About Max Tegmark, Swedish-American physicist, cosmologist and machine learning researcher

Max Tegmark Interviewed by 'Lex Fridman Podcast #155' :  (Very good) 

https://www.youtube.com/watch?v=RL4j4KPwNGM

https://en.wikipedia.org/wiki/Max_Tegmark

Max Tegmark.jpg

Born 5 May 1967 (age 56)

Stockholm, Sweden

Nationality Swedish  American

Alma mater Royal Institute of Technology

UC Berkeley

Scientific career

Fields Cosmology, physics, machine learning

Institution MIT

Future of Life Institute    https://futureoflife.org/ 

Doctoral advisor Joseph Silk

Max Erik Tegmark[1] (born 5 May 1967) is a Swedish-American physicist, cosmologist and machine learning researcher. He is a professor at the Massachusetts Institute of Technology and the president of the Future of Life Institute.  https://futureoflife.org/  He is also a scientific director at the Foundational Questions Institute and a supporter of the effective altruism movement.[2][3][4][5]  ... 

ML Physics Platform NVIDIA Modulus is Open

 New things out of NVIDIA Resources, Fascinating,  Sample Uses?

Machine Learning Platform NVIDIA Modulus Is Now Open Source

By Bhoomi Gadhia, Ram Cherukuri and Kristen Perez

Physics-informed machine learning (physics-ML) is transforming high-performance computing (HPC) simulation workflows across disciplines, including computational fluid dynamics, structural mechanics, and computational chemistry. Because of its broad applications, physics-ML is well suited for modeling physical systems and deploying digital twins across industries ranging from manufacturing to climate sciences.

NVIDIA Modulus is a state-of-the-art physics-ML platform that blends physics with deep learning training data to build high-fidelity, parameterized surrogate models with near-real-time latency. The surrogate models built using NVIDIA Modulus help a wide range of solutions including weather forecasting, reducing power plant greenhouse gasses, and accelerating clean energy transitions.

NVIDIA Modulus customer success stories are proving the platform’s incredible utility across industries. However, physics-ML is a relatively new field in the deep learning arena, with significant challenges both at the research level as well as at the application front. This is due to the unique requirements needed to satisfy physics-ML rules:

The need for a deep learning model to obey the governing principles of a physical system.

The need for new deep learning model architectures for a specific class of problems, such as those that can satisfy fluid mechanics laws.

The need for generalizable model architectures and algorithms that can serve across different applications.

These challenges require innovation and research across several domains. More importantly, these problems require a strong collaboration between respective domains, industries, and deep learning experts. This level of collaboration requires tools and technologies that remove barriers between researchers, teams, and even industries, to enable the community to build on each other’s work.

Because simulations are critical to these disciplines and the industries that employ them, there is a demand for building and demonstrating confidence that AI can meet and surpass the current simulation approaches. This requires transparency so research can identify the limitations and provide breakthroughs to enable more transformative technologies.

Now, to facilitate the collaboration, transparency, and accountability needed, NVIDIA Modulus has become an open-source platform available for physics-ML.

Physics-ML open-source workflows

This new open-source environment provides significant benefits for AI developers and domain experts across industries in several ways:

Collaboration: An open-source workflow enables you to collaborate more easily with colleagues and share your work with a wider community. By making data, code, and methods openly accessible, you can work together more effectively to address complex physics-ML questions.

Transparency: Open-source workflows can increase the transparency and reproducibility of physics-ML research. By publishing code and data, you can enable other researchers to verify and replicate your results, which can help to build greater trust in the scientific findings.

Innovation: Open-source workflows can facilitate innovation by enabling you to build on other researchers’ work more easily. By providing access to a shared repository of tools and techniques, open-source workflows can help to accelerate the pace of discovery and enhance the quality of research outputs.

Accessibility: Open-source workflows can help to make research more accessible to a wider range of stakeholders, including drug development managers, national lab directors, policymakers, journalists, and members of the public. By providing clear, accessible information about physics-based modeling research, open-source workflows can help build greater awareness and understanding.

Overall, an open-source workflow can help AI developers and engineering and science domain experts to work more collaboratively, transparently, innovatively, and with greater accessibility to enhance the impact and relevance of their research.

Accessing NVIDIA Modulus open-source software

NVIDIA Modulus is available as open-source software (OSS) under the simple Apache 2.0 license. .... ' 

Re a Further Look at BioPhysics

How does life and physics fit together?    Just received in an Alumni mailing discussing,

THE PHYSICS OF Us

Physicists are studying how living matter works, and find that it breaks the standard rules and produces fascinating new phenomena.

