All articles tagged with #model collapse
Originally Published 7 months ago — by Slashdot
The article discusses the emerging issue of AI model collapse, where AI systems, especially in search, are producing increasingly unreliable and distorted results due to errors accumulating over time, raising concerns about the long-term effectiveness of AI investments.
Originally Published 2 years ago — by MarkTechPost
Researchers from Britain and Canada introduce the phenomenon of model collapse, a degenerative learning process where models forget improbable events over time, even when no change has occurred. They provide case studies of model failure in the context of the Gaussian Mixture Model, the Variational Autoencoder, and the Large Language Model. Model collapse can be triggered by training on data from another generative model, leading to a shift in distribution. Long-term learning requires maintaining access to the original data source and keeping other data not produced by LLMs readily available over time.
Originally Published 2 years ago — by Business Insider
AIs trained solely on other AIs will eventually produce gibberish content, according to a group of British and Canadian scientists. The phenomenon, called "model collapse," occurs when AIs are trained on AI-generated content, causing errors and instances of nonsense to spiral. The scientists warn that this will make it impossible for later AIs to distinguish between fact and fiction. The problem lies in the AI's perception of probability after being trained on an earlier AI, narrowing what the next AI understands to be possible. The scientists likened the effect to pollution, saying that the internet will be filled with "blah."
Originally Published 2 years ago — by Slashdot
Researchers from the UK and Canada have found that using AI-generated content to train AI models causes irreversible defects in the resulting models, leading to a degenerative process called model collapse. As AI-generated content proliferates, models that learn from it perform worse over time, producing more errors and less non-erroneous variety in their responses. This will make it harder to train newer models by scraping the web, giving an advantage to firms which already have access to human interfaces at scale.
Originally Published 2 years ago — by VentureBeat
Researchers warn that as AI-generated content proliferates around the internet, and AI models begin to train on it, instead of on primarily human-generated content, it causes irreversible defects in the resulting models, leading to "model collapse." This phenomenon occurs when the data AI models generate ends up contaminating the training set for subsequent models, resulting in models gaining a distorted perception of reality. To avoid model collapse, it is important to ensure fair representation of minority groups in datasets and introduce new, clean, human-generated datasets back into their training.