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Surfer: Helps businesses improve their website's traffic by developing a comprehensive content strategy (link)
HelloScribe: HelloScribe's AI tools elevate your content and spark fresh ideas for pros and you. (link)
Decoherence: Enhance music videos with Decoherence. Upload, customize, and mesmerize with captivating visuals (link)
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🖌️ Today’s Best New DALL-E Prompts
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🤯 Insight: The Use Of Synthetic Data In AI
Synthetic data is generated from data designed to mimic real-world datasets statistically. However, it comes with several challenges and limitations. The problem lies in the ambiguity of "real data" and the statement that synthetic data replicates "some or all of the statistical properties."
The use of "real data" raises questions about data adequacy and the truthfulness of the generated data. Crafting models for generating synthetic data is an art, making it prone to spurious relationships and biases introduced by the modeller's variable selection.
While synthetic data can be useful for certain purposes, relying solely on it may undermine the value of human data. It can also artificially inflate confidence in analyses and distort the representation of under-represented groups.
Despite some claims, synthetic data has promising applications, especially in privacy protection, where it can help resolve privacy-utility trade-offs of legacy anonymization techniques.
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