Danny Hillis gets science, George Dyson doesn’t
July 1, 2008
I don’t know why Chris Anderson’s Wired article has got me so bugged. Things rarely do. Perhaps it’s cause of the name behind the article. Perhaps its the fact that it’s been picked up by pretty much everyone, from Hacker News, to bloggers to Edge. It’s also amazing to see the responses (in blog posts and on Edge) by Kevin Kelly and George Dyson, especially the latter. For someone who is a science historian his statement at the end of the Edge version of the Anderson article is even worse than the Anderson article. If someone could really explain what he’s trying to say it would be much appreciated, cause I can’t make any sense of it. The following is an excellent example of what I am talking about.
It may be that our true destiny as a species is to build an intelligence that proves highly successful, whether we understand how it works or not.
“It may be our destiny” is NOT science and to “build an intelligence”. What is science is best described by Danny Hillis in a quote at the end of the same Edge piece, which I reproduce in its entirety here. The quote summarizes my own thinking completely.
Chris Anderson says that “this approach to science — hypothesize, model, test — is becoming obsolete”. No doubt the statement is intended to be provocative, but I do not see even a little bit of truth in it. I share his enthusiasm for the possibilities created by petabyte datasets and parallel computing, but I do not see why large amounts of data will undermine the scientific method. We will begin, as always, by looking for simple patterns in what we have observed and use that to hypothesize what is true elsewhere. Where our extrapolations work, we will believe in them, and when they do not, we will make new models and test their consequences. We will extrapolate from the data first and then establish a context later. This is the way science has worked for hundreds of years. – W. Daniel Hillis
There is a beauty to science, a structure, a certain ethos. Too many people seem to miss it completely, even those who should know better.
And unless someone comes up with another idiotic post about science, this is the last I am writing about this.
Posted by Deepak Singh | Filed Under Science
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Look no further than human clinical drug trials.
When the data to refute hypotheses become cheap - really cheap as with genetics data, you can take wild chances, do orders of magnitude more experiments, adopt a very open approach to competing hypotheses, and learn so much more as to make what's happening seem fundamentally different than what came before.
Petabytes of data are to science as Netflix is to a movie fetish. I suspect this is the point Anderson is trying to make, but the article doesn't read that way because he left out the part about expensive experimentation being the root of scientific stagnation.
Almost makes me want to write an article of my own...
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The problem with experimentation is scale. Where you can (genomics) prices go down rapidly. In other cases, for whatever reasons, the costs have remained high for years, perhaps due to over regulation, perhaps due to inefficiencies and a lack of scale. Lots of data is a good thing. Rethinking our approach is a good thing. Forgetting the fundamentals; not so good
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