Interpretability Practitioners
Sungsoo Ray Hong joins us to discuss the paper Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs.
26 Kesä 202032min
Facial Recognition Auditing
Deb Raji joins us to discuss her recent publication Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.
19 Kesä 202047min
Robust Fit to Nature
Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.
12 Kesä 202038min
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 Kesä 202032min
Robustness to Unforeseen Adversarial Attacks
Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.
30 Touko 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 Touko 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 Touko 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 Touko 202034min
