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Recent algorithms can help to identify correlations in large amounts of data faster than ever. Such algorithms are often summarized under the umbrella term “artificial intelligence” (AI). It is important to understand that such AI methods cannot make binary decisions, but calculate probabilities; mathematically speaking, we are operate in the field of stochastics. This has important implications for the evaluation of results from learning systems that make use of AI algorithms, because their results must always be interpreted as statistical probabilities for certain events and not as 100% decisions in one or the other direction.

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Learning Laboratory Systems: How Artificial Intelligence Supports Laboratory Work

  • 章节

  • 发表日期：

    2026年

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  • Matthieu-P. Schapranow

摘要

Recent algorithms can help to identify correlations in large amounts of data faster than ever. Such algorithms are often summarized under the umbrella term “artificial intelligence” (AI). It is important to understand that such AI methods cannot make binary decisions, but calculate probabilities; mathematically speaking, we are operate in the field of stochastics. This has important implications for the evaluation of results from learning systems that make use of AI algorithms, because their results must always be interpreted as statistical probabilities for certain events and not as 100% decisions in one or the other direction.