The amazing supercomputer simulation in the video above takes you through 13 billion years of cosmic history, modeling the violent and dynamic processes that created the large-scale structure of our universe.

Using a sophisticated type of mathematics in a way that it has never been used before in neuroscience, a team from the Blue Brain Project has uncovered a universe of multi-dimensional geometrical structures and spaces within the networks of the brain. This research, published in Frontiers in Computational Neuroscience, has significant implications for our understanding of the brain.

What's the difference between plants and animals? If your answer is that animals are intelligent and plants aren't, you've got another thing coming. Evidence is mounting that plants are way more complex than scientists previously realized. Here's just one more example of their intelligence: some plants can hear when they're being eaten, and they defend themselves in response.

Tohoku University researchers have developed a computational simulation that shows that using ultrafast laser pulses to excite electrons in a magnetic material switches them into a transient non-magnetic state. This coul

Salk scientists have created a new version of the CRISPR/Cas9 genome editing technology that allows them to activate genes without creating breaks in the DNA, potentially circumventing a major hurdle to using gene editin

On Sunday, December 3rd, we saw a peak in one of our detections, JS:Miner-I, which blocks a cryptocurrency miner that uses the cryptonight algorithm to mine Monero, a popular cryptocurrency. The algorithm is suitable for using PC CPU for mining, and the miner is run using JavaScript. We blocked JS:Miner-I from launching on our users’ PCs, preventing 34.7 million attacks in just one day. The extreme peak wasn’t the only thing that caught our attention; all the detections were launched within Google’s Chrome browser.

An international group of biologists from Israel and Sweden has obtained a detailed view of a scallop’s visual system -- an arrangement of up to 200 eyes they say is strikingly similar to a reflecting telescope.

Quantum simulators, which are special-purpose quantum computers, will help researchers identify materials with new and useful properties. This enticing future has just taken a step forward thanks to a collaboration between Google and researchers at universities in California, Singapore and Greece.

The international team used photons in Google's quantum chip to simulate the surprising and beautiful pattern of the 'Hofstadter butterfly', a fractal structure characterizing the behaviour of electrons in strong magnetic fields. The results, published 1 December in Science, show how quantum simulators are starting to live up to their promise as powerful tools.

"We've always had this idea that we can use photons to simulate and better understand nature. Our collaboration puts this into practice," says Dimitris Angelakis at the Centre for Quantum Technologies, National University of Singapore.

The feat was performed on Google's chain of nine superconducting quantum bits (qubits) by collaborators at Google and the University of California Santa Barbara in the United States, the National University of Singapore and Technical University of Crete, Greece. It shows how a quantum simulator can reproduce all kinds of exotic complex quantum behavior. This will enable researchers to simulate - and thus engineer - materials with exotic electronic conduction properties, potentially opening up a range of new applications.

Quantized eigenenergies and their associated wave functions provide extensive information for predicting the physics of quantum many-body systems. Using a chain of nine superconducting qubits, Google and others implement a technique for resolving the energy levels of interacting photons. They benchmark this method by capturing the main features of the intricate energy spectrum predicted for two-dimensional electrons in a magnetic field—the Hofstadter butterfly. They introduce disorder to study the statistics of the energy levels of the system as it undergoes the transition from a thermalized to a localized phase. This work introduces a many-body spectroscopy technique to study quantum phases of matter.

In a role reversal reminiscent of the rule that news equals man bites dog, the giant trevally – or king-fish – has proved to be a cold-faced marine world killer that can pluck birds right out of the sky. Native to the Indian and Pacific Ocean atolls, the predatory creature can weigh up to 80 kilograms. The whopper featured on David Attenborough’s Blue Planet II programme on Sunday, some of whose viewers reacted in horror to its eating habits.

Researchers exploring AI systems are making news and familiarizing the public with terms like reinforcement learning and machine learning. Recent headlines are still making some heads turn in surprise. AI software is "learning" how to replicate itself and to build its own AI child.

Imagine this: you tell a computer system how the pieces move — nothing more. Then you tell it to learn to play the game. And a day later — yes, just 24 hours — it has figured it out to the level that beats the strongest programs in the world convincingly! DeepMind, the company that recently created the strongest Go program in the world, turned its attention to chess, and came up with this spectacular result.

AI tools could help us turn information gleaned from genetic sequencing into life-saving therapies. Almost 15 years after scientists first sequenced the human genome, making sense of the enormous amount of data that encodes human life remains a formidable challenge. But it is also precisely the sort of problem that machine learning excels at.

