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Given a collection of knowledge triples that utilize types and relations from a formal ontology, it has finally become possible to automatically classify, integrate, and interconnect entities and concepts, much like a human expert would.
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By indexing problems with this framework (we call it "semantics of constraints: SOC"), it becomes possible to automatically extract the "differences between problems" necessary for the comparison and sequencing of problems.
Automatic perturbations do not depend on a specific field, and by describing the relations between the attribute values in advance, it becomes possible to automatically generate a perturbation by reversing the attribute value relation of perturbation from dissolved matter (a lot) to dissolved matter (a little).
In this way, it has become possible to establish an infrastructure that can enable the testing of interlocking table automatically.
By automatically detecting facial features, it becomes possible to enlarge or ensmallen (that's totally a word) the subject's eyes, nose or mouth, or even re-shape a whole face.
In this way, it becomes possible to import data from all sources virtually and even to create reports automatically from workflows.
It also becomes possible to cheat.
Remember that once a payment has been sent electronically, it isn't possible to automatically reverse it.
Then, it is still possible to automatically create a stimuli generator that delivers these data.
It is not possible to automatically find all possible clusters in all the subspaces using this method.
Starting from an annotated specification in UML, it is possible to automatically derive a performance model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com