AI Readiness: Foundations of Artificial Intelligence

AI Fundamentals: The Advent of AI to Machine Learning to Neural Nets Hessie Jones takes you through this session, which traces the journey of artificial intelligence from what early thought leaders envisioned to the rise of neural networks, a point in time when accelerating computing power accelerated the technology and helped us emerge from the AI winter. It will introduce the core understanding of neural networks and Geoff Hinton's vision for his technology to behave like humans. It will introduce the evolution of machine learning types and how they inform predictions. By the end of the session, participants will understand the technology's vision and its evolution. The Math Behind AI Saurab Tripathi introduces the core mathematical ideas behind AI and machine learning in an accessible way. We’ll cover how data is represented (continuous vs. discrete), the difference between regression and classification tasks, and how model performance is measured through accuracy and error. We’ll also explore how models learn through optimization, and provide a simple introduction to embeddings, how words and sentences are converted into numbers that AI systems can understand. By the end, participants will have a clearer, more intuitive understanding of how AI works under the hood. Saurabh Tripathi is a Senior Machine Learning Engineer at Samsara with over 10 years of experience in AI and machine learning. He holds a Master's in Analytics from Northwestern University and specializes in computer vision, NLP, multimodal AI, edge AI and MLOps. He has shipped multiple production AI systems and holds a patent in the field. AI Fundamentals: The Advent of AI to Machine Learning to Neural Nets GET SOCIAL! Connect with RIC Centre on LinkedIn: http://ow.ly/gOepA Follow @RICCentre on Twitter:   / riccentre   Like RIC Centre on Facebook:   / riccentre