Monday, August 6, 2012

A Quantum Leap in Open Science with Michael Nielsen

 


  by Pat Cotey


If you haven't seen a TED talk, you are missing out on some great ideas and insights. I listened to Michael Nielsen discuss his thoughts on open science again yesterday and just had to share it with you. Even if you have seen it, take the time to watch and listen again (Transcription Below). You won't be disappointed! 


For those of you who have not been initiated, the TED Conference was started in 1984 and was devoted to the mission of "Ideas Worth Spreading" and brought folks together to discuss Technology, Entertainment and Design. Well, TED has spread and there are now two major conferences that you can attend in person or online.  Campuses and other communities sponsorTEDx events and most are posted online so you can search a topic and give yourself the "ultimate brain spa" to quote TED attendees.  


Michael, a pioneer of quantum computation, pitches a call to action for scientists to share science for the greater good. He asks that we all take a step and participate actively in an open science platform or begin an open science project or just inquire as to how colleagues are working on science actively. We have the opportunity to reinvent science using the new tools we have for sharing science and working collaboratively.  By doing so, we can move research forward more quickly to learn, discover and cure problems we all face.


Check out the TED website for more info, http://www.ted.com/


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Michael Nielson "Open Science Now!" Transcription:



I'd like to begin my talk with a story.  It's a story that begins but does not end with a
mathematician named Tim Gowers. 
Gowers is one of the world's most renowned mathematicians.  He's a professor at Cambridge
University and the recipient of the Fields Medal, often called the Nobel Prize
of Mathematics. 


Gowers is also a blogger. 
And in January of 2009, he used his blog to post a very striking
question: Is massively collaborative mathematics
possible? So what he was proposing in this post was to use his blog to attack a
difficult, unsolved mathematical problem, a problem which he said he would love
to solve completely in the open using his blog to post his ideas and his
partial progress.  What was more;
he issued an open invitation inviting anybody in the world who thought that
they had an idea to contribute, to post their idea in the comments section of
the blog.  Okay, his hope was that
by combining the ideas of many minds, he could make easy work of his hard
mathematical problem.  He called
this experiment the Polymath Project.


Well, the Polymath Project got off to a slow start.  In the first seven hours, nobody posted
any comments.  But then, a
mathematician from the University of British Columbia named Jozsef Solymosi
posted a short comment and it seemed to break the ice because a few minutes
later, a high school teacher named Jason Dyer posted a suggestion and a few
minutes after that, another mathematician named Terence Tao, also a Fields
medalist, posted an idea.  And
things really started to move quickly at this point.  Over the next 37 days, 27 different people would post 800
substantive comments, containing 170,000 words.


I was not a serious participant, but I was following along closely
from the start and it was just amazing. 
The speed with which an idea would be tentatively proposed and then
really rapidly developed by other people and improved, sometimes discarded, is
just amazing.  Gowers described the
process as being to ordinary research as driving is to pushing a car.


At the end of the 37 days, Gowers used his blog to announce that they
have solved the core problem—in fact they have solved the harder generalization
of the problem.  The Polymath
Project had succeeded.  So what the
Polymath Project suggests, at least to me, is that we can use the Internet to
build tools that actually expand our ability to solve the most challenging
intellectual problems.  Well, to
put it in another way—we can build tools which actively amplify our collective
intelligence in much the same way as from Millennia; we've used physical tools
to amplify our strength.


What I would like to talk about today and what I would like to explore
today is what this means for science. 
It is much more important than just solving a single mathematical
problem.  It means an expansion in
the range of scientific problems we can hope to attack at all.  It means potentially an acceleration in
the rate of scientific discovery. 
It means a change in the way we construct knowledge itself. 


So before I get too overexcited however, I would like to talk about some
of the challenges, some of the problems. 
In particular, I would like to describe a failure of this approach.  So it occurred in 2005, or it started
in 2005, a grad student at Caltech named John Stockton had a very good idea for
what he called the Quantum Wiki or Q-wiki for short.  It's a great idea. 
What he did with the Q-wiki—the idea of the Q-wiki—was that it was going
to be a great repository of human knowledge, much like Wikipedia, but instead
of being focused on general knowledge, it was going to be focused on specialist
knowledge in quantum computing. 
It's going to be kind of a super textbook for the field with information
about all of the latest research, about what the big open problems in the field
were, people's speculation about how to solve the problems, and so on.  Like Wikipedia, the intention was that it
would be written by the users, in this case, by experts in quantum computing.  I was present at the conference of
Caltech in 2005 when it was announced and some of the people who I spoke to
were very skeptical but some of the people were very excited by the idea. They
were impressed by the implementation, they were impressed by amount of initial
seed material which had been put on the site, and most of all they were excited
by the vision.  But just because
they were excited didn’t mean that they wanted to take the time themselves to
contribute.  They hoped that other
people would do so, and in the end, nobody essentially was really all that interested
in contributing.  If you look
today, except in a few small corners, the Q-wiki is essentially dead.