Monday, December 5, 2022, By Susan Ahlborn. Illustrations by Marina Muun,   UPenn Omnia 

The James Webb telescope is showing us our universe in vibrant new detail. Some physicists, though, are looking in another direction: at us and other living matter here on Earth, from the cilia in lungs to the vasculature in leaves to the neurons in brains. What they’re finding is equally marvelous, and it’s challenging some of the current understanding of physics. 

Ultimately, they’re working to discover the rules that govern how matter lives and evolves, and their research may lead to better medicine, robotics based on biology, and an expanded understanding of the physical and biological world. 

How can an intelligent system arise from the collective dynamics of its basic components?

“We’re using physics principles to understand life and living matter,” says Eleni Katifori, Associate Professor of Physics and Astronomy, who studies vasculature in plants and animals. “But we are also using living matter as an inspiration to discover new physics, for asking the right questions or new questions.”

“Biology has already invented a lot of things. Living matter, from bacteria to leaves to humans, works in ways that physicists don’t understand, much less can duplicate,” adds Arnold Mathijssen, Assistant Professor of Physics and Astronomy. His goal is to unravel the physics of pathogens, design biomedical materials, and understand the collective functionality of living systems out of equilibrium. “It’s fundamental research. For example, how can an intelligent system arise from the collective dynamics of its basic components? It’s also directly relevant to our society, as in, what is the probability of SARS-CoV-2 transmission within a food supply chain?”

Leading the Way

The study of biophysics is not new; Luigi and Lucia Galvani were already investigating animal electricity in the late 1700s. In the last 20 years, though, technological advances have allowed researchers to see microscopic phenomena in living tissue with unprecedented detail, record simultaneously from thousands of neurons, and even track the large-scale behavior of ecosystems. All of these new methods produce vast amounts of quantitative data from which we can infer the laws of living matter. But it wasn’t until 2022 that the National Academies of Science, Engineering, and Medicine recognized biological physics as a separate field. 

Penn Arts & Sciences physicists have been studying living matter for decades, bringing Penn to the front of this area. Philip Nelson, Professor of Physics and Astronomy, wrote key books in the field, starting with Biological Physics: Energy, Information, Life in 2014. He’s been honored with the Emily Gray Award of the Biophysical Society for his “far-reaching and significant contributions.” Arjun Yodh, James M. Skinner Professor of Science, has received the Michael S. Feld Biophotonics Award of the Optical Society of America for his pioneering work in demonstrating and clinically translating biomedical optics. A.T. Charlie Johnson, Rebecca W. Bushnell Professor of Physics and Astronomy, is using biological molecules as chemical recognition elements in disease diagnosis, security, and environmental monitoring. Marija Drndic, Fay R. and Eugene L. Langberg Professor of Physics, explores mesoscopic and nanoscale structures, including the detection and analysis of DNA and microRNA.

In 2021, Penn Arts & Sciences and Penn Engineering made a unique investment in this interdisciplinary study with the new Center for Soft and Living Matter. Led by Director Andrea J. Liu, Hepburn Professor of Physics, and Associate Director Douglas J. Durian, Mary Amanda Wood Professor of Physics and Astronomy, the center brings together more than 60 faculty from the two schools. And Penn’s Computational Neuroscience Initiative, cofounded by Vijay Balasubramanian, Cathy and Marc Lasry Professor of Physics and Astronomy, involves researchers from Arts & Sciences, the Perelman School of Medicine, and Engineering. 

“If you look at a different scale or regime, new phenomena always pop up,” says Balasubramanian, a theoretical physicist who also holds a secondary appointment in neuroscience in the Perelman School of Medicine. “It’s the interactions between the components of living systems that make them so interesting, unlike, for example simple gases in a room. The brain contains a hundred billion interacting neurons. Molecules can talk to the whole organism, pheromones can change the behavior of entire colonies of organisms, stress can change gene expression. So, living systems interact across scales of organization unlike most physical systems that we are used to.”  .... ' 

The Year in Physics from Quanta Mag

Physics, like AI,  is increasingly important to understand and apply ... 

The Year in Physics

In a year filled with sweet new observations in astronomy and tantalizing breakthroughs in condensed matter physics, the brand-new space telescope takes the cake.

