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AI learning methods other than Brute Force

 



In this article, we discuss the various learning methods by which Artificial Intelligence can learn. We discuss methods like learning a subject completely by branching out to different topics and learning a subject starting from a high-quality source and then to a low-quality source of data available on the internet.

Introduction

We know that Generative AI tools like ChatGPT and Bard already have learned a good part of their knowledge by brute force. We are suggesting a few methods by which it can learn otherwise. These methods should help it to optimize itself, if not learn from scratch.

Re-Inforced learning by adding weights

ChatGPT and Bard should add weights to their important sources of information. These sources could be the most visited websites, the most referred websites, acclaimed books from renowned authors, research papers or articles from well-known magazines, Articles, and even the most watched YouTube Channels. These sources need to be given weights and they should be bound with the already existing framework to bring out an even more effective generative AI in the future.

Learn a topic completely and validate it

ChatGPT and Bard should approach a topic like humans. If it learns about a topic it should learn it completely. Like if it learns about “Quantum Entanglement”, it should learn everything about it and validate it by comparing the learned data with credible high-quality publications and articles affirming its knowledge about it. While Learning math it needs to learn the method and verify the mathematical results by going through different data on the web and comparing the results of its calculation.

Learn Hierarchically

Generative AI should start learning first from high-quality data and then go down to low-quality data. While learning hierarchically, more weight should be given to data up the ladder than at the bottom. Contrasting information should be dealt with by reducing the weight of that high-quality information with that of the weight of corresponding low-quality information.

Learn by branching out

While learning a topic, when faced with new data, branch out and collect all information about it and come back to the same branch point. Armed with the information collected, proceed to learn the new data ahead of it. This ensures that AI gains full and thorough knowledge about the topic it is learning.

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