Lifeboat Foundation

An artificial intelligence system from Google’s sibling company DeepMind stumbled on a new way to solve a foundational math problem at the heart of modern computing, a new study finds. A modification of the company’s game engine AlphaZero (famously used to defeat chess grandmasters and legends in the game of Go) outperformed an algorithm that had not been improved on for more than 50 years, researchers say.

The new research focused on multiplying grids of numbers known as matrices. Matrix multiplication is an operation key to many computational tasks, such as processing images, recognizing speech commands, training neural networks, running simulations to predict the weather, and compressing data for sharing on the Internet.

In February 2019, JQI Fellow Alicia Kollár, who is also an assistant professor of physics at UMD, bumped into Adrian Chapman, then a postdoctoral fellow at the University of Sydney, at a quantum information conference. Although the two came from very different scientific backgrounds, they quickly discovered that their research had a surprising commonality. They both shared an interest in graph theory, a field of math that deals with points and the connections between them.

Chapman found graphs through his work in quantum error correction —a field that deals with protecting fragile quantum information from errors in an effort to build ever-larger quantum computers. He was looking for new ways to approach a long-standing search for the Holy Grail of quantum error correction: a way of encoding quantum information that is resistant to errors by construction and doesn’t require active correction. Kollár had been pursuing new work in graph theory to describe her photon-on-a-chip experiments, but some of her results turned out to be the missing piece in Chapman’s puzzle.

Their ensuing collaboration resulted in a new tool that aids in the search for new quantum error correction schemes—including the Holy Grail of self-correcting quantum error correction. They published their findings recently in the journal Physical Review X Quantum.

HOUSTON, Oct. 18, 2022 – The nonprofit Space Center Houston is advancing a Facilities Master Plan to support the growing need for space exploration learning and training in two massive structures that will also give the public a front row seat into the development of robotics, rovers, lunar landers and reduced gravity systems. Today, the center offered a glimpse of the facility that will include two enclosed simulated cosmic terrains of the Moon and Mars, as well as modular surface labs and STEM learning centers. An elevated exhibit hall over the two surfaces will offer the public immersive experiences to observe astronaut training first-hand while experiencing the future of space exploration as humans return to the Moon and eventually on to Mars.

Space Center Houston is responding to the opportunities and challenges in a rapidly evolving space sector, including the need for facilities built for current and future missions, while sharing this excitement with the public and addressing critical gaps in the development of the STEM workforce through its education programs. The facility will bring together guests, NASA, commercial space partners, colleges, universities and global space agencies to collaborate on new technologies that are propelling present and future human spaceflight.

For 30 years, Space Center Houston has chronicled the journey of human spaceflight while empowering and inspiring people to pursue careers in science, technology, engineering and mathematics. “Space is expanding once again and a new space age is upon us,” said William T. Harris, President and CEO Space Center Houston. “With new ambitions, new players and new challenges, we will shift our focus from being a curator of past achievements to also facilitating new feats in space.”

“We got a thousand times improvement [in training performance per chip] over the last 10 years, and a lot of it has been due to number representation,” Bill Dally, chief scientist and senior vice president of research at Nvidia said at the recent IEEE Symposium on Computer Arithmetic.

Gates will provide grants to prepare teachers better for teaching math and to curriculum companies and nonprofits to develop higher-quality teaching materials. The foundation will also support research into math education and make grants to help high-school math courses prepare students for college and the workplace.

A big problem with math as it is taught today is that students learn in isolation and can feel crushed when they get the wrong answer to a problem, says Shalinee Sharma, co-founder of Zearn, an educational nonprofit and Gates grantee who, with Hughes, spoke with reporters this week. Zearn uses computer-based lessons that incorporate a lot of visuals to keep students interested and provides feedback on progress to help teachers tailor lessons for individual students. A new approach in which students work in teams to solve problems, she said, can turn all students into “math kids.”

“When all kids are ‘math kids,’ making mistakes will be OK,” she said. “It won’t be embarrassing. In fact, making mistakes will be considered normal and an essential part of math learning.”

Try out my quantum mechanics course (and many others on math and science) on https://brilliant.org/sabine. You can get started for free, and the first 200 will get 20% off the annual premium subscription.

Welcome everybody to our first episode of Science News without the gobbledygook. Today we’ll talk about this year’s Nobel Prize in Physics, trouble with the new data from the Webb telescope, what’s next after NASA’s collision with an asteroid, new studies about the environmental impact of Bitcoin and exposure to smoke from wildfires, a test run of a new electric airplane, and dogs that can smell mathematics.

Continue reading “What’s next after NASA’s asteroid crash? A New Study on the Environmental Impact of Bitcoin & more” »

Recent explorations of unique geometric worlds reveal perplexing patterns, including the Fibonacci sequence and the golden ratio.

In an effort to clarify how deductive reasoning is accomplished, an fMRI study was performed to observe the neural substrates of logical reasoning and mathematical calculation. Participants viewed a problem statement and three premises, and then either a conclusion or a mathematical formula. They had to indicate whether the conclusion followed from the premises, or to solve the mathematical formula. Language areas of the brain (Broca’s and Wernicke’s area) responded as the premises and the conclusion were read, but solution of the problems was then carried out by non-language areas. Regions in right prefrontal cortex and inferior parietal lobe were more active for reasoning than for calculation, whereas regions in left prefrontal cortex and superior parietal lobe were more active for calculation than for reasoning. In reasoning, only those problems calling for a search for counterexamples to conclusions recruited right frontal pole. These results have important implications for understanding how higher cognition, including deduction, is implemented in the brain. Different sorts of thinking recruit separate neural substrates, and logical reasoning goes beyond linguistic regions of the brain.

Matrix multiplication is at the heart of many machine learning breakthroughs, and it just got faster—twice. Last week, DeepMind announced it discovered a more efficient way to perform matrix multiplication, conquering a 50-year-old record. This week, two Austrian researchers at Johannes Kepler University Linz claim they have bested that new record by one step.

In 1969, a German mathematician named Volker Strassen discovered the previous-best algorithm for multiplying 4×4 matrices, which reduces the number of steps necessary to perform a matrix calculation. For example, multiplying two 4×4 matrices together using a traditional schoolroom method would take 64 multiplications, while Strassen’s algorithm can perform the same feat in 49 multiplications.