'Eureka machine' puts scientists in the shade by working out laws of nature

[mr blur]

Scientists have created a "Eureka machine" that can work out the laws of nature by observing the world around it – a development that could dramatically speed up the discovery of new scientific truths.

The machine took only hours to come up with the basic laws of motion, a task that occupied Sir Isaac Newton for years after he was inspired by an apple falling from a tree.

Scientists at Cornell University in New York have already pointed the machine at baffling problems in biology and plan to use it to tackle questions in cosmology and social behaviour.

The work marks a turning point in the way science is done. Eureka moments, which supposedly began in Archimedes' bath more than 2,000 years ago, might soon be happening not in the minds of geniuses, but through the warm hum of electronic circuitry.

"We've reached a point in science where there's a lot of data to deal with. It's not Newton looking at an apple, or Galileo looking at heavenly bodies any more, it's more complex than that," said Hod Lipson, the computer engineer who led the project.

"This takes the grunt out of science by sifting through data and looking for the laws that govern how something behaves."

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http://www.guardian.co.uk/science/2009/apr/02/eureka-laws-nature-artificial-intelligence-ai

[bluedawg12]

1. That's cool.

I remember a young mathametican on Charlie Rose who was well respected and had funding to study cancer based on mathamatical models. A fresh look at old problems is always welcome.

Just remember, we rule the machine.

[bluedawg12]

2. Question: Can we adapt to discoveries as fast as machines will make them?

Are we in that much of a hurry and why?

Look at nuclear energy, bio warfare, the enivornment.


Maybe we a rushing headlong for a future we are not yet adapted to?


.........

http://sciencenow.sciencemag.org/cgi/content/full/2009/402/1
Robotic Scientists Make First Discoveries
By Constance Holden
ScienceNOW Daily News
2 April 2009


Using algorithms programmed by scientists, Adam formulates hypotheses about the origins of "orphan enzymes": enzymes for which scientists have been unable to identify the encoding genes. The robot then plans and executes experiments to test its hypotheses--selecting yeast mutants from a collection, incubating cells, and measuring their growth rates. As King's team reports this week in Science, Adam came up with 20 hypotheses about genes encoding 13 enzymes, 12 of which it confirmed.

The second paper, also in Science, reports a similar feat in physics. Cornell University computer scientists Michael Schmidt and Hod Lipson devised an algorithm that will deduce laws about the motion of a nonlinear dynamical system--for example, a pendulum suspended from the end of another pendulum. The dynamics of such a system can be captured in a mathematical function called a Hamiltonian, which is essentially an expression of the system's energy. Schmidt and Lipson's robot can deduce the Hamiltonian and other key mathematical quantities for a system by observing its motion.

Both papers show how to "automate parts of the science enterprise that haven't been automated much before," says computer scientist David Waltz of Columbia University. In both cases, he points out, the system not only will generate a hypothesis but then can go on to test it and revise it as a result of the testing. "There's an interesting message from this," says Waltz--and perhaps a daunting one for some scientists. In the future, he says, scientists, in order to carry out their work, "might have to learn how to program computers and express knowledge about the world the way people in artificial intelligence have done."

At the moment, neither robot is likely to bag a Nobel Prize. Schmidt and Lipson's system only "discovers" Hamiltonians and other functions that a sharp graduate student might quickly figure out. Adam has traced the previously unknown origins of several enzymes but hasn't made a conceptual breakthrough. "Qualitatively, humans are still winning hands down because of the amount of knowledge any human scientist brings to a question," says computer scientist Bruce Buchanan of the University of Pittsburgh in Pennsylvania. But he predicts that as programs continue to improve, automated systems may come up with discoveries humans would never have imagined.
......

[progressoid]

3. Coolio.

I'm sending that one to my brother!