A few days ago, the Centers for Medicare and Medicaid Services (CMS) released some unprecedented data on the US healthcare system. The data consists of 9 million rows showing how much each doctor in the US charged Medicare, for what, and how much Medicare paid out. It doesn’t quite cover everything (for example, services with less than 11 beneficiaries were removed for privacy reasons), but its the best thing we’ve got.
Sometime today, I got the idea to try to do automatic speech recognition. Speech recognition, even though it is widely used (and is on our phones), still seems kind of sci-fi-ish to me. The thought of running it on your own computer is still pretty exciting.
Wow, it’s been way too long since I have updated this blog! I am going to start making more frequent updates, and I have some cool things in the pipeline, so bear with me.
I’ve had a lot of different jobs over the past 4 years, and I’ve had some incredible experiences along the way. Lately, I’ve been struggling with what to do next. Or perhaps more accurately, I’ve been struggling with how to decide what to do next. Decisions that seem obvious in hindsight are tough to come to grips with beforehand, and it’s led me to think about what metric I am trying to maximize. I admit that it’s odd to think of life as a way to increase certain metrics, but aren’t we doing this already in a different way? A lot of people (myself included) will at some point say that all we care about is money. Isn’t that just us saying that money is the metric we want to maximize? Now that I am older and wiser (yeah, right), I find myself increasingly concerned with maximizing my own happiness.
I haven’t blogged in a while, mostly because I have been trying to figure out what I should do next. One thing that I have been working on lately that I am very passionate about is Movide. Movide is a student-centric learning platform. You might yawn at this point and wonder why Movide matters. It’s a natural reaction, given the crowded learning tools marketplace. Movide, matters, I think, because it is an open source attempt to change the LMS and learning tool paradigm.
I recently posted about visualizing the voting patterns of senators. In the post, I scraped voting data for each senator on every vote in the 113th Congress from the Senate website, and then assigned a code of 0 for a no vote on a particular issue, 1 for a yes vote, 2 for abstention, and 3 if the senator was not in office at the time of the vote (ie, a senator was switched mid-term).
We’ve all written essays, primarily while we were in school. The sometimes enjoyable process of researching the topic and composing the paper can take hours and hours of careful work. Given this, people react badly to the notion that their essays may be scored not by a human teacher, but by machine.
I very seldom pay attention to politics directly, because politics have always seemed a bit circular and cyclical to me. Most of the political news that I take in ends up worming its way into the news sources that I do consume, like the excellent longform.org. Even given my limited intake of political news, one trend that I have noticed lately is the increasing number of references to the Senate as “polarized” or “divided.” Here is a link to an interesting series of charts on polarization. Is it possible to quantify this polarization? Can quantifying the polarization enable us to draw interesting conclusions?
I recently posted about automatically making music. The algorithm that I made pulled out interesting sequences of music from existing songs and remixed them. While this worked reasonably well, it also didn’t have full control over the basics of the music; it wasn’t actually specifying which instruments to use, or what notes to play.
Update: you can find the next post in this series here.