AI Oracle: Predictive Text for Creative Writing

Imagine an AI assistant for writers, inspired by the predictive text interface of HAL 9000 and the grand narratives of Hyperion. This project, 'AI Oracle', functions as a sentence-level predictive text engine trained on a curated dataset of literary works. The user provides an initial prompt (a sentence or paragraph), and the AI, trained on a specific author's style (e.g., Dan Simmons), generates several possible subsequent sentences.

The story behind this concept is the idea of unlocking creativity through AI. Writers often face 'blank page' syndrome. 'AI Oracle' offers a jumpstart, not by writing the story for them, but by providing suggestions that align with a specific literary style. The user can then choose, modify, or reject these suggestions, iteratively building their narrative.

Concept:

1. Data Acquisition: The first step involves gathering text from books. Initially, focus on a single author like Dan Simmons (Hyperion) or works with a distinct style, which reduces training data requirements.
2. Model Training: Train a relatively simple language model (e.g., a Recurrent Neural Network or Transformer) to predict the next word or sentence given a sequence of words. Pre-trained models can be fine-tuned.
3. Stylistic Control: Implement a mechanism to bias the model towards the specific author's style. This could involve using stylistic features as additional inputs to the model or using different training datasets representing different styles.
4. User Interface: Create a simple web interface (using Streamlit or Flask) where users can input their initial text and see the AI-generated suggestions.
5. Iterative Refinement: The user selects one of the AI-generated sentences, which becomes the new input, and the AI generates the next set of suggestions, and so on. This creates an iterative co-creation process.

How it Works:

The user provides a prompt to the system through the interface. The prompt is sent to the backend where the pre-trained model predicts the next possible sequence. The model will have been trained on the text of one or more authors. The output consists of a few sentences for each prompt given by the user. The user can then choose their preferred output, make any edits, and ask the model to generate text based on their newly formed text, continuing until the scene is completed.

Niche: This caters to creative writers, scriptwriters, and game developers who seek inspiration or a way to overcome writer's block.
Low Cost: Can be implemented with readily available open-source tools (Python, TensorFlow/PyTorch, Streamlit/Flask) and limited computational resources. Cloud services (Google Colab) can handle the training.
High Earning Potential: It can be monetized through a subscription model, offering different tiers based on the number of supported authors/styles or by selling custom-trained models for specific writing styles.