Nico, Emil, and Moritz founded ReRun with the mission of making powerful visualization tools free and easily accessible for roboticists. Nico and Emil talk about how these powerful tools help debug the complex problem scopes faced by roboticists. Nikolaus West Co-Founder & CEO Niko is a second-time founder and software engineer with a computer vision background from Stanford. Emil Ernerfeldt Co-Founder & CTO Emil fell in love with coding over 20 years ago and hasn't looked back since. He's the creator of egui, an easy-to-use immediate mode GUI in Rust, that we're using to build Rerun.

In the example ETL pipeline below, three data files are transformed, loaded into a staging table, and finally aggregated into a final table. A common issue for ETL failures is missing data files for the latest day's run. If the data comes from an external source, check with the provider and confirm if the files are running late. If the data is internal such as application events or the company website activity, confirm with the team responsible if there were issues that could've caused delayed or missing data. Once you get the missing data your ETL issue is resolved.

On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task.

Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance. We build on these efforts and take them further. We offer a principled experimental design methodology, based on linear mixed models, to study and separate the effects of multiple factors of variation in machine learning experiments. This approach allows to account for the effects of architecture, optimizer, hyper-parameters, intentional randomization, as well as unintended lack of determinism across reruns. We illustrate that methodology by analyzing Matching Networks, Prototypical Networks and TADAM on the miniImagenet dataset.

Remember the days when the summer television schedule was dominated by reruns, lowbrow reality programs, and even more reruns? Today's summer television landscape is both exciting and diverse, and it's already giving the fall–spring lineup a serious run for its money. Below, we've outlined highlights from the summer slate, as well as a list of when all shows will be returning -- we'll continue to update this post in the coming weeks as more programming information becomes available.

Online news sites often include a comments section where readers are allowed to leave their thoughts. These comments often contain interesting and insightful conversations between readers about the news article. However the richness of these conversations is often lost among other meaningless comments, and moreover all comments are found at the bottom of the web page. In this article, we discuss how our system inserts reader conversations into the news article to create a multimedia presentation called Shout Out. Shout Out features two virtual news anchors: one anchor reads the news and when appropriate the anchor pauses to have a conversation about the news with another anchor. This current iteration of Shout Out combines natural language techniques and reader conversations to create an engaging system.