It's not quite live-blogging, but it's close. I've been finding Storify to be a nice way to organize my thinking during the first morning of the Canada 3.0 Conference.
Showing posts with label canada 3.0. Show all posts
Showing posts with label canada 3.0. Show all posts
Tuesday, April 24, 2012
Monday, May 9, 2011
Motion Capture Animation
This short video and an accompanying audio podcast, tell the story of how motion capture, and 3D technology are being used to fill the demand for special effects, animation, and videogame production. The interview with Tobias Wiegant took place at Canada 3.0.
If you found the video of interest, you might like to review the full length audio podcast.
If you found the video of interest, you might like to review the full length audio podcast.
Wednesday, May 4, 2011
The Machine is Indeed Using Us
Does this title sound familiar? It's very similar to Michael Wesch's viral video from a few years ago: The Machine is Us/ing Us. When I first saw this video in 2008, it inspired me to write a series of posts about how users of the World Wide Web, were Teaching the Machine. Now, just three years later, it seems as if the machine has become smart enough to customize the information it provides to each of us.
Yesterday, after hearing Wesch describe how his video went viral, participants at Canada 3.0 were called by Sonija Monga, to reflect on how we derive meaning and insights from our networks.
By virtue of my membership in a network that is already functioning as a bit of a like-minded hive, I discovered an answer to my question thanks to a tweet from Alec Couros, Take nine minutes to consider Eli Pariser's warning: Beware online "filter bubbles"
I don't know if the machine can yet answer these questions, but there are many things we need to think about:
Is there a problem with each user being the recipient of customized service from a news provider; an online store; a search engine?
Does the machine know enough to provide us not only with relevant results, but also with an unbiased determination of the most important content?
How good are our personal learning network at discovering content from varying points of view?
How might a young person's unseen profile and early online habits, affect their future online experiences?
Yesterday, after hearing Wesch describe how his video went viral, participants at Canada 3.0 were called by Sonija Monga, to reflect on how we derive meaning and insights from our networks.
By virtue of my membership in a network that is already functioning as a bit of a like-minded hive, I discovered an answer to my question thanks to a tweet from Alec Couros, Take nine minutes to consider Eli Pariser's warning: Beware online "filter bubbles"
I don't know if the machine can yet answer these questions, but there are many things we need to think about:
Is there a problem with each user being the recipient of customized service from a news provider; an online store; a search engine?
Does the machine know enough to provide us not only with relevant results, but also with an unbiased determination of the most important content?
How good are our personal learning network at discovering content from varying points of view?
How might a young person's unseen profile and early online habits, affect their future online experiences?
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