I've been lounging in San Diego at my favorite conference, the Information Theory and Applications workshop. It's so friendly that even the sea lions are invited (the Marine Room is where we had the conference banquet).
Sadly this year I was mired in deadlines and couldn't take advantage of the wonderful talks on tap and the over 720 people who attended. Pro tip, ITA: Could you try to avoid the ICML/KDD/COLT deadlines next time :) ?
ITA always has fun events that our more "serious" conferences should learn time. This time, the one event I attended was a Man vs Machine cookoff. Which I thought was particularly apropos since I had just written a well-received article with a cooking metaphor for thinking about algorithms and machine learning.
The premise: Chef Watson (IBM's Watson, acting as a chef) designs recipes for dinner (appetizer/entree/dessert) with an assist from a human chef. Basically the chef puts in some ingredients and Watson suggests a recipe (not from a list of recipes, but from its database of knowledge of chemicals, what tastes 'go well together' and so on. This was facilitated by Kush Varshney from IBM, who works on this project.
Each course is presented as a blind pairing of Watson and Human recipes, and its our job to vote for which one we think is which.
It was great fun. We had four special judges, and each of us had a placard with red and blue sides to make our votes. After each course, Kush gave us the answer.
The final score: 3-0. The humans guessed correctly for each course. The theme was "unusualness": the machine-generated recipes had somewhat stranger combinations, and because Watson doesn't (yet) know about texture, the machine-recipes had a different mouthfeel to them.
This was probably the only time I've heard the words 'Bayesian surprise' and 'eigenvector' used in the context of food reviews.