Myriam Wares for Quanta Magazine  By Natalie Wolchover, Senior Editor, December 22, 2022

Introduction

The year began right as the James Webb Space Telescope was unfurling its sunshield — the giant, nail-bitingly thin and delicate blanket that, once open, would plunge the observatory into frigid shade and open up its view of the infrared universe. Within hours of the ball dropping here in New York City, the sunshield could have caught on a snag, ruining the new telescope and tossing billions of dollars and decades of work into the void. Instead, the sunshield opened perfectly, getting the new year in physics off to an excellent start.

JWST soon started to glimpse gorgeous new faces of the cosmos. On July 11, President Biden unveiled the telescope’s first public image — a panoramic view of thousands of galaxies various distances away in space and time. Four more instantly iconic images were released the next day. Since then, the telescope’s data has been distributed among hundreds of astronomers and cosmologists, and cosmic discoveries and papers are pouring forth.

Astronomy is swimming in fresh data of all kinds. In May, for instance, the Event Horizon Telescope released the first-ever photo of the supermassive black hole in the heart of our galaxy — one of several recent observations that are helping astrophysicists figure out how galaxies operate. Other telescopes are mapping the locations of millions of galaxies, an effort that recently yielded surprising evidence of an asymmetry in galaxy distribution.

Breakthroughs are coming fast in condensed matter physics, too. An experiment published in September all but proved the origin of high-temperature superconductivity, which could help in the field’s perennial quest for an even warmer version of the phenomenon that could work at room temperature. That’s also a goal of research on two-dimensional materials. This year, a kind of flat crystal that once helped lubricate skis has emerged as a powerful platform for exotic, potentially useful quantum phenomena.

Particle physicists, who seek new fundamental ingredients of the universe, have been less lucky. They’ve continued to unravel features of particles we already know of — including the proton, the subject of a wonderful visual project we published this fall. But theorists have few if any concrete clues about how to go beyond the Standard Model of particle physics, the stiflingly comprehensive set of equations for the quantum world that’s been the theory to beat for half a century. Hope is a virtue, though, and at least one possible crack in the Standard Model did open up this year. Let’s start the 2022 greatest-hits list there.   .... ' 

Reading Existential Physics

Just started reading ...  good intro .... 

Understanding physics and its practical application is becoming important.  Book about how quantum effects are being applied in computing and cryptography. 

Existential Physics: A Scientist's Guide to Life's Biggest Questions Audible Logo Audible Audiobook – Unabridged

By Sabine Hossenfelder (Author), Gina Daniels (Narrator), Penguin Audio (Publisher)

A contrarian scientist wrestles with the big questions that modern physics raises, and what physics says about the human condition.

Not only can we not currently explain the origin of the universe, it is questionable we will ever be able to explain it. The notion that there are universes within particles, or that particles are conscious, is ascientific, as is the hypothesis that our universe is a computer simulation. On the other hand, the idea that the universe itself is conscious is difficult to rule out entirely. 

According to Sabine Hossenfelder, it is not a coincidence that quantum entanglement and vacuum energy have become the go-to explanations of alternative healers, or that people believe their deceased grandmother is still alive because of quantum mechanics. Science and religion have the same roots, and they still tackle some of the same questions: Where do we come from? Where do we go to? How much can we know? The area of science that is closest to answering these questions is physics. Over the last century, physicists have learned a lot about which spiritual ideas are still compatible with the laws of nature. Not always, though, have they stayed on the scientific side of the debate. 

In this lively, thought-provoking book, Hossenfelder takes on the biggest questions in physics: Does the past still exist? Do particles think? Was the universe made for us? Has physics ruled out free will? Will we ever have a theory of everything? She lays out how far physicists are on the way to answering these questions, where the current limits are, and what questions might well remain unanswerable forever. Her book offers a no-nonsense yet entertaining take on some of the toughest riddles in existence, and will give the listener a solid grasp on what we know—and what we don’t know.

* This audiobook includes a downloadable PDF with key visual figures included in the book. 

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2022 Sabine Hossenfelder (P)2022 Penguin Audio

Existential Physics Reading

 Understanding physics and its practical application is becoming important.   Here a good intro I am reading,  notably about how quantum effects are being applied in computing and cryptography. 

Existential Physics: A Scientist's Guide to Life's Biggest Questions Audible Logo Audible Audiobook – Unabridged

By Sabine Hossenfelder (Author), Gina Daniels (Narrator), Penguin Audio (Publisher)

4.4 out of 5 stars    289 ratings

A contrarian scientist wrestles with the big questions that modern physics raises, and what physics says about the human condition.

