The neural networks are harder to fool thanks to adversarial training. Los Alamos National Laboratory researchers have developed a novel method for comparing neural networks that looks into the 'black box' of artificial intelligence to help researchers comprehend neural network behavior. Neural net
The neural networks are harder to fool thanks to adversarial training.researchers have developed a novel method for comparing neural networks that looks into the “black box” of artificial intelligence to help researchers comprehend neural network behavior. Neural networks identify patterns in datasets and are utilized in applications as diverse as virtual assistants, facial recognition systems, and self-driving vehicles.
Researchers at Los Alamos are looking at new ways to compare neural networks. This image was created with an artificial intelligence software called Stable Diffusion, using the prompt “Peeking into the black box of neural networks.” Credit:presented at the Conference on Uncertainty in Artificial Intelligence. The paper is an important step in characterizing the behavior of robust neural networks in addition to studying network similarity.
Therefore, in order to improve neural networks, researchers are searching for strategies to increase network robustness. One cutting-edge method involves “attacking” networks as they are being trained. The AI is trained to overlook abnormalities that researchers purposefully introduce. In essence, this technique, known as adversarial training, makes it more difficult to trick the networks.
“We found that when we train neural networks to be robust against adversarial attacks, they begin to do the same things,” Jones said.