Google has now released a tool called DeepVariant that uses the latest AI techniques to build a more accurate picture of a person’s genome from sequencing data. DeepVariant helps turn high-throughput sequencing readouts into a picture of a full genome. It automatically identifies small insertion and deletion mutations and single-base-pair mutations in sequencing data.

High-throughput sequencing became widely available in the 2000s and has made genome sequencing more accessible. But the data produced using such systems has offered only a limited, error-prone snapshot of a full genome. It is typically challenging for scientists to distinguish small mutations from random errors generated during the sequencing process, especially in repetitive portions of a genome. These mutations may be directly relevant to diseases such as cancer.

A number of tools exist for interpreting these readouts, including GATK, VarDict, and FreeBayes. However, these software programs typically use simpler statistical and machine-learning approaches to identifying mutations by attempting to rule out read errors. “One of the challenges is in difficult parts of the genome, where each of the tools has strengths and weaknesses,” says Brad Chapman, a research scientist at Harvard’s School of Public Health who tested an early version of DeepVariant. “These difficult regions are increasingly important for clinical sequencing, and it’s important to have multiple methods.”

DeepVariant was developed by researchers from the Google Brain team, a group that focuses on developing and applying AI techniques, and Verily, another Alphabet subsidiary that is focused on the life sciences. The team collected millions of high-throughput reads and fully sequenced genomes from the Genome in a Bottle (GIAB) project, a public-private effort to promote genomic sequencing tools and techniques. They fed the data to a deep-learning system and painstakingly tweaked the parameters of the model until it learned to interpret sequenced data with a high level of accuracy.

Last year, DeepVariant won first place in the PrecisionFDA Truth Challenge, a contest run by the FDA to promote more accurate genetic sequencing. “The success of DeepVariant is important because it demonstrates that in genomics, deep learning can be used to automatically train systems that perform better than complicated hand-engineered systems,” says Brendan Frey, CEO of Deep Genomics.

The release of DeepVariant is the latest sign that machine learning may be poised to boost progress in genomics. Deep Genomics is one of several companies trying to use AI approaches such as deep learning to tease out genetic causes of diseases and to identify potential drug therapies (see “An AI-Driven Genomics Company Is Turning to Drugs”).

Deep Genomics aims to develop drugs by using deep learning to find patterns in genomic and medical data. Frey says AI will eventually go well beyond helping to sequence genomic data. “The gap that is currently blocking medicine right now is in our inability to accurately map genetic variants to disease mechanisms and to use that knowledge to rapidly identify life-saving therapies,” he says.

Another prominent company in this area is Wuxi Nextcode, which has offices in Shanghai, Reykjavik, and Cambridge, Massachusetts. Wuxi Nextcode has amassed the world’s largest collection of fully sequenced human genomes, and the company is investing heavily in machine-learning methods.

DeepVariant will also be available on the Google Cloud Platform. Google and its competitors are furiously adding machine-learning features to their cloud platforms in an effort to lure anyone who might want to tap into the latest AI techniques (see “Ambient AI Is About to Devour the Software Industry”).

In general, AI figures to help many aspects of medicine take big leaps forward in the coming years. There are opportunities to mine many different kinds of medical data—from images or medical records, for example— to predict ailments that a human doctor might miss (see “The Machines Are Getting Ready to Play Doctor” and “A New Algorithm for Palliative Care”).

Source code and analysis for CIA software projects including those described in the Vault7 series. This publication will enable investigative journalists, forensic experts and the general public to better identify and understand covert CIA infrastructure components.

Researchers at the University of Pennsylvania have used mouse models to demonstrate a new approach to restart cardiomyocyte replication after a heart attack: an injectable gel that slowly releases short gene sequences known as microRNAs into the heart muscle.

The same fusion reactions that power the sun also occur inside a tokamak, a device that uses magnetic fields to confine and control plasmas of 100-plus million degrees. Under extreme temperatures and pressure, hydrogen atoms can fuse together, creating new helium atoms and simultaneously releasing energy.

Fusion could be a source of inexhaustible energy. To create and study a burning plasma, a necessary step for fusion energy development, scientists and engineers are building a massive tokamak: ITER, currently under construction in France. Plasma physicists have sought for the past 35 years to answer a tough question: what causes a tokamak’s plasma to go from a weakly confined, strongly turbulent state to a more confined, calmer state when it is adequately heated? The answer may help scientists predict the optimal heating power necessary for ITER to demonstrate a self-sustained burning plasma and 500 megawatts of fusion power.

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