[LINK] A New Paradigm of Science

[stephen at melbpc.org.au]

Jan writes,

> Here's an interesting description of the need for a new approach to 
> science: The Fourth Paradigm - Data-Intensive Scientific Discovery. 
> There is mention of a positive use for cloud computing that isn't 
> privacy intrusive or 'risky' in that regard. Looks like it may be 
> worth a read, even though it does come from MS Research. Jan
> 
> http://www.nytimes.com/2009/12/15/science/15books.html


Interesting post, Jan, thanks. I had no luck coming in cold at this url,
(subscription required), but, could access it from the NYTimes top page
which produced the following url. Maybe it works first hit from outside?
http://www.nytimes.com/2009/12/15/science/15books.html?_r=1&ref=science

In essence, he says, we need, “to have a world in which all of the 
science literature is online, all of the science data is online, and they 
interoperate with each other.”

Books on Science: A Deluge of Data Shapes a New Era in Computing
By JOHN MARKOFF Published: December 14, 2009

 
In a speech given just a few weeks before he was lost at sea off the 
California coast in January 2007, Jim Gray, a database software pioneer 
and a Microsoft researcher, sketched out an argument that computing was 
fundamentally transforming the practice of science. 

Dr. Gray called the shift a “fourth paradigm.” The first three paradigms 
were experimental, theoretical and, more recently, computational science. 

He explained this paradigm as an evolving era in which an “exaflood” of 
observational data was threatening to overwhelm scientists. The only way 
to cope with it, he argued, was a new generation of scientific computing 
tools to manage, visualize and analyze the data flood. 

In essence, computational power created computational science, which 
produced the overwhelming flow of data, which now requires a computing 
change. It is a positive feedback loop in which the data stream becomes 
the data flood and sculptures a new computing landscape. 

In computing circles, Dr. Gray’s crusade was described as, “It’s the 
data, stupid.” It was a point of view that caused him to break ranks with 
the supercomputing nobility, who for decades focused on building machines 
that calculated at picosecond intervals.

He argued that government should instead focus on supporting cheaper 
clusters of computers to manage and process all this data. This is 
distributed computing, in which a nation full of personal computers can 
crunch the pools of data involved in the search for extraterrestrial 
intelligence, or protein folding.

The goal, Dr. Gray insisted, was not to have the biggest, fastest single 
computer, but rather “to have a world in which all of the science 
literature is online, all of the science data is online, and they 
interoperate with each other.” 

He was instrumental in making this a reality, particularly for astronomy, 
for which he helped build vast databases that wove much of the world’s 
data into interconnected repositories that have created, in effect, a 
worldwide telescope.

Now, as a testimony to his passion and vision, colleagues at Microsoft 
Research, the company’s laboratory that is focused on science and 
computer science, have published a tribute to Dr. Gray’s perspective 
in “The Fourth Paradigm: Data-Intensive Scientific Discovery.” It is a 
collection of essays written by Microsoft’s scientists and outside 
scientists, some of whose research is being financed by the software 
publisher. 

The essays focus on research on the earth and environment, health and 
well-being, scientific infrastructure and the way in which computers and 
networks are transforming scholarly communication. 

The essays also chronicle a new generation of scientific instruments that 
are increasingly part sensor, part computer, and which are capable of 
producing and capturing vast floods of data. 

For example, the Australian Square Kilometre Array of radio telescopes, 
CERN’s Large Hadron Collider and the Pan-Starrs array of telescopes are 
each capable of generating several petabytes of digital information each 
day, although their research plans call for the generation of much 
smaller amounts of data, for financial and technical reasons. (A petabyte 
of data is roughly equivalent to 799 million copies of the novel “Moby 
Dick.”)

“The advent of inexpensive high-bandwidth sensors is transforming every 
field from data-poor to data-rich,” Edward Lazowska, a computer scientist 
and director of the University of Washington eScience Institute, said in 
an e-mail message. The resulting transformation is occurring in the 
social sciences, too. 

“As recently as five years ago,” Dr. Lazowska said, “if you were a social 
scientist interested in how social groups form, evolve and dissipate, you 
would hire 30 college freshmen for $10 an hour and interview them in a 
focus group.”

“Today,” he added, “you have real-time access to the social structuring 
and restructuring of 100 million Facebook users.”

The shift is giving rise to a computer science perspective, referred to 
as “computational thinking” by Jeannette M. Wing, assistant director of 
the Computer and Information Science and Engineering Directorate at the 
National Science Foundation. 

Dr. Wing has argued that ideas like recursion, parallelism and 
abstraction taken from computer science will redefine modern science. 

Implicit in the idea of a fourth paradigm is the ability, and the need, 
to share data. In sciences like physics and astronomy, the instruments 
are so expensive that data must be shared. Now the data explosion and the 
falling cost of computing and communications are creating pressure to 
share all scientific data.

“To explain the trends that you are seeing, you can’t just work on your 
own patch,” said Daron Green, director of external research for Microsoft 
Research. “I’ve got to do things I’ve never done before: I’ve got to 
share my data.”

That resonates well with the emerging computing trend known as “the 
cloud,” an approach being driven by Microsoft, Google and other companies 
that believe that, fueled by the Internet, the shift is toward 
centralization of computing facilities.

Both Microsoft and Google are hoping to entice scientists by offering 
cloud services tailored for scientific experimentation. Examples include 
Worldwide Telescope from Microsoft and Google Sky, intended to make a 
range of astronomical data available to all.

Similar digital instruments are emerging in other fields. In one 
chapter, “Toward a Computational Microscope for Neurobiology,” Eric 
Horvitz, an artificial intelligence researcher for Microsoft, and William 
Kristan, a neurobiologist at the University of California, San Diego, 
chart the development of a tool they say is intended to help understand 
the communications among neurons.

“We have access to too much data now to understand what’s going on,” Dr. 
Horvitz said. “My goal now is to develop a new kind of telescope or 
microscope.” 

By imaging the ganglia of leeches being studied in Dr. Kristan’s 
laboratory, the researchers have been able to identify “decision” cells, 
responsible for summing up a variety of inputs and making an action, like 
crawling. Someday, Dr. Horvitz hopes to develop the tool into a three-
dimensional display that makes it possible to overlay a set of inferences 
about brain behavior that can be dynamically tested.

The promise of the shift described in the fourth paradigm is a blossoming 
of science. Tony Hey, a veteran British computer scientist now at 
Microsoft, said it could solve a common problem of poor use of graduate 
students. “In the U.K.,” Dr. Hey said, “I saw many generations of 
graduates students really sacrificed to doing the low-level IT.”

The way science is done is changing, but is it a shift of the magnitude 
that Thomas Kuhn outlined in “The Structure of Scientific Revolutions”?

In his chapter, “I Have Seen the Paradigm Shift, and It Is Us,” John 
Wilbanks, the director of Science Commons, a nonprofit organization 
promoting the sharing of scientific information, argues for a more 
nuanced view of data explosion.

“Data is not sweeping away the old reality,” he writes. “Data is simply 
placing a set of burdens on the methods and the social habits we use to 
deal with and communicate our empiricism and our theory.”


Message sent using MelbPC WebMail Server