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Posts Tagged ‘research’

A thoughtful piece on industrial labs and research styles. My favorite bit is the part about “empowering researchers”. All the managers dealing with researchers should read this paper by Roy Levin.

Windows On Theory

The closing of MSR-SV two months ago raised a fair bit of discussion, and I would like to contribute some of my own thoughts. Since the topic of industrial research is important, I would like the opportunity to counter some misconceptions that have spread. I would also like to share my advice with anyone that (like me) is considering an industrial research position (and anyone that already has one).

What?

On Thursday 09/18/2014, an urgent meeting was announced for all but a few in MSR-SV. The short meeting marked the immediate closing of the lab. By the time the participants came back to their usual building, cardboard boxes were waiting for the prompt packing of personal items (to be evacuated by the end of that weekend). This harsh style of layoffs was one major cause for shock and it indeed seemed unprecedented for research labs of this sort. But I find the following…

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It feels a bit strange to write this post’s title, because I don’t find myself defending Facebook very often. But there seems to be some discontent in the socialmediaverse at the moment over a new study in which Facebook data scientists conducted a large-scale–over half a million participants!–experimental manipulation on Facebook in order to show that emotional contagion occurs on social networks. The news that Facebook has been actively manipulating its users’ emotions has, apparently, enraged a lot of people…

In defense of Facebook | [citation needed].

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If it doesn’t, you’re not doing it right.

And on why research is not like sex.

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“The proposal is that certain conferences make it mandatory to publish datasets that were used for the experiments. This is a very bad idea and two things are getting confused here: scientific progress and common access. These two are not identical. Reproducibility is often confused with common access.”

Read more here, by Alex Smola.

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Person that does not know how machine learning works,
but knows what to do when machine learning does not work.

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California Dreamin’

The american dream awaits.

Silicon Valley, here I come!

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Data Intensive Scalable Computing
DISC=Data Intensive Scalable Computing

ML=Machine Learning
DM=Data Mining
IR=Information Retrieval
DS=Distributed Systems
DB=Data Bases

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