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Category: information science

Coca-Cola is ditching flesh and blood creatives in favour of software algorithms in an experiment to see whether AI bots have what it takes to beat their human masters.

Mariano Bosaz, Coca-Cola’s global senior digital director, is spearheading the move as part of wider efforts to push the bounds of technology to see what they are capable of.

In an interview with Adweek at Mobile World Congress in Barcelona Bosaz said: “Content creation is something that we have been doing for a very long time—we brief creative agencies and then they come up with stories that they audio visualize and then we have 30 seconds or maybe longer. In content, what I want to start experimenting with is automated narratives.”

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How do molecules rotate in a solvent? Answering this question is complicated, since molecular rotation is perturbed by a very large number of surrounding atoms. For a long time, large-scale computer simulations have been the main approach to model molecule-solvent interactions. However, they are extremely time consuming and sometimes infeasible. Now, Mikhail Lemeshko from the Institute of Science and Technology Austria (IST Austria) has proven that angulons—a certain type of quasiparticle he proposed two years ago—do, in fact, form when a molecule is immersed in superfluid helium. This offers a quick and simple description for rotation of molecules in solvents.

In physics, the concept of quasiparticles is used as a technique to simplify the description of many-particle systems. Namely, instead of modeling strong interactions between trillions of individual particles, one identifies building blocks of the system that are only weakly interacting with one another. These building blocks are called quasiparticles and might consist of groups of particles. For example, to describe air bubbles rising up in water from first principles, one would need to solve an enormous set of equations describing the position and momentum of each water molecule. On the other hand, the bubbles themselves can be treated as individual particles—or quasiparticles—which drastically simplifies the description of the system. As another example, consider a running horse engulfed in a cloud of dust. One can think of it as a quasiparticle consisting of the horse itself and the dust cloud moving along with it.

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We can even use a vacuum to explain how Quantum is in all things while solving one of the remaining discrepancies in physics.

In the observable universe, number of particles are estimated to be 1080 and if there were some discrepancy in Physics with the explanations of the observable universe or with its each particle then it should confine to factor 1080. I submit that 1080 is a huge figure that forms if one puts eighty zeros after 1. But if the discrepancy is of the factor 10120 then either it is beyond the total number of particles constituting the universe or the physicists might have gravely erred in their calculations. It might be a freak happening that resulted in such a huge quantity. After all, freaks are also the creations of nature or probably the nature itself has erred here. This discrepancy of 10120 is the largest and worst cosmological confusion which can be abbreviated CC and rightly so for cosmological constant as it is the cosmological constant based on Quantum mechanical model. Quantum mechanical model says, energy density of the vacuum is in the range of 10113 Joules per metre cube whereas General Relativity calculates it in the range of 10^−9 Joule per metre cube. An attempt is made to resolve this discrepancy using Spacetime transformation and gravitational gamma Г. Gravitational gamma Г is a term that appears in Schwarzschild solution of general relativity equations.

I submit that vacuum is not nothing but is everything and quantum mechanical model of the vacuum has very large energy density. In the words of John Archibald Wheeler, “Empty space is not empty… The density of field fluctuation energy in the vacuum argues that elementary particles represent percentage‐ wise almost completely negligible change in the locally violent conditions that characterise the vacuum.” That means there are violent conditions or fluctuations although vacuum on large scale appears smooth. Spacetime model has the capability of creating matter, forces, fields and particles. In fact, matter even the entire universe is assumed as spacetime as has been explained in my earlier article, “Matter Is No More Than Fluctuations In Vacuum*.”

I submit that the energy density of the vacuum, due to uncertainty principle, varies from point to point causing fluctuations. In other words, energy density at different points goes on varying even though there is no mass causing gravity. But even then at two points, due to fluctuations of energy density, rate of time may vary, causing difference in time by Planck time. These fluctuations can only be felt at a distance limited to Planck length or at microscopic scale but on macroscopic scale the space time, according to Quantum Mechanical model, appears smooth with no disturbances.

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What deep learning algorithms can tell us about the visual narratives of the world’s news imagery, from depictions of violence to the importance of people to visual context – a look inside what we see about the world around us.

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From digital currency to machine learning, the financial industry is being rocked by exponential technologies. Blockchain, artificial intelligence, big data, robotics, quantum computing, crowdfunding, and computing systems are allowing startups to solve consumer needs in new ways.

The downfall of the world’s largest institutions may not be imminent, but these new technologies are breaking up the previously rock solid foundation of finance, and allowing the fintech world to spring through the cracks. What’s happening now will rewrite the future of finance for years to come. By recognizing this reality and planning for it now, financial professionals can learn to thrive in an increasingly uncertain global economy.

