Indeed, Musk suggested that synthetic data — data generated by AI models themselves — is the path forward. “The only way to supplement [real-world data] is with synthetic data, where the AI creates [training data],” he said. “With synthetic data … [AI] will sort of grade itself and go through this process of self-learning.”
Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN, developed by a team led by Prof. Dr. Frank Hutter from the University of Freiburg. This artificial intelligence (AI) uses learning methods inspired by large language models.
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Generative AI projects will move from pilot phase to production for many companies this year, which means the workforce will be affected in ways never before imagined, says Sarah Hoffman, director of AI research at AlphaSense.
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