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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.
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."