Researchers at UC San Francisco have enabled a man who is paralyzed to control a robotic arm through a device that relays signals from his brain to a computer.  

He was able to grasp, move and drop objects just by imagining himself performing the actions.  

The researchers' new method, called chemical tomography, provides insights into tissue function by analyzing volatile organic compounds (VOCs). These molecules are present in exhaled breath, saliva, sweat, and other bodily fluids. Prof. Haick is a leading global expert in the use of VOCs for early disease detection. His prior research has led to the development of multiple diagnostic technologies based on VOC analysis.

In this study, VOC monitoring enabled the dynamic molecular and functional mapping of a human breast tissue organoid, revealing key protein and genomic data associated with the transformation of healthy breast tissue into cancerous tissue.

The system detects VOCs using a graphene-based sensor array, with the collected data analyzed through generative artificial intelligence (AI). The inspiration for this technology comes from the compound eye of insects – a structure composed of multiple small eyes that send numerous images to the insect’s brain for analysis. In the system, the graphene sensors function as the compound eye, while AI acts as the brain, processing and interpreting the data.

The enormous computing resources needed to train neural networks for artificial intelligence (AI) result in massive power consumption. Researchers at the Technical University of Munich (TUM) have developed a method that is 100 times faster and therefore much more energy efficient. Instead of taking an iterative approach, the parameters are computed directly based on probabilities. The results so far are comparable in quality to existing iterative methods.