JMLR and the Web, by Boston Globe

[Sebastian Thrun]

This article in Boston Globe should be of interest to many in the
Connectionists community. It provides a writer's perspective on the recent
creation of the Journal of Machine Learning Research (JMLR), and the motivation
behind the mass resignation of Machine Learning's editorial board. 

JMLR sets an example of what I believe will ultimately happen to many journals.
The Web reaches many more people than current publishing mechanisms. As JAIR
has proven, using the Web doesn't negatively affect the quality of a journal.

By moving from paper to the Web, I hope we ultimately change the way we publish
research results. It will be much easier to annotate papers by animations,
software, and on-line discussions. It will be possible to link to follow-up
research, publish revisions if necessary, and link to related scientific
findings that came along after an original paper was published. And, of course,
the whole world can access all of this, not just a selected few.

Best wishes,
sebastian thrun


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 SPREADING THE GOSPEL OF FREE SCIENCE

     Author: By Nicholas Thompson, GLOBE CORRESPONDENT Date: 11/20/2001
Page: C1 Section: Health Science

Leslie Kaelbling hardly looks like a threat to the scientific establishment.

The perky MIT professor rides to work on a bicycle that neatly folds into a
briefcase-sized rectangle and spends her days trying to make machines that
can learn. As associate director of MIT's Artificial Intelligence
Laboratory, she is surrounded by quirky creations, from tiny fish-like
robots made partly from frog tissue to robots that look like humans and, one
day, may think like them, too.

But Kaelbling leads another revolution in her spare time. As editor of the
upstart Journal of Machine Learning Research, Kaelbling offers some of the
latest debates and developments in artificial intelligence to anyone with
access to the Internet - free. By contrast, the scholarly journals that
largely set the world's science agenda sometimes charge more than $1,000 a
year for a subscription.

Kaelbling would be just another quixotic idealist with her unpaid staff -
the journal "doesn't even have a bank account right now," she acknowledges -
except for one thing. Thirty-nine board members of the leading artificial
intelligence journal in her field, Machine Learning, announced their
resignations last month to join her crusade.

Kaelbling and her supporters argue that putting all top current research
online could well inspire crucial insights from people who wouldn't
otherwise have access; a statistician in Mongolia may spur the next
breakthrough, after all.

"The only thing we care about in the world is that people read our work,"
Kaelbling said.

The rivalry between Kaelbling's journal and Machine Learning - which costs
$1,050 a year for institutions and $120 for individuals - is part of a much
broader debate about how much scientific information should be free. For
instance, the federal government's Human Genome Project and private Celera
Genomics have locked horns repeatedly over Celera's plan to withhold some
key information from the human genetic blueprint that could be sold to
pharmaceutical companies looking for potential new drugs.

More closely parallel to Kaelbling's work, the National Institutes of Health
have created a database called PubMed Central intended to allow anyone in
the world to freely search and retrieve the full text of any published
scientific article, with archives extending back for decades. Several months
ago, a coalition of about 30,000 scientists, led partly by former NIH
director and Nobel laureate Harold Varmus, pledged to boycott any journals
that didn't meet PubMed Central's standards for freely distributing data.
The coalition backed off a little in early September, but the conflict still
looms.

The free, Internet-based Journal of Machine Learning Research challenges an
elaborate system of disseminating scientific information that touches
everything from what appears on the nightly news to which researchers become
stars. A few well-known journals such as Science, Nature, and the New
England Journal of Medicine considerably influence scientific discussion and
have a near hammerlock on determining what science appears in the mainstream
press. A slew of others - including Machine Learning - shape their
respective subject areas, helping determine who gets tenure, where grants
go, and how their fields move forward.

Kaelbling and others note that the scientific community differs in several
ways from journalism, where support for free online access to magazines and
newspapers has withered. For one, most scientific authors don't get paid for
publishing, even in the journals with costly subscription prices. Instead,
they receive their funding from universities, corporations, or government
grants, and publish mainly for prestige and to advance their fields.
Kaelbling earns her salary from the Massachusetts Institute of Technology
and part of her job description requires her to offer public service to the
community, such as editing the Journal of Machine Learning Research.

