Understanding Neural Networks

Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically he ...  Show more

Anas Buhayh discusses multi-stakeholder fairness in recommender systems and the S'mores framework—a simulation allowing users to choose between mainstream and niche algorithms. His research shows specialized recommenders improve utility for niche users while raising questions abo ...  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 poin ...

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 Nume ...

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!