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Google Begat the End of the Scientific Method?
TheSauce writes "In a fairly concise one-pager from Chris Anderson, at Wired, the editor posits that all of our current (or now previous) models for collecting data are dead. The content is compelling. It notes that we've entered the Age of the Petabyte — where one can collect immense amounts of data that are paradigm agnostic. It goes on to add a comment from the head of Google's R&D, that we need an update to George Box's maxim: 'All models are wrong, and increasingly you can succeed without them.' Have we reached a time where all of our tool-sets are now made moot by vast clouds of information and strictly applied maths?"
It's simple really: The article seems to be saying that we have access to such a ludicrously large amount of data that trying to draw any real meaning from it is pointless. So, we employ a "shotgun" approach at reading the data, and voila, we get data that at least appears to be interesting.
Of course, since we have no particular purpose in mind when we do this, and no particular method other than "random", we end up with mostly useless data (in the example given, we have a bunch of random gene sequences that must belong to previously unknown species, but we know nothing about those species other than that we found some random DNA that probably belongs to them, and have no particularly good way of finding out more).
The article seems to be saying that since we have so much data, we can now draw correlations between different pieces of data and call it science. No reason is given why this is useful other than that we have so much of it, and Google is somehow involved. Apparently when you have enough data, "correlation does not equal causation" is no longer true. Again, no coherent reason is given for this stance.
I think the article makes the same mistake a lot of ill-informed people that get excited by big numbers make: It seems to believe that data is in and of itself an end goal, when really vast amounts of data are useless unless it can help us as humans answer questions that we want answered. Yes, knowing that there are lots of species of organisms in the air that we didn't know about before is sort of interesting I guess, but it doesn't really tell us anything useful.
Above all, the article proves that you can be almost entirely incoherent and still get your article published in Wired if it says something about how Google is changing the world.
I suppose you could start where he, again, tries to present the argument that correlation really is "good enough" - causation be damned. What he is blattering on about is that you can infer lots of things via statistical analysis - even complex things. That's certainly true. Where he fails (and it's an EPIC fail) is his assertion that this method is a general phenomena, suitable for every day use.
The other major failure of TFA is that I can't find a car analogy anywhere.
Faster! Faster! Faster would be better!
Yeah, I don't know what "paradigm agnostic" means specifically, but I think it's a mistake to think that "data is data".
Not all data is created equally. You have to ask how it was collected, according to what rules, and with what purpose. I can collect all sorts of data by stupid means, and have it be unsuitable for proving anything. It's even possible that I could collect a bunch of data in an appropriate way, accounting for the variables which matter for my particular experiment, and have that data be inappropriate for other uses.
Of course, if what's intended by "paradigm agnostic" is that we no longer pay attention to those things, then I hope we're not becoming paradigm agnostic. I'm just bringing this up because I think some people think numbers don't lie, and that when you analyze data, either your conclusions will be infallible or your analysis is flawed. On the contrary, data can not only be bad, but it can be inappropriate.
It's an idiotic notion. We've had vast amounts of data for well over a century now, more than we can hope to fully measure and catalog in a life time. Everything from fossils to space probe readings to seismic measurements fill up data archives, in some cases literally warehouses full of data tapes, artifacts and paper. The way you deal with this sort of thing never changes. Providing the data is stored in a reasonable fashion, if you have a theory, you can go back and look at the old measurements, artifacts, bones, whatever and test your theory against the data. The only difference is that rather than going out and making the observations yourself, your using someone else's (or some computer that just transmitted its data).
The world's burning. Moped Jesus spotted on I50. Details at 11.
Just undoing a slip of the mouse moderation.
That's one disadvantage of the current mod system - no chance to fix mistakes
Climate Progress - Hell and High Water
Well I think the point they make is that with this kind of mathematical tools running against this huge sets of data, you get models out that you couldn't have thought of about. This is real AI. During the last days we had entries here on Slashdot about how AI is not advancing, but this kind of thing is very advanced AI and it is new.
I'll explain myself. The biggest job that a brain does (lets not consider a human brain so we don't get into the consciousness/mind type of conversation) is to find statistical correlations from the input data and extracting models from this correlations that can be used to predict the future. This is exactly what this tools are doing.
Before this tools, by looking at the data you would go: mmmm, this is interesting, lets check it out. That is, you would come up with a model and try to find out if it predicts the data. Then we started to use computers to check our models, and from what this WTFey article says, it is the computer the one coming out with the model now, starting from raw data.
When his defense asked, "Which computer has Jon Johansen trespassed upon?" the answer was: "His own."
But that goes for any visualisation technique - look to Edward Tufte or Stephen Few for detailed examples of how even the simple xy-graph can be abused.
I must admit, as an applied mathematician who makes models of physical things for a living, this sort of research threatens to steal my bread and butter. It may be self-centered, but I think modeling is, beside experiment, half of science.
Simplified models are so valuable to our understanding because they tell us what information we can remove, which parts of a problem are important and which parts may be ignored. They allow us to not just make predictions, but they guide future experimentalists as to what sorts of changes will impact the system and which won't.
To be fair, it's more of a cycle: experiments generate data, models are constructed to explain the data. These models make predictions (and hopefully useful simplifications) that can be checked by further experiments to validate them. At the end of the process, we've produced a clearer picture of how a system works. Enough information maybe for someone building something slightly different to not have to test the aspects covered by the model.
I view these data-mining techniques like the scientific computing techniques of the last 30 years or so, only the inverse. Sci Comp nerds wanted to do away with experiments. They thought they could numerically simulate (relatively) exact models (like Navier-Stokes for fluid motion rather than one of its more tractable, understandable simplifications) and use the generated data instead of experimental data. The trouble was that no one will believe that the crazy new phenomenon discovered by your program is real until they see it in the lab, until they construct a simplified model that has the same behavior -- i.e. the same science as before.
The new data-mining idea is the same, but for the modeling end of things. "No models, please," they say. They'll just data-mine the experimental results and "discover" whatever the model missed. Except people will want to do experiments to verify the discovery. They'll want to build models so they can know they're doing the right experiments, and so on.
At the end, I think Sci Comp and data-mining are fantastic new tools that have a lot to offer science, but I don't think either eliminates the need for old fashioned modeling.
Use the Firehose to mod down Second Life stories!
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