Redumbdancy

I am an idiot.

For most of today, I have been trying to find a way to recover almost 60 GB of images that I’m pretty sure have been sacrificed to the MTBF gods. A little while ago I ran into a bit of a problem where my Aperture library was growing too large for my laptop’s primary disk, so I moved it onto an external drive temporarily while I could figure out a backup solution.

I had planned on implementing at least a partial backup solution at some point within the next couple of weeks (something about which I will write a little later in this post). I wanted to take the time to properly plan it, ensure it was redundant and as bullet-proof as I could make it, and then fully implement it and get it ready to go.

Unfortunately, this is exactly the time when that external hard disk decided it didn’t feel like working anymore.

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Happiness Metrics?

(I titled the post that way just to get my coworkers’ attention. It probably worked.)

My son wrote a “book” for me this morning with a story of his own creation. In the back of the book was an opinion poll:

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Notes for “Rebuilding the Tower of Babel”

  • Example of the Biblical story
    • Curse of the languages – that people could no longer cooperate
    • Three lessons
      • Don’t mess with God
      • Cooperation
  • How do we identify that apples are apples and oranges are oranges?
    • We put our experiences together in order to distinguish the two things
    • Context
    • The “database of things” around our experiences
  • What happens when we don’t agree?
    • We crash Martian orbiters
  • What about a computer?
    • When a computer encounters a piece of data, it has no context
    • Is the number 20 an hour, a number, the result of an algorithm?
  • Can’t computers use HTML as a sort of a database to provide context for all the things they look at?
    • No; HTML was designed for humans to understand; not for computers
  • People understand relationships between data; computers don’t unless we tell them
  • How do we get computers to understand and give them context?
    • How do you explain the semantic web and semantic technologies?
    • Tagging important data with a context and a vocabulary
  • Humans frequently don’t speak the same language at all
    • We have different preconceptions and different filters
    • We have to build ontologies, vocabularies, and tag everything and connect it to the semantic database so computers can understand it
  • If you are running predictive analysis on a whole bunch of data, you can use semantic analysis to unify datasets from very distant places and use it to understand what’s predictive
  • Potential application: healthcare and science
    • Semantic data makes the data highly valuable, because it allows other groups to “piggyback” on work that is being done without needing a “translation” of the work
    • Any company can define their own ontology, but if we let a company control them, it becomes hard to use them
  • Publishing and Web apps
    • What if we started tagging news semantically so a computer can understand the relationships?
    • Starts with making pure browsing, search, and discovery better
    • MS Pivot as an example of data that is semantically tagged
  • Public health
    • What if we semantically tagged symptoms for everything across the country?
    • Could identify the patterns related to disease and illness outbreak
  • AI
    • Semantics is the foundation for very robust AI
    • This is why computers can’t respond to stimuli the same way we do
    • Example – have people watch something and take semantic data of the emotional response – use those curves to model relationship in a computer
  • Companies using – Merck, Biogen Idec, GroupM, Chevron
  • Projects
    • DBPedia – structured data from Wikipedia
    • GoodRelations – product information, etc.
    • Swoogle – semantic resource search engine
    • Cambridge Semantics – enabled spreadsheets used by Group M
    • Pivot
  • What does this all add up to?
    • A better understanding of our world and the patterns that drive it
    • Accelerated convergence of industries
    • Accelerated innovation and discovery