Fri, 28 September 2018
A false discovery rate (FDR) is a methodology that can be useful when struggling with the problem of multiple comparisons.
In any experiment, if the experimenter checks more than one dependent variable, then they are making multiple comparisons. Naturally, if you make enough comparisons, you will eventually find some correlation.
Classically, people applied the Bonferroni Correction. In essence, this procedure dictates that you should lower your p-value (raise your standard of evidence) by a specific amount depending on the number of variables you're considering. While effective, this methodology is strict about preventing false positives (type i errors). You aren't likely to find evidence for a hypothesis that is actually false using Bonferroni. However, your exuberance to avoid type i errors may have introduced some type ii errors. There could be some hypotheses that are actually true, which you did not notice.
This episode covers an alternative known as false discovery rates. The essence of this method is to make more specific adjustments to your expectation of what p-value is sufficient evidence.
Fri, 21 September 2018
Digital videos can be described as sequences of still images and associated audio. Audio is easy to fake. What about video?
A video can easily be broken down into a sequence of still images replayed rapidly in sequence. In this context, videos are simply very high dimensional sequences of observations, ripe for input into a machine learning algorithm.
The availability of commodity hardware, clever algorithms, and well-designed software to implement those algorithms at scale make it possible to do machine learning on video, but to what end? There are many answers, one interesting approach being the technology called "DeepFakes".
The Deep of Deepfakes refers to Deep Learning, and the fake refers to the function of the software - to take a real video of a human being and digitally alter their face to match someone else's face. Here are two examples:
This software produces curiously convincing fake videos. Yet, there's something slightly off about them. Surely machine learning can be used to determine real from fake... right? Siwei Lyu and his collaborators certainly thought so and demonstrated this idea by identifying a novel, detectable feature which was commonly missing from videos produced by the Deep Fakes software.
In this episode, we discuss this use case for deep learning, detecting fake videos, and the threat of fake videos in the future.
Fri, 14 September 2018
In this episode, Kyle reviews what we've learned so far in our series on Fake News and talks briefly about where we're going next.
Thu, 6 September 2018
Two weeks ago we discussed click through rates or CTRs and their usefulness and limits as a metric. Today, we discuss a related metric known as quality score.
While that phrase has probably been used to mean dozens of different things in different contexts, our discussion focuses around the idea of quality score encountered in Search Engine Marketing (SEM). SEM is the practice of purchasing keyword targeted ads shown to customers using a search engine.
Most SEM is managed via an auction mechanism - the advertiser states the price they are willing to pay, and in real time, the search engine will serve users advertisements and charge the advertiser.
But how to search engines decide who to show and what price to charge? This is a complicated question requiring a multi-part answer to address completely. In this episode, we focus on one part of that equation, which is the quality score the search engine assigns to the ad in context. This quality score is calculated via several factors including crawling the destination page (also called the landing page) and predicting how applicable the content found there is to the ad itself.