This talk provides a high-level introduction to artificial intelligence and deep learning, covering the basics of machine learning and the key concepts of deep learning. We will explore the different types of deep learning architectures, including fully connected networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), 3D CNNs and transformers, highlighting their most common use cases and applications. We will then focus on visual AI, introducing CNNs as a fundamental architecture for image and video analysis. We will discuss the building blocks of CNNs and explore example architectures such as Inception, ResNet and EfficientNet. Finally, we will highlight some recent trends in visual AI such as vision transformers (ViT), hybrid architectures and visual-language models (VLM). By the end of this talk, attendees will have a solid understanding of the fundamentals of deep learning and visual AI, as well as recent advancements and current trends in the field.