In March of this year I was lucky to travel to my first academic conference (thanks very much to EPSRC Rise and AAAI). Feeling a little bit nervous, extremely jet-lagged, and completely in awe of Stanford’s gorgeous campus, I attended the symposium on “Interpretable AI for Well-Being: Understanding Cognitive Bias and Social Embeddedness”.

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

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 Marvin Zhang and Sharad Vikram

Imagine a robot trying to learn how to stack blocks and push objects using visual inputs from a camera feed. In order to minimize cost and safety concerns, we want our robot to learn these skills with minimal interaction time, but efficient learning from complex sensory inputs such as images is difficult. This work introduces SOLAR, a new model-based reinforcement learning (RL) method that can learn skills – including manipulation tasks on a real Sawyer robot arm – directly from visual inputs with under an hour of interaction. To our knowledge, SOLAR is the most efficient RL method for solving real world image-based robotics tasks.

“From the Archive” features historical content shining a light on past successes in AI.

This week we feature RoboCup highlights from 1997 to 2011.

By Anusha Nagabandi and Ignasi Clavera

Humans have the ability to seamlessly adapt to changes in their environments: adults can learn to walk on crutches in just a few seconds, people can adapt almost instantaneously to picking up an object that is unexpectedly heavy, and children who can walk on flat ground can quickly adapt their gait to walk uphill without having to relearn how to walk. This adaptation is critical for functioning in the real world.