Everyone Can Understand Machine Learning… and More!

Everyone Can Understand Machine Learning… and More!

Everyone Can Understand Machine Learning… and More! Some cool announcements, what is new in the AI world, and our monthly picks.

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Work in the AI field is moving forward very quickly. Today Papers with Code announced their partnership with arXiv, where code links are now shown on arXiv articles, and authors can submit code through arXiv, making it a great addition to avid researchers and practitioners.

NeurIPS also announced a cool challenge, the 2020 ML Reproducibility Challenge sponsored by Papers with Code, encouraging people who work with ML to participate (including enthusiasts!). If you’d like to learn more, check outtheir announcement, it sounds pretty neat.

In other news, if you are into reinforcement learning, we encourage you to check out MineRL— their research group just announced the extension of their competition till February 2021. So if you’d like to build some sample-efficient AI agents in Minecraft, definitely check them out!

Also, Scale AIannounced that they are sponsoring students and independent researchers creating promising datasets. If you are interested, check out the announcement by Aerin Kim.

Last but not least, don’t forget that October 11 is the deadline for the 2020 Amazon Research Awards. If you like to apply or learn more, you can do so on Amazon Science.

Is your company interested in supporting Towards AI’s efforts? For sponsorship opportunities, please email us.

Now into the monthly picks! We pick these articles based on readership, fans, and views a specific piece gets. We hope you enjoy reading them as much as we did. Also, we started doing something new! We will pick our top ten performing articles, and our editors will choose one to two essays that didn’t have outstanding performance, but due to its quality — made the cut for the month.

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