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 aliasinkhorn rated 13 months ago - The End of Theory: The Data Deluge Makes the Scientific Method Obsolete
From the page: But faced with massive data, this approach to science -- hypothesize, model, test -- is becoming obsolete. Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic le... more
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25 Reviews
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- psykonaut rated 12 months agoscience
- "This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves."
- aRTy-nz rated 12 months agoscience, theory
- This is so funny, because it is totally undermining ad upsetting the reactionaries of the scientific establishment. And yet it is correct.
- Ceylonese1 rated 12 months agoscience, statistics
- From the page: "J. Craig Venter"
Interesting ideas.
- timnull rated 13 months agoscience
- Sorry, but this is total BS.
You can replicate the universe with data, and you still won't understand the universe.
You can't learn squat without the scientific method: hypothesis, testing, theory, more hypotheses, more testing, more theories, etc and so forth.
This idea is the flipside of Creationism. Remember the Creationist saying, "All theories are equally good." Now these guys are saying, "Theories are unnecessary."
They all want us to stop thinking critically and become robotons!
- aliasinkhorn rated 13 months agoscience, theory, correlations, data
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The End of Theory: The Data Deluge Makes the Scientific Method Obsolete
From the page: But faced with massive data, this approach to science -- hypothesize, model, test -- is becoming obsolete. Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic level, but still useful). A hundred years ago, statistically based quantum mechanics offered a better picture -- but quantum mechanics is yet another model, and as such it, too, is flawed, no doubt a caricature of a more complex underlying reality. The reason physics has drifted into theoretical speculation about n-dimensional grand unified models over the past few decades (the beautiful story phase of a discipline starved of data) is that we don't know how to run the experiments that would falsify the hypotheses -- the energies are too high, the accelerators too expensive, and so on.
Now biology is heading in the same direction. The models we were taught in school about dominant and recessive genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton's laws. The discovery of gene-protein interactions and other aspects of epigenetics has challenged the view of DNA as destiny and even introduced evidence that environment can influence inheritable traits, something once considered a genetic impossibility.
In short, the more we learn about biology, the further we find ourselves from a model that can explain it.
There is now a better way. Petabytes allow us to say: Correlation is enough. We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.
The best practical example of this is the shotgun gene sequencing by J. Craig Venter. Enabled by high-speed sequencers and supercomputers that statistically analyze the data they produce, Venter went from sequencing individual organisms to sequencing entire ecosystems. In 2003, he started sequencing much of the ocean, retracing the voyage of Captain Cook. And in 2005 he started sequencing the air. In the process, he discovered thousands of previously unknown species of bacteria and other life-forms.
If the words discover a new species call to mind Darwin and drawings of finches, you may be stuck in the old way of doing science. Venter can tell you almost nothing about the species he found. He doesn't know what they look like, how they live, or much of anything else about their morphology. He doesn't even have their entire genome. All he has is a statistical blip -- a unique sequence that, being unlike any other sequence in the database, must represent a new species.
This sequence may correlate with other sequences that resemble those of species we do know more about. In that case, Venter can make some guesses about the animals -- that they convert sunlight into energy in a particular way, or that they descended from a common ancestor. But besides that, he has no better model of this species than Google has of your MySpace page. It's just data. By analyzing it with Google-quality computing resources, though, Venter has advanced biology more than anyone else of his generation.
Brilliant article. It is about time 'thinking' breaks the bondage of theories and taxonomies that become constrictive psuedo-belief systems; in any event, theories are nothing more than tentative explanatories.
I think theory has its place in the tool kit of exploration; it ruins 'knowledge' when it becomes a de facto law. This excerpt in the article explains why:
'The models we were taught in school about dominant and recessive genes steering a strictly Mendelian process have turned out to be an even greater simplification of reality than Newton's laws... In short, the more we learn about biology, the further we find ourselves from a model that can explain it.'
Highly recommended read.
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- tentmaker3 rated 13 months agoscience, technology
- This is a pretty good simple description of how advances in computing power and the amount of data we both have and can process are going to shortly begin changing everything. Genetic algorithms are at the complex end of this, but the simple end is that we no longer have to "solve" problems in the old analytical sense of the word, when we can just "throw the spaghetti against the wall and see what sticks". In other words, insteading of searching for the right pathway to solve a problem, we can try all the possible solutions until one works and we can do it in a short enough period of time to make this approach reasonable. This will change everything. Within a few years, we'll be solving problems at a much faster rate.
- ZenChaoist rated 13 months agoscience
- From the page: "In February, the National Science Foundation announced the Cluster Exploratory, a program that funds research designed to run on a large-scale distributed computing platform developed by Google and IBM in conjunction with six pilot universities. The cluster will consist of 1,600 processors, several terabytes of memory, and hundreds of terabytes of storage, along with the software, including Google File System, IBM's Tivoli, and an open source version of Google's MapReduce. Early CluE projects will include simulations of the brain and the nervous system and other biological research that lies somewhere between wetware and software."
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