Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. To ease these burdens, we propose the task of expository text generation, which seeks to automatically generate an accurate and stylistically consistent expository text for a topic by intelligently searching a knowledge source. We solve our task by developing IRP, a framework that overcomes the limitations of retrieval-augmented models and iteratively performs content planning, fact retrieval, and rephrasing. Through experiments on three diverse, newly-collected datasets, we show that IRP produces factual and organized expository texts that accurately inform readers.

We pride ourselves on our ability to build incrementally, adding steadily to the millions of petabytes of knowledge we've collectively been able to harness. Now imagine the giant is in fact a million little people in a trench coat held together by duct tape on a rickety fifty-foot ladder; Descartes & Newton in shambles. Science, since time immemorial, has relied on the systemic replication of any presented result or finding. Reproducing experiments and their reported results remains a cornerstone of the validation of any scientific theory. Following this understanding, the scientific community realized the severe Replication Crisis we're facing in the fields of psychology and medicine: since experiments conducted within those fields involve a lot of subtle and uncontrollable factors, reproducing results from the past becomes nearly impossible, with the unfortunate consequence of putting many proposed hypotheses into question.

Deep learning has profoundly impacted many areas of machine learning. However, it took a while for its impact to be felt in the field of recommender systems. In this article, we outline some of the challenges encountered and lessons learned in using deep learning for recommender systems at Netflix. We first provide an overview of the various recommendation tasks on the Netflix service. We found that different model architectures excel at different tasks.

Introduction to knowledge representation, expert systems and selected topics including natural language processing, vision and robotics.

The ongoing pandemic has forced designers to reimagine how they reveal their creations and Balenciaga has gone that extra mile to create an immersive experience. On December 6, the fashion house will debut its fall 2021 collection online in an original video game, Afterworld: The Age of Tomorrow. The game is an allegorical adventure set in the year 2031, exploring the collection's theme of destiny through'mythological pasts' and'projected futures.' In a statement, the Spanish label billed Afterworld as the'largest volumetric video project ever undertaken,' incorporating photogrammetry and cutting-edge computer technology. The game will be free and playable directly on Internet browsers.