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The Jargon File, a compendium of hacker lore, defines "bit rot" as a jocular explanation for the degradation of a software program over time even if "nothing has changed"; the idea behind this is almost as if the bits that make up the program were subject to radioactive decay. [1]
Enshittification, also known as crapification and platform decay, is the term used to describe the pattern in which online products and services decline in quality over time. Initially, vendors create high-quality offerings to attract users, then they degrade those offerings to better serve business customers, and finally degrade their services ...
Model collapse in generative models is reduced when data accumulates. Some researchers and commentators on model collapse warn that the phenomenon could fundamentally threaten future generative AI development: As AI-generated data is shared on the Internet, it will inevitably end up in future training datasets, which are often crawled from the Internet.
It is also referred to as data decay, data rot or bit rot. [1] This results in a decline in data quality over time, even when the data is not being utilized. The concept of data degradation involves progressively minimizing data in interconnected processes, where data is used for multiple purposes at different levels of detail.
The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay, contains chapters on elementary error-correcting codes; on the theoretical limits of error-correction; and on the latest state-of-the-art error-correcting codes, including low-density parity-check codes, turbo codes, and fountain codes.
Variable length arithmetic represents numbers as a string of digits of a variable's length limited only by the memory available. Variable-length arithmetic operations are considerably slower than fixed-length format floating-point instructions.
When concept drift is detected, the current model is no longer up-to-date and must be replaced by a new one to restore prediction accuracy. [11] [12] A shortcoming of reactive approaches is that performance may decay until the change is detected. Tracking solutions seek to track the changes in the concept by continually updating the model.
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