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Archive for the ‘mathematics’ category: Page 64

Dec 5, 2022

How Features of Our Consciousness Seem to Define Our Laws of Physics and Maths (Stephen Wolfram)

Posted by in categories: computing, mathematics, neuroscience, physics

Our Wolfram Physics Project has provided a surprisingly successful picture of the underlying (deeply computational) structure of our physical universe. I’ll talk here about how our perception of that underlying structure is determined by what seem to be key features of our consciousness—and how this leads to detailed laws of physics as we experience them. Our Physics Project has led to the concept of the ruliad—the entangled limit of all possible computations—which seems to represent a common underlying structure from which both physics and mathematics emerge. I’ll talk about the comparison between physical and mathematical observers, and how their common features in consciousness lead to implications for general laws of “bulk mathematics”.

Dec 5, 2022

Stephen Wolfram on the Wolfram Physics TOE, Blackholes, Infinity, and Consciousness

Posted by in categories: alien life, cryptocurrencies, economics, information science, mathematics, particle physics, robotics/AI

Stephen Wolfram is at his jovial peak in this technical interview regarding the Wolfram Physics project (theory of everything).
Sponsors: https://brilliant.org/TOE for 20% off. http://algo.com for supply chain AI.

Link to the Wolfram project: https://www.wolframphysics.org/

Continue reading “Stephen Wolfram on the Wolfram Physics TOE, Blackholes, Infinity, and Consciousness” »

Dec 3, 2022

‘Infinity Computer’ Calculates Area Of Sierpinski Carpet Exactly

Posted by in categories: computing, mathematics

Year 2012 😗


A Sierpinksi carpet is one of the more famous fractal objects in mathematics. Creating one is an iterative procedure. Start with a square, divide it into nine equal squares and remove the central one. That leaves eight squares around a central square hole. In the next iteration, repeat this process with each of the eight remaining squares and so on (see above). One interesting problem is to find the area of a Sierpinski triangle. Clearly this changes with each iteration. Assuming the original square has area equal to 1, the area after the first iteration is 8/9. After the second iteration, it is (8÷9)^2; after the third it is (8÷9)^3 and so on.

Nov 28, 2022

Predicting the Structures of Proteins

Posted by in categories: bioengineering, biological, mathematics, physics, robotics/AI

Kathryn Tunyasuvunakool grew up surrounded by scientific activities carried out at home by her mother—who went to university a few years after Tunyasuvunakool was born. One day a pendulum hung from a ceiling in her family’s home, Tunyasuvunakool’s mother standing next to it, timing the swings for a science assignment. Another day, fossil samples littered the dining table, her mother scrutinizing their patterns for a report. This early exposure to science imbued Tunyasuvunakool with the idea that science was fun and that having a career in science was an attainable goal. “From early on I was desperate to go to university and be a scientist,” she says.

Tunyasuvunakool fulfilled that ambition, studying math as an undergraduate, and computational biology as a graduate student. During her PhD work she helped create a model that captured various elements of the development of a soil-inhabiting roundworm called Caenorhabditis elegans, a popular organism for both biologists and physicists to study. She also developed a love for programming, which, she says, lent itself naturally to a jump into software engineering. Today Tunyasuvunakool is part of the team behind DeepMind’s AlphaFold—a protein-structure-prediction tool. Physics Magazine spoke to her to find out more about this software, which recently won two of its makers a Breakthrough Prize, and about why she’s excited for the potential discoveries it could enable.

All interviews are edited for brevity and clarity.

Nov 26, 2022

Mathematics and sex | Clio Cresswell | TEDxSydney

Posted by in categories: evolution, mathematics, neuroscience, sex

Never miss a talk! SUBSCRIBE to the TEDx channel: http://bit.ly/1FAg8hB

Mathematics and sex are deeply intertwined. From using mathematics to reveal patterns in our sex lives, to using sex to prime our brain for certain types of problems, to understanding them both in terms of the evolutionary roots of our brain, Dr Clio Cresswell shares her insight into it all.

Continue reading “Mathematics and sex | Clio Cresswell | TEDxSydney” »

Nov 24, 2022

A New Sound Levitation Breakthrough

Posted by in categories: biological, mathematics, particle physics

Sound waves, like an invisible pair of tweezers, can be used to levitate small objects in the air. Although DIY acoustic levitation kits are readily available online, the technology has important applications in both research and industry, including the manipulation of delicate materials like biological cells.

Researchers at the University of Technology Sydney (UTS) and the University of New South Wales (UNSW) have recently demonstrated that in order to precisely control a particle using ultrasonic waves, it is necessary to take into account both the shape of the particle and how this affects the acoustic field. Their findings were recently published in the journal Physical Review Letters.

Sound levitation happens when sound waves interact and form a standing wave with nodes that can ‘trap’ a particle. Gorkov’s core theory of acoustophoresis, the current mathematical foundation for acoustic levitation, makes the assumption that the particle being trapped is a sphere.

Nov 24, 2022

Decades-old math theorem cracks US government encryption algorithm

Posted by in categories: computing, encryption, government, information science, mathematics, quantum physics, security

The information security landscape is rapidly changing in response to quantum computing technology, which is capable of cracking modern encryption techniques in minutes, but a promising US government encryption algorithm for the post-quantum world was just cracked in less than an hour thanks to a decades-old math theorem.

In July 2022, the US National Institute of Standards and Technology (NIST) chose a set of encryption algorithms that it hoped would stand up to the encryption-cracking power of quantum computers and tasked researchers with probing them for vulnerabilities, offering a $50,000 prize for anyone who was able to break the encryption.

Nov 24, 2022

Using math to decode biology’s programming language

Posted by in categories: biological, mathematics, media & arts

There is a deep root of mathematics within biology. How this came to be, you’ll have to watch the video to find out.

Created by Prompt Suathim (2nd year undergrad, Integrated Science, UBC)

Continue reading “Using math to decode biology’s programming language” »

Nov 23, 2022

AI Reveals New Possibilities in Matrix Multiplication

Posted by in categories: mathematics, robotics/AI

Inspired by the results of a game-playing neural network, mathematicians have been making unexpected advances on an age-old math problem.

Nov 21, 2022

Researchers at MIT Solve a Differential Equation Behind the Interaction of Two Neurons Through Synapses to Unlock a New Type of Speedy and Efficient Artificial Intelligence AI Algorithm

Posted by in categories: information science, mathematics, robotics/AI

Continuous-time neural networks are one subset of machine learning systems capable of taking on representation learning for spatiotemporal decision-making tasks. Continuous differential equations are frequently used to depict these models (DEs). Numerical DE solvers, however, limit their expressive potential when used on computers. The scaling and understanding of many natural physical processes, like the dynamics of neural systems, have been severely hampered by this restriction.

Inspired by the brains of microscopic creatures, MIT researchers have developed “liquid” neural networks, a fluid, robust ML model that can learn and adapt to changing situations. These methods can be used in safety-critical tasks such as driving and flying.

However, as the number of neurons and synapses in the model grows, the underlying mathematics becomes more difficult to solve, and the processing cost of the model rises.

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