Changes between Version 20 and Version 21 of Public/WhitePaperMachineTalk


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Timestamp:
May 5, 2023, 5:33:41 PM (12 months ago)
Author:
Boris Horner
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  • Public/WhitePaperMachineTalk

    v20 v21  
    2020AIs are not instructed this way. An NLP AI brings along the ability to "understand" the text (or better: to behave as if it did). Therefore, we do not have to write lengthy software code to tell it how to (for example) find all the nouns in an input text in all possible languages and replace them with the correct terms. We must just provide the correct terms and how to find the wrong terms. A good way to teach such things are by a combination of instructions and examples, and also examples how not to do it. The interesting thing is: if we teach an AI things based on an English example, and if the same thing makes sense in German as well, the AI is likely to be able to do, in spite it's never been trained with German text. So, the AI can do all the basic lingual processing and also powerful reasoning, we must "just" provide data it doesn't have by default and information on what we expect it to do with the user's input data.
    2121
    22 The AI context in this screenshot has mainly been trained with German data, and had just a short training example in English:
    23 [[Image(Public/ImageContainer:AutoDITA.jpg, align=center, width=734px, margin-top=10, margin-bottom=20, link=)]]
     22The AI context in this image has mainly been trained with German data, and had just a short training example in English. The DITA training was only one example per topic type (admittedly, yet without any of the advanced markup - we'll add hazarstatements, examples and preconditions later):
     23[[Image(Public/ImageContainer:AutoDITA.jpg, align=center, width=749px, margin-top=10, margin-bottom=20, link=)]]
    2424
    2525In //ChatGPT//, training data can be uploaded as a [https://en.wikipedia.org/wiki/JSON JSON] file, which can be understood as the "AI program". The development process itself is, again, closer to the traditional way - you change the training data, test, implement improvements and test again.