Nine Inch Nails “Me I’m Not” remixed with US military, math, science, and computer footage from the Prelinger Archives.
Scientists have long studied the work of Subrahmanyan Chandrasekhar, the Indian-born American astrophysicist who won the Nobel Prize in 1983, but few know that his research on stellar and planetary dynamics owes a deep debt of gratitude to an almost forgotten woman: Donna DeEtte Elbert.
From 1948 to 1979, Elbert worked as a “computer” for Chandrasekhar, tirelessly devising and solving mathematical equations by hand. Though she shared authorship with the Nobel laureate on 18 papers and Chandrasekhar enthusiastically acknowledged her seminal contributions, her greatest achievement went unrecognized until a postdoctoral scholar at UCLA connected threads in Chandrasekhar’s work that all led back to Elbert.
Elbert’s achievement? Before anyone else, she predicted the conditions argued to be optimal for a planet or star to generate its own magnetic field, said the scholar, Susanne Horn, who has spent half a decade building on Elbert’s work.
Inflated expectations around the capabilities of AI technologies may lead people to believe that computers can’t be wrong. The truth is AI failures are not a matter of if but when. AI is a human endeavor that combines information about people and the physical world into mathematical constructs. Such technologies typically rely on statistical methods, with the possibility for errors throughout an AI system’s lifespan. As AI systems become more widely used across domains, especially in high-stakes scenarios where people’s safety and wellbeing can be affected, a critical question must be addressed: how trustworthy are AI systems, and how much and when should people trust AI?
Certain physical problems such as the rupture of a thin sheet can be difficult to solve as computations breakdown at the point of rupture. Here the authors propose a regularization approach to overcome this breakdown which could help dealing with mathematical models that have finite time singularities.
We all start from a single cell, the fertilized egg. From this cell, through a process involving cell division, cell differentiation and cell death a human being takes shape, ultimately made up of over 37 trillion cells across hundreds or thousands of different cell types.
While we broadly understand many aspects of this developmental process, we do not know many of the details.
A better understanding of how a fertilized egg turns into trillions of cells to form a human is primarily a mathematical challenge. What we need are mathematical models that can predict and show what happens.
A team at Los Alamos National Laboratory has developed a novel approach for comparing neural networks that looks within the “black box” of artificial intelligence to help researchers understand neural network behavior. Neural networks recognize patterns in datasets; they are used everywhere in society, in applications such as virtual assistants, facial recognition systems and self-driving cars.
“The artificial intelligence research community doesn’t necessarily have a complete understanding of what neural networks are doing; they give us good results, but we don’t know how or why,” said Haydn Jones, a researcher in the Advanced Research in Cyber Systems group at Los Alamos. “Our new method does a better job of comparing neural networks, which is a crucial step toward better understanding the mathematics behind AI.”
Jones is the lead author of the paper “If You’ve Trained One You’ve Trained Them All: Inter-Architecture Similarity Increases With Robustness,” which was presented recently at the Conference on Uncertainty in Artificial Intelligence. In addition to studying network similarity, the paper is a crucial step toward characterizing the behavior of robust neural networks.
Vaneev posits that: “‘intelligent impulses’ or even ‘human mind’ itself (because a musician can understand these impulses) existed long before the ‘Big Bang’ happened. This discovery is probably both the greatest discovery in the history of mankind, and the worst discovery (for many) as it poses very unnerving questions that touch religious grounds.”
The Voxengo developer sums up his findings as follows: “These results of 1-bit PRVHASH say the following: if abstract mathematics contains not just a system of rules for manipulating numbers, but also a freely-defined fixed information that is also ‘readable’ by a person, then mathematics does not just ‘exist’, but ‘it was formed’, because mathematics does not evolve (beside human discovery of new rules and patterns). And since physics cannot be formulated without such mathematics, and physical processes clearly obey these mathematical rules, it means that a Creator/Higher Intelligence/God exists in relation to the Universe. For the author personally, everything is proven here.”
Vaneev says that he wanted to “share my astonishment and satisfaction with the results of this work that took much more of my time than I had wished for,” but that you don’t need to concern yourself too much with his findings if you don’t want to.”
face_with_colon_three circa 2018.
Understanding the fundamental constituents of the universe is tough. Making sense of the brain is another challenge entirely. Each cubic millimetre of human brain contains around 4 km of neuronal “wires” carrying millivolt-level signals, connecting innumerable cells that define everything we are and do. The ancient Egyptians already knew that different parts of the brain govern different physical functions, and a couple of centuries have passed since physicians entertained crowds by passing currents through corpses to make them seem alive. But only in recent decades have neuroscientists been able to delve deep into the brain’s circuitry.
On 25 January, speaking to a packed audience in CERN’s Theory department, Vijay Balasubramanian of the University of Pennsylvania described a physicist’s approach to solving the brain. Balasubramanian did his PhD in theoretical particle physics at Princeton University and also worked on the UA1 experiment at CERN’s Super Proton Synchrotron in the 1980s. Today, his research ranges from string theory to theoretical biophysics, where he applies methodologies common in physics to model the neural topography of information processing in the brain.
(October 29, 2012) Keith Devlin concludes the course by discussing the development of mathematical cognition in humans as well as the millennium problems.
Originally presented in the Stanford Continuing Studies Program.
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University of Texas at Dallas physicists and their collaborators at Yale University have demonstrated an atomically thin, intelligent quantum sensor that can simultaneously detect all the fundamental properties of an incoming light wave.
The research, published April 13 in the journal Nature, demonstrates a new concept based on quantum geometry that could find use in health care, deep-space exploration and remote-sensing applications.
“We are excited about this work because typically, when you want to characterize a wave of light, you have to use different instruments to gather information, such as the intensity, wavelength and polarization state of the light. Those instruments are bulky and can occupy a significant area on an optical table,” said Dr. Fan Zhang, a corresponding author of the study and associate professor of physics in the School of Natural Sciences and Mathematics.
