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Intuitive, well documented, and very powerful. A library to keep an eye on. Check the example Belgian elections, June 13, 2010 - Twitter opinion mining

Echoing this, in 2009 Google researchers Alon Halevy, Peter Norvig, and Fernando Pereira penned an article under the title The Unreasonable Effectiveness of Data. In it, they describe the surprising insight that given enough data, often the choice of mathematical model stops being as important - that particularly for their task of automated language translation, "simple models and a lot of data trump more elaborate models based on less data." If we make the leap and assume that this insight can be at least partially extended to fields beyond natural language processing, what we can expect is a situation in which domain knowledge is increasingly trumped by "mere" data-mining skills. I would argue that this prediction has already begun to pan-out: in a wide array of academic fields, the ability to effectively process data is superseding other more classical modes of research.

Francesco please look at this and get back wrt to the /. question .... thanks

Interesting databases to play with data mining algorithms

of relevance to this discussion: Koomey's Law, a Moore's Law equivalent for computing's energetic efficiency http://www.economist.com/node/21531350 http://hardware.slashdot.org/story/11/09/13/2148202/whither-moores-law-introducing-koomeys-law

New results in NLP might allow to automate scientific discoveries by data mining of papers. Work considers 3.3M abstracts from material science, physics and chemistry and claims to discover new materials before they are published later on.

ACT did that from diigo post digging in the retreat of 2014! Still without NLP.

That's cool! Didn't know.

Seems like astrologists were somehow right... Ptolemy would be proud.

Greetings from July :-) On an unrelated note... this chart made me suddenly realise I've been always thinking of the year as passing counter-clockwise and starting at the bottom. Very strongly. Seems like some tempo-spatial association. Anybody has a similar feeling?

Responding to calls from the National Science Foundation (NSF) and White House Office of Science and Technology Policy (OSTP), a working group of the American Statistical Association has developed a whitepaper detailing how statisticians and computer scientists can contribute to administration research initiatives and priorities. The whitepaper includes a lot of topics central to machine learning and data mining, so please take a look.

I guess Norvig is trumping Chomsky big time if this is the attitude of the NSF :)))

first science results reported in Nature (as far as I know) from the Yutu-2 and Chang'e mission .... and they look very good!

Sure they are very useful! It will be even better if they manage to fit the data to modeled circulation of the lunar magma ocean that was formed posterior to the "Theia" body collision with Earth. The collision was the cause of the magma ocean in the first place. The question now is how this circulation pattern of the lava-moon "froze" in time upon phase transition to solid. Because, what crystallizes last in sequence, is more rich in "incompatible" with the crystal structure, elements, we might combine data+models to predict their location. Those incompatible tracers are mainly radioactively decaying elements that produce heat (google publications about lunar KREEP elements (potassium (K), rare earth elements(REE), and phosphorus(P)). By knowing where the KREEP is: - we know where to dig for them mining (if they are useful for something, eg. Phosphorus for plants to be grown on the Moon) - we avoid planning to build the future human colony on top of radioactives, of course. The hope is that the Moon, due to lack of plate tectonics, has preserved this "signature of the freezing sequence". Let's see.

thanks Nasia! very interesting comment

Old post from Luis brought back from graveyard..... At least two good ideas to put there: 1) tipping points prediction 2) planetary phases for trajectory transfer and probably many more if we think about it a bit more

Blog posts describing the approaches followed by the contestants that ranked 1st, 2nd and 3rd.

Another underestimated source of CO2, are turbulent waters. "The stronger the turbulences at the water's surface, the more CO2 is released into the atmosphere. The combination of maps and data revealed that, while the CO2 emissions from lakes and reservoirs are lower than assumed, those from rivers and streams are three times as high as previously believed." Alltogether the emitted CO2 equates to roughly one-fifth of the emissions caused by humans. Yet more stuff to model...

Nasia, I thought about your statement carefully - and I cannot agree with you. Water is not a greenhouse gas. It is instead a liquid. Also, I can't believe you keep feeding the troll! :-P But on a more topical note: I think it is an over-simplification to call water a greenhouse gas - water is one of the most important mechanisms in the way Earth handles heat input from the sun. The latent heat that you mention actually cools Earth: solar energy that would otherwise heat Earth's surface is ABSORBED as latent heat by water which consequently evaporates - the same water condenses into rain drops at high altitudes and releases this stored heat. In effect the water cycle is a mechanism of heat transport from low altitude to high altitude where the chance of infrared radiation escaping into space is much higher due to the much thinner layer of atmosphere above (including the smaller abundance of greenhouse gasses). Also, as I know you are well aware, the cloud cover that results from water condensation in the troposphere dramatically increases albedo which has a cooling effect on climate. Furthermore the heat capacity of wet air ("humid heat") is much larger than that of dry air - so any advective heat transfer due to air currents is more efficient in wet air - transporting heat from warm areas to a natural heat sink e.g. polar regions. Of course there are also climate heating effects of water like the absorption of IR radiation. But I stand by my statement (as defended in the above) that rain cools the atmosphere. Oh and also some nice reading material on the complexities related to climate feedback due to sea surface temperature: http://journals.ametsoc.org/doi/abs/10.1175/1520-0442(1993)006%3C2049%3ALSEOTR%3E2.0.CO%3B2

I enjoy trolling conversations when there is a gain for both sides at the end :-) . I had to check upon some of the facts in order to explain my self properly. The IPCC report states the greenhouse gases here, and water vapour is included: http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faq-2-1.html Honestly, I read only the abstract of the article you posted, which is a very interesting hypothesis on the mechanism of regulating sea surface temperature, but it is very localized to the tropics (vivid convection, storms) a region of which I have very little expertise, and is difficult to study because it has non-hydrostatic dynamics. The only thing I can comment there is that the authors define constant relative humidity for the bottom layer, supplied by the oceanic surface, which limits the implementation of the concept on other earth regions. Also, we may confuse during the conversation the greenhouse gas with the Radiative Forcing of each greenhouse gas: I see your point of the latent heat trapped in the water vapour, and I agree, but the effect of the water is that it traps even as latent heat an amount of LR that would otherwise escape back to space. That is the greenhouse gas identity and an image to see the absorption bands in the atmosphere and how important the water is, without vain authority-based arguments that miss the explanation in the end: http://www.google.nl/imgres?imgurl=http://www.solarchords.com/uploaded/82/87-33833-450015_44absorbspec.gif&imgrefurl=http://www.solarchords.com/agw-science/4/greenhouse--1-radiation/33784/&h=468&w=458&sz=28&tbnid=x2NtfKh5OPM7lM:&tbnh=98&tbnw=96&zoom=1&usg=__KldteWbV19nVPbbsC4jsOgzCK6E=&docid=cMRZ9f22jbtYPM&sa=X&ei=SwynUq2TMqiS0QXVq4C4Aw&ved=0CDkQ9QEwAw

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Impressive. Really nice work!!!

I used to use http://www.wordle.net/

Francesco could you please have a look at this? technology behind? semantic? useful also for us?

The latest from Schmidt / Lipson / Eureqa. A significant improvement over their previous work is that now "The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree."