https://github.com/sam120204/hackos2
https://github.com/sam120204/hackos2
Last synced: 2 months ago
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- Host: GitHub
- URL: https://github.com/sam120204/hackos2
- Owner: Sam120204
- Created: 2024-11-09T19:12:26.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-10T02:39:54.000Z (about 1 year ago)
- Last Synced: 2025-01-14T17:33:44.874Z (12 months ago)
- Language: Python
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# HackOS2 - ARC Transformation Project
This project tackles the Abstraction and Reasoning Corpus (ARC) challenge, a set of tasks designed to evaluate an AI’s ability to recognize and generalize patterns in a way similar to human intelligence. Through pattern recognition, the project aims to develop a model that can analyze grids of numbers and generalize transformations that apply to unseen cases.
## Purpose
The ARC Transformation Project focuses on simulating reasoning abilities in AI by enabling it to learn abstract visual transformations from training examples. The core goal is to create a system that, when presented with a set of grid-based tasks, can:
1. **Identify Patterns**: Learn generalized transformation rules from provided training data.
2. **Apply Transformations**: Use the learned patterns to accurately transform test grids that it has never seen before.
This project is part of an initiative to advance AI's ability to tackle open-ended reasoning tasks and to measure its performance on abstract problem-solving in a human-comparable way.
## Key Features
- **Pattern Recognition**: Utilizes AI to derive a unified transformation pattern from a set of training grids, providing insight into visual transformations without manual input.
- **Automated Transformation Application**: Once a transformation pattern is identified, it is applied to new test grids, producing predicted outputs.
- **Flexibility**: Designed to be adaptable across different tasks within the ARC dataset, making it possible to evaluate various transformation rules and complexities.
## Project Structure
The project consists of several main components:
- **Data Loading**: Imports and manages ARC datasets for training and testing.
- **Transformation Generation**: Uses AI to extract generalized transformation rules from training examples.
- **Prediction**: Applies the learned transformations to test cases to predict the correct output.
## Requirements
To run the project, you’ll need:
- **Python 3.x**
- An **OpenAI API key** to access the necessary models for generating and applying transformations.
- Additional dependencies, which can be installed using:
```bash
pip install -r requirements.txt