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enjoy ....

ok, but if we first have to define consciousness before starting dealing with any physics, isn't a large pause looming ahead, proposed by the author?

I kind of subscribe to Tononi's information theory approach to consciousness: http://www.biolbull.org/content/215/3/216.full And of course the watered down wikipedia version for whoever lacks the patience to read the whole treatise http://en.wikipedia.org/wiki/Integrated_information_theory

Two research teams have designed an optical gate that can 'switch off' a stream of photons as well as store them ... good news for optical communications as well as future photon-based quantum information systems.

NASA and ESA spacecraft have gathered new information about the basic properties of the comet's nucleus (not 67p) and directly detected the effects on the Martian atmosphere. Dust from the comet impacted Mars and was vaporized high in the atmosphere, producing what was likely an impressive meteor shower. Eight different types of metal ions have been detected, including sodium, magnesium and iron. The satellites will now continue to look for long-term perturbations to Mars' atmosphere.

nice video and nice story, no revolution in physics and somehow surprising that not done/tried earlier (maybe just again good MIT public relations work?)

CSI writers will have to up the ante now.

it was probably already done... by the NSA

A free software tool that could revolutionize the drone industry to design and build your own drone/aircraft/fighter with a 3-D online software that generates aerodynamic information (maybe optimization also) about your own design.

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."

How do humans construct their mental representations of the passage of time? The universalist account claims that abstract concepts like time are universal across humans. In contrast, the linguistic relativity hypothesis holds that speakers of different languages represent duration differently. The precise impact of language on duration representation is, however, unknown. Here, we show that language can have a powerful role in transforming humans' psychophysical experience of time. Contrary to the universalist account, we found language-specific interference in a duration reproduction task, where stimulus duration conflicted with its physical growth. When reproducing duration, Swedish speakers were misled by stimulus length, and Spanish speakers were misled by stimulus size/quantity. These patterns conform to preferred expressions of duration magnitude in these languages (Swedish: long/short time; Spanish: much/small time). Critically, Spanish-Swedish bilinguals performing the task in both languages showed different interference depending on language context. Such shifting behavior within the same individual reveals hitherto undocumented levels of flexibility in time representation. Finally, contrary to the linguistic relativity hypothesis, language interference was confined to difficult discriminations (i.e., when stimuli varied only subtly in duration and growth), and was eliminated when linguistic cues were removed from the task. These results reveal the malleable nature of human time representation as part of a highly adaptive information processing system.

Loss of connectivity in the multisensory integration cortical areas after short term microgravity experience, which could explain astronauts decrease of performance in sensorimotor tasks and spatial working memory. However, the effect should wear off after a few days in microgravity and after adaptation to incongruent vestibular information. ISS experiment needed...

A new idea called the "information bottleneck" is helping to explain the puzzling success of today's artificial-intelligence algorithms - and might also explain how human brains learn.

This has been around for over a year. The current trend in deep learning is "deeper is better". But a consequence of this is that for a given network depth, we can only feasibly evaluate a tiny fraction of the "search space" of NN architectures. The current approach to choosing a network architecture is to iteratively add more layers/units and keeping the architecture which gives an increase in the accuracy on some held-out data set i.e. we have the following information: {NN, accuracy}. Clearly, this process can be automated by using the accuracy as a 'signal' to a learning algorithm. The novelty in this work is they use reinforcement learning with a recurrent neural network controller which is trained by a policy gradient - a gradient-based method. Previously, evolutionary algorithms would typically be used. In summary, yes, the results are impressive - BUT this was only possible because they had access to Google's resources. An evolutionary approach would probably end up with the same architecture - it would just take longer. This is part of a broader research area in deep learning called 'meta-learning' which seeks to automate all aspects of neural network training.

Btw that techxplore article was cringing to read - if interested read this article instead: https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html