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Compression Is a Tool to Promote Simplicity and Efficiency in Your Models
We are using compression as a tool to promote simplicity and efficiency in our models. But they are not perfectly compressed because they need to include things that are seemingly useless today, but may turn out to be useful in the future. An analogy that you can make is with investing, for instance. If I look at the past 20 years of stock market data and I use a compression algorithm to figure out the best trading strategy, it's going to be you buy Apple stock then maybe the past few years you buy Tesla stock or something. Is that strategy still going to be true for the next 20 years? Well, actually probably not.