Not only can we not currently explain the origin of the universe, it is questionable we will ever be able to explain it. The notion that there are universes within particles, or that particles are conscious, is ascientific, as is the hypothesis that our universe is a computer simulation. On the other hand, the idea that the universe itself is conscious is difficult to rule out entirely. 

According to Sabine Hossenfelder, it is not a coincidence that quantum entanglement and vacuum energy have become the go-to explanations of alternative healers, or that people believe their deceased grandmother is still alive because of quantum mechanics. Science and religion have the same roots, and they still tackle some of the same questions: Where do we come from? Where do we go to? How much can we know? The area of science that is closest to answering these questions is physics. Over the last century, physicists have learned a lot about which spiritual ideas are still compatible with the laws of nature. Not always, though, have they stayed on the scientific side of the debate. 

In this lively, thought-provoking book, Hossenfelder takes on the biggest questions in physics: Does the past still exist? Do particles think? Was the universe made for us? Has physics ruled out free will? Will we ever have a theory of everything? She lays out how far physicists are on the way to answering these questions, where the current limits are, and what questions might well remain unanswerable forever. Her book offers a no-nonsense yet entertaining take on some of the toughest riddles in existence, and will give the listener a solid grasp on what we know—and what we don’t know.

* This audiobook includes a downloadable PDF with key visual figures included in the book. 

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2022 Sabine Hossenfelder (P)2022 Penguin Audio

Will AI Discover new Laws of Physics?

 A possibility.

Will AI Discover new Laws of Physics?  in NewScientist

Algorithms can pore over astrophysical data to identify underlying equations. Now, physicists are trying to figure out how to imbue these “machine theorists” with the ability to find deeper laws of nature

This article has been viewed 3468 times in the last 24 hours.

PHYSICS 21 November 2022 By Thomas Lewton

SPEAKING at the University of Cambridge in 1980, Stephen Hawking considered the possibility of a theory of everything that would unite general relativity and quantum mechanics – our two leading descriptions of reality – into one neat, all-encompassing equation. We would need some help, he reckoned, from computers. Then he made a provocative prediction about these machines’ growing abilities. “The end might not be in sight for theoretical physics,” said Hawking. “But it might be in sight for theoretical physicists.”

Artificial intelligence has achieved much since then, yet physicists have been slow to use it to search for new and deeper laws of nature. It isn’t that they fear for their jobs. Indeed, Hawking may have had his tongue firmly in his cheek. Rather, it is that the deep-learning algorithms behind AIs spit out answers that amount to a “what” rather than a “why”, which makes them about as useful for a theorist as saying the answer to the question of life, the universe and everything is 42.

Except that now we have found a way to make deep-learning algorithms speak physicists’ language. We can leverage AI’s ability to scour vast data sets in search of hidden patterns and extract meaningful results – namely, equations. “We’re moving into the discovery phase,” says Steve Brunton at the University of Washington in Seattle.  ... ' 

Physicists Rewrite the Fundamental Law That Leads to Disorder

Technical and intriguing

The second law of thermodynamics is among the most sacred in all of science, but it has always rested on 19th century arguments about probability. New arguments trace its true source to the flows of quantum information.

Is the rise of entropy merely probabilistic, or can it be straightened out by use of clear quantum axioms?

By Maggie Chiang for Quanta Magazine

In all of physical law, there’s arguably no principle more sacrosanct than the second law of thermodynamics — the notion that entropy, a measure of disorder, will always stay the same or increase. “If someone points out to you that your pet theory of the universe is in disagreement with Maxwell’s equations — then so much the worse for Maxwell’s equations,” wrote the British astrophysicist Arthur Eddington in his 1928 book The Nature of the Physical World. “If it is found to be contradicted by observation — well, these experimentalists do bungle things sometimes. But if your theory is found to be against the second law of thermodynamics I can give you no hope; there is nothing for it but to collapse in deepest humiliation.” No violation of this law has ever been observed, nor is any expected.

But something about the second law troubles physicists. Some are not convinced that we understand it properly or that its foundations are firm. Although it’s called a law, it’s usually regarded as merely probabilistic: It stipulates that the outcome of any process will be the most probable one (which effectively means the outcome is inevitable given the numbers involved).

Yet physicists don’t just want descriptions of what will probably happen. “We like laws of physics to be exact,” said the physicist Chiara Marletto of the University of Oxford. Can the second law be tightened up into more than just a statement of likelihoods?

