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Classical computers struggle to crack modern encryption. But quantum computers using Shor’s Algorithm make short work of RSA cryptography. Find out how.
Category: information science – Page 266
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.
For example, L. M. LoPucki6 points out, citing Shawn Bayern’s work on conferring legal personhood on AI7, 8, “Professor Shawn Bayern demonstrated that anyone can confer legal personhood on an autonomous computer algorithm merely by putting it in control of a limited liability company (LLC). The algorithm can exercise the rights of the entity, making them effectively rights of the algorithm. The rights of such an algorithmic entity (AE) would include the rights to privacy, to own property, to enter into contracts, to be represented by counsel, to be free from unreasonable search and seizure, to equal protection of the laws, to speak freely, and perhaps even to spend money on political campaigns. Once an algorithm had such rights, Bayern observed, it would also have the power to confer equivalent rights on other algorithms by forming additional entities and putting those algorithms in control of them.”6. (See Note 1.)
Evidence that quantum searches are an ordinary feature of electron behavior may explain the genetic code, one of the greatest puzzles in biology.
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 universe, 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 stars.
“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.”
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.
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 chemical 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.
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).
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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.
The original problem, set in 1954 at the University of Cambridge, looked for Solutions of the Diophantine Equation x3+y3+z3, with k being all the numbers from one to 100.
Beyond the easily found small solutions, the problem soon became intractable as the more interesting answers—if indeed they existed—could not possibly be calculated, so vast were the numbers required.
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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Greetings with some good news for the women’s world. Just recently, one of the most prestigious mathematics prizes in the world – The Abel Prize was awarded to a woman for the first time ever. Yes! Karen Uhlenbeck is a mathematician and a professor at the University of Texas and is now the first woman to win this prize in mathematics. You go Karen!
The award, which is modeled by the Nobel Prize, is awarded by the king of Norway to honor mathematicians who have made an influence in their field including a cash prize of around $700,000. The award to Karen cites for “the fundamental impact of her work on analysis, geometry and mathematical physics.” This award exists since 2003 but has only been won by men since.
Among her colleagues, Dr. Uhlenbeck is renowned for her work in geometric partial differential equations as well as integrable systems and gauge theory. One of her most famous contributions were her theories of predictive mathematics and in pioneering the field of geometric analysis.