Lifeboat Foundation

Figuring out how our reality took shape over billions of years is no easy task for scientists. Theories about how the Big Bang played out and the immediate aftermath are a dime a dozen, but researchers led by a team from the University of Arizona think they might stumble upon some of the secrets of galaxy formation by asking a supercomputer to simulate millions of virtual universes and seeing which ones come closest to what we see today.

In a new research paper published in Monthly Notices of the Royal Astronomical Society, the team explains how they used a supercomputer system nicknamed the “Universe Machine” to watch billions of (virtual) years of galaxy formation play out before their eyes.

A method for locating seams of gold and other heavy metals is the unlikely spin-off of Swinburne’s involvement in a huge experiment to detect dark matter down a mine in Stawell, Victoria.

Associate Professor Alan Duffy, from Swinburne’s Centre for Astrophysics and Supercomputing and a member of the Sodium iodide with Active Background REjection (SABRE) project, said cosmic radiation was effectively creating an X-ray of the Earth between the underground detector and the surface.

In the mine, the SABRE experiment seeks to detect particles of dark matter, something no one has conclusively achieved yet. Any signal from dark matter would be miniscule, and so the SABRE team created a phenomenally sensitive detector, which, it turns out, is also sensitive to a host of cosmic particles that can help us to locate gold.

How do galaxies such as our Milky Way come into existence? How do they grow and change over time? The science behind galaxy formation has remained a puzzle for decades, but a University of Arizona-led team of scientists is one step closer to finding answers thanks to supercomputer simulations.

Observing real galaxies in space can only provide snapshots in time, so researchers who want to study how galaxies evolve over billions of years have to revert to computer simulations. Traditionally, astronomers have used this approach to invent and test new theories of galaxy formation, one-by-one. Peter Behroozi, an assistant professor at the UA Steward Observatory, and his team overcame this hurdle by generating millions of different universes on a supercomputer, each of which obeyed different physical theories for how galaxies should form.

The findings, published in the Monthly Notices of the Royal Astronomical Society, challenge fundamental ideas about the role dark matter plays in galaxy formation, how galaxies evolve over time and how they give birth to stars.

Microsoft is investing $1 billion in OpenAI to support us building artificial general intelligence (AGI) with widely distributed [https://openai.com/charter/]

Economic benefits. We’re partnering to develop a hardware and software platform within Microsoft Azure which will scale to AGI. We’ll jointly develop new Azure.

AI supercomputing technologies, and Microsoft will become our exclusive cloud provider—so we’ll be working hard together to further extend Microsoft Azure’s capabilities in large-s.

Physicists at the University of Innsbruck are proposing a new model that could demonstrate the supremacy of quantum computers over classical supercomputers in solving optimization problems. In a recent paper, they demonstrate that just a few quantum particles would be sufficient to solve the mathematically difficult N-queens problem in chess even for large chess boards.

It’s difficult to simulate quantum physics, as the computing demand grows exponentially the more complex the quantum system gets — even a supercomputer might not be enough. AI might come to the rescue, though. Researchers have developed a computational method that uses neural networks to simulate quantum systems of “considerable” size, no matter what the geometry. To put it relatively simply, the team combines familiar methods of studying quantum systems (such as Monte Carlo random sampling) with a neural network that can simultaneously represent many quantum states.

Artificial Intelligence (AI) is an emerging field of computer programming that is already changing the way we interact online and in real life, but the term ‘intelligence’ has been poorly defined. Rather than focusing on smarts, researchers should be looking at the implications and viability of artificial consciousness as that’s the real driver behind intelligent decisions.

Consciousness rather than intelligence should be the true measure of AI. At the moment, despite all our efforts, there’s none.

Significant advances have been made in the field of AI over the past decade, in particular with machine learning, but artificial intelligence itself remains elusive. Instead, what we have is artificial serfs—computers with the ability to trawl through billions of interactions and arrive at conclusions, exposing trends and providing recommendations, but they’re blind to any real intelligence. What’s needed is artificial awareness.

Elon Musk has called AI the “biggest existential threat” facing humanity and likened it to “summoning a demon,”^[1] while Stephen Hawking thought it would be the “worst event” in the history of civilization and could “end with humans being replaced.”^[2] Although this sounds alarmist, like something from a science fiction movie, both concerns are founded on a well-established scientific premise found in biology—the principle of competitive exclusion.^[3]

Continue reading “Is artificial consciousness the solution to AI?” »

Autonomous vehicles aren’t perfect, so to help upgrade their intelligence and prevent fatal accidents, Nvidia created the DGX SuperPod, an AI-optimized supercomputer that will help design a better self-driving car.

While intense magnetic fields are naturally generated by neutron stars, researchers have been striving to achieve similar results for many years. UC San Diego mechanical and aerospace engineering graduate student Tao Wang recently demonstrated how an extremely strong magnetic field, similar to that on the surface of a neutron star, can be not only generated but also detected using an X-ray laser inside a solid material.

Wang carried out his research with the help of simulations conducted on the Comet supercomputer at the San Diego Supercomputer Center (SDSC) as well as Stampede and Stampede2 at the Texas Advanced Computing Center (TACC). All resources are part of a National Science Foundation program called the Extreme Science and Engineering Discovery Environment (XSEDE).

“Wang’s findings were critical to our recently published study’s overall goal of developing a fundamental understanding of how multiple laser beams of extreme intensity interact with matter,” said Alex Arefiev, a professor of mechanical and aerospace engineering at the UC San Diego Jacobs School of Engineering.