The Coevolution of Human and Machine learning
- Publication Type:
- Thesis
- Issue Date:
- 2023
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Machine learning has made remarkable progress in the past decade, and its application scope and depth continue to expand. However, it also faces significant crises and challenges. Firstly, although machine learning has high accuracy for model output, they rely heavily on large amounts of annotated data. In situations with limited data or no annotations, ensuring the accuracy for the model output becomes a significant goal for specific industries. Secondly, machine learning applications often encounter scenarios with insufficient data or low quality data. When such data is used to train models, it can lead to significant deviations, resulting in inaccurate output and a decline in people's trust in machine learning. Thirdly, many machine learning models operate in a black box environment, where the models often do not provide explanations or the explanations provided are too complex. Without appropriate feedback, humans cannot understand the learning status of the model and cannot effectively intervene in the model’s learning. Therefore, it is difficult for machine learning to gain human trust. This issue can have a direct impact on the application and advancement of machine learning. To address these challenges, under the guidance of my supervisors, I conducted research on the coevolution of human and machine learning. Our research goal is to enable human and machine learning models to progress together, achieving better performance, gaining people's understanding and trust ,and then applying it.
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