A number of independent groups appear to have done just that. They may have woven the second law out of the fundamental principles of quantum mechanics — which, some suspect, have directionality and irreversibility built into them at the deepest level. According to this view, the second law comes about not because of classical probabilities but because of quantum effects such as entanglement. It arises from the ways in which quantum systems share information, and from cornerstone quantum principles that decree what is allowed to happen and what is not. In this telling, an increase in entropy is not just the most likely outcome of change. It is a logical consequence of the most fundamental resource that we know of — the quantum resource of information.  ... ' 

Is a Warp Drive Possible?

Possible? Outrageous?   Or does the science say we can exceed the speed of light if we just don't accelerate though it from a speed below it?  The speed of light as a barrier?  Technical. 

Warp Drive News. Seriously!   By Sabine Hossenfelder,  441K subscribers

You can help finance my videos by supporting me on Patreon:

https://www.patreon.com/Sabine

What is a warp drive? Are they scientifically possible? How does the Alcubierre drive work? That's what we talk about today.

The paper has now been published here:  https://iopscience.iop.org/article/10...

An open-access version is here:  https://arxiv.org/pdf/2102.06824.pdf

You can find more information about the authors' work here:

https://appliedphysics.org 

When I talk about energy conservation around 7 mins, I am NOT referring to the conservation of the stress-energy-tensor. This is conservation is always fulfilled if Einstein's field equations are fulfilled by way of the (contracted) Bianchi identities. I am talking about the conservation of the four-momentum (the integrated quantity) that is associated with the warp-drive bubble. In an asymptotically flat space-time, it should be conserved. If the bubble accelerates, it isn't. (As you'd expect.)

#physics #space #science

0:00 Intro and Motivation

0:46 What is a Warp Drive?

2:47 The Alcubierre Drive

3:54 General Relativity Background

6:09 The Problem with the Alcubierre Drive

7:26 New Paper

9:37 What the new paper may be good for ... 

Good Advanced Science explaining: By Sabine Hossenfelder

Relatively recently discovered, science but mostly what would be called physics.    Well done, but relatively technical.  Ideal to catch up on the updates of the physics you learned in high school or college. 

In her channel "Science without the gobbledygook," Sabine talks straight about science: No hype, no spin, no tip-toeing around inconvenient truths. New video each Saturday.

Sabine Hossenfelder has a PhD in physics and is presently a Research Fellow at the Frankfurt Institute for Advanced Studies (FIAS). She is author of the book "Lost in Math: How Beauty Leads Physics Astray" (Basic Books, 2018) and blogs at backreaction.blogspot.com

This is a personal channel. Opinions expressed here do not represent the opinions of FIAS.....

https://www.youtube.com/c/SabineHossenfelder  on yYoutube

What are Time Crystals?

In the NextWeb:  Technical view. physics 

Google’s ‘time crystals’ could be the greatest scientific achievement of our lifetimes

Eureka! A research team featuring dozens of scientists working in partnership with Google‘s quantum computing labs may have created the world’s first time crystal inside a quantum computer.

This is the kind of news  that makes me want to jump up and do a happy dance.

These scientists may have produced an entirely new phase of matter. I’m going to do my best to explain what that means and why I personally believe this is the most important scientific breakthrough in our lifetimes.

However, for the sake of clarity, there’s two points I need to make first:

Time crystals are a wickedly difficult concept to understand and even harder to explain.

The Google team might have created time crystals. This is pre-print research and has yet to receive full peer-review. Until the rest of the scientific community has time to review and replicate the work, we can’t say for sure it’s legitimate.

What’s a time crystal? In colloquial terms, it’s a big screw you to Sir Isaac Newton. Time crystals are a new phase of matter. For the sake of simplicity, let’s imagine a cube of ice. When you put a cube of ice in glass of water, you’re introducing two separate entities (the ice cube and the liquid water) to each other at two different temperatures.

Everyone knows that the water will get colder (that’s why we put the ice in there) and, over time, the ice will get warmer and turn into water. Eventually you’ll just have a glass of room-temperature water.

We call this process “thermal equilibrium.”

Most people are familiar with Newton’s first law of motion, it’s the one that says “an object at rest tends to stay at rest and an object in motion tends to stay in motion.”

An important side-effect of this law of physics is that it means a perpetual motion machine is classically impossible.