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BMI is coming fast and will replace many devices we have today. Advances we making in deep brain development are huge markers that pushes the BMI needle forward for the day when IoT, Security, and big data analytics is a human brain’s and a secured Quantum Infrastructure and people (not servers sitting somewhere) owns and manages their most private of information. I love calling it the age of people empowerment as well as singularity.

Small study in 16 people suggests technique is safe and might help improve mood, anxiety and wellbeing, while increasing weight.

Deep brain stimulation might alter the brain circuits that drive anorexia nervosa symptoms and help improve patients’ mental and physical health, according to a small study published in The Lancet Psychiatry.

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(Phys.org)—Physicists are getting a little bit closer to answering one of the oldest and most basic questions of quantum theory: does the quantum state represent reality or just our knowledge of reality?

George C. Knee, a theoretical physicist at the University of Oxford and the University of Warwick, has created an algorithm for designing optimal experiments that could provide the strongest evidence yet that the quantum state is an ontic state (a state of ) and not an epistemic state (a state of knowledge). Knee has published a paper on the new strategy in a recent issue of the New Journal of Physics.

While physicists have debated about the nature of the quantum state since the early days of quantum theory (with, most famously, Bohr being in favor of the ontic interpretation and Einstein arguing for the epistemic one), most modern evidence has supported the view that the quantum state does indeed represent reality.

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Interestingly, Seager, who studies bio signatures in exoplanet atmospheres, has suggested that two inhabited planets could reasonably turn up during the next decade, based on her modified version of the Drake equation, Space.com notes. Her equation focuses on the search for planets with biosignature gases — gases produced by life that can accumulate in a planet atmosphere to levels that can be detected with remote space telescopes.

Artist’s concept of exoplanet Kepler-452b, the first near-Earth-size world to be found in the habitable zone of a star similar to our Sun. (credit: NASA Ames/JPL-Caltech/T. Pyle)

NASA will hold a news conference at 1 p.m. EST Wednesday, Feb. 22, to present new findings on exoplanets — planets that orbit stars other than our sun. As of Feb. 21, NASA has discovered and confirmed 3,440 exoplanets.

The briefing participants are Thomas Zurbuchen, associate administrator of the Science Mission Directorate at NASA Headquarters in Washington; Michael Gillon, astronomer at the University of Liege in Belgium; Sean Carey, manager of NASA’s Spitzer Science Center at Caltech/IPAC, Pasadena, California; Nikole Lewis, astronomer at the Space Telescope Science Institute in Baltimore; and Sara Seager, professor of planetary science and physics at Massachusetts Institute of Technology, Cambridge. Details of the findings are embargoed by the journal Nature until 1 p.m.

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It wasn’t that long ago that building and training neural networks was strictly for seasoned computer scientists and grad students. That began to change with the release of a number of open-source machine learning frameworks like Theano, Spark ML, Microsoft’s CNTK, and Google’s TensorFlow. Among them, TensorFlow stands out for its powerful, yet accessible, functionality, coupled with the stunning growth of its user base. With this week’s release of TensorFlow 1.0, Google has pushed the frontiers of machine learning further in a number of directions.

TensorFlow isn’t just for neural networks anymore

In an effort to make TensorFlow a more-general machine learning framework, Google has added both built-in Estimator functionality, and support for a number of more traditional machine learning algorithms including K-means, SVM (Support Vector Machines), and Random Forest. While there are certainly other frameworks like SparkML that support those tools, having a solution that can combine them with neural networks makes TensorFlow a great option for hybrid problems.

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Cybercrime & Cybersecurity 0 20

There is a reason why they’re not in the private sector developing QC. Noticed all represented no one developing and delivering QC commercially. There is a reason why folks like this become nay sayers as it is hard when you’re not able to deliver and not hireable by the private sector to deliver QC. With such a huge demand for QC experts and in security; you have to wonder why these folks have not been employed in a QC Tech company especially when you see tech grabbing every professor they can to develop QC and especially cyber security. Also, I still never saw any bases or details scientifically for their argument why specifically where and how QC will not block hacking just a bunch of professors throwing out words and high level speculations.

SAN FRANCISCO—Cryptographers said at the RSA Conference Tuesday they’re skeptical that advances in quantum computing and artificial intelligence will profoundly transform computer security.

“I’m skeptical there will be much of an impact,” Ron Rivest, a MIT professor and inventor of several symmetric key encryption algorithms, said early at the annual Cryptographers’ Panel here.

Susan Landau, a professor who specializes in cybersecurity policy and computer science at Worcester Polytechnic Institute, said that while artificial intelligence can be helpful when it comes to processing lots of data effectively, she doesn’t think it will be useful in fingering out series attacks or anomalous situations.

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