My daily readings 09/25/2012

  • Exactly, I sensed it many years ago. We see/read a lot, gain a litte.

    tags: note digest

    • On a daily basis, we’re exposed to hundreds of articles, tweets, emails and advertisements. We are, as some put it, an “overcommunicated” society.
    • We’re exposed to more content than at any time in our lives yet the amount of time to consume it isn’t increasing.
    • With all of this content vying for our attention at virtually every hour of the day, I believe the future is not real-timeInstead, we will find ways to artificially stem the constant flow of information through algorithmic summarization. We will find ways to bring information we are truly interested in back to us at a pace and time that is more manageable. Instant notifications will be reserved for those few precious individuals and apps that absolutely need our attention, rather than those that simply want it.
    • There are already small efforts to make this happen. Look at the recent addition of Twitter’s weekly digest, Facebook’s relatively recent switch to a Top News feed, or—my personal favorite—The Slow Web Movement. We’re at the very forefront of this trend and our understanding of how to coalesce all of this information into valuable, individually relevant, and timely packages is still in its infancy.
    • The real-time web is a bit like a fire hydrant—either the valve is opened or closed, but there’s no filter to stem the flow; we become the filter for the massive flow of information. Content should always feel like a gift, not a burden. To turn it into a gift, we need to start focusing on ways to control the flow.
  • tags: iOS editor

  • tags: TTS

  • tags: TTS

    • OpenEars is an shared-source iOS framework for iPhone voice recognition and speech synthesis (TTS). It lets you easily implement round-trip English language speech recognition and text-to-speech on the iPhone and iPad and uses the open source CMU Pocketsphinx, CMU Flite, and CMUCLMTK libraries. Highly-accurate large-vocabulary recognition (that is, trying to recognize any word the user speaks out of many thousands of known words) is not yet a reality for local in-app processing on the iPhone given the hardware limitations of the platform; even Siri does its large-vocabulary recognition on the server side. However, Pocketsphinx (the open source voice recognition engine that OpenEars uses) is capable of local recognition on the iPhone of vocabularies with hundreds of words depending on the environment and other factors, and performs very well with command-and-control language models. The best part is that it uses no network connectivity because all processing occurs locally on the device.

Posted from Diigo. The rest of my favorite links are here.

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