Data Skeptic

Paxos is a protocol for arriving a consensus in a distributed computing system which accounts for unreliability of the nodes.  We discuss how this might be used in the real world in the event of a massive disaster.

Direct download: paxos.mp3
Category:general -- posted at: 8:00am PDT

Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems.

The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of the example are likely to reveal the relevant features in the local input space to reveal details about why the model arrives at it's conclusion.

In this episode, Marco Tulio Ribeiro joins us to discuss how LIME (Locally Interpretable Model-Agnostic Explanations) can help users trust machine learning models. The accompanying paper is titled "Why Should I Trust You?": Explaining the Predictions of Any Classifier.

Direct download: trust-in-ml.mp3
Category:general -- posted at: 8:00am PDT

Analysis of variance is a method used to evaluate differences between the two or more groups.  It works by breaking down the total variance of the system into the between group variance and within group variance.  We discuss this method in the context of wait times getting coffee at Starbucks.

Direct download: anova.mp3
Category:general -- posted at: 8:00am PDT

When humans describe images, they have a reporting bias, in that the report only what they consider important. Thus, in addition to considering whether something is present in an image, one should consider whether it is also relevant to the image before labeling it.

Ishan Misra joins us this week to discuss his recent paper Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels which explores a novel architecture for learning to distinguish presence and relevance. This work enables web-scale datasets to be useful for training, not just well groomed hand labeled corpora.

Direct download: ishan.mp3
Category:general -- posted at: 8:00am PDT