Archive for the ‘information science’ category: Page 10

May 29, 2020

Solution to century-old math problem could predict transmission of infectious diseases

Posted by in categories: biotech/medical, information science, mathematics

A Bristol academic has achieved a milestone in statistical/mathematical physics by solving a 100-year-old physics problem—the discrete diffusion equation in finite space.

May 29, 2020

Quantum-Resistant Cryptography: Our Best Defense Against An Impending Quantum Apocalypse

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

As far back as 2015, the National Institute of Standards and Technology (NIST) began asking encryption experts to submit their candidate algorithms for testing against quantum computing’s expected capabilities — so this is an issue that has already been front of mind for security professionals and organizations. But even with an organization like NIST leading the way, working through all those algorithms to judge their suitability to the task will take time. Thankfully, others within the scientific community have also risen to the challenge and joined in the research.

It will take years for a consensus to coalesce around the most suitable algorithms. That’s similar to the amount of time it took ECC encryption to gain mainstream acceptance, which seems like a fair comparison. The good news is that such a timeframe still should leave the opportunity to arrive at — and widely deploy — quantum-resistant cryptography before quantum computers capable of sustaining the number of qubits necessary to seriously threaten RSA and ECC encryption become available to potential attackers.

The ongoing development of quantum-resistant encryption will be fascinating to watch, and security professionals will be sure to keep a close eye on which algorithms and encryption strategies ultimately prove most effective. The world of encryption is changing more quickly than ever, and it has never been more important for the organizations dependent on that encryption to ensure that their partners are staying ahead of the curve.

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May 29, 2020

Eye-catching advances in some AI fields are not real

Posted by in categories: information science, robotics/AI

When tuned up, old algorithms can match the abilities of their successors.

May 29, 2020

Algorithm tracks down buried treasure among existing compounds

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

A machine-learning algorithm has been developed by scientists in Japan to breathe new life into old molecules. Called BoundLess Objective-free eXploration, or Blox, it allows researchers to search chemical databases for molecules with the right properties to see them repurposed. The team demonstrated the power of their technique by finding molecules that could work in solar cells from a database designed for drug discovery.

Chemical repurposing involves taking a molecule or material and finding an entirely new use for it. Suitable molecules for chemical repurposing tend to stand apart from the larger group when considering one property against another. These materials are said to be out-of-trend and can display previously undiscovered yet exceptional characteristics.

‘In public databases there are a lot of molecules, but each molecule’s properties are mostly unknown. These molecules have been synthesised for a particular purpose, for example drug development, so unrelated properties were not measured,’ explains Koji Tsuda of the Riken Centre for Advanced Intelligence and who led the development of Blox. ‘There are a lot of hidden treasures in databases.’

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May 27, 2020

Why are neural networks so powerful?

Posted by in categories: information science, robotics/AI

It is common knowledge that neural networks are very powerful and they can be used for almost any statistical learning problem with great results. But have you thought about why is this the case? Why is this method more powerful in most scenarios than many other algorithms?

May 26, 2020

Why the Future of Machine Learning Is a Master Algorithm

Posted by in categories: information science, robotics/AI

Pedro Domingos has devoted his life to learning how computers learn. He says a breakthrough is coming.

May 26, 2020

Deep learning accurately stains digital biopsy slides

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

Tissue biopsy slides stained using hematoxylin and eosin (H&E) dyes are a cornerstone of histopathology, especially for pathologists needing to diagnose and determine the stage of cancers. A research team led by MIT scientists at the Media Lab, in collaboration with clinicians at Stanford University School of Medicine and Harvard Medical School, now shows that digital scans of these biopsy slides can be stained computationally, using deep learning algorithms trained on data from physically dyed slides.

Pathologists who examined the computationally stained H&E images in a blind study could not tell them apart from traditionally stained slides while using them to accurately identify and grade prostate cancers. What’s more, the slides could also be computationally “de-stained” in a way that resets them to an original state for use in future studies, the researchers conclude in their May 20 study published in JAMA Network Open.

This process of computational digital staining and de-staining preserves small amounts of tissue biopsied from cancer patients and allows researchers and clinicians to analyze slides for multiple kinds of diagnostic and prognostic tests, without needing to extract additional tissue sections.

May 23, 2020

This Chinese AI start-up can tell how much you crave drugs

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

Beijing start-up develops AI algorithm to determine how prone the VR participant is to drug use by tracking their pulse, brainwaves and electrical conductance of the skin.

May 22, 2020

China has started a grand experiment in AI education. It could reshape how the world learns

Posted by in categories: education, information science, mathematics, robotics/AI

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam.

“I used to think math was terrifying,” he says. “But through tutoring, I realized it really isn’t that hard. It helped me take the first step down a different path.”

May 22, 2020

A fault-tolerant non-Clifford gate for the surface code in two dimensions

Posted by in categories: computing, information science, quantum physics

Fault-tolerant logic gates will consume a large proportion of the resources of a two-dimensional quantum computing architecture. Here we show how to perform a fault-tolerant non-Clifford gate with the surface code; a quantum error-correcting code now under intensive development. This alleviates the need for distillation or higher-dimensional components to complete a universal gate set. The operation uses both local transversal gates and code deformations over a time that scales with the size of the qubit array. An important component of the gate is a just-in-time decoder. These decoding algorithms allow us to draw upon the advantages of three-dimensional models using only a two-dimensional array of live qubits. Our gate is completed using parity checks of weight no greater than four. We therefore expect it to be amenable with near-future technology. As the gate circumvents the need for magic-state distillation, it may reduce the resource overhead of surface-code quantum computation considerably.

A scalable quantum computer is expected to solve difficult problems that are intractable with classical technology. Scaling such a machine to a useful size will necessarily require fault-tolerant components that protect quantum information as the data is processed (14). If we are to see the realization of a quantum computer, its design must respect the constraints of the quantum architecture that can be prepared in the laboratory. In many cases, for instance, superconducting qubits (57), this restricts us to two-dimensional architectures.

Leading candidate models for fault-tolerant quantum computation are based on the surface code (3, 8) due to its high threshold (9) and multitude of ways of performing Clifford gates (10). Universal quantum computation is possible if this gate set is supplemented by a non-Clifford gate. Among the most feasible approaches to realize a non-Clifford gate is by the use of magic-state distillation (11). However, this is somewhat prohibitive as a large fraction of the resources of a quantum computer will be expended by these protocols (12, 13).

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