Artificial Intelligence, Supervised Learning, and User Experience
For machine-learning (ML) scientists to train artificial intelligence (AI) systems and algorithms, they need data. They collect many of the datasets they use to build AI systems from human behaviors and people’s interactions with the technology they use in their everyday lives.
Whether the data comprise a set of liver-disease diagnoses and outcomes, come from a consumer survey on attitudes toward marijuana usage, or derive from active/passive data collection of spoken phrases, AI systems always need training data to ensure their algorithms produce the right outcomes. Such data can frequently be hard to come by. And even once ML scientists have acquired a dataset, how can we be sure that it includes what an AI system needs? Read More
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