Automated science, deep data and the paradox of information

by dublinben · #science
posted 407 days ago · shared by: 7
A lot of great pieces have been written about the relatively recent surge in interest in big data and data science, but in this piece I want to address the importance of deep data analysis: what we can learn from the statistical outliers by drilling down and asking, "What's different here? What's special about these outliers and what do they tell us about our models and assumptions?”

The reason that big data proponents are so excited about the burgeoning data revolution isn't just because of the math. Don't get me wrong, the math is fun, but we're excited because we can begin to distill patterns that were previously invisible to us due to a lack of information.

That's big data.

by NotPhil 407 days ago  ·  link
A comment from the page of the Radiolab episode the article mentioned:

The article makes a big point of saying that the Eureqa is able to calculate F=ma or put differently: There is some quantity called force that is proportional to the instantaneous change in the instantaneous change of position of the pendulum. The Eureqa machine is capable of using calculus as a given. What made this task so hard for Newton was he had to create the calculus of instantaneous change. The problem that Eureqa solved is much more like that which a first year physics student solves (only, it did it with a much more impressive dataset) in that, thanks to Sir Isaac Newton, we have all the rules of calculus and mathematics to assist us.
by thundara 386 days ago  ·  link
Man, the discussion of modelling cells remind me a lot of a big picture I saw at a presentation a few months ago. It showed a few key proteins in a cell just with lines linking them to all the other proteins they "affected".

It takes a lot of work to analyze exactly how two proteins will bind together, or produce substances that alter the other in some way. But, just having a large system of differential equations can be just as useful as determining in what orientation two domains of two proteins interact and with what affinity.

Supposedly that's the budding new field of biology, I had a conversation with my boss a few months ago about it!

by mk 406 days ago  ·  link
From this, we built what we're calling the "cognome", a mapping between brain structure, function, and disease.

I always wince when I see a new 'nome'. :)

Nice article though. I can see a new discipline arising in the not-so-distant-future that regards application of data. With all this information, we are going to have to have tools and language that better enables us to determine if and how it might be put to use. Oddly, computers might do that for us as well.