14 Matching Annotations
  1. Jan 2019
    1. Contrary to mainstream thinking that this new technology is unregulated, it’s really quite the opposite. These systems apply the strictest of rules under highly deterministic and predictable models that are regulated through mathematics. In the future, industry will be regulated not just by institutions and committees but by algorithms and mathematics. The new technology will gradually out-regulate the regulators and, in many cases, make them obsolete because the new system offers more certainty. Antonopoulos explains that “the opposite of authoritarianism is not chaos, but autonomy.”

      <big>评:</big><br/><br/>1933 年德国包豪斯设计学院被纳粹关闭,大部分师生移民到美国,他们同时也把自己的建筑风格带到了美利坚。尽管人们在严格的几何造型上感受到了冷漠感,但是包豪斯主义致力于美术和工业化社会之间的调和,力图探索艺术与技术的新统一,促使公众思考——「如何成为更完备的人」?而这一点间接影响到了我们现在所熟知的美国式人格。<br/><br/>区块链最终会超越「人治」、达到「算法自治」的状态吗?类似的讨论声在人工智能领域同样不绝于耳。「绝对理性」站到了完备人格的对立面,这种冰冷的特质标志着人类与机器交手后的败退。过去有怀疑论者担心,算法的背后实际上由人操控,但随着「由算法生成」的算法,甚至「爷孙代自承袭」算法的出现,这样的担忧逐渐变得苍白无力——我们有了更大的焦虑:是否会出现 “blockchain-based authoritarianism”?

  2. Nov 2018
    1. how does misrepresentative information make it to the top of the search result pile—and what is missing in the current culture of software design and programming that got us here?

      Two core questions in one? As to "how" bad info bubbles to the top of our search results, we know that the algorithms are proprietary—but the humans who design them bring their biases. As to "what is missing," Safiya Noble suggests here and elsewhere that the engineers in Silicon Valley could use a good dose of the humanities and social sciences in their decision-making. Is she right?

  3. Jul 2018
    1. The new organs process this enormous amount of information to break you down into categories, which are sometimes innocuous like, “Listens to Spotify” or “Trendy Moms”, but can also be more sensitive, identifying ethnicity and religious affiliation, or invasively personal, like “Lives away from family”. More than this, the new organs are being integrated with increasingly sophisticated algorithms, so they can generate predictive portraits of you, which they then sell to advertisers who can target products that you don’t even know you want yet. 
    1. Perelman says his Babel Generator also proves how easy it is to game the system. While students are not going to walk into a standardized test with a Babel Generator in their back pocket, he says, they will quickly learn they can fool the algorithm by using lots of big words, complex sentences, and some key phrases - that make some English teachers cringe. "For example, you will get a higher score just by [writing] "in conclusion,'" he says.
    2. "The idea is bananas, as far as I'm concerned," says Kelly Henderson, an English teacher at Newton South High School just outside Boston. "An art form, a form of expression being evaluated by an algorithm is patently ridiculous."
  4. Apr 2018
    1. This fall, my colleagues and I released gobo.social, a customizable news aggregator. Gobo presents you with posts from your friends, but also gives you a set of sliders that govern what news you see and what’s hidden from you. Want more serious news, less humor? Move a slider. Need to hear more female voices? Adjust the gender slider, or press the “mute all men” button for a much quieter internet. Gobo currently includes half a dozen ways to tune your news feed, with more to come.

      Gobo, a proof of concept.

  5. Mar 2018
  6. Mar 2017
    1. for not very large numbers

      Would an approach using the Sieve or Eratosthenes work better for very large numbers? Or the best shot would be a probabilistic primality test?

  7. Dec 2016
  8. Oct 2016
  9. Aug 2016
  10. Apr 2016
    1. While there are assets that have not been assigned to a cluster If only one asset remaining then Add a new cluster Only member is the remaining asset Else Find the asset with the Highest Average Correlation (HC) to all assets not yet been assigned to a Cluster Find the asset with the Lowest Average Correlation (LC) to all assets not yet assigned to a Cluster If Correlation between HC and LC > Threshold Add a new Cluster made of HC and LC Add to Cluster all other assets that have yet been assigned to a Cluster and have an Average Correlation to HC and LC > Threshold Else Add a Cluster made of HC Add to Cluster all other assets that have yet been assigned to a Cluster and have a Correlation to HC > Threshold Add a Cluster made of LC Add to Cluster all other assets that have yet been assigned to a Cluster and have Correlation to LC > Threshold End if End if End While

      Fast Threshold Clustering Algorithm

      Looking for equivalent source code to apply in smart content delivery and wireless network optimisation such as Ant Mesh via @KirkDBorne's status https://twitter.com/KirkDBorne/status/479216775410626560 http://cssanalytics.wordpress.com/2013/11/26/fast-threshold-clustering-algorithm-ftca/

  11. Nov 2015
    1. I don't totally agree with the fact that writers the creation of language is the target of a writer, I think language is just a means, the "algorithm" that "plays" with words/semanthincs, as any machine can do