Stephen Wolfram joins Brian Greene to explore the computational basis of space, time, general relativity, quantum mechanics, and reality itself.
This program is part of the Big Ideas series, supported by the John Templeton Foundation.
Participant: Stephen Wolfram. Moderator: Brian Greene.
0:00:00 — Introduction. 01:23 — Unifying Fundamental Science with Advanced Mathematical Software. 13:21 — Is It Possible to Prove a System’s Computational Reducibility? 24:30 — Uncovering Einstein’s Equations Through Software Models. 37:00 — Is connecting space and time a mistake? 49:15 — Generating Quantum Mechanics Through a Mathematical Network. 01:06:40 — Can Graph Theory Create a Black Hole? 01:14:47 — The Computational Limits of Being an Observer. 01:25:54 — The Elusive Nature of Particles in Quantum Field Theory. 01:37:45 — Is Mass a Discoverable Concept Within Graph Space? 01:48:50 — The Mystery of the Number Three: Why Do We Have Three Spatial Dimensions? 01:59:15 — Unraveling the Mystery of Hawking Radiation. 02:10:15 — Could You Ever Imagine a Different Career Path? 02:16:45 — Credits.
Does the use of computer models in physics change the way we see the universe? How far reaching are the implications of computation irreducibility? Are observer limitations key to the way we conceive the laws of physics? In this episode we have the difficult yet beautiful topic of trying to model complex systems like nature and the universe computationally to get into; and how beyond a low level of complexity all systems, seem to become equally unpredictable. We have a whole episode in this series on Complexity Theory in biology and nature, but today we’re going to be taking a more physics and computational slant. Another key element to this episode is Observer Theory, because we have to take into account the perceptual limitations of our species’ context and perspective, if we want to understand how the laws of physics that we’ve worked out from our environment, are not and cannot be fixed and universal but rather will always be perspective bound, within a multitude of alternative branches of possible reality with alternative possible computational rules. We’ll then connect this multi-computational approach to a reinterpretation of Entropy and the 2nd law of thermodynamics. The fact that my guest has been building on these ideas for over 40 years, creating computer language and AI solutions, to map his deep theories of computational physics, makes him the ideal guest to help us unpack this topic. He is physicist, computer scientist and tech entrepreneur Stephen Wolfram. In 1987 he left academia at Caltech and Princeton behind and devoted himself to his computer science intuitions at his company Wolfram Research. He’s published many blog articles about his ideas, and written many influential books including “A New kind of Science”, and more recently “A Project to Find the Fundamental Theory of Physics”, and “Computer Modelling and Simulation of Dynamic Systems”, and just out in 2023 “The Second Law” about the mystery of Entropy. One of the most wonderful things about Stephen Wolfram is that, despite his visionary insight into reality, he really loves to be ‘in the moment’ with his thinking, engaging in socratic dialogue, staying open to perspectives other than his own and allowing his old ideas to be updated if something comes up that contradicts them; and given how quickly the fields of physics and computer science are evolving I think his humility and conceptual flexibility gives us a fine example of how we should update how we do science as we go.
What we discuss: 00:00 Intro. 07:45 The history of scientific models of reality: structural, mathematical and computational. 14:40 Late 2010’s: a shift to computational models of systems. 20:20 The Principle of Computational Equivalence (PCE) 24:45 Computational Irreducibility — the process that means you can’t predict the outcome in advance. 27:50 The importance of the passage of time to Consciousness. 28:45 Irreducibility and the limits of science. 33:30 Godel’s Incompleteness Theorem meets Computational Irreducibility. 42:20 Observer Theory and the Wolfram Physics Project. 45:30 Modelling the relations between discrete units of Space: Hypergraphs. 47:30 The progress of time is the computational process that is updating the network of relations. 50:30 We ’make’ space. 51:30 Branchial Space — different quantum histories of the world, branching and merging. 54:30 We perceive space and matter to be continuous because we’re very big compared to the discrete elements. 56:30 Branchial Space VS Many Worlds interpretation. 58:50 Rulial Space: All possible rules of all possible interconnected branches. 01:07:30 Wolfram Language bridges human thinking about their perspective with what is computationally possible. 01:11:00 Computational Intelligence is everywhere in the universe. e.g. the weather. 01:19:30 The Measurement problem of QM meets computational irreducibility and observer theory. 01:20:30 Entanglement explained — common ancestors in branchial space. 01:32:40 Inviting Stephen back for a separate episode on AI safety, safety solutions and applications for science, as we did’t have time. 01:37:30 At the molecular level the laws of physics are reversible. 01:40:30 What looks random to us in entropy is actually full of the data. 01:45:30 Entropy defined in computational terms. 01:50:30 If we ever overcame our finite minds, there would be no coherent concept of existence. 01:51:30 Parallels between modern physics and ancient eastern mysticism and cosmology. 01:55:30 Reductionism in an irreducible world: saying a lot from very little input.
