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May 16, 2013

Mozart Meets MapReduce

Isaac Lopez

Big data has been around since the beginning of time, says Thomas Paulmichl, founder and CEO of Sigmaspecto, who says that what has changed is how we process the information.  In a talk during Big Data Week, Paulmichl encouraged people to open up their perspective on what big data is, and how it can be applied.

During the talk, he admonished people to take a human element into big data. Paulmichl demonstrated this by examining the work of musical prodigy, Mozart – who Paulmichl noted is appreciated greatly by both music scientists, as well as the common music listener.

“When Mozart makes choices on writing a piece of work, the number of choices that he has and the kind of neural algorithms that his brain goes through to choose things is infinitesimally higher that what we call big data – it’s really small data in comparison,” he said.

Taking Mozart’s  The Magic Flute as an example, Paulmichl, discussed the framework that Mozart used to make his choices by examining a music sheet outlining the number of bars, the time signature, the instrument and singer voicing.

“So from his perspective, he sits down, and starts to make what we as data scientists call quantitative choices,” explained Paulmichl. “Do I put a note here, down here, do I use a different instrument; do I use a parallel voicing for different violins – so these are all metrics that his brain has to decide.”

Exploring the mathematics of the music, Paulmichl concluded that in looking at The Magic Flute, Mozart had 4.72391E+21 creative variations (and then some) that he could have taken with the direction of it over the course of the piece. “We’re not talking about a trillion dataset; we’re talking about a sextillion or more,” he says adding that this is a very limited cut of the quantitative choice that his brain makes at every composition point.

Paul related this process to MapReduce, attempting to demonstrate that the principles of the framework are nothing new, but rather techniques that have been underlying much of humanity’s cultural output.  “Our brains use similar algorithms that we can compare when we look at using MapReduce and big data technologies on our traditional data set.”

He notes that brains aren’t computers, but rather neural nets that operate on a different paradigm.

“Your brain doesn’t process stuff in a parallel fashion.  There are a few parallel neurons firing– you know color, shape etc, but it’s a very conservative parallel process – but it is parallel in a sense if you collapse time into one data point, and then your brain becomes a MapReduce machine because you are in a loop solving a compression algorithm that becomes better and better by analyzing the same bit.”

It is an interesting point of view from the founder and CEO of a company that analyzes unstructured data streams using machine learning and artificial intelligence algorithms. You can view the talk here:

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