Communications - May 2012

volves formidable challenges, such as how to scale up algorithms like Eureqa to handle more complex models. While Langley considers Lipson’s work to be technically solid, it deals with only a seven-dimensional model. What happens if the computer has to deal with hundreds of equations, all interacting with one another? The computer’s runtime could grow exponentially with the complexity of the model. Computer scientists need to develop methods to deal with much more complex models, Langley says.

A Revolution in science?

It is not difficult to envision how computers could speed up scientific discovery by plowing through vast arrays of data and “spitting out hypotheses” for testing, suggests Bruce Buchanan, professor of computer science, philosophy, and medicine at the University of Pittsburgh. In the 1960s, Buchanan was involved in Dendral, an early scientific discovery computer program that used mass spectrometry to identify unknown organic molecules. Simply sorting out the interesting molecules from vast arrays of information, the way Google identifies the most popular results of a search query, would be useful. “There are so many cases where discoveries could have been made sooner if people were looking at different data, asking better questions,” Buchanan says.

The larger question is whether computers could ever go beyond what phi-

Robots will be
able to conduct
almost any
experiment in
a wet biology lab
in about 20 years,
says Ross King.

losopher Thomas Kuhn called “normal science” and achieve a real scientific revolution. Elihu Abrahams, a condensed matter physicist at Rutgers University, and Princeton University physicist Philip Anderson, expressed their doubts in a letter to Science after its publication of separate computational discovery papers by Lipson and King in 2009. “Even if machines did contribute to normal science,” they wrote, “we see no mechanism by which they could create a Kuhnian revolution and thereby establish new physical law.” Abrahams says he has seen nothing since then to change his mind.

King admits the field has not yet
reached that point. Adam “knows far
more facts than any human, but it
doesn’t have a deep understanding of
biology,” he says. “I think you need a
deep understanding to actually manip-
ulate concepts in novel ways.”
Computational discovery can cer-
tainly make science more efficient and
cost effective, and free up scientists to
do more deep thinking, he argues. But
for machines, King says, “the things
which the great scientists do are still a
long way off.”

Technology

Cloud to
Create
Jobs Boom

Instead of causing massive unemployment, cloud computing will generate 13. 8 million jobs worldwide by the end of 2015, according to a new study from IDC.

Given its potential as an outsourcing catalyst, the cloud has often been linked to future staffing cuts. But the study, which is sponsored by Microsoft, contends that the cloud will fuel growth initiatives by greatly reducing “legacy drag”—the maintenance of legacy systems and routine upgrades—which now accounts for 75% of technology spending.

“Most [CIos] look at migration to cloud computing as a way to free up existing resources to work on more innovative projects, not to do away with the positions entirely,” according to the study, which was written by the IDC research team of John f. Gantz, Stephen Minton, and Anna Toncheva.

IDC came up with its projections by analyzing cloud-spending patterns in more than 40 nations and combining this with data such as available country work force, unemployment rates, and other IT spending developments.

Additional study results: ˲ Cloud-related business revenue could increase to $1.1 trillion by 2015. ˲ Small and medium-sized companies will establish the most jobs, with 7. 5 million new jobs within this time. Why? Because they are less impacted by legacy drag and are more likely to invest in public cloud services due to capital constraints. ˲ China and India will generate 6. 75 million jobs by the end of 2015. In contrast, less than 1. 2 million positions will be created in North America. ˲ Public-sector opportunities will grow, but at a much slower pace with just more than 500,000 jobs. Global governments, the authors write, will lag behind because of their “slow pace [in] adopting the new computing style.” — Dennis McCafferty