The rise of so-called "smart factory" technologies is leading to a race to modernize manufacturing plant and warehouse floors. Old equipment is being replaced by newer, more advanced machinery as manufacturers look to keep pace with the competition -- and wrestle with high turnover rates. According to a survey by Plex Systems, 50% of manufacturers accelerated their adoption of automation and digital systems during the pandemic. A separate report from The Harris Poll, commissioned by Google, found that two-thirds of manufacturers were using AI in their day-to-day operations as of June 2021. Take those numbers with a grain of salt -- they're not from impartial sources, after all.

"Assembly" may sound like one of the simpler tests in the manufacturing process, but as anyone who's ever put together a piece of flat-pack furniture knows, it can be surprisingly (and frustratingly) complex. Invisible AI is a startup that aims to monitor people doing assembly tasks using computer vision, helping maintain safety and efficiency -- without succumbing to the obvious all-seeing-eye pitfalls. A $3.6 million seed round ought to help get them going. The company makes self-contained camera-computer units that run highly optimized computer vision algorithms to track the movements of the people they see. By comparing those movements with a set of canonical ones (someone performing the task correctly), the system can watch for mistakes or identify other problems in the workflow -- missing parts, injuries and so on.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. To help modify current brain-computer interfaces, Zachary Danziger, an assistant professor in the Department of Biomedical Engineering housed within the College of Engineering & Computing, was awarded a $1.6 million grant from the National Institutes of Health (NIH). Danziger is creating a less invasive and more interactive model, called the joint angle brain-computer interface (jaBCI), to help improve brain-computer interface's brain decoding algorithms before the device is implanted in the brain. This model is referred to as the jaBCI because it is powered by people's finger joint angles instead of neurons. The system can potentially help people who are paralyzed to regain their independence and ability to communicate.