English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Abstraction and Reasoning Corpus (ARC) was recently introduced byFran\c{c}ois Chollet as a tool to measure broad intelligence in both humans andmachines. It is very challenging, and the best approach in a Kaggle competitioncould only solve 20% of the tasks, relying on brute-force search for chains ofhand-crafted transformations. In this paper, we present the first stepsexploring an approach based on descriptive grid models and the MinimumDescription Length (MDL) principle. The grid models describe the contents of agrid, and support both parsing grids and generating grids. The MDL principle isused to guide the search for good models, i.e. models that compress the gridsthe most. We report on our progress over a year, improving on the generalapproach and the models. Out of the 400 training tasks, our performanceincreased from 5 to 29 solved tasks, only using 30s computation time per task.Our approach not only predicts the output grids, but also outputs anintelligible model and explanations for how the model was incrementally built.

First Steps of an Approach to the ARC Challenge based on Descriptive Grid Models and the Minimum Description Length Principle