Lifeboat Foundation

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

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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 neurons 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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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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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.

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.

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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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.

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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New algorithms show how very simple robots can be made to work together as a group.

• By Ben Rollins, Quanta Magazine on February 18, 2018