Have you ever wondered how our brains process and remember so much information? Or considered the feats of compression in the digital world, where massive files are shrunk into compact packages for storage and transmission? It turns out these two seemingly distinct processes are intimately linked through the concept of 'understanding,' which is the foundation of a groundbreaking new approach to compression. Large Language Models (LLMs), renowned for their ability to process and generate human-like text, are now being used to significantly improve data compression. Researchers have developed a new paradigm called LMCompress, which leverages the understanding of LLMs to achieve unprecedented compression ratios. Traditional compression methods rely on identifying and exploiting predictable patterns in data. However, these techniques, while effective, have limitations based on computable features of the data. LMCompress, in contrast, borrows from the uncomputable Solomonoff induction, using LLMs to simulate human-like understanding. This enables the algorithm to compress various kinds of data—text, images, audio, and video—at rates far surpassing traditional methods like JPEG, FLAC, or H.264. The process begins by converting the data into a sequence of tokens. These tokens are then fed into a generative LLM, which predicts the probability of each token's occurrence. Based on these predictions, arithmetic coding is used to create the compressed representation of the original data. Testing across a range of datasets, LMCompress doubled the compression ratios of traditional codecs for images and videos, quadrupled them for text, and consistently outperformed other LLM-based methods. One of the most significant advantages of this approach is its adaptability. By training or fine-tuning the LLMs on domain-specific data, compression efficiency can be drastically improved. For instance, using medical or legal texts to train the model led to significantly better compression within those domains. The success of LMCompress points towards a future where understanding is central to data compression. This has wide-ranging implications, from reducing storage needs and transmission bandwidth to potentially revolutionizing fields like 6G communication and data encryption. The exploration of LMCompress for lossy compression remains an intriguing area for future development. Imagine videos compressed to unprecedented levels while maintaining high visual quality. While the current research focuses on lossless compression, the principles underlying LMCompress hold enormous promise for a wide array of data compression applications. As LLMs continue to evolve and our understanding of how they can be applied to data compression deepens, we can expect even more significant breakthroughs in how we store, transmit, and interact with digital information.