References: “The Second Law: Resolving the Mystery of the Second Law of Thermodynamics”, Stephen Wolfram.
Neil deGrasse Tyson breaks down intriguing new evidence along with other curious parallels that could point to the universe being inside a black hole. Is the edge of our universe an event horizon on a black hole in some other universe?
Timestamps: 00:00 — What is a Black Hole? 1:26 — Mass of the Universe vs. A Black Hole This Size. 2:36 — The Net Rotation of the Universe. 5:55 — What This Means. 6:48 — Closing.
Researchers, in a recent Physical Review Letters paper, introduce a new mechanism that may finally allow ultralight dark photons to be considered serious candidates for dark matter, with promising implications for detection efforts.
Around 85% of all matter is believed to be dark matter, yet this elusive substance continues to puzzle scientists because it cannot be observed directly.
One of the candidates for dark matter particles is dark photons. These hypothetical particles are similar to regular photons but have mass and interact only weakly with normal matter.
UC Berkeley astronomers found a hidden black hole roaming far from the galaxy’s core. It may eventually merge with the central black hole and release gravitational waves. Astronomers have identified nearly 100 cases of massive black holes feasting on stars, almost all located in the dense centers
A fireside with Elon Musk at AI Startup School in San Francisco.
Before rockets and robots, Elon Musk was drilling holes through his office floor to borrow internet. In this candid talk, he walks through the early days of Zip2, the Falcon 1 launches that nearly ended SpaceX, and the “miracle” of Tesla surviving 2008.
He shares the thinking that guided him—building from first principles, doing useful things, and the belief that we’re in the middle of an intelligence big bang.
Chapters:
00:00 — Intro. 01:25 — His origin story. 02:00 — Dream to help build the internet. 04:40 — Zip2 and lessons learned. 08:00 — PayPal. 14:30 — Origin of SpaceX 18:30 — Building rockets from first principles. 23:50 — Lessons in leadership. 27:10 — Building up xAI 39:00 — Super intelligence and synthetic data. 39:30 — Multi-planetary future. 43:00 — Nueralink, AI safety and the singularity.
In 1912, astronomer Victor Hess discovered strange, high-energy particles known as “cosmic rays.” Since then, researchers have hunted for their birthplaces. Today, we know about some of the cosmic ray “launch pads”, ranging from the Sun and supernova explosions to black holes and distant active galactic nuclei. What astronomers are now searching for are sources of cosmic rays within the Milky Way Galaxy.
In a pair of presentations at the recent American Astronomical Society meeting, a team led by Michigan State University’s Zhuo Zhang, proposed an interesting place where cosmic rays originate: a pulsar wind nebula in our own Milky Way Galaxy. A pulsar is a rapidly rotating neutron star, formed as a result of a supernova explosion. High-energy particles and the neutron star’s strong magnetic field combine to interact with the nearby interstellar medium. The result is a pulsar wind nebula that can be detected across nearly the whole electromagnetic spectrum, particularly in X-rays. It makes sense that this object would be a source of cosmic rays. Pulsars are found throughout the Galaxy, which makes them a useful category in the search for cosmic ray engines in the Milky Way.
The Vela Pulsar is a good example of a pulsar wind nebula. The pulsar is at the center, and the surrounding cloudiness is the nebula. Courtesy NASA.
There are new hints that the fabric of space-time may be made of “memory cells” that record the whole history of the universe. If true, it could explain the nature of dark matter and much more
Binary neutron star mergers, cosmic collisions between two very dense stellar remnants made up predominantly of neutrons, have been the topic of numerous astrophysics studies due to their fascinating underlying physics and their possible cosmological outcomes. Most previous studies aimed at simulating and better understanding these events relied on computational methods designed to solve Einstein’s equations of general relativity under extreme conditions, such as those that would be present during neutron star mergers.
Researchers at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute), Yukawa Institute for Theoretical Physics, Chiba University, and Toho University recently performed the longest simulation of binary neutron star mergers to date, utilizing a framework for modeling the interactions between magnetic fields, high-density matter and neutrinos, known as the neutrino-radiation magnetohydrodynamics (MHD) framework.
Their simulation, outlined in Physical Review Letters, reveals the emergence of a magnetically dominated jet from the merger, followed by the collapse of the binary neutron star system into a black hole.
Back in 2019, the Event Horizon Telescope (EHT) team revealed the first-ever image of a supermassive black hole in the galaxy M87. In 2022, they followed up with the iconic image of Sagittarius A at the heart of the Milky Way. While these images were groundbreaking, the data behind them held even deeper insights that were hard to decode.
Neural Networks Meet Black Hole Physics
Previous studies by the EHT Collaboration used only a handful of realistic synthetic data files. Funded by the National Science Foundation (NSF) as part of the Partnership to Advance Throughput Computing (PATh) project, the Madison-based CHTC enabled the astronomers to feed millions of such data files into a so-called Bayesian neural network, which can quantify uncertainties. This allowed the researchers to make a much better comparison between the EHT data and the models.
