The ‘resonant’ planets could provide insight about how such systems form and evolve—and why our own solar system is not synced up.
Category: mathematics – Page 42
Order vs Disorder, Jordan Peterson’s Yin Yang analogy, & Stephen Wolfram’s 4 classes of cellular automata are explored. The edge of chaos is the phase transition zone between order and disorder which is found across a broad range of complex systems. We discuss Norman Packard, Christopher Langton, John Beggs, Stuart Kauffman, Mihaly Csikszentmihalyi, and M. Mitchell Waldrop. Wolfram’s Rule 110 and John Conway’s Game of Life, both Turing complete, make appearances.
0:00 Intro.
0:59 Lambda & Wolfram’s 4 Classes.
3:32 Criticality, Avalanches, & John Beggs.
4:44 Homework? More like FUNwork!
5:08 Flow by Mihaly Csikszentmihalyi.
5:35 Jordan Peterson (Yin-Yang)
9:39 M. Mitchell Waldrop’s Complexity.
Play with cellular automata here:
https://math.hws.edu/eck/js/edge-of-chaos/CA.html.
David J. Eck, Hobart and William Smith Colleges flow state by MIHALY CSIKSZENTMIHALYI ► The Secret to Happiness & Psychology of Optimal Experience https://www.youtube.com/watch?v=9e31Tdvz_FU&list=PLyQeeNuuRL…sWGsWQOG6H
🚾 Works Cited.
What can a quantum information theorist say about the certainty of arithmetic and the universality of mathematical truth?
Has OpenAI invented an AI technology with the potential to “threaten humanity”? From some of the recent headlines, you might be inclined to think so.
Reuters and The Information first reported last week that several OpenAI staff members had, in a letter to the AI startup’s board of directors, flagged the “prowess” and “potential danger” of an internal research project known as “Q*.” This AI project, according to the reporting, could solve certain math problems — albeit only at grade-school level — but had in the researchers’ opinion a chance of building toward an elusive technical breakthrough.
There’s now debate as to whether OpenAI’s board ever received such a letter — The Verge cites a source suggesting that it didn’t. But the framing of Q* aside, Q* in actuality might not be as monumental — or threatening — as it sounds. It might not even be new.
If OpenAI’s new model can solve grade-school math, it could pave the way for more powerful systems.
Ever since last week’s dramatic events at OpenAI, the rumor mill has been in overdrive about why the company’s chief scientific officer, Ilya Sutskever, and its board decided to oust CEO Sam Altman.
Carlos Bravo-Prieto1,2,3, Ryan LaRose4, M. Cerezo1,5, Yigit Subasi6, Lukasz Cincio1, and Patrick J. Coles1
1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87,545, USA. 2 Barcelona Supercomputing Center, Barcelona, Spain. 3 Institut de Ciències del Cosmos, Universitat de Barcelona, Barcelona, Spain. 4 Department of Computational Mathematics, Science, and Engineering & Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48,823, USA. 5 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, USA 6 Computer, Computational and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM 87,545, USA
Get full text pdfRead on arXiv Vanity.
Open-source supercomputer algorithm predicts patterning and dynamics of living materials and enables studying their behavior in space and time.
Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called “active matter.” The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.
Scientists from the Max Planck Institute of Molecular Cell.
An open-source advanced supercomputer algorithm predicts the patterning and dynamics of living materials, allowing for the exploration of their behaviors across space and time.
Biological materials consist of individual components, including tiny motors that transform fuel into motion. This process creates patterns of movement, leading the material to shape itself through coherent flows driven by constant energy consumption. These perpetually driven materials are called “active matter.”
The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.
Hopfions, magnetic spin structures predicted decades ago, have become a hot and challenging research topic in recent years. In a study published in Nature, the first experimental evidence is presented by a Swedish-German-Chinese research collaboration.
“Our results are important from both a fundamental and applied point of view, as a new bridge has emerged between experimental physics and abstract mathematical theory, potentially leading to hopfions finding an application in spintronics,” says Philipp Rybakov, researcher at the Department of Physics and Astronomy at Uppsala University, Sweden.
A deeper understanding of how different components of materials function is important for the development of innovative materials and future technology. The research field of spintronics, for example, which studies the spin of electrons, has opened up promising possibilities to combine the electrons’ electricity and magnetism for applications such as new electronics.
Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called active matter.
The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flow, and form of living materials. The active matter theory consists of many challenging mathematical equations.
Scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD), and the TU Dresden have now developed an algorithm, implemented in an open-source supercomputer code, that can for the first time solve the equations of active matter theory in realistic scenarios.