There's a new $20,000 challenge on Kaggle that uses a dataset titled "The Abstraction and Reasoning Corpus". The aim of this episode is to get you up to speed as fast as possible so that you can participate. This challenge is hosted by Francois Chollet, the creator of the popular deep learning library Keras, and there are still 3 months to go. The goal is to create an algorithm that can learn the relationship between input-output pairs of colored tiles. Sounds simple at first, but upon closer inspection, creating an algorithm that's able to do so given such a small dataset requires genuine AI innovation. Deep Learning won't work because it requires big datasets and expensive compute, so even though this challenge is difficult, it's also the most accessible! We'll be learning about the dataset, related paper, and the various techniques that Kagglers have already attempted. Then, we'll learn about Neural Program Synthesis and future directions that could ultimately solve this problem. Enjoy!

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Kaggle Challenge:
www.kaggle.com/c/abstraction-and-reasoning-challen…

ARC Dataset:
github.com/fchollet/ARC

Convolutional Network Kernel:
www.kaggle.com/tarunpaparaju/arc-competition-eda-p…

Cellular Automata Kernel:
www.kaggle.com/arsenynerinovsky/cellular-automata-…

Genetic Algorithm Kernel:
www.kaggle.com/zenol42/dsl-and-genetic-algorithm-a…

Really great paper on Neural Program Synthesis:
arxiv.org/abs/1802.02353

Intro to Convolutional Networks:
   • Convolutional Neural Networks - The M...  

Intro to Genetic Algorithms:
   • 9.1: Genetic Algorithm: Introduction ...  

Intro to Cellular Automata:
   • 7.1: Cellular Automata - The Nature o...  

Simple PointerNet example:
github.com/keon/pointer-networks/blob/master/Point…

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Credits:
Kaggle & Kagglers
Francois Chollet
Google Image Search for the image assets

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