When the turk squares the apple and inference does no better there's no
crop, only crap...


With that... a mixed data db, even when the associations are more than
binary, might require a certain kind of ANN, modeled so that it
resembles a 'random forest' (growing the tree at discrete times - with
or without the Sleep States) and also a reactor when propagating the
inference particles. It takes a large correspondence table before
building or packing the model.


https://en.wikipedia.org/wiki/Random_forest


On 20.08.2019 21:17, Stefan Reich via AGI wrote:
lol... "crap ANNs". You convinced me there...

On Tue, 20 Aug 2019 at 00:04, Secretary of Trades
<[email protected] <mailto:[email protected]>> wrote:

    :)

    It's annotation (a text entry). From Amazon's "mechanical turk".

    A hilarious way of referring the current norm of primitive and
    supervised learning molded on crap ANNs.


    On 20.08.2019 00:11, Stefan Reich via AGI wrote:
    > I find your explanation hard to follow. What is "turking"? Is this
    > another one of those "nothing must be manual because we are
    making AI"
    > argument? Those are really useless.
    >
    > On Mon, 19 Aug 2019 at 22:36, Secretary of Trades
    > <[email protected] <mailto:[email protected]>
    <mailto:[email protected]
    <mailto:[email protected]>>> wrote:
    >
    >     Hearing, but not for control. For segment cuts to be
    associated with
    >     image cuts (e.g. 'red numbers').
    >
    >     Also, the red numbers should not be sourced by any kind of
    turking
    >     (such
    >     as the color channel threshold kind of is). Instead a hard
    or soft
    >     focusing should source the image portion of interest.
    >
    >     As one would bring an apple in front of a child's eyes and
    say "Apple"
    >     or "An apple" ...
    >
    >     Such a set-up would be unsupervised enough (no text in the
    >     database and
    >     no other kind of turking/ annotation), but instead an
    association
    >     between an image db (where non-OCRed text can be included)
    and a sound
    >     cuts db (where spoken text could also be included). The
    learning model
    >     can be complex enough so that a spoken phrase that describes
    the apple
    >     fruit to be linked (or associated) not to an image but to
    only a tiny
    >     segment of the same spoken phrase (the 'apple' noun, when
    isolated).
    >
    >     So, instead of sourcing for control, the hearing module should
    >     source to
    >     a smart association algorithm that runs continuously (so that
    >     associations can be made at discrete times).
    >
    >
    >     On 19.08.2019 15:37, Stefan Reich via AGI wrote:
    >     > You mean voice control? Yeah I have code for that.
    >     >
    >     > On Mon, 19 Aug 2019 at 05:54, Secretary of Trades
    >     > <[email protected]
    <mailto:[email protected]> <mailto:[email protected]
    <mailto:[email protected]>>
    >     <mailto:[email protected]
    <mailto:[email protected]>
    >     <mailto:[email protected]
    <mailto:[email protected]>>>> wrote:
    >     >
    >     >     It sees. It must also hear.
    >     >
    >     >
    >     >     On 19.08.2019 01:31, Stefan Reich via AGI wrote:
    >     >     > https://www.youtube.com/watch?v=W6eJSCGEkSE
    >     >     >
    >     >     > --
    >     >     > Stefan Reich
    >     >     > BotCompany.de // Java-based operating systems
    >     >     > *Artificial General Intelligence List
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    >     >
    >     >
    >     > --
    >     > Stefan Reich
    >     > BotCompany.de // Java-based operating systems
    >     > *Artificial General Intelligence List
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    >
    > --
    > Stefan Reich
    > BotCompany.de // Java-based operating systems
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--
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BotCompany.de // Java-based operating systems
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