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interesting study showing seasonal changes to brain functions Agata, you didn't tell us about this yet :-) "the present study provides compelling evidence for previously unappreciated annual varia- tions in the cerebral activity required to sustain ongoing cognitive processes in healthy volunteers. The data further show that this annual rhythmicity is cognitive-process-specific (i.e., the phase of the rhythm changes between cognitive tasks), speaking for a complex impact of season on human brain function. Annual var- iations in cognitive brain function may contribute to explain intraindividual cognitive changes that could emerge at specific times of year."

Thank you for this interesting study. I will make a brief intro about it during our Wednesday meeting. Especially, that spring is coming...;)

"Without the relevant data for reasoning about the state of AI technology, we are essentially "flying blind" in our conversations and decision-making related to AI [...] The AI Index is an initiative to track, collate, distill and visualize data relating to artificial intelligence. "

Finish him!

The paper I talked about with the proof of a gigantic lava tube discovered in 2017 from data of the Japanese lunar mission

Given the size of this thing we may consider a project on city urban planning inside. I would see a rocket-port outside the crater, some lift systems to get in, a closed dome with artificial atmosphere where to develop the city possible trekking routes on the lunar surface, a mirror system to get sunlight in, an energy factory just outside the entry.

Scientific use cases show promise, but challenges remain for complex data analytics.

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

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.

beautiful! anybody knows of similar trials to fool EO data classifiers?

I am not aware of any similar trials, yet this study remind me of generative adversarial talking heads https://arxiv.org/pdf/1803.07716.pdf https://www.youtube.com/watch?v=p1b5aiTrGzY

Nice paper ...

NASA's oldest interstellar spacecraft is suddenly measuring changes more dramatic than any it has seen during its 35 year journey

this is very nice! in either of the explanations Voyager will likely be in good enough shape to send data when outside of the boundary layer ...

Anybody knows them? What mathematic tools are behind their products?

Wouldn't have guessed

Francesco, I missed your comments :) :)

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

I know at least of a few people who share this point of view :)

I think it is worth noting that Dyson's is not saying that climate change is an illusion - it is evident that a lot of CO2 is emitted into the atmosphere and hence something will change. His point is that we just don't know what will change and by how much and that (much) more experimental data is necessary to make predictive models.

On missing experimental work: just read in the news that condensation in cirrus clouds has been studied recently and that the models where incorrect as to what the significance of organic substances and soot is in cirrus cloud formation. http://www.sciencemag.org/content/early/2013/05/08/science.1234145

"chances are that if 700 passengers are flown annually, up to 10 of them might not survive the flight in the first years of the operations." most remarkable also the question who is to blame if a dead and burned space tourist corps comes crashing down from the sky into your car.

This calls for some we serious simulations by the Petkow code it seems to me ...

I still would need some serious input data...

"A platform for public participation in and discussion of the human perspective on machine-made moral decisions" Machine Ethics is basically the return of philosophy through code. Here you can learn a bit about it, and help the MIT collect data on how humans make choices when faced with ethical dilemmas, and how we perceive AIs making such choices.

from one of the reddit threads discussing this: "bit fishy, crazy if real". "Incredible claims: - Train only using about 10% of imagenet-12, i.e. around 120k images (i.e. they use 6k images per arm) - get to the same or better accuracy as the equivalent VGG net - Training is not via backprop but more simpler PCA + Sparsity regime (see section 4.1), shouldn't take more than 10 hours just on CPU probably "

clicking the link says the manuscript was withdrawn :))

This "one-shot learning" paper by Googe Deepmind also claims to be able to learn from very few training data. Thought it might be interesting for you guys: https://arxiv.org/pdf/1605.06065v1.pdf

There has been much recent interest in accelerating materials discovery. High-throughput calculations and combinatorial experiments have been the approaches of choice to narrow the search space. The emphasis has largely been on feature or descriptor selection or the use of regression tools, such as least squares, to predict properties. The regression studies have been hampered by small data sets, large model or prediction uncertainties and extrapolation to a vast unexplored chemical space with little or no experimental feedback to validate the predictions. Thus, they are prone to be suboptimal. Here an adaptive design approach is used that provides a robust, guided basis for the selection of the next material for experimental measurements by using uncertainties and maximizing the 'expected improvement' from the best-so-far material in an iterative loop with feedback from experiments. It balances the goal of searching materials likely to have the best property (exploitation) with the need to explore parts of the search space with fewer sampling points and greater uncertainty.

Can AI create the next wonder material? http://www.nature.com/news/can-artificial-intelligence-create-the-next-wonder-material-1.19850?WT.ec_id=NATURE-20160505&spMailingID=51301208&spUserID=MTEzODM0NjYzMzgS1&spJobID=920498312&spReportId=OTIwNDk4MzEyS0

A nice read. IBM Watson wowed the tech industry with a 2011 win against two of television show Jeopardy greatest champions. Using something that seemed like a sort of tree search for me IBM DeepQA algorithm managed to ingest sparse data (clues), process it getting one answer, understand what that answer means and come up with the question that leads to that answer. Now, IBM tells us that the same system can tackle medical diagnosis and financial risk problems.

Interesting. these guys are apparently gonna try developing pulsar navigation. They propose to solve the low apparent brightness problem using relatively complex signal processing and filtering to limit the antenna size etc. The say they've already had some promising results using ground based data. worth a science coffee perhaps?

Absolutely. Sante can you get in contact with them?