Understanding Neural Networks

In this episode, Professor Pål Grønås Drange from the University of Bergen, introduces the field of Parameterized Complexity - a powerful framework for tackling hard computational problems by focusing on specific structural aspects of the input. This framework allows researchers ... Show More

In this episode, we learn why simply analyzing the structure of a network is not enough, and how the dynamics - the actual mechanisms of interaction between components - can drastically change how information or influence spreads. Our guest, Professor Baruch Barzel of Bar-Ilan Un ... Show More

In this episode we'll discuss how to use Github data as a network to extract insights about teamwork. Our guest, Gabriel Ramirez, manager of the notifications team at GitHub, will show how to apply network analysis to better understand and improve collaboration within his enginee ... Show More

Today we’re joined by Sophia Sanborn, a postdoctoral scholar at the University of California, Santa Barbara. In our conversation with Sophia, we explore the concept of universality between neural representations and deep neural networks, and how these principles of efficiency pro ... Show More

In this episode, I explore the cutting-edge technology of graph neural networks (GNNs) and how they are revolutionizing the field of artificial intelligence. I break down the complex concepts behind GNNs and explain how they work by modeling the relationships between data points ... Show More

In this episode, we talk with Jeff Hawkins—an entrepreneur and scientist, known for inventing some of the earliest handheld computers, the Palm and the Treo, who then turned his career to neuroscience and founded the Redwood Center for Theoretical Neuroscience in 2002 and Numenta ... Show More

Andy Kitchen is a consultant and researcher in machine learning specializing in neural networks. He sits down with Scott and explains why Machine Learning matters, and why you and I should start learning it ourselves, right away, with TensorFlow! 

What does it mean to solve a problem in our universe? That's a trickier question than you might think, with some fairly high-stakes ramifications in the worlds of computing and even philosophy. In this episode of Stuff to Blow Your Mind, Robert and Joe explore the inherent logic ... Show More

Le deep learning ou l’apprentissage profond est un type d'intelligence artificielle dérivé du machine learning qui signifie lui apprentissage automatique. Ici, la machine est capable d'apprendre par elle-même, contrairement à la programmation où elle se contente d'exécuter à la l ... Show More

Welcome to episode #126 of Eye on AI with Craig Smith and Noam Chomsky. Are neural nets the key to understanding the human brain and language acquisition? In this conversation with renowned linguist and cognitive scientist Noam Chomsky, we delve into the limitations of large lang ... Show More

The intersection between cutting-edge neuroscience and the emerging field of network science has been growing tremendously over the past decade. Olaf Sporns, editor of Network Neuroscience, and Distinguished Professor, Provost Professor of Department of Psychological and Brain Sc ... Show More

In this episode of our podcast, we dive deep into the fascinating world of Graph Neural Networks. First, we explore Hierarchical Networks, which allow for the efficient representation and analysis of complex graph structures by breaking them down into smaller, more manageable com ... Show More