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Emergent day to you. 2010-04-22 is my knowmad birthday. Think I understood the word. More to emerge.
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    Monica Anderson explains.

    Reductionism is an amazingly powerful strategy for leveraging the work of scientists and for disseminating the results in the form of re-usable models of structure and causality. But for some of the "remaining hard problem domains" such as Life (biology, psychology, ecology, etc), the World (world modeling, economies, sociology), Intelligence (understanding the brain, intelligence, and creating Artificial General Intelligences - AGI) and the problem of determining the semantics of language (e.g. text) Reductionism has failed. I claim that reductionist models cannot be created in these domains (which have been named "Bizarre Domains") and that we must use Model Free (Holistic) Methods for these domains. This has important implications for AGI research strategies.

    "AI is the last holdout of pure reductionism, if you will."


    AGI: Artificial General Intelligence. In later lectures she uses AN: Artificial iNtuition.

    Slow down and enjoy the presentation. 31:22
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    Hans Rosling, genius of data visualization at Gapminder leads us to grasp surprising big data and aim for adequate context. One of his key messages for me: what we can do to effectively help people in one place and condition would not necessarily work well elsewhere. That is why I feel complex adaptive people networks have a growing role in improving the world.
    Image credit: Gapminder World





    Video,
    Hans Rosling: No more boring data 20 minutes TED talk - 2006

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    A New Direction In AI Research

    Monica Anderson
    proposes adding a new target to ongoing AI research efforts: We need to focus more of our attention on Understanding as opposed to Reasoning. Understanding requires using Model Free Methods. As a bonus towards the end, Ms. Anderson also speculates about the so-called AI singularity and discusses whether SkyNet like scenarios, where computers take over the world, are plausible.

    Mechanical manipulation of formulas, even in the most strictly logical contexts require intuition-based guidance in order to progress towards a goal, such as simplification.
    ...
    Intuition is an invisible but non-mystical process....

    Intuition is fallible, that is why it is not a good basis to use in science...

    Intuition is the art of guessing wisely, based on a lifetime of gathered experience, stored as patterns...

    Monica Anderson



    Does it make sense to you?

    Why does the mind enjoy #simplicity? Because once you get it (conceptual understanding), things do get simple.

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