

More is less - tiki12revolt
http://web.mit.edu/newsoffice/2010/hidden-variables-0602.html
Complex computer models can involve thousands of variables. But paradoxically, adding more variables can sometimes make them easier to work with.
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jayruy
The article is so vague it seems even the author isn't particularly sure of
the point.

Notions of factor models and hidden markov models have been around in the
statistics literature for ages. Computer science's contribution to the
discussion has been framing these methods as machine learning - and leading
the foray into unsupervised learning. But I'm not sure if unsupervised
learning techniques are being put to use in real-world data analysis, I
believe the theoretical foundations are still a bit opaque.

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hnote
Additional "variables" they are introducing are in fact algorithms acting on
available data. A fortunate choice of an algorithm amounts to providing a good
prior on the space of algorithms, which are used to estimate the Kolmogorov
complexity of the data, or, in other words, explain it. As in the example with
stocks, adequate algorithms can be more complex than just copy-paste usually
used when doing compression/pattern recognition...

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nysauhem
"Generally, computer science is concerned with questions of computational
complexity: Given a particular algorithm, you want to know whether a computer
can execute it quickly, slowly or never."

Tsk tsk MIT for confusing computational complexity with analysis of
algorithms. Or perhaps with sloppy use of the words "algorithm" and "execute",
when "problem" and "solve" would be more accurate.

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faramarz
The whole notion of _Less is More_ has to do with the end result, at the
consumption/interaction level. Of course there's complexity behind the
minimalism, and that's the true art of it.

This article isn't touching on anything new, I was expecting definitive
contradictions.

