Large language models (LLMs) are impressive, but their complexity makes them difficult to understand and trust. This 'black box' nature limits their use in critical areas like medicine and science, where explainability is paramount. Plus, their massive size makes them resource-intensive. A new research paper introduces 'Induction-Gram,' a method that combines the efficiency of traditional n-gram models with a neural 'induction head' inspired by LLMs. This induction head intelligently searches recent text for similar patterns, providing an understandable basis for its predictions. The results are promising: Induction-Gram significantly outperforms other interpretable language models in predicting the next word in a sequence. It even speeds up the processing of larger LLMs through a technique called speculative decoding. The researchers further tested Induction-Gram's potential in neuroscience, using it to predict brain activity associated with language processing. Again, Induction-Gram showed marked improvement over simpler models, offering exciting possibilities for understanding how our brains process language. While there's still work to be done, particularly in enhancing its reasoning capabilities, Induction-Gram represents a significant step toward building more transparent, efficient, and trustworthy AI.