Data-Driven Education Insights

  Russell, S. (2020). Human compatible: Artificial intelligence and the problem of control . Penguin Books. Buy the book here . Highlights: The standard model of AI, where machines optimize a fixed objective, is fundamentally flawed and becomes untenable as AI becomes more powerful. To ensure AI remains beneficial, machines should be designed to be uncertain about human preferences and learn them through observation, leading to deferential and cautious behavior. The three principles for beneficial machines are: the machine's only objective is to maximize the realization of human preferences, the machine is initially uncertain about those preferences, and human behavior is the only source of information about human preferences. Inverse reinforcement learning (IRL) is a key technique for machines to learn human preferences from observed behavior. The off-switch problem (machines resisting being turned off) can be solved by machines that are uncertain about human preferences, as they

  Introduction of the Project This project aims to conduct a comprehensive linguistic analysis of a Ph.D. dissertation using advanced text mining and natural language processing techniques. By employing a diverse set of analytical methods, including word frequency analysis, sentiment analysis, topic modeling, named entity recognition, and readability assessment, among others, the study seeks to uncover deeper insights into the dissertation’s content, structure, and stylistic features. This multi-faceted approach will not only provide a quantitative understanding of the text but also offer qualitative insights into the dissertation’s themes, coherence, and overall academic contribution. The project’s findings are expected to demonstrate the potential of computational linguistics in enhancing the evaluation and understanding of complex academic texts, potentially paving the way for more sophisticated tools in academic writing and assessment. A. Summary of the Dissertation My Ph.D. disser