Advanced AI Training — Deep Intro to Graph Neural Nets GNN

eXacognition AI has released on their Youtube channel the next session video in the Deep Intro to AI Bootcamp series of introductory AI training presentations on advanced Artificial Intelligence design that I created & presented earlier this year to their client base. I personally perceive Graph Neural Networks less as graphs and more as relative spatial relationships of anything perceived by a cognition both human and artificial. Within the foundation of these networks lies the foundation of other AI tech like RNNs, GANs & Variational Autoencoders (VAE) and that hidden GNN foundation is a specific type of cognitive structure used to comprehend the nature of relative correlation & variance that is especially helpful in generalization or the move to Artificial General Intelligence. However a more immediate use of this hidden GNN foundation is in artificial comprehension of flowing variant and layered perceptual context. This is a critical step on the pathway and evolution from narrow AI to Superintelligence. I discuss VAE’s in more detail in the next session on Generative AI but before that, we need to understand and appreciate the true value of relative cognitive spatial awareness hidden deep inside the structure and math behind a Graph Neural Network. <a href="http://eXacognition AI has released on their Youtube channel the next session video in the Deep Intro to AI Bootcamp series of introductory AI training presentations on advanced Artificial Intelligence design that I created & presented earlier this year to their client base. I personally perceive Graph Neural Networks less as graphs and more as relative spatial relationships of anything perceived by a cognition both human and artificial. Within the foundation of these networks lies the foundation of other AI tech like RNNs, GANs & Variational Autoencoders (VAE) and that hidden GNN foundation is a specific type of cognitive structure used to comprehend the nature of relative correlation & variance that is especially helpful in generalization or the move to Artificial General Intelligence. However a more immediate use of this hidden GNN foundation is in artificial comprehension of flowing variant and layered perceptual context. This is a critical step on the pathway and evolution from narrow AI to Superintelligence. I discuss VAE’s in more detail in the next session on Generative AI but before that, we need to understand and appreciate the true value of relative cognitive spatial awareness hidden deep inside the structure and math behind a Graph Neural Network.">Website</a>