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A teaching guide for an AI tool that chooses from options

This guide explores a teaching example of an AI tool that chooses from options rather than writing an answer. It shows the basic parts needed to build software that sorts information by picking from set categories.

Original by Kye GomezGetting started

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Our summary

Open Jev is a teaching project that shows how to build an AI tool that chooses from options rather than writing an answer. People use this type of design to quickly sort incoming messages, like deciding if a customer needs billing help or technical support.

The software is designed to read the background information once and save it in memory. Then, separate decision tools look at that saved information to answer different questions at the same time. The author suggests training the software with uncertain examples to preserve disagreement.

This project is only for learning how the design might work. It is completely untrained and uses random numbers, so it cannot make real decisions yet. It is useful for students and researchers who want to study the structure before trying to build a working version.

Key takeaways

  1. The software reads information once and uses separate tools to make different choices at the same time.
  2. The author suggests using uncertain training examples, but this untrained project cannot prove if that method works.
  3. The author notes that any working version must be tested with new examples before acting on its own.

This is an educational project with random settings, not a working AI assistant. It is a guess at how the original software works, and actual testing remains unfinished.

GitHub repository

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