How can I create a general purpose AI that can analyze a movie script and extract concepts like characters, location, time, emotion etc so that we can use generative ai to generate storyboards, shot divisions, conceptual posters etc?

Creating a general-purpose AI capable of analyzing a movie script and extracting concepts like characters, location, time, emotion, and generating storyboards, shot divisions, and conceptual posters involves complex and interdisciplinary tasks. While I can provide a high-level overview of the general process, it's important to note that developing such an AI system requires significant expertise in natural language processing, machine learning, computer vision, and creative domain knowledge. Here are some general steps you might consider:

1. Data collection and preprocessing: Gather a diverse dataset of movie scripts, along with associated metadata such as character names, locations, and emotions. Preprocess the data by removing noise, standardizing the format, and annotating key elements.

2. Natural language processing (NLP): Utilize NLP techniques to parse the movie script and extract relevant information. This involves tasks such as named entity recognition to identify characters and locations, sentiment analysis for emotions, and parsing temporal information for time-related concepts.

3. Knowledge representation: Develop a knowledge representation system to organize and store the extracted concepts. This could involve using ontologies or graph databases to capture relationships between characters, locations, and emotions.

4. Machine learning models: Train machine learning models to recognize and classify different elements in the script. For example, you might employ text classification algorithms to identify emotions or character sentiment. Use supervised learning with labeled data or explore unsupervised learning approaches for tasks like character clustering.

5. Computer vision (optional): If you want to generate visual elements like storyboards or posters, you may need to integrate computer vision techniques. This could involve scene analysis to identify relevant imagery, object recognition to detect objects mentioned in the script, or image generation models for creating visual representations.

6. Generative AI: Develop generative AI models, such as recurrent neural networks or transformers, to generate storyboards, shot divisions, or conceptual posters based on the extracted concepts and knowledge representation. This requires training the model on a large dataset of existing storyboards, shot divisions, or posters to learn the patterns and generate coherent output.

7. Iterative refinement: Continuously evaluate and refine the AI system's performance by incorporating user feedback, improving models, and updating the knowledge representation to enhance accuracy and generate high-quality results.

It's crucial to recognize that building a comprehensive and accurate AI system for this purpose is a challenging and ongoing research area. It requires expertise in multiple domains and often benefits from collaboration with experts in film production, creative arts, and AI development.