According to classical physics, the universe is always moving towards entropy. In other words: if we isolate an ice cube and a room-temperature glass of water from all other external forces, the water will always melt the ice cube.

The entropy (the movement towards change) of any system will always remain the same if there are no processes, and it will always increase if there are processes.  Since our universe has stars exploding, black holes sucking, and people lighting things on fire – chemical processes – entropy is always increasing.   Except when it comes to time crystals. Time crystals don’t give a damn what Newton or anyone else thinks. They’re lawbreakers and heart takers. They can, theoretically, maintain entropy even when they’re used in a process.  ...  ' 

Using AI in Particle Physics

An old interest of mine, how can AI be applied?   An investigation.  A further question:  How can AI be used generally elsewhere as a means to 'drill' into a complex space, with known rules,  play with their testing and extension?     ... 

Using AI to Drill Down in Physics  By Bennie Mols, Commissioned by CACM Staff, July 8, 2021

If a computer can teach itself to play the age-old board game Go better than the human world champion, if a computer can even conjure up a genius new Go move, couldn't a computer also discover new physics?

Jesse Thaler, an associate professor of physics at the Massachusetts Institute of Technology (MIT), investigates the potential of artificial intelligence (AI) in particle physics. In 2020, Thaler also became the director of the National Science Foundation's AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), which is dedicated to advancing physics knowledge and galvanizing AI research innovation.

At the moment, the Standard Model of particle physics is the best description of three of the four fundamental forces of nature, and of a large family of elementary particles. Finding deviations from the Standard Model might lead physicists to discover new particles or new interactions, and AI might be able to play an important role in this.

In a Zoom interview, Thaler talks about the present and the future of applying AI to particle physics.

Why does particle physics need AI?

Particle physics is a data-rich field. In CERN's Large Hadron Collider (LHC), particles collide every 25 nanoseconds. Processing and analyzing these collisions has to be done in an automated and robust way. We are seeing now that our traditional tools have reached their limits. AI has the potential to exploit new features of these datasets."

Where are we right now with AI in particle physics?

Right now, AI has augmented and improved on strategies that particle physics has already used for many decades. AI gives physicists a better ability to reconstruct particles from the collision debris and interpret the results.

What type of AI is used in particle physics?

It's almost all machine learning, and the biggest distinction to make is between supervised and unsupervised machine learning. Thus far in particle physics, we have focused on supervised learning, based on training sets with labeled data.  ... '

Wolfram Physics: One Year Update: How’s It Going?

With a physics background, always found this somewhat ungraspable, but on it goes.  And now an update.   I have yet to hear that traditional physics is applauding this, so I guess that is still my main objection.   And I don't have the time to do my own fundamental research.   So here it is:

Wolfram Physics:  On Year Update:  How’s It Going?

When we launched the Wolfram Physics Project a year ago today, I was fairly certain that—to my great surprise—we’d finally found a path to a truly fundamental theory of physics, and it was beautiful. A year later it’s looking even better. We’ve been steadily understanding more and more about the structure and implications of our models—and they continue to fit beautifully with what we already know about physics, particularly connecting with some of the most elegant existing approaches, strengthening and extending them, and involving the communities that have developed them.

And if fundamental physics wasn’t enough, it’s also become clear that our models and formalism can be applied even beyond physics—suggesting major new approaches to several other fields, as well as allowing ideas and intuition from those fields to be brought to bear on understanding physics.

Needless to say, there is much hard work still to be done. But a year into the process I’m completely certain that we’re “climbing the right mountain”. And the view from where we are so far is already quite spectacular.

We’re still mostly at the stage of exploring the very rich structure of our models and their connections to existing theoretical frameworks. But we’re on a path to being able to make direct experimental predictions, even if it’ll be challenging to find ones accessible to present-day experiments. But quite independent of this, what we’ve done right now is already practical and useful—providing new streamlined methods for computing several important existing kinds of physics results.

The way I see what we’ve achieved so far is that it seems as if we’ve successfully found a structure for the “machine code” of the universe—the lowest-level processes from which all the richness of physics and everything else emerges. It certainly wasn’t obvious that any such “machine code” would exist. But I think we can now be confident that it does, and that in a sense our universe is fundamentally computational all the way down. But even though the foundations are different, the remarkable thing is that what emerges aligns with important mathematical structures we already know, enhancing and generalizing them.

From four decades of exploring the computational universe of possible programs, my most fundamental takeaway has been that even simple programs can produce immensely complex behavior, and that this behavior is usually computationally irreducible, in the sense that it can’t be predicted by anything much less than just running the explicit computation that produced it. And at the level of the machine code our models very much suggest that our universe will be full of such computational irreducibility.

