Children and young people are growing up in an increasingly digital age, where technology pervades every aspect of their lives. From robotic toys and social media to the classroom and home, artificial intelligence (AI) is a ubiquitous part of daily life. It's vital therefore that ethical guidelines protect them and ensure they get the best from this emerging technology. Generation Z, who have grown up with AI, are uniquely placed to offer an insight into the potential issues of AI targeted at children and help create governance guidelines. With that in mind the World Economic Forum has set up the AI Youth Council, a global diverse group comprising young people interested in AI.

Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for large games that allow complex and constrained locomotion movements, where the number of transitions grows exponentially with the number of states. In this paper, we present a novel approach, based on deep recurrent neural networks, to automatically generate such transitions given a \textit{past context} of a few frames and a target character state to reach. We present the Recurrent Transition Network (RTN), based on a modified version of the Long-Short-Term-Memory (LSTM) network, designed specifically for transition generation and trained without any gait, phase, contact or action labels. We further propose a simple yet principled way to initialize the hidden states of the LSTM layer for a given sequence which improves the performance and generalization to new motions. We both quantitatively and qualitatively evaluate our system and show that making the network terrain-aware by adding a local terrain representation to the input yields better performance for rough-terrain navigation on long transitions. Our system produces realistic and fluid transitions that rival the quality of Motion Capture-based ground-truth motions, even before applying any inverse-kinematics postprocess. Direct benefits of our approach could be to accelerate the creation of transition variations for large coverage, or even to entirely replace transition nodes in an animation graph. We further explore applications of this model in a animation super-resolution setting where we temporally decompress animations saved at 1 frame per second and show that the network is able to reconstruct motions that are hard to distinguish from un-compressed locomotion sequences.

The haunting genius of George Orwell's Nineteen Eighty-Four is that it suggests how some ideas can become literally unimaginable when the language that describes them is destroyed. He wrote of freedoms that political power could make simply impossible to talk, write and think about – because there was no language in which to do so. It's a brilliant idea with multiple applications in every age – suggesting that we need always to be ready to interrogate the reasons why ideas are being reconfigured, compromised or destroyed. Related: 'Bedblocking' – what happens when profits are put ahead of people Here is an unexpected candidate: care. It's a small word, so pervasive and overloaded with meanings that its significance has often been easy to overlook. It's the care given by parents that nurtures us into adulthood, and it's the care given by others that supports us in old age and as we die; and in-between, care is the oft overlooked scaffolding of our lives, on which wellbeing and daily life depend.