Interessanter Artikel über die Aktivitäten von Hyundai im Bereich Artificial Intelligence.
While Hyundai will develop its own deep learning technology, its researchers will use Allegro.ai’s solutions to better understand how the puzzle pieces come together. “By making these tools commercially available, companies can access them, which means things are going to happen faster,” Bar-Lev predicted.
The first (and most often cited) application of deep learning in the automotive world is powering an autonomous car. For it to work, a car needs to understand what it’s doing, what other cars are doing, and the type of environment it’s operating in. And, as Bar-Lev pointed out, driving a car in the United States is a completely different experience than driving in Abu Dhabi, or Guatemala City, or downtown Paris.
Teaching a car how to drive is a lot like teaching a teen how to drive in the sense that experience is key. For a 15-year old, the experience comes by spending hours behind the wheel next to an instructor. For a car, it requires feeding the software a mammoth amount of annotated data that teaches it what trees, trucks, and railroad crossings look like.
Allegro.ai doesn’t deal in data. The companies who want to build self-driving cars need to figure out how to gather it. It simply provides a platform that lets engineers annotate it and feed it to a car more efficiently and at scale. On a second but more lasting level, the same basic technology can be used to teach a car how to recognize who is in the car at any given time and what they are doing.
“If a car is on-demand, it needs to somehow know what’s going on in the cabin. It needs to make sure that no one is littering the cabin, that no one is doing something they’re not supposed to be doing,” explained Bar-Lev. This type of technology is also used in semi-autonomous systems to tell whether the driver is looking at the road ahead, counting crows on power lines, or sleeping.
Finally, deep learning technology can also help automakers build better cars. Robots trained in quality control can identify even the tiniest scratches in the paint, misaligned body panels, or leaks before a car leaves the assembly line. Humans currently do this job. AI-enabled robots could replace them or complement them, depending on the company and the use case it makes for deep learning technology.