Another scientific revolution
This is a very interesting article discussing the possibility of the computer, not only dealing with the data that scientific concepts produce, but even coming up with the concepts in the first place. Imagine, Isaac Newton transfigured into a giant computer!
The scientific method | Computing the future | Economist.com
WHAT makes a scientific revolution? Thomas Kuhn famously described it as a “paradigm shift”—the change that takes place when one idea is overtaken by another, usually through the replacement over time of the generation of scientists who adhered to an old idea with another that cleaves to a new one. These revolutions can be triggered by technological breakthroughs, such as the construction of the first telescope (which overthrew the Aristotelian idea that heavenly bodies are perfect and unchanging) and by conceptual breakthroughs such as the invention of calculus (which allowed the laws of motion to be formulated). This week, a group of computer scientists claimed that developments in their subject will trigger a scientific revolution of similar proportions in the next 15 years.
[...] Such solutions, however, are merely an extension of the existing paradigm of collecting and ordering data by whatever technological means are available, but leaving the value-added stuff of interpretation to the human brain. What really interested Dr Emmott's team was whether computers could participate meaningfully in this process, too. That truly would be a paradigm shift in scientific method.
And computer science does, indeed, seem to be developing a role not only in handling data, but also in analysing and interpreting them.
[...] Stephen Muggleton, the head of computational bio-informatics at Imperial College, London, has, meanwhile, taken the involvement of computers with data handling one step further. He argues they will soon play a role in formulating scientific hypotheses and designing and running experiments to test them. The data deluge is such that human beings can no longer be expected to spot patterns in the data. Nor can they grasp the size and complexity of one database and see how it relates to another. Computers—he dubs them “robot scientists”—can help by learning how to do the job. A couple of years ago, for example, a team led by Ross King of the University of Wales, Aberystwyth, demonstrated that a learning machine performed better than humans at selecting experiments that would discriminate between hypotheses about the genetics of yeast.