Secondly, progress in any scientific field relies to a huge extent on the
amount of available information. More available information equals more and
better science. Some of Kaelbling's colleagues, for example, want to design
robots that actually think like humans: Rodney Brooks, the director of
Kaelbling's laboratory, helped inspire Steven Spielberg's vision for the
movie "A.I." That enormously complicated task would be helped with input
from as many different scientists as possible. Putting everything online
seems like the obvious thing to do, Kaelbling argued.

Still, numerous scientists and publishers fear that moving away from
printed, subscription-based journals could derail standards and practices
that have worked well for years. Others fear that such a change could
shutter prestigious and important journals.

In the machine-learning community, swords have already crossed. In their
letter resigning from Machine Learning, the rebellious members, who
represented about two-thirds of the board, wrote: "Journals should
principally serve the needs of the intellectual community, in particular by
providing the immediate and universal access to journal articles that modern
technology supports, and doing so at a cost that excludes no one."

In the past, scholars in the field researched and wrote their articles,
submitted them to Machine Learning, and then waited up to a year to see them
in print. Probably most aggravating to the authors, Machine Learning's
owners, Netherlands-based Kluwer Academic Publishers, retained complete
copyright control. Authors couldn't even publish their articles on personal
Web pages - a fairly restrictive policy for the industry that the company
changed after the mass resignation.

With the Journal of Machine Learning Research, authors just e-mail their
pieces to Kaelbling, who then forwards them to assorted editors. These
volunteers, generally prestigious researchers in whatever particular
sub-field the article covers, then decide whether to accept or reject the
articles. If accepted, the articles appear online immediately and the
authors retain full copyrights.

"Mostly, it's just a bunch of work," said Kaelbling, before noting that she
still spends vastly more time with her MIT students and Erik the Red, a
robot resembling R2D2 that she is trying to teach to see and navigate
through hallways.

Despite Kaelbling's optimism, though, other scientists argue that there are
holes in her boat. Robert Holte, the editor of Machine Learning, supports
the new journal and suggests that both his journal and Kaelbling's can exist
harmoniously. But, he added, "What [the Journal of Machine Learning
Research] doesn't have right now is a history. You can have the most famous
people on your editorial board that you like. But until a journal has a
well-established track record proving its ability to attract a large number
of high quality, highly-cited papers, it cannot claim to be the community's
flagship journal."

Tenure committees, for example, know that being published in Machine
Learning means you've accomplished something significant. Until it earns a
reputation, Kaelbling's journal could just be two crackpots in a barn with a
cable modem.

In addition, advocates of print journals argue that online journals may not
hold to the same quality standards, becoming, in a sense, the scientific
equivalents of the Drudge Report.

"Paper journals have a strict limit on the number of papers they can
publish. With an online journal, there's always a temptation to accept
rather than reject," said Cornell professor Shai Ben-David, one member of
the editorial board of Machine Learning who chose not to resign.

Kaelbling acknowledged that her journal hasn't earned prestige yet, but she
insists that it will maintain strict standards. "It's the same people doing
the same work," she said, noting that the editorial board of the Journal of
Machine Learning Research is made up of many people who used to work for
Machine Learning. Kaelbling is one of the most highly respected scientists
in her field and, before resigning last year, she herself reviewed papers
for her print rival.

Kaelbling faces another potential problem in that no major for-profit
publisher supports and promotes her journal. Kluwer Academic Publishing, the
world's second largest scientific publisher with 731 journals under its
umbrella, stated that it supports Machine Learning by providing services
that include promotion, copy-editing, distribution and representation of the
journal at conferences.

Still, the nonprofit MIT Press does offer Kaelbling's journal its support,
publishing and promoting quarterly bound editions of the articles that have
appeared on the journal's Web site. "I don't think they are any less
effective than Kluwer," Kaelbling said.

MIT Press does publish and promote quarterly bound editions of the articles
that have appeared on Kaelbling's Web site, but has garnered fewer than 100
subscriptions so far. That doesn't phase Kaelbling, either. "Everyone's
going to have to do this," she said.

Even some scientists closely attached to old print publications agree.
According to Thomas Dietterich, a former editor of Machine Learning, and one
of the recent defectors to the new journal: "I am emotionally attached to
Machine Learning. I have every issue from the start to now. But things
change. In the computer business, we are used to technology turning things
upside down."