This simulation models a huge number of atoms in detail with the help of artificial intelligence.
By Alex Wilkins
Something not musk:
No one will ever be able to see a purely mathematical construct such as a perfect sphere. But now, scientists using supercomputer simulations and atomic resolution microscopes have imaged the signatures of electron orbitals, which are defined by mathematical equations of quantum mechanics and predict where an atom’s electron is most likely to be.
Scientists at UT Austin, Princeton University, and ExxonMobil have directly observed the signatures of electron orbitals in two different transition-metal atoms, iron (Fe) and cobalt (Co) present in metal-phthalocyanines. Those signatures are apparent in the forces measured by atomic force microscopes, which often reflect the underlying orbitals and can be so interpreted.
Continue reading “Supercomputing simulations spot electron orbital signatures” »
In case anyone is wondering how advances like ChatGPT are possible while Moore’s Law is dramatically slowing down, here’s what is happening:
Nvidia’s latest chip, the H100, can do 34 teraFLOPS of FP64 which is the standard 64-bit standard that supercomputers are ranked at. But this same chip can do 3,958 teraFLOPS of FP8 Tensor Core. FP8 is 8 times less precise than FP64. Also, Tensor Cores accelerate matrix operations, particularly matrix multiplication and accumulation, which are used extensively in deep learning calculations.
So by specializing in operations that AI cares about, the speed of the computer is increased by over 100 times!
Advancing Nuclear Energy Science And Technology For U.S. Energy, Environmental And Economic Needs — Dr. Katy Huff, Ph.D. — Assistant Secretary, U.S. Department of Energy Office of Nuclear Energy, U.S. Department of Energy.
Dr. Kathryn Huff, Ph.D. (https://www.energy.gov/ne/person/dr-kathryn-huff) is Assistant Secretary, Office of Nuclear Energy, U.S. Department of Energy, where she leads their strategic mission to advance nuclear energy science and technology to meet U.S. energy, environmental, and economic needs, both realizing the potential of advanced technology, and leveraging the unique role of the government in spurring innovation.
Defining computational neuroscience The evolution of computational neuroscience Computational neuroscience in the twenty-first century Some examples of computational neuroscience The SpiNNaker supercomputer Frontiers in computational neuroscience References Further reading
The human brain is a complex and unfathomable supercomputer. How it works is one of the ultimate mysteries of our time. Scientists working in the exciting field of computational neuroscience seek to unravel this mystery and, in the process, help solve problems in diverse research fields, from Artificial Intelligence (AI) to psychiatry.
Computational neuroscience is a highly interdisciplinary and thriving branch of neuroscience that uses computational simulations and mathematical models to develop our understanding of the brain. Here we look at: what computational neuroscience is, how it has grown over the last thirty years, what its applications are, and where it is going.
AI is having its moment on tech earnings calls for the second consecutive quarter, following the widely popular launch of OpenAI’s ChatGPT in late November. But not every company has the same plans for the new technology.
Nvidia (NVDA) is selling AI powered supercomputers. Microsoft (MSFT) is integrating ChatGPT into its search engine to compete with Google (GOOGL), which has its own AI searchbot.
Meta’s approach is slightly different. The core business for Meta since the early days of Facebook has been advertising sales, which still account for 98% of the company’s quarterly revenue. So naturally, enhancing advertisements with AI is where Meta believes the new technology can be most impactful.
Humanity has had a sustained human presence in space for decades now. Traveling the world can be done in mere hours, and each of us carries within our pockets a supercomputer that is linked to all of human knowledge. Our fingertips are now more powerful than the kings or queens of centuries past. For all of our flaws and challenges, we live in the protopia today.
Not dystopia, not utopia, but something else.
Musk warned of “stormy weather” ahead, flagged Tesla’s key challenges, and touted its Dojo supercomputer and autonomous driving tech as revolutionary.
Quantum computers have the potential to break common cryptography techniques, search huge datasets and simulate quantum systems in a fraction of the time it would take today’s computers. But before this can happen, engineers need to be able to harness the properties of quantum bits or qubits.
Currently, one of the leading methods for creating qubits in materials involves exploiting the structural atomic defects in diamond. But several researchers at the University of Chicago and Argonne National Laboratory believe that if an analogue defect could be engineered into a less expensive material, the cost of manufacturing quantum technologies could be significantly reduced. Using supercomputers at the National Energy Research Scientific Computing Center (NERSC), which is located at the Lawrence Berkeley National Laboratory (Berkeley Lab), these researchers have identified a possible candidate in aluminum nitride. Their findings were published in Nature Scientific Reports.
“Silicon semiconductors are reaching their physical limits—it’ll probably happen within the next five to 10 years—but if we can implement qubits into semiconductors, we will be able to move beyond silicon,” says Hosung Seo, University of Chicago Postdoctoral Researcher and a first author of the paper.
The new chip can wire together supercomputers for artificial intelligence networks.
American semiconductor manufacturing company Broadcom Inc. has released a new chip Jericho3-AI, which is being touted by the company as the highest-performance fabric for artificial intelligence (AI) networks. The new chip will wire together supercomputers.
G0d4ather/iStock.
Quantum computers have the potential to break common cryptography techniques, search huge datasets and simulate quantum systems in a fraction of the time it would take today’s computers. But before this can happen, engineers need to be able to harness the properties of quantum bits or qubits.
