A quantum computer has been connected to Europe's fastest supercomputer. It may be a step towards a new type of computing that combines traditional and quantum computers to quickly solve complex problems. The promise of quantum computers is that they will eventually complete calculations that are impossible for the most powerful conventional computers. Though many researchers are working on perfecting quantum computers, many are also suggesting that existing, imperfect quantum computers could be more useful if connected to traditional supercomputers.

A 2022 upgrade will bring that processor total to 16,000. Meta, the tech giant previously known as Facebook, revealed Monday that it's built one of the world's fastest supercomputers, a behemoth called the Research SuperCluster, or RSC. With 6,080 graphics processing units packaged into 760 Nvidia A100 modules, it's the fastest machine built for AI tasks, Chief Executive Mark Zuckerberg says. That processing power is in the same league as the Perlmutter supercomputer, which uses more than 6,000 of the same Nvidia GPUs and currently ranks as the world's fifth fastest supercomputer. And in a second phase, Meta plans to boost performance by a factor of 2.5 with an expansion to 16,000 GPUs this year.

Mark Zuckerberg has announced his social media empire is building what he claims is the world's fastest artificial intelligence supercomputer as part of plans to build a virtual metaverse. The Facebook founder said in a blogpost that the metaverse, a concept that blends the physical and digital world via virtual and augmented reality, will require "enormous" computing power. The AI supercomputer, dubbed AI Research SuperCluster (RSC) by Zuckerberg's Meta business, is already the fifth fastest in the world, the company said. "The experiences we're building for the metaverse require enormous compute [sic] power (quintillions of operations/second!) and RSC will enable new AI models that can learn from trillions of examples, understand hundreds of languages, and more," wrote Zuckerberg in a blogpost. Meta researchers added that they expected the RSC to become the fastest computer of its kind when it is completed in the summer.

It's been nearly two years since Tesla's first "Autonomy Day" event, at which CEO Elon Musk made numerous lofty predictions about the future of autonomous vehicles, including his infamous claim that the company would have "one million robotaxis on the road" by the end of 2020. This time, the event will be called "AI Day," and according to Musk, the "sole goal" is to persuade experts in the field of robotics and artificial intelligence to come work at Tesla. The company is known for its high rate of turnover, the latest being Jerome Guillen, a key executive who worked at Tesla for 10 years before recently stepping down. Attracting and retaining talent, especially top tier names, has proven to be a challenge for the company. The August 19th event is scheduled to start at 5PM PT / 8PM ET at Tesla's headquarters in Palo Alto, California.

Berzelius is now Sweden's fastest supercomputer for AI and machine learning, and has been installed in the National Supercomputer Centre at Linköping University. A donation of EUR 29.5 million from the Knut and Alice Wallenberg Foundation has made the construction of the new supercomputer possible. "It's very gratifying, but also a major challenge, that Linköping University is taking a national responsibility to connect all initiatives within high-performance computing and data processing. Our new supercomputer is a powerful addition to the important research carried out into such fields as the life sciences, machine learning and artificial intelligence", says Jan-Ingvar Jönsson, vice-chancellor of Linköping University. The new supercomputer – Berzelius – takes its name from the renowned scientist Jacob Berzelius, who came from Östergötland, the region of Sweden in which Linköping is located.

National Supercomputer Centre at Linköping University has installed Berzelius, Sweden's fastest supercomputer for artificial intelligence and machine learning. The above had been made possible with a donation of EUR 29.5 million from the Knut and Alice Wallenberg Foundation. According to Jan-Ingvar Jönsson, vice-chancellor of Linköping University, It's gratifying and a big challenge at the same time that Linköping University has taken a national responsibility to connect all initiatives within high-performance computing and data processing. Berzelius is a powerful addition to the vital research carried out in such fields. Berzelius owes its name to renowned scientist Jacob Berzelius from Östergötland.

Through a new partnership with GlaxoSmithKline, AstraZeneca and the U.K.'s National Health Service, the chip maker Nvidia plans to build Great Britain's most powerful supercomputer--and dedicate its use to artificial intelligence research in healthcare. Dubbed Cambridge-1, the machine is designed to deliver 400 petaflops of performance, or 400 quadrillion floating-point calculations per second. When presented with dense systems of linear equations used in AI--such as simulations of molecular models and chemical interactions among potential drug compounds--it is expected to provide 8 petaflops of supercomputing power, ranking it number 29 on the list of the world's fastest. It is slated to come online before the end of the year, with GSK and AstraZeneca among the first drugmakers to use the system. Researchers from King's College London, Oxford Nanopore and the Guy's and St. Thomas' NHS Foundation Trust will also have access.

Among the many departments and agencies within the United States federal government, the US Department of Energy (DOE) stands out as one of the most science, technology, and innovation-focused. This should come as little surprise to those who know the DOE's storied history with its breakthrough labs, world-leading research institutions, and highly educated staff. Since World War II, the DOE has been at the forefront of most of the groundbreaking and world-changing revolutions in science and technology including the development and harnessing of nuclear energy, innovations in genomics including the DOE initiative Human Genome Project, work in high-performance computing, and many other research-oriented efforts. In fact, the DOE supports more research in the physical sciences than any other US federal agency, providing more than 40% of US funding in computing, physics, chemistry, materials science, and other area through a system of national laboratories including Lawrence Berkeley National Laboratory, Oak Ridge National Laboratory, Argonne National Laboratory, Ames Laboratory, Brookhaven National Laboratory, Los Alamos National Laboratory, Sandia National Labs, Lawrence Livermore National Laboratory, the SLAC National Accelerator Laboratory, and dozens more institutions. Until very recently, the DOE also ran the world's top two fastest supercomputers: Summit and Sierra.

But for machine learning, Fugaku is already there. That's significant because AI researchers are scaling up machine learning algorithms at a quick pace.

In 2012, Geoffrey Hinton's research team used only two NVIDIA GPUs to train AlexNet, the revolutionary network architecture that handily won the ImageNet Large Scale Visual Recognition Challenge. It probably never occurred to these groundbreaking researchers that just seven years later, a new team of researchers would use almost 10,000 times more GPUs to train their AI model. A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world's fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs. They managed to achieve an impressive, near-linear scaling of 0.93 on distributed training and produce a model capable of atomically-accurate reconstruction of materials -- a longstanding scientific problem involving materials imaging. In June 2018 the US Department of Energy's Oak Ridge National Laboratory in Tennessee unveiled the world's fastest supercomputer Summit, whosecomputing power reaches 200 petaflops.