There is no end to what all human beings can do, and yet there is little we do better than growing on a consistent basis. This tendency to improve, no matter the situation, has empowered the world to hit upon some huge milestones, with technology emerging as quite a major member of the group. The reason why we hold technology in such a high regard is, by and large, predicated upon its skill-set, which ushered us towards a reality that nobody could have ever imagined otherwise. Nevertheless, if we look beyond the surface for a second, it will become clear how the whole runner was also very much inspired from the way we applied those skills across a real world environment. The latter component, in fact, did a lot to give the creation a spectrum-wide, and as a result, initiate a full-blown tech revolution. Of course, this revolution then went on to scale up the human experience through some outright unique avenues, but even after achieving a feat so notable, technology will somehow continue to bring forth the right goods. The same has turned more and more evident in recent times, and assuming one new discovery ends up with the desired impact, it will only put that trend on a higher pedestal moving forward.

The researching teams at University of California and United States Army Research Laboratory have successfully developed a new approach, which will enhance artificial intelligence-powered computer vision capabilities by adding physics-based awareness into the mix. You see, while AI systems have largely relied upon data-based machine learning to gauge their surroundings and carry out any given tasks, the idea of roping in physics-based awareness promises to bolster the precision we usually observe around such operations. According to certain reports, the researchers have devised three particular methods we can use to make the stated merger happen. For starters, they have suggested incorporating physics into AI data sets. This involves leveraging tag objects with additional information, such as how fast they can move or how much they weigh, similar to characters in video games. Next up, we unpack a technique where we install physics into network architectures and run the data through a network filter which codes physical properties into what cameras pick up. Lastly, the teams coined an idea of getting physics-related elements to work alongside network loss function. Driven by knowledge on physics, the stated method uses those relevant bits of information to help AI interpret training data on what it witnesses.

“Visual machines—cars, robots, or health instruments that use images to perceive the world—are ultimately doing tasks in our physical world,” said Achuta Kadambi, the study’s corresponding author an assistant professor of electrical and computer engineering at the UCLA Samueli School of Engineering. “Physics-aware forms of inference can enable cars to drive more safely or surgical robots to be more precise.”

The researchers have already conducted preliminary tests on their approach, and going by the available results, it did make a meaningful difference when it came down to tracking and predicting an object’s motion with more accuracy than, let’s say, what we have been able to achieve so far.

For the future, though, the goal is to continue working on this technology and provide it that ultimate capability to learn the laws of physics on its own.