Data is critical to making AI and machine learning work. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Data Augmentation, Data Labeling, Bounding box, Sensor fusion.
Data Augmentation are the techniques used to enhance existing data through the use of additional data, manipulations on existing data, or combinations of data in various ways. Data Labeling is the necessary additional metadata that is applied to training data to provide the meaning necessary to train supervised learning machine learning models. Labels are specific to the type of data and the required purpose or output of the machine learning system. Additionally, data labels are applied by people who manually use their human knowledge to apply the right labels. Or, by systems that can infer the label based on previously trained supervised or semi-supervised approaches.
In this episode we explain the terms above in greater detail, including the terms bounding box and sensor fusion. We also explain how these terms relate to AI and why it’s important to know about them.
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