AI Can Run Simulation Faithful to Physical Laws

 A kind of 'physics twin'? 

AI Can Run Simulation Faithful to Physical Laws

Kobe University (Japan),  December 18, 2020

Researchers at Japan's Kobe University developed an artificial intelligence (AI) model that can simulate phenomena based on an unknown mechanism or formula, while preserving the laws of physics. The technology aims to enable predictions for phenomena that have been difficult to simulate, like wave motion or fracture mechanics, and speed up the simulations themselves. The researchers created a new digital version of backpropagation using automatic differentiation, which makes it possible to preserve physical laws in the digital world. The researchers also used geometric approaches to apply the technique to the prediction of a wider range of phenomena. ... ' 

Exceptions to the Law of Entropy

More of a Science idea, but we actually found it useful to include in creativity work.   And in work that related  to the application of AI to process.   I post it here to remind myself of the connection and pass it along to others out there.  Is it creeping into other 'engineering' efforts.  For example, is Quantum an aspect of the below?

Stochastic thermodynamics finds exceptions to the law of entropy  By Tim Andersen, Ph.D.  in Medium

 Thermodynamics is one of the most venerable physical theories. 18th and 19th century physicists like Carnot, Clausius, Gibbs, Helmholtz, and Boltzmann established it as a cornerstone of how macroscopic systems made of many, many constituent particles behave, how heat is transported from one system to another, and how engines do work and with what efficiency.

It is from thermodynamics that we get laws like: there are no perpetual motion machines (machines with 100% or more efficiency or that have non-zero efficiency in thermal equilibrium).

Unlike the laws of mechanics or many field theories, however, thermodynamics has always been recognized as containing both strict and de facto laws. The primary laws are:

   - Energy can be neither created nor destroyed. (Strict)

   - Entropy can never decrease. (De Facto)

There are qualifiers to this. In the 1st law, the system must be closed. For the second, we are talking about bringing two or more isolated systems together.

The conservation of energy is a consequence of time translation symmetry of our universe. All times in physical laws can be shifted by a constant amount. We get that from Emmy Noether’s theorem. No physical law breaks the conservation of energy.

The second law is a “de facto” law of physics. I use this term where others might use the word “statistical”. But to say it is statistical is an understatement compared to other fields like sociology. Until about 25 years ago, a violation of the 2nd law was almost thought to be impossible.   ... "

Numbers, Mathematics and the Reality of Science

Why can numbers do such a good job of describing reality?   Can they describe all of reality?

Recently republished:  https://medium.com/@ruth.ym.ng/do-numbers-exist-251e9b61508

Do Numbers Exist?  by Ruth Ng
November 2nd 2018

In 1960, Eugene Wigner began the closing paragraph of his paper The Unreasonable Effectiveness of Mathematics in the Natural Sciences with a beautiful summary of the problem philosophers face when it comes to the existence of numbers. He said:

“The miracle of the appropriateness of the language of mathematics for the formulation of the laws of physics is a wonderful gift which we neither understand nor deserve.”

He’s talking about the sheer power, the disproportionate usefulness and beauty of mathematics. Its ability to seemingly describe reality in a way our ordinary language never could is uncanny.

The Collatz Conjecture is one such curiosity, as is the Fibonacci sequence. (If these sorts of things interest you, my favourite books on this kind of thing are Ian Stewart’s Incredible Numbers, Freiberger & Thomas’ Numericon and David Acheson’s 1089.) ... 

Do We Need a Theory of Everything?

Nicely put.  The math does not to be easy, or visual or nice.   Just predict real things correctly.

Do We Need a Theory of Everything? in Nautilus
Posted By  Sabine Hossenfelder

I get constantly asked if I could please comment on other people’s theories of everything. That could be Garrett Lisi’s E8 theory or Eric Weinstein’s geometric unity or Stephen Wolfram’s idea that the universe is but a big graph, and so on. Good, then. Let me tell you what I think about this. But I’m afraid it may not be what you wanted to hear.

Before we start, let me remind you what physicists mean by a “Theory of Everything.” For all we currently know, the universe and everything in it is held together by four fundamental interactions. That’s the electromagnetic force, the strong and the weak nuclear force, and gravity. All other forces that you are familiar with, say, the van der Waals force, or muscle force, or the force that’s pulling you down an infinite sequence of links on Wikipedia, these are all non-fundamental forces that derive from the four fundamental interactions. At least in principle.

