Post by account_disabled on Dec 26, 2023 4:18:52 GMT
Less than half of respondents said their organization understands the processes required to train algorithms or the data requirements of the algorithms. Generating business value from artificial intelligence is directly related to the effective training of artificial intelligence algorithms. Many current AI applications start with one or more “naked” algorithms that only become intelligent after being trained, mostly on company-specific data. Successful training depends on having a sound information system that brings together relevant training data. Many of the pioneers already have robust data and analytics infrastructure and a broad understanding of what is needed to develop the data used to train AI algorithms.
In contrast researchers and experimenters struggle because they lack analytical expertise and because data is largely kept in silos and difficult to integrate. While more than half of Pioneer organizations are investing heavily in data and training, organizations from other maturity clusters are Job Function Email List investing much less. For example, only a quarter of researchers have made significant investments in AI technology, the data needed to train AI algorithms, and the processes to support that training. Misconceptions about AI data Our research revealed a number of data-related misconceptions. It is a misconception that complex AI algorithms alone can provide valuable business solutions without sufficient data.
Jacob Spoelstra, director of data science at Microsoft, observed: I think people's understanding of what machine learning can do is not mature enough. One mistake we often see is that organizations don’t have the historical data their algorithms need to extract patterns to make robust predictions. For example, they ask us to build a predictive maintenance solution for them, and we find that very few, if any, failures are logged. They expect AI to be able to predict when failure will occur, even though there are no examples to learn from.
In contrast researchers and experimenters struggle because they lack analytical expertise and because data is largely kept in silos and difficult to integrate. While more than half of Pioneer organizations are investing heavily in data and training, organizations from other maturity clusters are Job Function Email List investing much less. For example, only a quarter of researchers have made significant investments in AI technology, the data needed to train AI algorithms, and the processes to support that training. Misconceptions about AI data Our research revealed a number of data-related misconceptions. It is a misconception that complex AI algorithms alone can provide valuable business solutions without sufficient data.
Jacob Spoelstra, director of data science at Microsoft, observed: I think people's understanding of what machine learning can do is not mature enough. One mistake we often see is that organizations don’t have the historical data their algorithms need to extract patterns to make robust predictions. For example, they ask us to build a predictive maintenance solution for them, and we find that very few, if any, failures are logged. They expect AI to be able to predict when failure will occur, even though there are no examples to learn from.