Although neural networks have been studied for decades, over the past couple of years there have been many small but significant changes in the default techniques used. For example, ReLU (rectified ...

The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations. Regularization is a ...

Around the Hackaday secret bunker, we’ve been talking quite a bit about machine learning and neural networks. There’s been a lot of renewed interest in the topic recently because of the success of ...

Over the last several years we have seen many new hardware architectures emerge for deep learning training but this year, inference will have its turn in the spotlight. For those chips that can manage ...

Language generators such as ChatGPT are gaining attention for their ability to reshape how we use search engines and change the way we interact with artificial intelligence. However, these algorithms ...

The growing energy use of AI has gotten a lot of people working on ways to make it less power hungry. One option is to develop processors that are a better match to the sort of computational needs of ...

Comparing states A schematic of a quantum spiking neural network made up of several qubits. It can compare two Bell states (shown in red) and store the output with the result in a single (green) qubit ...