This whole idea of a theory of everything is based on an unscientific premise.... "

New Physics: All the Way Down?

Descriptive.  But will it show us something really predictively new?

THE THIRD CULTURE
Computation All the Way Down In the Edge
A Conversation with Stephen Wolfram

We're now in this situation where people just assume that science can compute everything, that if we have all the right input data and we have the right models, science will figure it out. If we learn that our universe is fundamentally computational, that throws us right into the idea that computation is a paradigm you have to care about. The big transition was from using equations to describe how everything works to using programs and computation to describe how things work. And that's a transition that has happened after 300 years of equations. The transition time to using programs has been remarkably quick, a decade or two. One area that was a holdout, despite the transition of many fields of science into the computational models direction, was fundamental physics.

If we can firmly establish this fundamental theory of physics, we know it's computation all the way down. Once we know it's computation all the way down, we're forced to think about it computationally. One of the consequences of thinking about things computationally is this phenomenon of computational irreducibility. You can't get around it. That means we have always had the point of view that science will eventually figure out everything, but computational irreducibility says that can't work. It says that even if we know the rules for the system, it may be the case that we can't work out what that system will do any more efficiently than basically just running the system and seeing what happens, just doing the experiment so to speak. We can't have a predictive theoretical science of what's going to happen.

STEPHEN WOLFRAM is a scientist, inventor, and the founder and CEO of Wolfram Research. He is the creator of the symbolic computation program Mathematica and its programming language, Wolfram Language, as well as the knowledge engine Wolfram|Alpha. His most recent endeavor is The Wolfram Physics Project. He is also the author, most recently, of A Project to Find the Fundamental Theory of Physics. ...."  More.

Wolfram Needs Help Modeling Universe

Just mentioned related work:

Wolfram, Modeling Our Universe, Needs Your Help
Popular Mechanics
By Courtney Linder

Physicist Stephen Wolfram has launched the Wolfram Physics Project to crowdsource research to model the universe's fundamental physical laws. The project incorporates “the most important works in physics,” including 800 pages of documents Wolfram authored, and 430 hours of video documenting brainstorming sessions between Wolfram and his colleagues. Wolfram and project participants will use computational models to simulate possible universes. The project webpage lists a Registry of Notable Universes compiling about 1,000 rules for the project. Said Wolfram, “I hope soon there might just be a rule entered in the Registry that has all the right properties, and that we’ll slowly discover that, yes, this is it—our universe finally decoded." .... '

Physics as Automata

Fascinating read.    And as a person with background in physics and computer science I find this fascinating.   Have read parts of Wolfram's 'big' book, which initially posed aspects of this solution.  Admit to being somewhat mysttified by the solution and what to do with it.   Technical, the article is a good start.

Finally We May Have a Path to the Fundamental Theory of Physics…
and It’s Beautiful

Website: Wolfram Physics Project

Technical Intro: A Class of Models with the Potential to Represent Fundamental Physics
How We Got Here: The Backstory of the Wolfram Physics Project
Visual summary of the Wolfram Physics Project

I Never Expected This
It’s unexpected, surprising—and for me incredibly exciting. To be fair, at some level I’ve been working towards this for nearly 50 years. But it’s just in the last few months that it’s finally come together. And it’s much more wonderful, and beautiful, than I’d ever imagined.

In many ways it’s the ultimate question in natural science: How does our universe work? Is there a fundamental theory? An incredible amount has been figured out about physics over the past few hundred years. But even with everything that’s been done—and it’s very impressive—we still, after all this time, don’t have a truly fundamental theory of physics.

Back when I used do theoretical physics for a living, I must admit I didn’t think much about trying to find a fundamental theory; I was more concerned about what we could figure out based on the theories we had. And somehow I think I imagined that if there was a fundamental theory, it would inevitably be very complicated.

But in the early 1980s, when I started studying the computational universe of simple programs I made what was for me a very surprising and important discovery: that even when the underlying rules for a system are extremely simple, the behavior of the system as a whole can be essentially arbitrarily rich and complex.

And this got me thinking: Could the universe work this way? Could it in fact be that underneath all of this richness and complexity we see in physics there are just simple rules? I soon realized that if that was going to be the case, we’d in effect have to go underneath space and time and basically everything we know. Our rules would have to operate at some lower level, and all of physics would just have to emerge. ...."