Archive for the ‘information science’ category: Page 182

Mar 9, 2018

How Fast Can Gravitational Wave Detection Get?

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

With machine learning and other algorithmic approaches, researchers are increasing the speed at which they detect the undulations of spacetime.

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Mar 6, 2018

Google backs its Bristlecone chip to crack quantum computing

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

Like every other major tech company, Google has designs on being the first to achieve quantum supremacy — the point where a quantum computer could run particular algorithms faster than a classical computer. Today it’s announced that it believes its latest research, Bristlecone, is going to be the processor to help it achieve that. According to the Google Quantum AI Lab, it could provide “a compelling proof-of-principle for building larger scale quantum computers.”

One of the biggest obstacles to quantum supremacy is error rates and subsequent scalability. Qubits (the quantum version of traditional bits) are very unstable and can be adversely affected by noise, and most of these systems can only hold a state for less than 100 microseconds. Google believes that quantum supremacy can be “comfortably demonstrated” with 49 qubits and a two-qubit error below 0.5 percent. Previous quantum systems by Google have given two-qubit errors of 0.6 percent, which in theory sounds like a miniscule difference, but in the world of quantum computing remains significant.

However, each Bristlecone chip features 72 qubits, which may help mitigate some of this error, but as Google says, quantum computing isn’t just about qubits. “Operating a device such as Bristlecone at low system error requires harmony between a full stack of technology ranging from software and control electronics to the processor itself,” the team writes in a blog post. “Getting this right requires careful systems engineering over several iterations.”

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Mar 6, 2018

The tyranny of algorithms is part of our lives: soon they could rate everything we do

Posted by in categories: finance, information science

Credit scores already control our finances. With personal data being increasingly trawled, our politics and our friendships will be next.

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Mar 5, 2018

Researchers find algorithm for large-scale brain simulations

Posted by in categories: biotech/medical, information science, neuroscience, supercomputing

An international group of researchers has made a decisive step towards creating the technology to achieve simulations of brain-scale networks on future supercomputers of the exascale class. The breakthrough, published in Frontiers in Neuroinformatics, allows larger parts of the human brain to be represented, using the same amount of computer memory. Simultaneously, the new algorithm significantly speeds up brain simulations on existing supercomputers.

The human brain is an organ of incredible complexity, composed of 100 billion interconnected nerve cells. However, even with the help of the most powerful supercomputers available, it is currently impossible to simulate the exchange of neuronal signals in networks of this size.

“Since 2014, our software can simulate about one percent of the in the human brain with all their connections,” says Markus Diesmann, Director at the Jülich Institute of Neuroscience and Medicine (INM-6). In order to achieve this impressive feat, the software requires the entire main memory of petascale supercomputers, such as the K computer in Kobe and JUQUEEN in Jülich.

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Mar 5, 2018

Google’s new Bristlecone processor brings it one step closer to quantum supremacy

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

Every major tech company is looking at quantum computers as the next big breakthrough in computing. Teams at Google, Microsoft, Intel, IBM and various startups and academic labs are racing to become the first to achieve quantum supremacy — that is, the point where a quantum computer can run certain algorithms faster than a classical computer ever could. Today, Google said that it believes that Bristlecone, its latest quantum processor, will put it on a path to reach quantum supremacy in the future.

The purpose of Bristlecone, Google says, it to provide its researchers with a testbed “for research into system error rates and scalability of our qubit technology, as well as applications in quantum simulation, optimization, and machine learning.

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Mar 4, 2018

New Algorithm Lets AI Learn From Mistakes, Become a Little More Human

Posted by in categories: information science, robotics/AI

OpenAI’s latest algorithm lets AI learn from its mistakes by re-framing past failures. This method helps AI to learn faster and do so better.

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Mar 2, 2018

The Ongoing Battle Between Quantum and Classical Computers

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

The quest for “quantum supremacy”—unambiguous proof that a quantum computer does something faster than an ordinary computer—has paradoxically led to a boom in quasi-quantum classical algorithms.

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Mar 2, 2018

Using big data analysis to significantly boost cancer treatment effectiveness

Posted by in categories: biotech/medical, computing, genetics, information science, life extension

Summary: Treatability of cancer was raised to over 80% by a new intelligent system that sifts through massive genetic datasets to pinpoint targets for cancer treatment, say these scientists. [This article first appeared on LongevityFacts. Author: Brady Hartman. ]

Scientists in Singapore have discovered a significantly improved way to treat cancer by listening to many different computer programs rather than just one.

Their new computer program reaches a consensus on how to treat a specific tumor, and it is significantly more accurate than existing predictive methods. The system isolates the Achilles heel of each individual tumor, helping doctors to choose the best treatment.

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Feb 26, 2018

Deep learning for biology

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

Finkbeiner’s success highlights how deep learning, one of the most promising branches of artificial intelligence (AI), is making inroads in biology. The algorithms are already infiltrating modern life in smartphones, smart speakers and self-driving cars. In biology, deep-learning algorithms dive into data in ways that humans can’t, detecting features that might otherwise be impossible to catch. Researchers are using the algorithms to classify cellular images, make genomic connections, advance drug discovery and even find links across different data types, from genomics and imaging to electronic medical records.

A popular artificial-intelligence method provides a powerful tool for surveying and classifying biological data. But for the uninitiated, the technology poses significant difficulties.

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Feb 20, 2018

DeepMind’s latest AI transfers its learning to new tasks

Posted by in categories: information science, robotics/AI

By using insights from one job to help it do another, a successful new artificial intelligence hints at a more versatile future for machine learning.

Backstory: Most algorithms can be trained in only one domain, and can’t use what’s been learned for one task to perform another, new one. A big hope for AI is to have systems take insights from one setting and apply them elsewhere—what’s called transfer learning.

What’s new: DeepMind built a new AI system called IMPALA that simultaneously performs multiple tasks—in this case, playing 57 Atari games—and attempts to share learning between them. It showed signs of transferring what was learned from one game to another.

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