And sad to say, this is quite the common story.  Many scientists in fields ranging from
genetics to string theory have tried to start science wikis along very similar
lines.  And typically they have
failed for essentially the same reason. 


It's not just science wikis either.  Inspired by Facebook, many organizations have tried to
create social networks for scientists which will connect scientists to other
people with similar interests, so they can share things like data or code,
their ideas, and so on.  Again, it
sounds like a good idea, but if you join one of these sites you will quickly
discover that they are essentially empty, they are virtual ghost towns.


So what is going on? What is the problem here? Why are these promising
sites failing.  Well, imagine that
you are an ambitious young scientist (In fact I know that some of you are
ambitious young scientists). 
Imagine you are an ambitious young scientist.  You really would like to get a job, a permanent job, a good
job doing the work that you love. 
But it's incredibly competitive to get such jobs.  Often there would be hundreds of very
highly qualified applicants to the positions.  And so you find yourself working sixty, seventy, eighty
hours a week doing the one thing that you know would get you such a job, and
that is writing scientific papers. 
You may think that the Q-wiki is a wonderful idea in principle, but you
also know that writing a single mediocre paper would do much more for your
career and your job prospects than a long series of brilliant contributions to
such a site.  So even though you
may like the idea, you may think it will advance science more quickly, you find
and you just can't conceive of it as being part of your job.  It's not.  The only things which can succeed in this kind of
environment are projects like the Polymath Project, which even though they
employ a non-conventional means to an end, they have an essential conservatism
about them.  The end product of the
Polymath Project was still a scientific paper; in fact it was several
papers.  So “unconventional means
but conventional end”. So there was a kind of conservatism about it.  But don't get me wrong; the Polymath
Project is terrific.  But it is a
pity that scientists can only use tools which have this kind of conservative
nature.


So let me tell you a story about an instance where we moved away from
this conservatism.  It is a rare
story, but the conservatism has been broken.  It occurred in the 1990s, when as you know, for the first
time, biologists were taking large amounts of genetic data particularly in the
human genome project.  And there
were sites online which would allow biologists to upload that data so it could
be shared with other people around the world and analyzed by other people.  Probably the best known of these is the
site GenBank which some of you may have had heard of or used.  And these sites, like GenBank, had the
problem in common with the Q-wiki that scientists (they are not paid or
rewarded for sharing their data; it's all about publishing papers), and so
there was a considerable reluctance to actually upload the data.  Everybody could see that this was
silly, but it was obvious that this was the right thing to do.  But just because it was obvious didn’t
mean that people were actually doing it. 
And so a meeting was convened in Bermuda in 1996, of many of the world's
leading molecular biologists and they sat and they discussed the problem for
several days and they came up with what are now called The Bermuda Principles,
which state that first, once human genetic data is taken in the lab, it should
be immediately uploaded to a site like GenBank, and two, that the data would be
in the public domain.


And these principles were given teeth because they were taken by the
big scientific grant agencies, the US National Institutes of Health, the UK
Welcome Trust.  Next they baked
into policy.  So it meant that if
you were a scientist who wanted to work on a human genome, you had to agree to
abide by these principles.  And
today, I am very pleased to say, as a result you can go online, anybody here
can download the human genome.  So
that's a terrific story, but the human genome is just a tiny, tiny fraction of
all scientific knowledge.  Even in
just other parts of genetics, there is so much knowledge that is still locked
up.  I spoke with one
bioinformatician, he told me that he had been "sitting on the genome of an
entire species for more than a year".  An entire species! And in other parts of science, it is
retained that scientists hoard their data, they hoard the computer code that
they write that could be useful potentially to other people, they hoard their
best ideas, and they often hoard even the descriptions of the problems that they
think are most interesting. 


And so what I and other people in the open science movement would like
to do is: we'd like to change this situation.  We would like to change the culture of science so that
scientists could become much more strongly motivated to share all of these
different kinds of knowledge.  We
want to change the values of individual scientists so that they start to see it
as part of their job to be sharing their data, to be sharing their code, to be
sharing their best ideas and their problems.


