15. Dezember 2020, 18:00 Uhr | Andreas Knoll
Ian Ferguson, Lynx Software Technologies: »Cobots are an exciting vision whose promise relies on the safe, secure implementation of AI.«
AI capabilities can change how systems behave and are controlled in amazing ways. What is fascinating here are the new possibilities for collaboration between computers (robots) and humans - using the best capabilities of each.
The emergence of Cobots, or collaborative robots, intended to interact with humans in a shared space or to work safely in close proximity is an important step. Cobots stand in stark contrast to traditional industrial robots which work autonomously with safety assured by isolation from human contact. Cobots are currently a very small percentage of the overall robotics market, but they are an area we (and a number of recognized analysts) believe will grow rapidly in the next five years, for applications in manufacturing, healthcare and retail. The Covid-19 pandemic is accelerating this trend, providing more acceptance towards increased digital transformation and automation.
Cobots need much closer control for real-time implementation of complex decisions in co-working environments. This is an area where there is a lot of focus on AI to improve the user experience with these types of machines. This decision making HAS to be made by the robot as an Edge device in order to achieve the speed and latency to cope with increasing data from more IoT sensors and the consequences of getting a decision wrong. The more pioneering manufacturing plants, however, are starting to rethink processes to make more efficient use of humans and robots together.
Edge computing will have a big impact on the development of AI. Right now, AI training produces vast volumes of data that are almost exclusively implemented and stored in the cloud. Placing compute at the edge creates a change to process and looks for patterns locally. I believe this can evolve the training models to become simpler and more effective. I’ve seen car manufacturers dramatically improve their quality-control processes through inference at the edge because they are able to catch any defects in real-time, before the product is put into commercialization.