June 22, 2017

The frightening, futurist portrayals of artificial intelligence and anthropomorphic robots portended in Hollywood films and sci-fi novels are fictional. In reality, AI is already changing our everyday lives, almost entirely in ways that benefit our society. Apple's Siri, voice recognition, Google's ability to recognize photos and videos of cats, weather forecasts, and email spam filtering, are all examples. Indeed, we've witnessed quantum leaps in the quantity and quality of a wide range of common technologi... View More >

June 19, 2017
Tabs vs Spaces

by Kyle Polich

Thanks to @daniel_budd and @coolaj86 for pointing me to the recent post from David Robinson on the stackoverflow blog titled Developers Who Use Spaces Make More Money Than Those Who Use Tabs<... View More >

June 12, 2017

This episode features discussion of database as a service, database migration, threat detection, R/python in SQL Server, and use ca... View More >

June 9, 2017

A dubious perk of working at the L.A. Times in 2016 was that some friends started associating the USC/L.A. Times poll with... View More >

May 26, 2017

Machine learning has been an essential tool for solving computer vision tasks such as image classification, object detection, instance recognition, and semantic segmentation, among others. The crux of machine learning approaches involves data. Training a machine requires enormous amounts of usable data. Why? Suppose you want to learn about monkeys and apes. Let's also assume you've never seen any monkeys or apes in your lifetime, until one day, someone shows you a picture of a monkey and an ape. It might be difficult to generalize from one picture and discern the differences between a monkey and an ape. If you saw perhaps 50 pictures of each species, you would have a greater chance of noticing that monkeys tend to be smaller than apes and that monkeys tend to have tails, whereas apes do not. Now if you saw thousands of pictures of both monkeys and apes, it might become very clear to you that the two are in fact, very different. For example, you might discover monkeys and apes have different nose structures, upper bodies, feet and so... View More >

May 21, 2017

I find that the broad media discussion of machine learning models, algorithmic decision making, and artificial intelligence is written by some very ill informed commentators on the subject. I do believe this is an important topic. We covered some of these ideas in episodes like Auditing Algorithms with Christian Sandvig and Predictive Policing with Kristian Lum. It's a central theme in the widely discussed Weapons of Math Destruction by Cathy O'Neil<... View More >

May 16, 2017

Buried in the Comey news last week was the release of a dataset by the U.S. Census Bureau that gives us one of the most complete looks at how people voted in the 2016 presidential election-the November 2016 Voting and Registration Supplement to the Current Population Sur... View More >

May 10, 2017
Deploying to the Edge

by Kyle Polich

A major theme of the Microsoft Build 2017 keynote was Microsoft's investment in cloud, AI, and edge. In this context, MS uses the term AI to describe object recognition, integration across a network of devices, and a layer of intelligence to generate notificati... View More >

May 7, 2017

Our recent episode Opinion Polling for Presidential Elections featured segments from an interview with Ernie Tedeschi. This post contains the full transcript for that interview conducted by Christine Zhang. The player below also contains a link to the full recording from the Data Skeptic Bonus F... View More >

May 4, 2017

Our recent episode Opinion Polling for Presidential Elections featured segments from an interview with Jill Darling. This post contains the full transcript for that interview conducted by Christine Zhang. The player below also contains a link to the full recording from the Data Skeptic Bonus Feed.... View More >

April 30, 2017

In a letter to Nature published in 1907, Francis Galton described an event that had taken place at a county fair, where he asked roughly 800 people to guess the weight of an ox. The average estimate was 1,197 pounds. The actual weight was 1,198, which meant that the average guess was a near-perfect estimate. Many people who participated from the crowd were considered experts, such as farmers and butchers, but many people were non-experts who were just attending the fair. Also, none of them guessed the correct weight, and only one person guessed 1,197. The next closest guess was 1,199, which was given by two participant... View More >

April 30, 2017

An interesting talking point in contrast to the Turing test is the Chinese Room Problem. It postulates a sealed room in which a person with no ability to read Chinese receives handwritten messages in Chinese. The room they are in is filled with books that contain possible responses to sentences in Chinese. The room's occupant is instructed with enough information to match the characters of input given to them, and lookup the corresponding output. They then copy the characters onto a sheet of paper and slip that pass that message out of the room. Assume there number of books of responses is large enough to contain a response for any possible input. The languages was selected (by an English speaker/reader) since Chinese is entirely foreign to them. The Spanish Room or German Room Problem wouldn't be quite as hard since the languages have similar enough origins that people can often get a sense of the meaning. With no background in Chinese writing, it seems unlikely an English speaker would interpret much, if anything at all, from inspecting the messa... View More >

April 28, 2017

On the eve of Election Day 2016, FiveThirtyEight's final "polls-only" forecast gave Donald Trump a 29 percent chance of winning the presidency. The NYT Upshot's model gave him a 15 percent chance. According to these two probabilities, a Hillary Clinton victory was likely (a 71 percent and 85 percent chance, respectively), though not a gi... View More >

April 24, 2017
Prediction Markets

by Kristine de Leon

In the final stretch leading up the U.K's referendum on the EU last year, traditional opinion polls suggested an extremely close race, fluctuating between staying in the EU and leaving up until the votes were cast. However, the political prediction (betting) markets told a different story, showing a wide lead on the odds of remaining. Two days before the referendum, a large number of opinion polls showed 'Leave' ahead, while the British prediction market Betfair was implying odds of 75 percent for 'Remain' and 25 percent for 'Lea... View More >

April 23, 2017

In this post, I'll present the quickest way to get up and running fast, doing analysis on real estate data using Python. We'll make a request to the OpenHouse API, retrieve some data, and then do a quick analysis. You won't need any prior experience beyond basic Python to follow this walkthro... View More >

April 22, 2017

EDITOR'S NOTE: Late in 2016, as the US election results came in and left many onlookers claiming that the polls "got it wrong", Data Skeptic promised that we'd cover the topic of polling after some time had passed. This post kicks off our week long coverage of election polling, culminating in a podcast on the subject this Fri... View More >