise operations and math functions
    • inner / outer products
    • interpolation, integration
    • fft
    • rounding
    • random variables
  • Matplotlib
    • figures and axes
    • figures and axes properties
    • plotting routines

Source: Scientific python cheat sheet by IPGP

The absurdly deep research underlying HBO’s satire.

When Dick Costolo attended the University of Michigan, in the nineteen-eighties, his major was computer science, but he was surprised to find that he also had a knack for improv comedy. After graduation, he moved to Chicago and took classes at the Second City Theatre. Unlike some of his peers there—Steve Carell, Tina Fey, Adam McKay—Costolo was not asked to join the theatre’s house company, and his comedy career dried up. He fell back on his skills as a coder and founded a series of tech startups, one of which was eventually acquired by Google, for a hundred million dollars. In 2010, he became the C.E.O. of Twitter, earning about ten million dollars in his first year. At a charity event, he ran into Steve Carell, and they reminisced about their days as bohemian improvisers. “I’m sorry it didn’t work out for you,” Carell joked.

In June of 2015, with Twitter’s stock price languishing, Costolo announced that he would leave the company. (According to the tech press, the board of directors had forced him out; Costolo maintains that leaving was his idea.) Three days later, HBO aired the second-season finale of its half-hour satire “Silicon Valley.” The season ended on a cliffhanger: the central character, the founder and C.E.O. of a tech company, was fired by his board. Costolo, a fan of the show, found the situation uncannily familiar. “I could relate to every person in that situation—the founder who’s leaving, the C.E.O. who’s coming in, the employees who are watching it happen,” he said.

Around that time, Costolo had breakfast in San Francisco with Kara Swisher, a tech reporter and power broker who has been called “Silicon Valley’s most feared and well-liked journalist.” Conversation turned to “Silicon Valley,” the show. “People in the Valley—at least, the people I know—talk about the show all the time,” Costolo told me. “Most of them love it, oddly. I think there are a lot of people telling themselves, with varying levels of accuracy, ‘They’re satirizing those annoying tech people—not me.’” Swisher, who knows everyone, was in frequent contact with the showrunners, Mike Judge and Alec Berg. “I’ll introduce you,” she told Costolo.

Source: How “Silicon Valley” Nails Silicon Valley – The New Yorker

tl;dr: this explains how Celery obsoletes GIL and coroutines Recently I’ve been rereading the great Unyielding post by Glyph. If you haven’t read it – go read it. I’ll be summing it up below but it’s worth your time to read the original. I’ve been looking at the problem

Source: How Celery fixed Python’s GIL problem