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Aaron Davis

Richard Olsen's Blog › Why everyone should learn to code [eventually] - 0 views

  • The bigger question is what do students need to learn, period.
  • Curriculum is designed to predict need.
  • Authenticate problem solving, ideation and play in our digital world requires the ability to program.
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    An interesting post discussing why everyone should learn to code. More fuel to the fire associated with the great poetry vs. coding debate.
Camilla Elliott

Elon Pew Future of the Internet Survey Report: Experts Predict Internet's Impact by 2025 - 1 views

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    "Internet experts and highly engaged netizens participated in answering an eight-question survey fielded by Elon University and the Pew Internet Project from late November 2013 through early January 2014."
Jenny Grabiec

Tornado Country | Scholastic News Online | Scholastic.com - 0 views

    • Jenny Grabiec
       
      I predict that....
Roland Gesthuizen

http://www.thedailyriff.com/articles/21-things-that-will-become-obsolete-in-education-b... - 5 views

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    I put together my own list of '21 Things That Will Become Obsolete in Education by 2020
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    Interesting list. I wonder what else we could add to this?
Roland Gesthuizen

Computational Model of Peace Predicts Social Violence, Harmony | Wired Science | Wired.com - 3 views

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    A systems model of how ethnic tensions flare into violence has passed a test in Switzerland, where harmony prevails except for one region flagged by the analysis. The model runs census data through an assembly line of high-powered mathematical processes, but at its root is one basic assumption: that community-level violence is primarily a function of geography, modulated by the overlap of political, topographical and ethnic borders.
Aaron Davis

Facebook's war on free will | Technology | The Guardian - 0 views

  • Though Facebook will occasionally talk about the transparency of governments and corporations, what it really wants to advance is the transparency of individuals – or what it has called, at various moments, “radical transparency” or “ultimate transparency”. The theory holds that the sunshine of sharing our intimate details will disinfect the moral mess of our lives. With the looming threat that our embarrassing information will be broadcast, we’ll behave better. And perhaps the ubiquity of incriminating photos and damning revelations will prod us to become more tolerant of one another’s sins. “The days of you having a different image for your work friends or co-workers and for the other people you know are probably coming to an end pretty quickly,” Zuckerberg has said. “Having two identities for yourself is an example of a lack of integrity.”
  • The essence of the algorithm is entirely uncomplicated. The textbooks compare them to recipes – a series of precise steps that can be followed mindlessly. This is different from equations, which have one correct result. Algorithms merely capture the process for solving a problem and say nothing about where those steps ultimately lead.
  • For the first decades of computing, the term “algorithm” wasn’t much mentioned. But as computer science departments began sprouting across campuses in the 60s, the term acquired a new cachet. Its vogue was the product of status anxiety. Programmers, especially in the academy, were anxious to show that they weren’t mere technicians. They began to describe their work as algorithmic, in part because it tied them to one of the greatest of all mathematicians – the Persian polymath Muhammad ibn Musa al-Khwarizmi, or as he was known in Latin, Algoritmi. During the 12th century, translations of al-Khwarizmi introduced Arabic numerals to the west; his treatises pioneered algebra and trigonometry. By describing the algorithm as the fundamental element of programming, the computer scientists were attaching themselves to a grand history. It was a savvy piece of name-dropping: See, we’re not arriviste, we’re working with abstractions and theories, just like the mathematicians!
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  • The algorithm may be the essence of computer science – but it’s not precisely a scientific concept. An algorithm is a system, like plumbing or a military chain of command. It takes knowhow, calculation and creativity to make a system work properly. But some systems, like some armies, are much more reliable than others. A system is a human artefact, not a mathematical truism. The origins of the algorithm are unmistakably human, but human fallibility isn’t a quality that we associate with it.
  • Nobody better articulates the modern faith in engineering’s power to transform society than Zuckerberg. He told a group of software developers, “You know, I’m an engineer, and I think a key part of the engineering mindset is this hope and this belief that you can take any system that’s out there and make it much, much better than it is today. Anything, whether it’s hardware or software, a company, a developer ecosystem – you can take anything and make it much, much better.” The world will improve, if only Zuckerberg’s reason can prevail – and it will.
  • Data, like victims of torture, tells its interrogator what it wants to hear.
  • Very soon, they will guide self-driving cars and pinpoint cancers growing in our innards. But to do all these things, algorithms are constantly taking our measure. They make decisions about us and on our behalf. The problem is that when we outsource thinking to machines, we are really outsourcing thinking to the organisations that run the machines.
  • The engineering mindset has little patience for the fetishisation of words and images, for the mystique of art, for moral complexity or emotional expression. It views humans as data, components of systems, abstractions. That’s why Facebook has so few qualms about performing rampant experiments on its users. The whole effort is to make human beings predictable – to anticipate their behaviour, which makes them easier to manipulate. With this sort of cold-blooded thinking, so divorced from the contingency and mystery of human life, it’s easy to see how long-standing values begin to seem like an annoyance – why a concept such as privacy would carry so little weight in the engineer’s calculus, why the inefficiencies of publishing and journalism seem so imminently disruptable
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    via Aaron Davis
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