news    articles    opinions    tutorials    concepts    |    about    contribute     republish

Uncategorized

by   -   August 14, 2019

Like yesterday, we bring you the best tweets covering major talks and events at IJCAI 2019.

by   -   August 13, 2019

Robots are helping conference-goers at IJCAI 2019 this week.

You can get a souvenir from “Dorabot” by showing your badge, or get rid of your glass at the welcome reception.

by   -   August 12, 2019

Meet Dimmy Wang who presented his work at the Deep Learning Workshop at IJCAI 2019. He graduated with a Masters from Tshinghua University and currently works as a researcher at Tencent Cloud AI.

by   -   July 21, 2019

In this episode of Computing Up, Michael Littman and Dave Ackley discuss the democratic debates and raise the question of whether progress is even possible.

by   -   June 24, 2019

The International Conference on Machine Learning took place this month in Long Beach, California. Over one week, the conference was home to 6000+ machine learning experts from academia and industry.

Almost all content at ICML 2019 was live streamed – which means you can hear talks from the likes of Jeff Clune, Raia Hadsell, or Yann Lecun. I’ve embedded as many streams as I could below for ease of navigation, but you should also check out the ICML Facebook Page for the full list.

by   -   June 24, 2019

The International Conference on Machine Learning took place this month in Long Beach, California. Over one week, the conference was home to 6000+ machine learning experts from academia and industry.

Almost all content at ICML 2019 was live streamed – which means you can hear talks from the likes of Jeff Clune, Raia Hadsell, or Yann Lecun. I’ve embedded as many streams as I could below for ease of navigation, but you should also check out the ICML Facebook Page for the full list.

by   -   June 22, 2019
Effect of Population Based Augmentation applied to images, which differs at different percentages into training.

In this blog post we introduce Population Based Augmentation (PBA), an algorithm that quickly and efficiently learns a state-of-the-art approach to augmenting data for neural network training. PBA matches the previous best result on CIFAR and SVHN but uses one thousand times less compute, enabling researchers and practitioners to effectively learn new augmentation policies using a single workstation GPU. You can use PBA broadly to improve deep learning performance on image recognition tasks.

We discuss the PBA results from our recent paper and then show how to easily run PBA for yourself on a new data set in the Tune framework.

by   -   June 1, 2019

In this episode of Computing Up, Michael Littman and Dave Ackley discuss intersubjectivity.

by   -   May 27, 2019


OECD and partner countries formally adopted the first set of intergovernmental policy guidelines on Artificial Intelligence (AI) today, agreeing to uphold international standards that aim to ensure AI systems are designed to be robust, safe, fair and trustworthy.

by   -   May 16, 2019

“From the Archive” features historical content shining a light on past successes in AI.

This week we feature RoboCup highlights from 1997 to 2011.




supported by: