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Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. [3]
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 129 ] and reweighting training data.
A "doc model" XSL transformation, provided by the chosen presentation style, is applied to define the files that will be generated. Sandcastle provides a reference build component stack (sandcastle.config) that builds in-memory indexes of the data, resolves shared content and links, and uses XSL to generate the final HTML output.
Training of largest language models might need more linguistic data than naturally available, or that the naturally occurring data is of insufficient quality. In these cases, synthetic data might be used. Microsoft's Phi series of LLMs is trained on textbook-like data generated by another LLM. [36]
Microsoft Azure, OCI, CoreWeave, and others are building large AI factories with Spectrum-X. The first Stargate data centers will use Spectrum-X. ... generate an enormous amount of synthetic data ...
The result has been called "procedural oatmeal", a term coined by writer Kate Compton, in that while it is possible to mathematically generate thousands of bowls of oatmeal with procedural generation, they will be perceived to be the same by the user, and lack the notion of perceived uniqueness that a procedural system should aim for.
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