ARC-AGI
A
ARC AGI
Overview :
ARC-AGI is a dataset designed to test whether an artificial intelligence system possesses the ability of abstract and reasoning like a human. It consists of 400 training tasks and 400 evaluation tasks, each stored in JSON format and including input-output pairs. This dataset can be used as a benchmark for artificial intelligence, program synthesis, or psychological intelligence testing.
Target Users :
The target audience is primarily AI researchers and developers, as well as scholars interested in testing human and artificial intelligence. This product can help them evaluate and improve the generalization and reasoning abilities of algorithms.
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Use Cases
Researchers use the ARC-AGI dataset to train deep learning models to improve their ability to solve abstract problems.
Educators utilize this dataset as a teaching tool to help students understand how artificial intelligence works.
Technology companies use ARC-AGI as a benchmark to evaluate the performance of their AI products.
Features
Provides an abstract and reasoning task dataset for training and evaluating AI algorithms.
Contains a browser interface that allows humans to manually solve tasks and test human intelligence.
Tasks are stored in JSON format, including training and testing input-output pairs.
Supports three attempts to construct an output grid to match the test input grid.
Uses colors (integers 0-9) to visualize cells in the grid.
Provides a testing interface that allows users to load tasks and attempt to solve them.
How to Use
Visit the ARC-AGI GitHub page and download the dataset.
Select a task JSON file and familiarize yourself with its training and testing input-output pairs.
Open the testing interface using the browser and load the selected task.
In the testing space, observe the demonstration of input-output pairs and understand the nature of the task.
Use the grid control tools to adjust the output grid size, duplicate the input grid, or reset the grid.
Use the symbol control tools to edit the color of grid cells by selecting and filling them.
After constructing the output grid, click the 'Submit' button to validate the answer.
After completing the current test, continue with the 'Next Test Input' button or start a new task with the 'Load Task' button.
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