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Information created artificially by computers rather than gathered from real-world measurements.
Synthetic data is generated using algorithms to match specific statistical properties of real data. It is often used to train machine learning models when real-world data is scarce, expensive, or difficult to collect. In scientific modeling, it can help test systems under extreme or unusual conditions.
Researchers at MIT have developed a new training method that makes AI climate emulators faster, more trustworthy, and cheaper to build.
Physicl secures $10 million in seed funding to help robot builders train physical AI models using high-fidelity 3D simulations.
Hugging Face introduced Palette, a method to train small open-source AI models to generate custom presentation decks using specific design rules.
