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Mothball the calibration effort.
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README-Calibration.md

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Calibrating Unsupervised Language Learning
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==========================================
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* Version of May 2021
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* Mothballed, late 2021
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Ongoing project, continuing activity. See the
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Mothballed project, halted activity. This was a good idea, but it
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foundered on severeal practical and several theoretical issues.
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Cannot move foreward, until these are addressed. See below, "lessons
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learned". for a summary of these issues.
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See also the
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[language learning wiki](http://wiki.opencog.org/w/Language_learning)
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for an alternate overview. See the diary at
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`learn-lang-diary/learn-lang-diary-part-two.lyx` for a progress log.
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for an alternate overview. See
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[Diary Part Two](learn-lang-diary/learn-lang-diary-part-two.pdf)
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for a log of the experimental efforts and results.
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Project Summary
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---------------
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adjustable parameters even "aligned" with the axes of human language
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complexity? How does one even judge this?
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* A serious, practical impediment is that there is no easy way to just
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"eyeball" the results. With English, one can just look at the stuff
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being generated, and its fairly clear when it's good and when its
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garbage. But with artificial langauges, it is impossible to glance at
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it and get any idea of what's going on. In order to track what's
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happpening, one would need to build a deep, complex and throuogh set
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of tools, carefully measuring everything at every step, to see if the
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process is working or has gone off the rails.
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Additional details, results, questions and head-scartching can be found
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in the [Language Learning Diary, Part
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Two](learn-lang-diary/learn-lang-diary-part-two.pdf).
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even dented the surface of grammar--corpus correspondence. Onward
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through the fog!
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That's all for now!
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-------------------
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THE END.

README-Natural-v2.md

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Integrated Natural Language System
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==================================
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AKA v2.

README.md

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See the [README-Natural](README-Natural.md) file for a description of
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the "open-loop" (un-calibrated) processing system. It describes the
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processing steps in detail. Getting good results requires tuning
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a variety of parameters, and so calibration needs to be run first.
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a variety of parameters, and so careful monitoring is required.
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See the [README-Calibration](README-Calibration.md) file for the "new"
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(next-gen) process of generating artificial languages with bounded
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statistical properties, learning them, and then measuring the accuracy
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of the learned language vs. the generated artificial language.
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See the [README-Natural-v2](README-Natural-v2.md) file for the new
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"integrated", "continuous learning" pipeline. Under development.
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The [README-Calibration](README-Calibration.md) proposed a technique
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for calibrating the learning pipeline, by generating artificial languages
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with known, bounded statistical properties, then learning them, and then
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measuring the accuracy of the learned language vs. the generated artificial
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language. This approach is currently abandoned. Its not a bad idea, just
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that naive conception won't work.
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Directories
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