Lifeboat Foundation

Black holes are one of the greatest mysteries of the universe—for example, a black hole with the mass of our sun has a radius of only 3 kilometers. Black holes in orbit around each other emit gravitational radiation—oscillations of space and time predicted by Albert Einstein in 1916. This causes the orbit to become faster and tighter, and eventually, the black holes merge in a final burst of radiation. These gravitational waves propagate through the universe at the speed of light, and are detected by observatories in the U.S. (LIGO) and Italy (Virgo). Scientists compare the data collected by the observatories against theoretical predictions to estimate the properties of the source, including how large the black holes are and how fast they are spinning. Currently, this procedure takes at least hours, often months.

An interdisciplinary team of researchers from the Max Planck Institute for Intelligent Systems (MPI-IS) in Tübingen and the Max Planck Institute for Gravitational Physics (Albert Einstein Institute/AEI) in Potsdam is using state-of-the-art machine learning methods to speed up this process. They developed an algorithm using a deep neural network, a complex computer code built from a sequence of simpler operations, inspired by the human brain. Within seconds, the system infers all properties of the binary black-hole source. Their research results are published today in Physical Review Letters.

“Our method can make very accurate statements in a few seconds about how big and massive the two black holes were that generated the gravitational waves when they merged. How fast do the black holes rotate, how far away are they from Earth and from which direction is the gravitational wave coming? We can deduce all this from the observed data and even make statements about the accuracy of this calculation,” explains Maximilian Dax, first author of the study Real-Time Gravitational Wave Science with Neural Posterior Estimation and Ph.D. student in the Empirical Inference Department at MPI-IS.

Computer engineers at Duke University have developed a new AI method for accurately predicting the power consumption of any type of computer processor more than a trillion times per second while barely using any computational power itself. Dubbed APOLLO, the technique has been validated on real-world, high-performance microprocessors and could help improve the efficiency and inform the development of new microprocessors.

The approach is detailed in a paper published at MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture, one of the top-tier conferences in computer architecture, where it was selected the conference’s best publication.

“This is an intensively studied problem that has traditionally relied on extra circuitry to address,” said Zhiyao Xie, first author of the paper and a Ph.D. candidate in the laboratory of Yiran Chen, professor of electrical and computer engineering at Duke. “But our approach runs directly on the microprocessor in the background, which opens many new opportunities. I think that’s why people are excited about it.”

Security experts around the world raced Friday to patch one of the worst computer vulnerabilities discovered in years, a critical flaw in open-source code widely used across industry and government in cloud services and enterprise software.

“I’d be hard-pressed to think of a company that’s not at risk,” said Joe Sullivan, chief security officer for Cloudflare, whose online infrastructure protects websites from malicious actors. Untold millions of servers have it installed, and experts said the fallout would not be known for several days.

New Zealand’s computer emergency response team was among the first to report that the flaw in a Java-language utility for Apache servers used to log user activity was being “actively exploited in the wild” just hours after it was publicly reported Thursday and a patch released.

The Artificial Intelligence industry should create a global community of hackers and “threat modelers” dedicated to stress-testing the harm potential of new AI products in order to earn the trust of governments and the public before it’s too late.

This is one of the recommendations made by an international team of risk and machine-learning experts, led by researchers at the University of Cambridge’s Center for the Study of Existential Risk (CSER), who have authored a new “call to action” published today in the journal Science.

They say that companies building intelligent technologies should harness techniques such as “red team” hacking, audit trails and “bias bounties”—paying out rewards for revealing ethical flaws—to prove their integrity before releasing AI for use on the wider public.

To watch the full interview, visit: http://www.yiddishbookcenter.org/oral…

Leonard Nimoy — Jewish actor best known for his role as Spock on the Star Trek science fiction series — explains the Jewish story behind the hand-gesture he made famous through his role as Spock in the Star Trek science fiction series.

China is drawing up a blacklist targeting new companies in sensitive sectors that use the variable interest entity structure, reported the Financial Times.

Car sharing should become the norm to end “20th-century thinking” that values private vehicle ownership, as part of the drive to cut carbon emissions, a government minister has said.

Trudy Harrison, a junior transport minister, said the transport system would soon be designed around “access to services rather than what you own”.

She said the UK was “reaching a tipping point where shared mobility in the form of car clubs, scooters and bike shares will soon be a realistic option for many of us to get around.”

Microsoft creates an AI bug detector that learns to hunt for and fix bugs without being trained on data from real bugs.

Researchers at the University of Texas have discovered a new way for neural networks to simulate symbolic reasoning. This discovery sparks an exciting path toward uniting deep learning and symbolic reasoning AI.

In the new approach, each neuron has a specialized function that relates to specific concepts. “It opens the black box of standard deep learning models while also being able to handle more complex problems than what symbolic AI has typically handled,” Paul Blazek, University of Texas Southwestern Medical Center researcher and one of the authors of the Nature paper, told VentureBeat.

This work complements previous research on neurosymbolic methods such as MIT’s Clevrer, which has shown some promise in predicting and explaining counterfactual possibilities more effectively than neural networks. Additionally, DeepMind researchers previously elaborated on another neural network approach that outperformed state-of-the-art neurosymbolic approaches.