AI’s ability to generate text, code, and translations is impressive, but its inner workings remain a mystery. This 'black box' nature makes it hard to understand why AI makes certain decisions, and even harder to control the output. A new research paper proposes a solution: incorporating fuzzy logic, a method that mimics human reasoning by embracing uncertainty, into the AI generation process. The researchers developed a system called GenFS (Generative Fuzzy System) that combines the learning power of deep learning models with the interpretability of fuzzy systems. Imagine an AI model trying to translate a sentence. Instead of just relying on statistical patterns, it breaks down the sentence into different fuzzy sets, like 'long sentence,' 'medium sentence,' and 'short sentence.' Each set has a 'delegate,' a representative example. The AI compares the input sentence to these delegates and uses the closest match to guide the translation. This approach allows the AI to use both learned data and pre-defined knowledge, making the generation process more transparent. The team tested their GenFS-based model, called FuzzyS2S, on twelve datasets across machine translation, code generation, and text summarization. FuzzyS2S outperformed traditional Transformer models and even beat state-of-the-art models like T5 and CodeT5 in several tests. It showed improved accuracy, particularly in machine translation, because it considers the relationships between tokens at different levels of detail. While the results are promising, the research team acknowledges that GenFS isn’t perfect. It still relies on deep learning components, which have their own black box aspects. Also, GenFS currently doesn’t work with multimodal data like images or audio. However, the researchers are optimistic about GenFS’s potential. They believe it could be applied to various generation tasks beyond text, including images, audio, and video, paving the way for more transparent and controllable AI generation.