Group items tagged

To catalyze, support, and disseminate research on questions at the foundations of physics and cosmology, particularly new frontiers and innovative ideas integral to a deep understanding of reality but unlikely to be supported by conventional funding sourc

fantastic images from the deep sea ...

Other possible study: get a textbook example of an image of a pen, evolve it just enough so NN can't recognize it anymore, while minimizing the distance between the original and evolved images. EDIT: Its been done already: http://cs.nyu.edu/~zaremba/docs/understanding.pdf

Of course, you can't really use them to extrapolate. The unknown unknown is always the trickiest :P They should just make another class "random bullshit", really and dump all of this stuff in there. I think there's a potential paper right there

interesting story with many juicy details on how they proceed ... (similarly interesting nickname for the "operation" chosen by our british friends) "The spies used the IP addresses they had associated with the engineers as search terms to sift through their surveillance troves, and were quickly able to find what they needed to confirm the employees' identities and target them individually with malware. The confirmation came in the form of Google, Yahoo, and LinkedIn "cookies," tiny unique files that are automatically placed on computers to identify and sometimes track people browsing the Internet, often for advertising purposes. GCHQ maintains a huge repository named MUTANT BROTH that stores billions of these intercepted cookies, which it uses to correlate with IP addresses to determine the identity of a person. GCHQ refers to cookies internally as "target detection identifiers." Top-secret GCHQ documents name three male Belgacom engineers who were identified as targets to attack. The Intercept has confirmed the identities of the men, and contacted each of them prior to the publication of this story; all three declined comment and requested that their identities not be disclosed. GCHQ monitored the browsing habits of the engineers, and geared up to enter the most important and sensitive phase of the secret operation. The agency planned to perform a so-called "Quantum Insert" attack, which involves redirecting people targeted for surveillance to a malicious website that infects their computers with malware at a lightning pace. In this case, the documents indicate that GCHQ set up a malicious page that looked like LinkedIn to trick the Belgacom engineers. (The NSA also uses Quantum Inserts to target people, as The Intercept has previously reported.) A GCHQ document reviewing operations conducted between January and March 2011 noted that the hack on Belgacom was successful, and stated that the agency had obtained access to the company's

I knew I wasn't using TOR often enough...

Cool! It seems that after all it is best to restrict employees' internet access only to work-critical areas... @Paul TOR works on network level, so it would not help here much as cookies (application level) were exploited.

12 new NIAC 1 studies - many topics familiar to us ... please have a look at those closest to your expertise to see if there is anything new/worth investigating (and in general to be knowledgeable on them since we will get questions sooner or later on them)
Principal Investigator Proposal Title Organization City, State, Zip Code
Atchison, Justin Swarm Flyby Gravimetry Johns Hopkins University Baltimore, MD 21218-2680
Boland, Eugene Mars Ecopoiesis Test Bed Techshot, Inc. Greenville, IN 47124-9515
Cash, Webster The Aragoscope: Ultra-High Resolution Optics at Low Cost University of Colorado Boulder, CO 80309-0389
Chen, Bin 3D Photocatalytic Air Processor for Dramatic Reduction of Life Support Mass & Complexity NASA ARC Moffett Field, CA 94035-0000
Hoyt, Robert WRANGLER: Capture and De-Spin of Asteroids and Space Debris Tethers Unlimited Bothel, WA 98011-8808
Matthies, Larry Titan Aerial Daughtercraft NASA JPL Pasadena, CA 91109-8001
Miller, Timothy Using the Hottest Particles in the Universe to Probe Icy Solar System Worlds John Hopkins University Laurel, MD 20723-6005
Nosanov, Jeffrey PERISCOPE: PERIapsis Subsurface Cave OPtical Explorer NASA JPL Pasadena, CA 91109-8001
Oleson, Steven Titan Submarine: Exploring the Depths of Kraken NASA GRC Cleveland, OH 44135-3127
Ono, Masahiro Comet Hitchhiker: Harvesting Kinetic Energy from Small Bodies to Enable Fast and Low-Cost Deep Space Exploration NASA JPL Pasadena, CA 91109-8001
Streetman, Brett Exploration Architecture with Quantum Inertial Gravimetry and In Situ ChipSat Sensors Draper Laboratory Cambridge, MA 02139-3539
Wiegmann, Bruce Heliopause Electrostatic Rapid Transit System (HERTS) NASA MSFC Huntsville, AL 35812-0000

Eh, the swarm flyby gravimetry is very similar to the "measuring gravitational fields" project I proposed in the brewery

Software that roughly mimics the way the brain works could give smartphones new smarts-leading to more accurate and sophisticated apps for tracking everything from workouts to emotions. The software exploits an artificial-intelligence technique known as deep learning, which uses simulated neurons and synapses to process data.

We humans take for granted our remarkable ability to predict things that happen around us. Here, a deep learning model trained from real-world videos and a 3D graphics engine was able to infer physical properties of objects against humans.

This is the biggest breakthrough in game AI (and one of the biggest in AI in general) since Deep Blue beat Kasparov in chess: For the first time, a human professional player was defeated in the game of Go. The approach was a combination of tree search and deep neural networks. Very proud of a former colleague on the team at Google Deepmind!

Funny enough, facebook also had a very similar paper around the same time.

There we go: Deep Learning predicts disruptions in plasmas. The paper related to this article is here: https://arxiv.org/abs/1802.02242

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

Neural Networks to analyse sleeplessness.

This has attracted a lot of attention in the DL community lately. Article: https://lilianweng.github.io/lil-log/2017/09/28/anatomize-deep-learning-with-information-theory.html Paper: https://arxiv.org/pdf/1703.00810.pdf

Dataset automatically constructed from public images uploaded by users on FB/Instagram with hashtags used as labels! They refer to this as 'weakly supervised learning'. Then neural net fine-tuned for ImageNet and achieved record 85.4% accuracy.

Random branches are collected, scanned to 3D, and connected with servos. Then a neural network is trained to control this "robot".

rather impressive! and frightening

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

Interesting long read on the future of deep-sea mining

The generated inputs are quite strange, I wonder where else something like this occurs.