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Archive for the ‘information science’ category

Sep 13, 2019

Solving the Schrödinger equation with deep learning

Posted by in categories: information science, particle physics, quantum physics, robotics/AI

The code used below is on GitHub.

In this project, we’ll be solving a problem familiar to any physics undergrad — using the Schrödinger equation to find the quantum ground state of a particle in a 1-dimensional box with a potential. However, we’re going to tackle this old standby with a new method: deep learning. Specifically, we’ll use the TensorFlow package to set up a neural network and then train it on random potential functions and their numerically calculated solutions.

Why reinvent the wheel (ground state)? Sure, it’s fun to see a new tool added to the physics problem-solving toolkit, and I needed the practice with TensorFlow. But there’s a far more compelling answer. We know basically everything there is to know about this topic already. The neural network, however, doesn’t know any physics. Crudely speaking, it just finds patterns. Suppose we examine the relative strength of connections between input neurons and output. The structure therein could give us some insight into how the universe “thinks” about this problem. Later, we can apply deep learning to a physics problem where the underlying theory is unknown. By looking at the innards of that neural network, we might learn something new about fundamental physical principles that would otherwise remain obscured from our view. Therein lies the true power of this approach: peering into the mind of the universe itself.

Sep 13, 2019

Hacking at Quantum Speed with Shor’s Algorithm

Posted by in categories: cybercrime/malcode, encryption, information science, quantum physics

X.x.


Classical computers struggle to crack modern encryption. But quantum computers using Shor’s Algorithm make short work of RSA cryptography. Find out how.

Sep 12, 2019

Your Software Could Have More Rights Than You

Posted by in categories: information science, law, robotics/AI

Much like US corporations do now.


Debates about rights are frequently framed around the concept of legal personhood. Personhood is granted not just to human beings but also to some non-human entities, such as corporations or governments. Legal entities, aka legal persons, are granted certain privileges and responsibilities by the jurisdictions in which they are recognized, and many such rights are not available to non-person agents. Attempting to secure legal personhood is often seen as a potential pathway to get certain rights and protections for animals1, fetuses2, trees and rivers 3, and artificially intelligent (AI) agents4.

It is commonly believed that a new law or judicial ruling is necessary to grant personhood to a new type of entity. But recent legal literature 5–8 suggests that loopholes in the current law may permit legal personhood to be granted to AI/software without the need to change the law or persuade a court.

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Sep 12, 2019

An important quantum algorithm may actually be a property of nature

Posted by in categories: biological, genetics, information science, quantum physics

Evidence that quantum searches are an ordinary feature of electron behavior may explain the genetic code, one of the greatest puzzles in biology.

Sep 10, 2019

Are black holes made of dark energy?

Posted by in categories: cosmology, information science, physics

Two University of Hawaii at Manoa researchers have identified and corrected a subtle error that was made when applying Einstein’s equations to model the growth of the universe.

Physicists usually assume that a cosmologically large system, such as the , is insensitive to details of the small systems contained within it. Kevin Croker, a postdoctoral research fellow in the Department of Physics and Astronomy, and Joel Weiner, a faculty member in the Department of Mathematics, have shown that this assumption can fail for the compact objects that remain after the collapse and explosion of very large .

“For 80 years, we’ve generally operated under the assumption that the universe, in broad strokes, was not affected by the particular details of any small region,” said Croker. “It is now clear that general relativity can observably connect collapsed stars—regions the size of Honolulu—to the behavior of the universe as a whole, over a thousand billion billion times larger.”

Sep 9, 2019

Microsoft Vision AI Developer Kit Simplifies Building Vision-Based Deep Learning Projects

Posted by in categories: information science, robotics/AI, security, transportation

Computer vision is one of the most popular applications of artificial intelligence. Image classification, object detection and object segmentation are some of the use cases of computer vision-based AI. These techniques are used in a variety of consumer and industrial scenarios. From face recognition-based user authentication to inventory tracking in warehouses to vehicle detection on roads, computer vision is becoming an integral part of next-generation applications.

Computer vision uses advanced neural networks and deep learning algorithms such as Convolutional Neural Networks (CNN), Single Shot Multibox Detector (SSD) and Generative Adversarial Networks (GAN). Applying these algorithms requires a thorough understanding of neural network architecture, advanced mathematics and image processing techniques. For an average ML developer, CNN remains to be a complex branch of AI.

Apart from the knowledge and understanding of algorithms, CNNs demand high end, expensive infrastructure for training the models, which is out of reach for most of the developers.

Sep 7, 2019

AI learns the language of chemistry to predict how to make medicines

Posted by in categories: biotech/medical, chemistry, information science, robotics/AI

Researchers have designed a machine learning algorithm that predicts the outcome of chemical reactions with much higher accuracy than trained chemists and suggests ways to make complex molecules, removing a significant hurdle in drug discovery.

University of Cambridge researchers have shown that an algorithm can predict the outcomes of complex reactions with over 90% accuracy, outperforming trained chemists. The algorithm also shows chemists how to make target compounds, providing the chemical “map” to the desired destination. The results are reported in two studies in the journals ACS Central Science and Chemical Communications.

A central challenge in drug discovery and materials science is finding ways to make complicated organic molecules by chemically joining together simpler building blocks. The problem is that those building blocks often react in unexpected ways.

Sep 6, 2019

Particle physicists team up with AI to solve toughest science problems

Posted by in categories: information science, particle physics, robotics/AI, science

Researchers from SLAC and around the world increasingly use machine learning to handle Big Data produced in modern experiments and to study some of the most fundamental properties of the universe (Symmetry magazine).

Sep 6, 2019

Sum of three cubes for 42 finally solved—using real life planetary computer

Posted by in categories: computing, information science

O.o.


Hot on the heels of the ground-breaking ‘Sum-Of-Three-Cubes’ solution for the number 33, a team led by the University of Bristol and Massachusetts Institute of Technology (MIT) has solved the final piece of the famous 65-year-old maths puzzle with an answer for the most elusive number of all—42.

Continue reading “Sum of three cubes for 42 finally solved—using real life planetary computer” »

Sep 6, 2019

How the United States Is Developing Post-Quantum Cryptography

Posted by in categories: computing, encryption, government, information science, internet, quantum physics, security

When practical quantum computing finally arrives, it will have the power to crack the standard digital codes that safeguard online privacy and security for governments, corporations, and virtually everyone who uses the Internet. That’s why a U.S. government agency has challenged researchers to develop a new generation of quantum-resistant cryptographic algorithms.

Many experts don ’t expect a quantum computer capable of performing the complex calculations required to crack modern cryptography standards to become a reality within the next 10 years. But the U.S. National Institute of Standards and Technology (NIST) wants to stay ahead by getting new cryptographic standards ready by 2022. The agency is overseeing the second phase of its Post-Quantum Cryptography Standardization Process to narrow down the best candidates for quantum-resistant algorithms that can replace modern cryptography.

“Currently intractable computational problems that protect widely-deployed cryptosystems, such as RSA and Elliptic Curve-based schemes, are expected to become solvable,” says Rafael Misoczki, a cryptographer at the Intel Corporation and a member of two teams (named Bike and Classic McEliece) involved in the NIST process. “This means that quantum computers have the potential to eventually break most secure communications on the planet.”

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