Computer scientists have developed a way to measure staff comfort and concentration in flexible working spaces using artificial intelligence. While hot desking and activity-based working allow cost savings and greater flexibility - and are said to increase staff collaboration and satisfaction - studies also show the noise and lack of privacy can be distracting. With coronavirus restrictions beginning to ease in some parts of the world and employers planning the return to office-based work, a new sensor-based system developed by RMIT and Arup can offer insights on how to get the best out of these flexible working spaces.

Continuous advancements in the development of AI technology are encouraging more and more companies to introduce artificial intelligence into the workplace. Businesses which were early-adopters of AI have already seen huge successes across multiple KPIs and are now running highly effective business strategies. But the introduction of AI can be a worrying prospect for employees, and it is natural to be wary of things we don't fully understand. Therefore, it's important that companies which do look to harness the power of AI, effectively convey the benefits that it will bring to the current workforce. Here are just some of the ways which artificial intelligence can create a better working environment for your employees.

A picture may be worth a thousand words, but sound is just as important to how we experience the world as how we see it -- that's why a team at Disney Research is working on a computer vision system that can not only recognize what an image is, but how it sounds, too. In an initial study presented at the European Conference on Computer Vision, the group's system successfully managed to pair appropriate audio with images of doors closing, glasses clinking and vehicles driving down the road. Audio association might be easy for humans, but teaching a computer to do it is actually pretty challenging. Disney researchers trained AI to recognize the sound of images by feeding it a collection of videos demonstrating an object making a specific sound, but background noise, narration or sound made from other objects could easily confuse the system. If the system was fed samples with most of the uncorrelated sounds filtered out, however, it did a pretty good job of suggesting the right sound for each image.