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duncan barker

Optics InfoBase - Ultrathin, metamaterial-based laser cavities - 0 views

www.opticsinfobase.org/abstract.cfm

technology space phy NRG

shared by duncan barker on 07 Sep 10 - No Cached
LeopoldS liked it
  • duncan barker on 07 Sep 10
     
    I'm not sure if meta materials can be used to enhance lasers in space........Leopold
  • LeopoldS on 07 Sep 10
     
    well .... will have to read this more carefully, but it seems, why not, no principal difference in space on this aspect ... any idea on this José? (in case you read this, which I doubt very much) - or maybe Luzi?
Tobias Seidl

Nanometric butterfly wings created (10/9/2009) - 0 views

www.biomimicrynews.com/...ic_butterfly_wings_created.asp

BIO NAN insects butterflies optics

shared by Tobias Seidl on 14 Oct 09 - Cached
  • Tobias Seidl on 14 Oct 09
     
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  • pacome delva on 19 Oct 09
     
    cool. It would be very to make a review of interesting photonic properties we could benefit from nature, and the possible use for space. It's nice to see that some structured can already be replicated...! Is there a structure to give angulaer momentum to light ?
Luís F. Simões

In Head-Hunting, Big Data May Not Be Such a Big Deal - NYTimes.com - 1 views

www.nytimes.com/...ay-not-be-such-a-big-deal.html

google hiring data mining

shared by Luís F. Simões on 05 Jul 13 - No Cached
LeopoldS liked it
  • Years ago, we did a study to determine whether anyone at Google is particularly good at hiring. We looked at tens of thousands of interviews, and everyone who had done the interviews and what they scored the candidate, and how that person ultimately performed in their job. We found zero relationship. It’s a complete random mess
Luís F. Simões

At Google X, a Top-Secret Lab Dreaming Up the Future - NYTimes.com - 3 views

www.nytimes.com/...ab-dreaming-up-the-future.html

AI robotics elevator spaceelevator car Web of Things

shared by Luís F. Simões on 15 Nov 11 - No Cached
  • These are just a few of the dreams being chased at Google X, the clandestine lab where Google is tackling a list of 100 shoot-for-the-stars ideas. In interviews, a dozen people discussed the list; some work at the lab or elsewhere at Google, and some have been briefed on the project. But none would speak for attribution because Google is so secretive about the effort that many employees do not even know the lab exists.
  • tvinko on 15 Nov 11
     
    hmmm, I was wondering how many ESA employees do know that ACT does exist....
  • Isabelle DB on 16 Nov 11
     
    And my son studying at Stanford (he just sent me the same link !) follows the courses this semester of two of the teachers mentioned in the article, Thrun - very good and Ng - excellent
Juxi Leitner

Make: Online : Programmable blobs - 4 views

blog.makezine.com/...programmable_blobs.html

technology programmable meta materials

shared by Juxi Leitner on 09 Nov 10 - No Cached
  • Ma Ru on 09 Nov 10
Marcus Maertens

Google AI Blog: Curiosity and Procrastination in Reinforcement Learning - 2 views

ai.googleblog.com/...ty-and-procrastination-in.html

AI fun

shared by Marcus Maertens on 14 May 19 - No Cached
  • Marcus Maertens on 14 May 19
     
    What happens if you put a TV in the maze your robot is supposed to navigate (driven by curiosity)?
  • Athanasia Nikolaou on 17 May 19
     
    Does the fact that I follow this process of learning, make me a meta-learner? Or a pre-robot?
jcunha

When AI is made by AI, results are impressive - 6 views

m.techxplore.com/...2017-12-ai-results.html

technology google nasnet ai

shared by jcunha on 11 Dec 17 - No Cached
LeopoldS liked it
  • dharmeshtailor on 12 Dec 17
     
    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.
  • dharmeshtailor on 12 Dec 17
     
    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
Luís F. Simões

Why Is It So Hard to Predict the Future? - The Atlantic - 1 views

www.theatlantic.com/...588040

future forecast innovation collaboration

shared by Luís F. Simões on 29 Nov 20 - No Cached
LeopoldS liked it
  • The Peculiar Blindness of Experts Credentialed authorities are comically bad at predicting the future. But reliable forecasting is possible.
  • The result: The experts were, by and large, horrific forecasters. Their areas of specialty, years of experience, and (for some) access to classified information made no difference. They were bad at short-term forecasting and bad at long-term forecasting. They were bad at forecasting in every domain. When experts declared that future events were impossible or nearly impossible, 15 percent of them occurred nonetheless. When they declared events to be a sure thing, more than one-quarter of them failed to transpire. As the Danish proverb warns, “It is difficult to make predictions, especially about the future.”
  • Tetlock and Mellers found that not only were the best forecasters foxy as individuals, but they tended to have qualities that made them particularly effective collaborators. They were “curious about, well, really everything,” as one of the top forecasters told me. They crossed disciplines, and viewed their teammates as sources for learning, rather than peers to be convinced. When those foxes were later grouped into much smaller teams—12 members each—they became even more accurate. They outperformed—by a lot—a group of experienced intelligence analysts with access to classified data.
  • ...1 more annotation...
  • This article is adapted from David Epstein’s book Range: Why Generalists Triumph in a Specialized World.
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