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Tyler Hawkes
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Good perspective on the true opportunity cost of some decisions. 

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+J. Michael Boyle +Alton Alexander this looks interesting
Estimating a causal effect accurately is hard, especially when a randomized experiment is not available. We've been working on this problem for some time at Google. One approach we've been developing and using internally is based on Bayesian structural time-series models.

We use these models to construct a synthetic control — what would have happened to our outcome metric in the absence of the intervention whose effect we're interested in. This approach makes it possible to estimate the causal effect that can be attributed to the intervention, as well as its evolution over time.

Today, we're excited to announce the release of CausalImpact, a new open-source R package that makes Bayesian causal inference in time series simple and fast. Visit the Google Open Source blog, linked below, to learn more.

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As an ESL Tutor...

I agree

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This is why I avoid meetings... 

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Pretty good rant on programming :-)
Websites that are glorified shopping carts with maybe three dynamic pages are maintained by teams of people around the clock, because the truth is everything is breaking all the time, everywhere, for everyone. Right now someone who works for Facebook is getting tens of thousands of error messages and frantically trying to find the problem before the whole charade collapses. There's a team at a Google office that hasn't slept in three days. Somewhere there's a database programmer surrounded by empty Mountain Dew bottles whose husband thinks she's dead. And if these people stop, the world burns. Most people don't even know what sysadmins do, but trust me, if they all took a lunch break at the same time they wouldn't make it to the deli before you ran out of bullets protecting your canned goods from roving bands of mutants.

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Let It Go - Frozen - Alex Boyé (Africanized Triba…:

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A Billion Words: Because today's language modeling standard should be higher
Posted by +Dave Orr, Product Manager, and +Ciprian Chelba, Research Scientist

Language is chock full of ambiguity, and it can turn up in surprising places. Many words are hard to tell apart without context: most Americans pronounce “ladder” and “latter” identically, for instance.

One key way computers use context is with language models ( These are used for predictive keyboards, but also speech recognition, machine translation, spelling correction, query suggestions, and so on.

We believe that the field could benefit from a large, standard set with benchmarks for easy comparison and experiments with new modeling techniques. To that end, we are releasing scripts that convert a set of public data into a language model consisting of over a billion words, with standardized training and test splits, described in an arXiv paper (  

Along with the scripts, we’re releasing the processed data in one convenient location, along with the training and test data. This will make it much easier for the research community to quickly reproduce results, and we hope will speed up progress on these tasks. The benchmark scripts and data are freely available, and can be found at

For all the researchers out there, try out this model, run your experiments, and let us know how it goes -- or publish, and we’ll enjoy finding your results at conferences and in journals. Head over to the Google Research Blog to learn more.

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Looks like a good course to learn functional programming. The instructor is the language designer. 
Free Functional Programming Principles class starts tomorrow (April 25). Let me know if you enroll, so we can discuss the class material together.
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