Learning curve
Though he catches flak for it, Garrett Kenyon, a physicist at Los Alamos National Laboratory, calls artificial intelligence “overhyped.” The algorithms that underlie everything from Alexa’s voice recognition to credit card fraud detection typically owe their skills to deep learning, in which the software learns to perform specific tasks by churning through vast databases of examples. These programs, Kenyon points out, don’t organize and process information the way human brains do, and they fall short when it comes to the versatile smarts needed for fully autonomous robots, for example. “We have a lot of fabulous devices out there that are incredibly useful,” Kenyon says. “But I would not call any of that particularly intelligent.”
Kenyon and many others see hope for smarter computers in an upstart technology called neuromorphic computing. In place of standard computing architecture, which processes information linearly, neuromorphic chips emulate the way our brains process information, with myriad digital neurons working in parallel to send electrical impulses, or spikes, to networks of other neurons. Each silicon neuron fires when it receives enough spikes, passing along its excitation to other neurons, and the system learns by reinforcing connections that fire regularly while paring away those that don’t. The approach excels at spotting patterns in large amounts of noisy data, which can speed learning. Because information processing takes place throughout the network of neurons, neuromorphic chips also require far less shuttling of data between memory and processing circuits, boosting speed and energy efficiency.
Two groups have already shown neuromorphic chips can match the capabilities of some of the most advanced AI programs on the market. Today’s workhorse AI software relies on a deep learning algorithm known as a backpropagation neural network (BPNN), which enables AI systems to learn from their mistakes as they are trained. In a preprint posted on arXiv in August, Andrew Sornborger, a physicist at Los Alamos, and colleagues reported programming the first-generation Loihi to carry out backpropagation. The chip learned to interpret a commonly used visual data set of handwritten numerals as quickly as conventional BPNNs, while drawing just 1/100 as much power. (Full story)
Supercomputing effort to model the complex interactions affecting climate change in Arctic coastal regions
Earth's rapidly changing Arctic coastal regions have an outsized climatic effect that echoes around the globe. Tracking processes behind this evolution is a daunting task even for the best scientists.
Coastlines are some of the planet's most dynamic areas—places where marine, terrestrial, atmospheric and human actions meet. But the Arctic coastal regions face the most troubling issues from human-caused climate change from increasing greenhouse gas emissions, says Los Alamos National Laboratory (LANL) scientist Andrew Roberts.
"Arctic coastal systems are very fragile," says Roberts, who leads the high-performance computing systems element of a broader Department of Energy (DOE) Office of Science effort, led by its Biological and Environmental Research (BER) office, to simulate changing Arctic coastal conditions. "Until the last several decades, thick, perennial Arctic sea ice appears to have been generally stable. Now, warming temperatures are causing it to melt.” (Full story)
developed algorithms that took top prizes in a national
competition to help improve resiliency of the electrical grid.
Two
scientists at Los Alamos National Laboratory took top prizes in a national
competition for developing algorithms to help improve the resiliency and
efficiency of the electrical grid. The algorithm developed by Hassan Hijazi of
the Applied Mathematics and Plasma Physics Group took first place in all four
divisions, while the one developed by Carleton Coffrin of the Laboratory’s
Information Systems and Modeling Group placed second in two of the four
divisions. Their work outperformed 14 other entries in the competition funded by Advanced Research Projects
Agency–Energy (ARPA-E), a United States government agency that promotes and funds
research and development of advanced energy technologies.
“Grid security is a national security issue, which is why this is important
work for Los Alamos,” said Nancy Jo Nicholas, associate Laboratory director for
Global Security at Los Alamos. “Every five minutes, optimization problems
arise in the U.S. electrical grid that require a mathematical solution.
Hassan’s and Carleton’s achievement will help advance national efforts to
create a more reliable, resilient, and secure electrical grid.” (Full
story)
Los Alamos National Laboratory’s 2021 Fellows are:
Elizabeth Hunke and Baolian Cheng, top row, and David A. Smith
and Blas Uberuaga, bottom row.
Four researchers have been named 2021 Los Alamos National Laboratory Fellows: Baolian Cheng, Elizabeth Hunke, David A. Smith and Blas Uberuaga.
“To be a Fellow at the Laboratory is to be a leader in our workplace and within the scientific community at large,” said Thom Mason, Laboratory director. “I am honored to recognize these four fellows and thank them for their extraordinary contributions and accomplishments.” (Full story)
LANL researchers, clockwise from top left:
Andrew Gaunt, Bill Daughton, Eva Birnbaum
and Cristiano Nisoli.
Four Los Alamos National Laboratory researchers will be honored with the Laboratory’s Fellows Prizes at a ceremony Oct. 6. Bill Daughton, Andrew Gaunt and Cristiano Nisoli will receive the Fellows Prize for Research, and Eva Birnbaum will receive the Fellows Prize for Leadership.
“I congratulate Bill, Andrew, Cristiano and Eva for being recognized with these prestigious awards,” said John Sarrao, deputy Laboratory director for Science, Technology and Engineering. “Bill’s significant advancements in internal confinement fusion, Andrew’s key role in transuranic chemistry, and Cristiano’s work in magnetic materials have profoundly influenced their respective fields and the Laboratory. Eva’s leadership in isotope production has impacted national priorities and differentiated Los Alamos.”
The Fellows Prizes for Research is awarded to individuals for outstanding research performed at the Laboratory that has been published within the last 10 years and that has had a significant impact on their discipline or program. The Fellows Prize for Leadership recognizes individuals for outstanding scientific and engineering leadership at the Laboratory and recognizes the value of such leadership that stimulates the interest of talented young staff members in the development of new technology. (Full story)
