Using an Artificial-Intelligence-Generated Program for Positive Efficiency in Filmmaking Education: Insights from Experts and Students

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Abstract

In recent years, despite the widespread success of artificial intelligence (AI) across various domains, its full potential in the field of education, particularly in filmmaking education, remains largely untapped. The purpose of this study is to explore the application of AI-generated programs in filmmaking education to address existing shortcomings in curriculum design. We employed a comprehensive approach, starting with an extensive review of existing filmmaking courses and AI-recommended courses. Subsequently, two rounds of in-depth interviews were conducted, involving both experts and students, to gain profound insights. We utilized user journey maps to visualize the participants’ experiences and feedback, complemented by a mixed-methods analysis approach for a comprehensive data assessment. The study revealed that both the experts and the students derived positive benefits from AI-recommended courses. This research not only provides a fresh perspective on the practical applications of AI in filmmaking education but also offers insights for innovation in the field of education. Theoretically, this study establishes a new foundation for the application of AI in education. In practice, it opens up new possibilities for filmmaking education and promotes the development of cutting-edge teaching methods. Despite limitations in sample size and geographical scope, this study underscores the immense potential of AI in filmmaking education. It provides directions for future research to deepen our understanding of AI’s impact on education.

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LSTM: A Search Space Odyssey

Bas Steunebrink, Jan Koutník, Rupesh K Srivastava … (2016)

Several variants of the long short-term memory (LSTM) architecture for recurrent neural networks have been proposed since its inception in 1995. In recent years, these networks have become the state-of-the-art models for a variety of machine learning problems. This has led to a renewed interest in understanding the role and utility of various computational components of typical LSTM variants. In this paper, we present the first large-scale analysis of eight LSTM variants on three representative tasks: speech recognition, handwriting recognition, and polyphonic music modeling. The hyperparameters of all LSTM variants for each task were optimized separately using random search, and their importance was assessed using the powerful functional ANalysis Of VAriance framework. In total, we summarize the results of 5400 experimental runs ( ≈ 15 years of CPU time), which makes our study the largest of its kind on LSTM networks. Our results show that none of the variants can improve upon the standard LSTM architecture significantly, and demonstrate the forget gate and the output activation function to be its most critical components. We further observe that the studied hyperparameters are virtually independent and derive guidelines for their efficient adjustment.

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Can artificial intelligence help for scientific writing?

Michele Salvagno, ChatGPT, Fabio Silvio Taccone … (2023)

This paper discusses the use of Artificial Intelligence Chatbot in scientific writing. ChatGPT is a type of chatbot, developed by OpenAI, that uses the Generative Pre-trained Transformer (GPT) language model to understand and respond to natural language inputs. AI chatbot and ChatGPT in particular appear to be useful tools in scientific writing, assisting researchers and scientists in organizing material, generating an initial draft and/or in proofreading. There is no publication in the field of critical care medicine prepared using this approach; however, this will be a possibility in the next future. ChatGPT work should not be used as a replacement for human judgment and the output should always be reviewed by experts before being used in any critical decision-making or application. Moreover, several ethical issues arise about using these tools, such as the risk of plagiarism and inaccuracies, as well as a potential imbalance in its accessibility between high- and low-income countries, if the software becomes paying. For this reason, a consensus on how to regulate the use of chatbots in scientific writing will soon be required.