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Of interest Ana ?

Let's keep a eye on this... Animals use odor cues to navigate through their environments, helping them locate targets and assess danger. Much of how animal brains organize, read out, and respond to odor stimuli across spatial and temporal scales is not well understood. To tackle these questions, Odor2Action uses a highly interdisciplinary team science approach. Our work uses fruit fly, honeybee, and mouse models to determine how neural representations of odor are generated, reformatted, and translated to generate useful behaviors that guide how animals interact with their environment.

reminds me of the methan smelling source finding study we did ...

Material point method is an efficient and promising method for simulating complex stuff. Not used much in Astro, a lot in gaming, cartoons etc.... Worth having a look in comparison with SPH in simulation (for example those connected to the HERAS mission)

This is an interesting viewpoint on the applicability (usefulness) of AI models devised by competitions, backed up by easy statistics. Worth a read!

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

Just read some of the samples of text generated with their neural networks, insane.

It's really lucky that it was OpenAI who made that development and Elon Musk is so worried about AI. This way at least they try to assess the whole spectrum of abilities and applications of this model before releasing the full research to the public.

They released a smaller model, I got it running on Sandy. It's fairly straight forward: https://github.com/openai/gpt-2

Wisdom of crowds under a new perspective: a motivation for the island model?

Prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity (reward for unfamiliar states). Learns some games without any extrinsic reward!

Fun failure case: agent gets stuck in front of TV.

Not read this article but on a related note: Curiosity and various metrics for it have been explored for some time in robotics (outside of RL) as a framework for exploring (partially) unfamiliar environments. I came across some papers on this topic applied to UAVs when prep'ing for a PhD app. This one (http://www.cim.mcgill.ca/~yogesh/publications/crv2014.pdf) comes to mind - which used a topic modelling approach.

Our work sheds light on many interesting directions of future exploration in the development of quantum-inspired algorithms for unsupervised machine learning, which are promisingly possible to realize on quantum devices.

Paper: https://arxiv.org/abs/1806.02404

Researchers have used machine learning to predict the chaotic evolution of a model flame front.

"The Neuromorphic Computing Platform allows neuroscientists and engineers to perform experiments with configurable neuromorphic computing systems. The platform provides two complementary, large-scale neuromorphic systems built in custom hardware at locations in Heidelberg, Germany (the "BrainScaleS" system, also known as the "physical model" or PM system) and Manchester, United Kingdom (the "SpiNNaker" system, also known as the "many core" or MC system)."

"By extension, GCMs are not fit for the purpose of justifying political policies to fundamentally alter world social, economic and energy systems. It is this application of climate model results that fuels the vociferousness of the debate surrounding climate models."

but you know wo these global warming policy foundation is, do you? they are the main advocacy group for climate change deniers in the UK, nothing scientific to start with; fine to post here reasonable scientific papers criticising global climate models but please not this shit

A college course at University of Washington on "Calling Bullshit". We should invite them to give a lunch lecture at ESA... Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences. While bullshit may reach its apogee in the political domain, this is not a course on political bullshit. Instead, we will focus on bullshit that comes clad in the trappings of scholarly discourse. Our learning objectives are straightforward. After taking the course, you should be able to: * Remain vigilant for bullshit contaminating your information diet. * Recognize said bullshit whenever and wherever you encounter it. * Figure out for yourself precisely why a particular bit of bullshit is bullshit. * Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit. * Provide your crystals-and-homeopathy aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit. We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.

love it: "Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit - and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit. We're sick of it. It's time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument."

Extension of standard model to explain a number of unexplained phenomena.

I got a fever. And the only prescription is more cat faces! ...../\_/\ ...(=^_^) ..\\(___) The article sounds quite interesting, though. I think the idea of a "fake" agent that tries to trick the classifier while both co-evolve is nice as it allows the classifier to first cope with the lower order complexity of the problem. As the fake agent mimics the real agent better and better the classifier has time to add complexity to itself instead of trying to do it all at once. It would be interesting if this is later reflected in the neural nets structure, i.e. having core regions that deal with lower order approximation / classification and peripheral regions (added at a later stage) that deal with nuances as they become apparent. Also this approach will develop not just a classifier for agent behavior but at the same time a model of the same. The later may be useful in itself and might in same cases be the actual goal of the "researcher". I suspect, however, that the problem of producing / evolving the "fake agent" model might in most case be at least as hard as producing a working classifier...

This paper from 2014 seems discribe something pretty similar (except for not using physical robots, etc...): https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf

Yes, this IS basically adversarial learning. Except the generator part instead of being a neural net is some kind of swarm parametrization. I just love how they rebranded it, though. :))

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