摘要： 2023 will bring exciting advances in AI and graph technology. One of the most compelling innovations will be the ability for quantum programs to be turned into graphs and vice versa. Natural language understanding will become part of AI models. The adoption of standards-based semantic layers will spike as they enable data selection through business terms. Graph neural networks (GNNs) will become standard in knowledge graphs and causal knowledge graphs will emerge.
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As we head into 2023, machine learning (ML) professionals are taking stock of the past year and identifying potential key opportunities moving forward.
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Artificial intelligence has already permeated our everyday lives. We see it everywhere, even when we’re not aware of it. From the algorithm that drives our online searches to the app we use on our phones, AI is all around us.
摘要： Researchers used a powerful deep-learning model to extract important data from electronic health records that could assist with personalized medicine.
摘要： Time series forecasting is one of the key topics of machine learning. The fact that so many prediction issues have a temporal component makes it crucial. In contrast to many other prediction tasks, time series issues are more challenging since the time component contributes more information.
