Black Boxes Are Not Required
Deep neural networks are undeniably effective. They rely on such a high number of parameters, that they are appropriately described as "black boxes". While black boxes lack desirably properties like i...
5 Juni 202032min
Robustness to Unforeseen Adversarial Attacks
Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.
30 Maj 202021min
Estimating the Size of Language Acquisition
Frank Mollica joins us to discuss the paper Humans store about 1.5 megabytes of information during language acquisition
22 Maj 202025min
Interpretable AI in Healthcare
Jayaraman Thiagarajan joins us to discuss the recent paper Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.
15 Maj 202035min
Understanding Neural Networks
What does it mean to understand a neural network? That's the question posted on this arXiv paper. Kyle speaks with Tim Lillicrap about this and several other big questions.
8 Maj 202034min
Self-Explaining AI
Dan Elton joins us to discuss self-explaining AI. What could be better than an interpretable model? How about a model wich explains itself in a conversational way, engaging in a back and forth with th...
2 Maj 202032min
Plastic Bag Bans
Becca Taylor joins us to discuss her work studying the impact of plastic bag bans as published in Bag Leakage: The Effect of Disposable Carryout Bag Regulations on Unregulated Bags from the Journal of...
24 Apr 202034min
Self Driving Cars and Pedestrians
We are joined by Arash Kalatian to discuss Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning.
18 Apr 202030min
