Lifeboat Foundation

Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company’s AIs used to beat human players in games such as Go and chess.

The AI discovered algorithms that break decades-old records for computational efficiency, and the team’s findings, published on 5 October in Nature¹, could open up new paths to faster computing in some fields.

“It is very impressive,” says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. “This work demonstrates the potential of using machine learning for solving hard mathematical problems.”

Quantum money underpinned by the mathematics of knots could be impossible to forge.

Since the discovery of genetics, people have dreamed of being able to correct diseases, select traits in children before birth, and build better human beings. Naturally, many serious technical and ethical questions surround this endeavor. Luckily, tonights’ guest is as good a guide as we could hope to have.

Dr. Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. He has done extensive research in the field of computational genomics, and is the founder of several startups.

Continue reading “Ep. 102: Genetic engineering and the biological basis of intelligence. | Steven Hsu” »

Back-of-the-envelope math suggests interstellar probes get faster every year.

International research centre led by Jain 108 that offers a global curriculum of Sacred Geometry, Philosophical Geometry and Pyramid Mathematics. Online Courses presented by Jain 108 now available.

Without satellite navigation, rockets can’t navigate to the moon without ground control, which is costly, cumbersome, and requires a lot of math.

In mathematical physics, a closed timelike curve (CTC) is a world line in a Lorentzian manifold, of a material particle in spacetime, that is “closed”, returning to its starting point. This possibility was first discovered by Willem Jacob van Stockum in 1937^[1] and later confirmed by Kurt Gödel in 1949,^[2] who discovered a solution to the equations of general relativity (GR) allowing CTCs known as the Gödel metric; and since then other GR solutions containing CTCs have been found, such as the Tipler cylinder and traversable wormholes.

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What is the Drake Equation? We are talking about The Odds of ALIEN LIFE.
Is there life out there in the Universe?
How are the chances to find Extraterrestrial life?

Continue reading “What Are The Odds Of Alien Life? The Drake Equation” »

Greg Yang is a mathematician and AI researcher at Microsoft Research who for the past several years has done incredibly original theoretical work in the understanding of large artificial neural networks. Greg received his bachelors in mathematics from Harvard University in 2018 and while there won the Hoopes prize for best undergraduate thesis. He also received an Honorable Mention for the Morgan Prize for Outstanding Research in Mathematics by an Undergraduate Student in 2018 and was an invited speaker at the International Congress of Chinese Mathematicians in 2019.

In this episode, we get a sample of Greg’s work, which goes under the name “Tensor Programs” and currently spans five highly technical papers. The route chosen to compress Tensor Programs into the scope of a conversational video is to place its main concepts under the umbrella of one larger, central, and time-tested idea: that of taking a large N limit. This occurs most famously in the Law of Large Numbers and the Central Limit Theorem, which then play a fundamental role in the branch of mathematics known as Random Matrix Theory (RMT). We review this foundational material and then show how Tensor Programs (TP) generalizes this classical work, offering new proofs of RMT. We conclude with the applications of Tensor Programs to a (rare!) rigorous theory of neural networks.

Continue reading “Greg Yang | Large N Limits: Random Matrices & Neural Networks | The Cartesian Cafe w/ Timothy Nguyen” »