Petabyte Age, Data Mining and Science

Natalie Glatzel has written a interesting post on the blog Tasty Data Goodies about an article of Chris Anderson, editor in chief at Wired. Chris' opinion is that scientific theory is now, in the age of Petabyte, becoming obsolete. He writes that "[...] science can advance even without coherent models [...]". Basically, according to Chris, mining huge amount of data to get knowledge kills scientific theory.

As written by Natalie Glatzel, data mining is not meant to replace science and discovery in general. She writes that

"Data mining can really only point us in the right direction of new discovery by showing us relationships between data points; it can't generate new discoveries alone."

My opinion is that the issue pointed by Chris Anderson is not due to the "petabyte age" but rather to the concepts behind data mining itself. Statisticians build a model and then test it. Data miners test the data and then tries to understand them. This is the basic difference between statistics and data mining. And this is distinct from the petabyte issue. Of course data mining is one possible answer to the petabyte age. But in the late 80's, data mining was already used on "small" data sets (comparing to nowadays). Finally, we should remind that there is a big difference between getting knowledge and using it! As written by Natalie Glatzel:

"Although data mining may change the rules of the science game, it's definitely not the end of theory."

For more information, here is the link to Natalie Glatzel's post.

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WIRED point of view on AI

In its October 2007 issue, WIRED has special section named "Geekipedia". In this supplement, WIRED summarizes 149 people, facts or concepts that they think are important. Among the list, one can find "Artificial Intelligence". The description is quite negative and focus on different aims that AI hasn't been able to achieve. I agree with them on the first half of the explanation regarding AI. They write that "[...] while researchers have built awesome technology, they've failed to grapple with philosophy". AI researchers can tell me if I'm wrong, but I think this sentence can be considered as acceptable. However, in the middle of the text, things start to go wrong.

Although the message WIRED intends to give about AI (i.e. AI hasn't yet achieved most of its initial aims), they give bad examples. They write that "[...] computers failed one commonsense task after another [...]". The problem doesn't come from this sentence, rather from examples of such "tasks".

First example: computer fails to understand natural languages. Oops! Very bad example. Although one can discuss the meaning of the word "understand", it is clear that speech processing, recognition and synthesis are examples of successful applications in machine learning. The second example they give is even worst. They write that a computer cannot distinguish a dog from a cat. Oops again! Face recognition is one of the best example of machine learning success story. And there is only one step from the Human to the animal. Indeed, I have a colleague in machine learning who is doing face recognition on a cat database... and it's working!

But the worst is yet to come (yes, believe me). The last paragraph explaining AI contains the following sentence: "Nowadays, Google "knows" pretty much anything you ask it. But its insanely fast and powerful work is modestly described as data-mining, not thinking". Out of the spelling, I'm surprised by the bad connotation given to the data mining term. So, although their description is not completely wrong, they haven't chosen the best examples to illustrate the limitations of AI. There are still a lot of things a computer cannot do, so examples are not missing...

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