Lifeboat Foundation

Two retired professors are sharing the mathematics version of the Nobel Prize for their lifelong contributions to the changing nature of math in the computing age. Both Hillel Furstenberg and Gregory Margulis spent decades applying ideas from probability theory to different kinds of discrete mathematics in order to shake loose new ways to solve seemingly intractable problems. The Abel Prize, awarded since just 2003, honors career mathematical accomplishments with a prize of about $700,000.

Wait—there’s not a Nobel Prize for mathematics? It’s true, and although you may have heard a lascivious story to explain why, no one really knows for sure.

Life is rife with patterns. It’s common for living things to create a repeating series of similar features as they grow: think of feathers that vary slightly in length on a bird’s wing or shorter and longer petals on a rose.

It turns out the brain is no different. By employing advanced microscopy and mathematical modeling, Stanford researchers have discovered a pattern that governs the growth of brain cells or neurons. Similar rules could guide the development of other cells within the body, and understanding them could be important for successfully bioengineering artificial tissues and organs.

Their study, published in Nature Physics, builds on the fact that the brain contains many different types of neurons and that it takes several types working in concert to perform any tasks. The researchers wanted to uncover the invisible growth patterns that enable the right kinds of neurons to arrange themselves into the right positions to build a brain.

Using data from the Hubble Space Telescope’s Cosmic Origins Spectrograph, the team was able to observe the distinctive absorption signature in the spectrum of light that passes through it, and the sight-lines of hundreds of distant quasars that pierce the volume of space occupied by the SDSS galaxies, says the university.

This lowly slime mold does a good job of characterizing the large-scale structure of the Universe over a wide range of scale, Burchett told me.

“I see how it works from a mathematical and [topological] perspective, but that doesn’t diminish my continued amazement that the slime mold-inspired method handles this difficult problem so elegantly and efficiently,” Burchett told me.

For all of science’s impressive advancements, one problem has stubbornly eluded us: Why do we have consciousness? How does inert unconscious matter give rise to the light of conscious experience? Neuroscientist Donald Hoffman has been pondering this question throughout his career. His thinking has gradually led him to a surprising possibility — that consciousness itself is fundamental to reality. Donald’s theory, however, differs from that of the growing number of other scientists and philosophers now arriving at this conclusion.

“We’ve been stuck on the same problem for centuries. It’s time to take a different approach.”

The fundamental nature of reality, Donald theorizes, is comprised of an infinite network of interacting conscious agents. Uniquely, Donald offers a precise mathematical definition of a conscious agent. He believes the theory may be used to reconstruct the universe and existing scientific discoveries purely through the interaction of these units of consciousness.

Circa 2002 4 lines of code to solve everything.

… But first it cracked him. The inside story of how Stephen went from boy genius to recluse to science renegade.

Word had been out that Stephen, the onetime enfant terrible of the science world, was working on a book that would Say It All, a paradigm-busting tome that would not only be the definitive account on complexity theory but also the opening gambit in a new way to view the universe. But no one had read it.

Continue reading “The Man Who Cracked The Code to Everything ...” »

NASA announced Johnson’s death on Monday.

Johnson was part of NASA’s “Computer Pool,” a group of mathematicians whose data powered NASA’s first successful space missions. The group’s success largely hinged on the accomplishments of its black women members.

Johnson was among a group of black women mathematicians who helped power NASA’s space travel in the early 1960s when the agency was still segregated.

Engineers at the University of Washington are working on a new type of rocket engine that holds the promise of being lighter, more efficient, and simpler to make than conventional liquid-fuel rockets. Called a Rotational Detonation Engine (RDE), one of the biggest hurdles to making it practical is to develop mathematical models that can describe how the very unpredictable engine design works in order to make it more stable.

An RDE is a rocket engine that is similar to the pulse jet engines that powered the infamous German V1 cruise missile of the Second World War, which used a simple combustion chamber with an exhaust pipe at one end and spring-mounted slats on the front face. In operation, air would come in through the slats, mix with fuel, which was then detonated, producing a pulse of thrust. An RDE takes this idea one step further.

Continue reading “Computer modeling brings simple, efficient rocket engine closer to reality” »

The sophistication of autonomous systems currently being developed across various domains and industries has markedly increased in recent years, due in large part to advances in computing, modeling, sensing, and other technologies. While much of the technology that has enabled this technical revolution has moved forward expeditiously, formal safety assurances for these systems still lag behind. This is largely due to their reliance on data-driven machine learning (ML) technologies, which are inherently unpredictable and lack the necessary mathematical framework to provide guarantees on correctness. Without assurances, trust in any learning enabled cyber physical system’s (LE-CPS’s) safety and correct operation is limited, impeding their broad deployment and adoption for critical defense situations or capabilities.

To address this challenge, DARPA’s Assured Autonomy program is working to provide continual assurance of an LE-CPS’s safety and functional correctness, both at the time of its design and while operational. The program is developing mathematically verifiable approaches and tools that can be applied to different types and applications of data-driven ML algorithms in these systems to enhance their autonomy and assure they are achieving an acceptable level of safety. To help ground the research objectives, the program is prioritizing challenge problems in the defense-relevant autonomous vehicle space, specifically related to air, land, and underwater platforms.

The first phase of the Assured Autonomy program recently concluded. To assess the technologies in development, research teams integrated them into a small number of autonomous demonstration systems and evaluated each against various defense-relevant challenges. After 18 months of research and development on the assurance methods, tools, and learning enabled capabilities (LECs), the program is exhibiting early signs of progress.

BF16, the new number format optimized for deep learning, promises power and compute savings with a minimal reduction in prediction accuracy.

BF16, sometimes called BFloat16 or Brain Float 16, is a new number format optimised for AI/deep learning applications. Invented at Google Brain, it has gained wide adoption in AI accelerators from Google, Intel, Arm and many others.

The idea behind BF16 is to reduce the compute power and energy consumption needed to multiply tensors together by reducing the precision of the numbers. A tensor is a three-dimensional matrix of numbers; multiplication of tensors is the key mathematical operation required for AI calculations.