Title: Shrinking AI for Greater Humanization The endless pursuit of making AI more human-like has resulted in groundbreaking improvements. However, the latest development in the AI world takes a different approach - making AI smaller with fewer words. Recently, researchers are experimenting with a novel idea of using minimal data to teach large language models. The goal is to make the language models sound more human by training them with fewer words. This groundbreaking research could revolutionize the way we interact with AI chatbots, making them more relatable, and easier to communicate with. The research aims to augment smaller datasets to provide the machines with basic knowledge they need to understand and communicate like humans. The idea is to teach AI chatbots to learn from a limited pool of data, unlike the huge datasets they currently learn from. The theory behind this initiative is that with fewer datasets, the AI can focus more on understanding the language and discerning the context, making them better at interacting and responding like humans. Large language models that have been trained on smaller datasets seem to be more context-aware and less robotic, leading researchers to conclude that the key to making AI more relatable is by training them to understand context rather than just focusing on big datasets. These developments represent a significant shift away from the traditional approach where we trained AI to memorize and regurgitate phrases, without context. It opens new possibilities for the chatbot industry by bringing the human touch that has been missing for so long. Could this be the start of a new generation of AI-powered chatbots that understand context and human emotions? The possibilities are endless. While the journey towards achieving greater humanization of AI may still be long, this research is a crucial step in that direction. Teaching fewer words to large language models might help them sound more human.
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