A study, published in Nature Machine Intelligence, outlines a hybrid methodology designed to improve how autonomous vehicles navigate and how robots execute precision actions.
Researchers from UCLA and the United States Army Research Laboratory have recently proposed a novel approach to enhance artificial intelligence (AI)-powered computer vision technologies. This approach combines the power of physics-based awareness with data-driven techniques, aiming to revolutionize how AI-based machinery interacts with its environment in real time.
What is the research about?
The researchers at UCLA aim to overcome certain limitations by integrating an understanding of physics into the development of neural networks. These networks, modeled after the human brain, crunch massive image datasets until they gain an understanding of what they “see.” The challenge lies in adding elements of physics awareness into these robust data-driven networks.
The UCLA-led study seeks to harness the power of both data and physics to create a hybrid AI with enhanced capabilities. “Visual machines are ultimately doing tasks in our physical world,” said the study's corresponding author Achuta Kadambi, 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.”
Talking about the study, the key is within the technique called computer vision, which allows AI to comprehend its surroundings by decoding data and inferring properties of the physical world from images.
What is a computer vision technique?
Traditionally, computer vision techniques have predominantly focused on data-based machine learning to drive performance. Here the images are formed through the physics of light and mechanics. And data-driven techniques often overlook the physical principles behind many computer vision challenges.
The research team outlined three ways to combine physics and data into computer vision AI:
- Incorporating physics into AI datasets
- Incorporating physics into network architectures
- Incorporating physics into the network loss function
These investigations have already yielded promising results. For instance, the hybrid approach allows AI to track and predict an object's motion more accurately and can produce high-resolution images from scenes obscured by adverse weather conditions.
The researchers believe that with continued progress, deep learning-based AIs may even begin to learn the laws of physics on their own. This research, supported in part by a grant from the Army Research Laboratory, represents a significant step forward in the field of AI and computer vision.
The Hybrid Approach in Action
According to a report by TechXplore, the hybrid approach proposed by UCLA researchers and the U.S. Army Research Laboratory has already shown promising results in real-world applications. The AI can track and predict an object's motion more accurately, and it can produce high-resolution images from scenes obscured by adverse weather conditions. This is a significant advancement in the field of AI and computer vision, as it allows for more precise and reliable machine interactions with the environment.
The Future of AI and Computer Vision
The researchers believe that with continued progress, deep learning-based AIs may even begin to learn the laws of physics on their own. This could revolutionize the field of AI and computer vision, opening up new possibilities for autonomous vehicles, robotics, and other AI-powered technologies. The study, supported in part by a grant from the Army Research Laboratory, represents a significant step forward in the field of AI and computer vision.
The Impact of the Hybrid Approach on AI Applications
The hybrid approach to AI and computer vision could have far-reaching implications for various applications. For instance, autonomous vehicles could navigate more safely, surgical robots could operate with greater precision, and AI-powered surveillance systems could monitor environments more effectively. As AI continues to evolve and improve, we can expect to see even more innovative applications of this technology in the future.
More information: Achuta Kadambi et al, Incorporating physics into data-driven computer vision, Nature Machine Intelligence (2023). DOI: 10.1038/s42256-023-00662-0
Credits: TechExplore
