Forecasting human motion of multiple persons is very challenging. It requires to model the interactions between humans and the interactions with objects and the environment. For example, a person might want to make a coffee, but if the coffee machine is already occupied the person will haveto wait. These complex relations between scene geometry and persons ariseconstantly in our daily lives, and models that wish to accurately forecasthuman behavior will have to take them into consideration. To facilitate research in this direction, we propose Humans in Kitchens, alarge-scale multi-person human motion dataset with annotated 3D human poses, scene geometry and activities per person and frame.Our dataset consists of over 7.3h recorded data of up to 16 persons at the same time in four kitchen scenes, with more than 4M annotated human poses, represented by a parametric 3D body model. In addition, dynamic scene geometry and objects like chair or cupboard are annotated per frame. As first benchmarks, we propose two protocols for short-term and long-term human motion forecasting.

Researchers from the Queensland University of Technology (QUT) in Australia have developed an algorithm that detects misogynistic content on Twitter. The team developed the system by first mining 1 million tweets. They then refined the dataset by searching the posts for three abusive keywords: whore, slut, and rape. Next, they categorized the remaining 5,000 tweets as either misogynistic or not, based on their context and intent. These labeled tweets were then fed to a machine learning classifier, which used the samples to create its own classification model.

The Find-the-Remote event was considered the most challenging of the events in the 1997 AAAI Mobile Robot Competition and Exhibition. It required a broad range of both hardware and software capabilities. I discuss the rules and rationale for the event as well as the results. It involved fetching a known set of objects from unknown, but constrained, locations in a known environment. In real life, such functions might be useful for in-home care of the elderly or the physically disabled. This event was extremely difficult because it forced teams to implement both manipulation (the grasping and moving of objects) and visual object recognition. Furthermore, it explicitly required teams to implement them for a wide range of objects. It therefore eliminated a broad range of special-purpose sensing and manipulation strategies that would be specific to one or another class of objects. It also required that objects be lifted from a variety of surfaces (real furniture) at a variety of heights.

One should not throw out the baby with the bathwater, according to an old aphorism. Some popular recent positions in AI thinking have done just this, we suggest, by rejecting the useful idea of mental representations in their overenthusiastic zeal to correct some simplifications and naïveties in the way traditional AI ideas have sometimes been understood. These "situated" perspectives correctly emphasize that agents live in a social world, using their environments to help guide their actions without needing to always plan their futures in detail; but they incorrectly conclude that the very idea of mental representation is mistaken. This perspective has its intellectual roots in parts of recent sociological thinking which reject the entire fabric of western science. We discuss these ideas and disputes in the form of an illustrated fable concerning nannies and babies.

Most of us have struggled to prise the lid off a jar of jam or been frustrated by the tricky safety caps on bottles of pills, but now there is robotic help at hand - literally. Engineers have developed a machine with two highly dexterous arms that are capable of unscrewing even the trickiest of lids. The talents of the RE2 Robotics High Dexterous Manipulation System extend even to tying knots, opening zips, making balloon animals and cutting snowflakes out of folded paper. A pair of robotic arms developed by engineers at RE2 Robotics is so dexterous it can unscrew the lids of jars (pictured) and even use a pair of scissors to cut out shapes from folded paper. Yet despite its handiness around the home, the robot is additionally capable of lifting three times its own weight, and the company behind it said it could be used to dismantle unexploded bombs.