This would lead to some very large cost savings if AI could compress video/audio even better than current compression algorithms. Data borders unite!
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When an algorithm or model can accurately guess the next piece of data in a sequence, it shows it’s good at spotting these patterns.
The study’s results suggest that even though Chinchilla 70B was mainly trained to deal with text, it’s surprisingly effective at compressing other types of data as well, often better than algorithms specifically designed for those tasks.
This opens the door for thinking about machine learning models as not just tools for text prediction and writing but also as effective ways to shrink the size of various types of data.
Over the past two decades, some computer scientists have proposed that the ability to compress data effectively is akin to a form of general intelligence.
The idea is rooted in the notion that understanding the world often involves identifying patterns and making sense of complexity, which, as mentioned above, is similar to what good data compression does.
The relationship between compression and intelligence is a matter of ongoing debate and research, so we’ll likely see more papers on the topic emerge soon.
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Data borders divide.
