The virtuous cycle of AI research

Many recent research efforts seek to construct neural networks capable of executing algorithmic computation, primarily to endow them with reasoning capabilities – which neural networks typically lack. Critically, every one of these papers generates its own dataset, which makes it hard to track progress, and raises the barrier of entry into the field. The CLRS benchmark, with its readily exposed dataset generators, and publicly available code, seeks to improve on these challenges. We've already seen a great level of enthusiasm from the community, and we hope to channel it even further during ICML. The main dream of our research on algorithmic reasoning is to capture the computation of classical algorithms inside high-dimensional neural executors.