Spiking Neural Networks (SNNs) are a promising new approach to neural network design that have the potential to revolutionize the field of machine learning. SNNs model the behavior of biological neurons by using spikes, or discrete events, to represent the activation of the neurons. Unlike traditional artificial neural networks, SNNs can be processed using simple, low-power circuits, making them well-suited for use in resource-constrained environments such as mobile devices, IoT sensors and robotics. This talk will provide an overview of the current state of SNNs, including their strengths, limitations, and open research questions, as well as provide examples of recent applications that demonstrate the potential of SNNs in various fields such as image recognition, speech recognition and robotics.