Lifeboat Foundation

Capturing details of faraway members of our universe is an understandably complicated affair, but translating these details into the stunning space images that we see from space agencies around the world is equally difficult. It is here that supercomputers step in, helping process the massive amounts of data that are captured by terrestrial and space telescopes. On August 11, that is exactly what Australia’s upcoming supercomputer, called Setonix, helped achieve.

As its first project, Setonix processed the image of a dying supernova — the last stages of a dying star — from data sent to it by the Australian Square Kilometer Array Pathfinder (Askap). The latter is a terrestrial radio telescope, which has 36 individual antennas working together to capture radio frequency data about objects that are far away in space.

Such data contains intricate details about the object being observed. This not only increases the volume of the data being captured by the telescope, but also puts increasing pressure on a supercomputer to process it into a composite image.

The US has retaken the top spot in the world supercomputer rankings with the exascale Frontier system at Oak Ridge National Laboratory (ORNL) in Tennessee.

The Frontier system’s score of 1.102 exaflop/s makes it “the most powerful supercomputer to ever exist” and “the first true exascale machine,” the Top 500 project said Monday in the announcement of its latest rankings. Exaflop/s (or exaflops) is short for 1 quintillion floating-point operations per second.

Frontier was more than twice as fast as a Japanese system that placed second in the rankings, which are based on the LINPACK benchmark that measures the “performance of a dedicated system for solving a dense system of linear equations.”

In its heyday, UIUC’s Blue Waters was one of the world’s top supercomputers. Anyone who was curious could drop by its 30,000-square-foot machine room for a tour, and spend half an hour strolling among the 288 huge black cabinets, supported by a 24-megawatt power supply, that housed its hundreds of thousands of computational cores.

Blue Waters is gone, but today UIUC is home to not just one, but tens of thousands of vastly superior computers. Although these wondrous machines put Blue Waters to shame, each one weighs just three pounds, can be fueled by coffee and sandwiches, and is only the size of its owner’s two hands curled together. We all carry them between our ears.

The fact is that humanity is far from having artificial computers that can match the capabilities of the human brain, outside a narrow range of well-defined tasks. Will we ever capture the brain’s magic? To help answer that question, MRL’s Axel Hoffmann recently led the writing of an APL Materials “Perspectives” article that summarizes and reflects on efforts to find so-called “quantum materials” that can mimic brain function.

Tesla has teased its Optimus humanoid robot prototype with a new image ahead of a full unveiling planned for September 30th.

Earlier this year, CEO Elon Musk announced “Tesla AI Day #2” with “many cool updates” on August 19.

The original “Tesla AI Day” held last year was an event focused on the company’s self-driving program. The automaker also unveiled its Dojo supercomputer and announced plans for the “Tesla Bot” humanoid robot – now known as Tesla Optimus.

Quantum computing, though still in its early days, has the potential to dramatically increase processing power by harnessing the strange behavior of particles at the smallest scales. Some research groups have already reported performing calculations that would take a traditional supercomputer thousands of years. In the long term, quantum computers could provide unbreakable encryption and simulations of nature beyond today’s capabilities.

A UCLA-led interdisciplinary research team including collaborators at Harvard University has now developed a fundamentally new strategy for building these computers. While the current state of the art employs circuits, semiconductors and other tools of electrical engineering, the team has produced a game plan based in chemists’ ability to custom-design atomic building blocks that control the properties of larger molecular structures when they’re put together.

The findings, published last week in Nature Chemistry, could ultimately lead to a leap in quantum processing power.

Scientists at the Lawrence Livermore National Laboratory (LLNL) Energetic Materials Center and Purdue University Materials Engineering Department have used simulations performed on the LLNL supercomputer Quartz to uncover a general mechanism that accelerates chemistry in detonating explosives critical to managing the nation’s nuclear stockpile. Their research is featured in the July 15 issue of the Journal of Physical Chemistry Letters.

Insensitive high explosives based on TATB (1,3,5-triamino-2,4,6-trinitrobenzene) offer enhanced safety properties over more conventional explosives, but physical explanations for these safety characteristics are not clear. Explosive initiation is understood to arise from hotspots that are formed when a shockwave interacts with microstructural defects such as pores. Ultrafast compression of pores leads to an intense localized spike in temperature, which accelerates chemical reactions needed to initiate burning and ultimately detonation. Engineering models for insensitive high explosives—used to assess safety and performance—are based on the hotspot concept but have difficulty in describing a wide range of conditions, indicating missing physics in those models.

Using large-scale atomically resolved reactive molecular dynamics supercomputer simulations, the team aimed to directly compute how hotspots form and grow to better understand what causes them to react.

Atoms are notoriously difficult to control. They zigzag like fireflies, tunnel out of the strongest containers and jitter even at temperatures near absolute zero.

Nonetheless, scientists need to trap and manipulate single atoms in order for quantum devices, such as atomic clocks or quantum computers, to operate properly. If individual atoms can be corralled and controlled in large arrays, they can serve as quantum bits, or qubits—tiny discrete units of information whose state or orientation may eventually be used to carry out calculations at speeds far greater than the fastest supercomputer.

Researchers at the National Institute of Standards and Technology (NIST), together with collaborators from JILA—a joint institute of the University of Colorado and NIST in Boulder—have for the first time demonstrated that they can trap single atoms using a novel miniaturized version of “optical tweezers”—a system that grabs atoms using a laser beam as chopsticks.

Quantum entanglement is one of the most fundamental and intriguing phenomena in nature. Recent research on entanglement has proven to be a valuable resource for quantum communication and information processing. Now, scientists from Japan have discovered a stable quantum entangled state of two protons on a silicon surface, opening doors to an organic union of classical and quantum computing platforms and potentially strengthening the future of quantum technology.

One of the most interesting phenomena in quantum mechanics is “quantum entanglement.” This phenomenon describes how certain particles are inextricably linked, such that their states can only be described with reference to each other. This particle interaction also forms the basis of quantum computing. And this is why, in recent years, physicists have looked for techniques to generate entanglement. However, these techniques confront a number of engineering hurdles, including limitations in creating large number of “qubits” (quantum bits, the basic unit of quantum information), the need to maintain extremely low temperatures (1 K), and the use of ultrapure materials. Surfaces or interfaces are crucial in the formation of quantum entanglement. Unfortunately, electrons confined to surfaces are prone to “decoherence,” a condition in which there is no defined phase relationship between the two distinct states.

A new language model similar in scale to GPT-3 is being made freely available and could help to democratise access to AI.

BLOOM (which stands for BigScience Large Open-science Open-access Multilingual Language Model) has been developed by 1,000 volunteer researchers from over 70 countries and 250 institutions, supported by ethicists, philosophers, and legal experts, in a collaboration called BigScience. The project, coordinated by New York-based startup Hugging Face, used funding from the French government.

The new AI took more than a year of planning and training, which included a final run of 117 days (11th March – 6th July) using the Jean Zay, one of Europe’s most powerful supercomputers, located in the south of Paris, France.