Interleaving
If you’re Google or Netflix, and you have a recommendation or search system as part of your bread and butter, what’s the best way to test improvements to your algorithm? A/B testing is the canonical a...
22 Jul 201916min
Federated Learning
This is a re-release of an episode first released in May 2017. As machine learning makes its way into more and more mobile devices, an interesting question presents itself: how can we have an algorit...
14 Jul 201915min
Endogenous Variables and Measuring Protest Effectiveness
This is a re-release of an episode first released in February 2017. Have you been out protesting lately, or watching the protests, and wondered how much effect they might have on lawmakers? It's a t...
7 Jul 201917min
Deepfakes
Generative adversarial networks (GANs) are producing some of the most realistic artificial videos we’ve ever seen. These videos are usually called “deepfakes”. Even to an experienced eye, it can be a ...
1 Jul 201915min
Revisiting Biased Word Embeddings
The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias aroun...
24 Jun 201918min
Attention in Neural Nets
There’s been a lot of interest lately in the attention mechanism in neural nets—it’s got a colloquial name (who’s not familiar with the idea of “attention”?) but it’s more like a technical trick that’...
17 Jun 201926min
Interview with Joel Grus
This week’s episode is a special one, as we’re welcoming a guest: Joel Grus is a data scientist with a strong software engineering streak, and he does an impressive amount of speaking, writing, and po...
10 Jun 201939min
Re - Release: Factorization Machines
What do you get when you cross a support vector machine with matrix factorization? You get a factorization machine, and a darn fine algorithm for recommendation engines.
3 Jun 201920min
