May 22, 2016
Posted by Shailesh Prasad in categories: information science, space
A modified version of the Drake Equation, and what it tells us.
A modified version of the Drake Equation, and what it tells us.
Math isn’t everyone’s strong suit, especially those who haven’t stretched that part of their brain since college. Thanks to the wonders of image recognition technology, we now have Mathpix, an iOS app that lets you point your phone camera at a problem and calculates solutions in seconds.
The interface looks like any standard camera app: simply drag the on-screen reticle over the equation and the app solves it and provides graph answers where appropriate. More useful is a step-by-step guide offering multiple methods to reach a solution, making this a bona fide educational tool. It uses image recognition to process problems and pings its servers to do the mathematical heavy lifting, so it likely requires an internet connection to work.
Mathpix was envisioned by Stanford PhD student Nico Jimenez, who was advised by Stanford grad Paul Ferrell. The app’s other developers are high schoolers Michael Lee and August Trollback, which is impressive for an app that claims to be the first to visually recognize and solve handwritten math problems.
New spin on all things that are Singular. Hmmm — so if Singularity becomes a religion; is Ray Kurzweil its God?
A colleague forwarded John Horgan’s recent Scientific American article, “The Singularity and the Neural Code.” Horgan argues that the intelligence augmentation and mind uploading that would lead to a technological singularity depend upon cracking the neural code. The problem is that we don’t understand our neural code, the software or algorithms that transform neurophysiology into the stuff of minds like perceptions, memories, and meanings. In other words, we know very little about how brains make minds.
I am glad that D. Whyte recognizes “If quantum computers are developed faster than anticipated, certification would mandate insecure modules, given the time to approve and implement new quantum resistant algorithms. Worse, it is conceivable that data encrypted by a certified module is more vulnerable than data encrypted by a non-certified module that has the option of using a quantum-safe encryption algorithm.”
Because many of us who are researching and developing in this space have seen the development pace accelerated this year and what was looking like we’re 10 years away is now looking like we’re less than 7 years.
Dr. William Whyte, Chief Scientist for Security Innovation, a cybersecurity provider and leader in the 2015 Gartner Magic Quadrant for Security Awareness Training, will be presenting at the Fourth International Cryptographic Module Conference in Ottawa, Ontario.
Australian physicists’ team has developed a new research assistant to carry out experiments in quantum mechanics in an artificial intelligence (AI) algorithm form, which quickly took control of the experiment, learned the job tasks and even innovated. In a statement, co-lead researcher Paul Wigley from the Australian National University (ANU) Research School of Physics and Engineering, said he didn’t expect that the machine would be able to conduct the experiment itself from scratch within an hour.
He added that in case a simple computer program had been used, it would have taken much more time than the age of the universe to go through all the combinations and work on it.
Scientists were looking forward to reconstruct an experiment that was awarded the 2001 Nobel Prize in Physics, which included very cold gas trapped in a laser beam called a Bose-Einstein condensate.
Given the fact that Los Alamos Labs have been and continue to advance cyber security work on the Quantum Internet as well as work in partnerships with other labs and universities; so, why isn’t Mason not collaborating with Los Alamos on developing an improved hacker proof net? Doesn’t look like the most effective and cost efficient approach.
Imagine burglars have targeted your home, but before they break in, you’ve already moved and are safe from harm.
Now apply that premise to protecting a computer network from attack. Hackers try to bring down a network, but critical tasks are a step ahead of them, thanks to complex algorithms. The dreaded “network down” or denial of service message never flashes on your screen.
Theoretical chemists at Princeton University have pioneered a strategy for modeling quantum friction, or how a particle’s environment drags on it, a vexing problem in quantum mechanics since the birth of the field. The study was published in the Journal of Physical Chemistry Letters (“Wigner–Lindblad Equations for Quantum Friction”). “It was truly a most challenging research project in terms of technical details and the need to draw upon new ideas,” said Denys Bondar, a research scholar in the Rabitz lab and corresponding author on the work.
Researchers construct a quantum counterpart of classical friction, a velocity-dependent force acting against the direction of motion. In particular, a translationary invariant Lindblad equation is derived satisfying the appropriate dynamical relations for the coordinate and momentum (i.e., the Ehrenfest equations). Numerical simulations establish that the model approximately equilibrates. (© ACS)
An algorithm developed by Google is designed to encode thought, which could lead to computers with ‘common sense’ within a decade, says leading AI scientist.
If you’ve ever seen a “recommended item” on eBay or Amazon that was just what you were looking for (or maybe didn’t know you were looking for), it’s likely the suggestion was powered by a recommendation engine. In a recent interview, Co-founder of machine learning startup Delvv, Inc., Raefer Gabriel, said these applications for recommendation engines and collaborative filtering algorithms are just the beginning of a powerful and broad-reaching technology.
Gabriel noted that content discovery on services like Netflix, Pandora, and Spotify are most familiar to people because of the way they seem to “speak” to one’s preferences in movies, games, and music. Their relatively narrow focus of entertainment is a common thread that has made them successful as constrained domains. The challenge lies in developing recommendation engines for unbounded domains, like the internet, where there is more or less unlimited information.
“Some of the more unbounded domains, like web content, have struggled a little bit more to make good use of the technology that’s out there. Because there is so much unbounded information, it is hard to represent well, and to match well with other kinds of things people are considering,” Gabriel said. “Most of the collaborative filtering algorithms are built around some kind of matrix factorization technique and they definitely tend to work better if you bound the domain.”
If you’ve ever tried to learn how to spin a pencil in your hand, you’ll know it takes some concerted effort—but it’s even harder for a robot. Now, though, researchers have finally built a ‘bot that can learn to do it.
The reason that tasks like spinning a stick are hard is that a lot happens in a very short time. As the stick moves, the forces exerted by the hand can easily send it flying out of control if they’re not perfectly co-ordinated. Sensing where the stick is and varying the hand’s motion is an awful lot for even the smartest algorithms to handle based on a list of rules.