We prove that except possibly for small exceptional sets, discrete(cid:173) time analog neural nets are globally observable, i.e. all their cor(cid:173) rupted pseudo-orbits on computer simulations actually reflect the true dynamical behavior of the network. Locally finite discrete (boolean) neural networks are observable without exception.

The new approach allows scientists to better understand neural network behavior. 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 networks identify patterns in datasets and are utilized in applications as diverse as virtual assistants, facial recognition systems, and self-driving vehicles. "The artificial intelligence research community doesn't necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don't know how or why," said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. "Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI." Researchers at Los Alamos are looking at new ways to compare neural networks.

We prove that except possibly for small exceptional sets, discretetime analog neural nets are globally observable, i.e. all their corrupted pseudo-orbits on computer simulations actually reflect the true dynamical behavior of the network. Locally finite discrete (boolean) neural networks are observable without exception.

We prove that except possibly for small exceptional sets, discretetime analog neural nets are globally observable, i.e. all their corrupted pseudo-orbits on computer simulations actually reflect the true dynamical behavior of the network. Locally finite discrete (boolean) neural networks are observable without exception.

We prove that except possibly for small exceptional sets, discretetime analogneural nets are globally observable, i.e. all their corrupted pseudo-orbitson computer simulations actually reflect the true dynamical behavior of the network. Locally finite discrete (boolean) neural networks are observable without exception.