New method for comparing neural networks exposes how artificial intelligence works
A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.
A team has developed a novel approach for comparing neural networks that looks within the 'black box' of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving…
Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and reasoning models like ChatGPT. The principle: during a training phase, the parameters of the network's artificial neurons are optimized in such a way that they can carry out specific tasks, such as…
Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have shown that a brief period of alignment between neural networks, a method they call guidance, can dramatically improve the performance of architectures previously thought…