So you know, if we can do this, then this kind of change in values,
then we will indeed start to see individual scientists rewarded for doing these
things.  There will be incentives
to do them.  It's a difficult thing
to do, however.  Yes, we are
talking about changing the culture of an entire large part of science.  But it has happened before, once in
history.  Right back at the dawn of
science, Galileo, 1609, he points his telescope up at the sky towards Saturn
and he sees for the first time in history what we now know are the rings of
Saturn.  Does he tell everybody in
the world? No.  He doesn’t do
that.  He writes down a description
privately and then he scrambles the letters in the description into an anagram
and he sends that anagram to several of his astronomer rivals.  And what this ensures is that if they
later make the same discovery, he can reveal the anagram and get the credit,
but in the meantime, he hasn't given up any knowledge at all.  And I'm sad to say that he was not
uncommon at the time.  Newton,
Huygens, Hooke, Leonardo—they all used similar devices, okay.


The printing press have been around for 150 years by this time, and
yet there was a great battle in the 17th and 18th centuries to change the
culture of science so that it became expected that when a science made a
discovery, they would reveal it in a journal.  And that's great, that change has happened, it's
terrific.  But today, we have new
technologies, we have new opportunities to share our knowledge in new ways, and
the ability to create tools that actually allow us to solve problems in
entirely new ways.


So we need to have a second "Open Science Revolution".  It is my belief, that any publicly
funded science should be open science. 
How can we achieve this change? Well, if you're a scientist (and I know
many of you are not scientists), but if you're a scientist then there are
things that you can do.  You can
get involved in an open science project, even if it's just for a small fraction
of your time.  You can find forums
online where you can share your knowledge in new ways—ways that allow other
people to build on that knowledge. 
You can also (if you're more ambitious) start an open science project of
your own.  If you're really bold,
you may wish to experiment with entirely new ways of collaborating in much the
same way as the Polymath Project did. 
But above all, what you should do is be very generous in giving credit
to those of your colleagues (you are practicing science in the open) and to
promote their work.


These only conservative scientific values that look down on these
activities—the sharing of data, the blogging or using of wikis and so on—you
can reject those conservative values and engage your scientific colleagues in
conversation to promote the value of these new ways of working to emphasize
that it takes bravery to do these things, particularly by young
scientists.  It is through such
conversation that the culture of science can be changed.


So if you are not a scientist, there are also things that you can
do.  My belief is that the single
most important thing that we can do to give impetus to open science is create a
general awareness amongst the population of the issue of open science and of
its critical importance.  If there
is that general awareness, then the scientific community will inevitably find,
it will be dragged by the population at large in the right direction.  There are simple things you can
do.  You can talk to your friends
and acquaintances who are scientists. 
Just ask them, what are they doing to work more openly? Or, you can use
your imagination and your personal palette to raise your awareness in other
ways.  We are talking about
changing not just what scientists do, but what grant agencies do, what
universities do, and what governments do, and you can influence all of those
things.


Our society faces a fundamental question: what kinds of knowledge are
we going to expect and incentivize our scientists to share? Will we continue as
we have done in the past? Or will we embrace new kinds of sharing, which lead
to new methods for solving problems—an acceleration in the process of science
entirely across the board?


My hope is that we will embrace open science and really seize this
opportunity that we have to reinvent discovery itself.




Thursday, August 2, 2012

4 Ways to Spot Phony Data In the Media

Whenever we turn on our televisions or read the paper, we see statistics and figures regarding everything from the probability of rain, to the rate of employment increase or decrease in the engineering sector, to the total number of babies born in the last forty-eight hours. Numbers are generally viewed as factual. If you add one and one, you always get two. However, like any other type of data, numbers can be manipulated, and how the data analysis is presented is as important, if not more so, than the “facts” that are generated from that analysis.


When viewing statistics, look for the following red flags, which may indicate that you are viewing manipulated data.


1. Before believing a statistic, make sure that the company or organization did not “throw out” data that was negative, or which did not prove the point they wanted to make.


2. Find out exactly what was surveyed or what questions were actually asked of participants. Often, the data presented may have been gathered based on a completely different question or issue.


3. Make sure the statistics actually apply to the group that was being used for the analysis. Asking 15 dentists whether dancers should receive healthcare will generate a very different response from asking 15 dancers whether dancers should receive healthcare.


4. Find out as much as you can about the polled group before believing the data. A supposedly “random” study of 1000 people is not so random if all of the people studied are students at the same university, in the same major, and are all the same age.


At FastFig, we believe strongly in data transparency; in fact, we are building a numerical platform centered around it.  We believe that by arming the world with solid facts, we will all be able to make better decisions to solve the world's problems, big and small.