Lifeboat Foundation

The scientific world has long acknowledged that proving mathematical theorems is an essential first step in developing artificial intelligence. To prove the truth or falsity of a conjecture, one must use symbolic thinking and sort through an unlimited number of alternatives. These tasks are beyond the capabilities of even the most sophisticated AI systems.

The state of the art in artificial intelligence today is to create machines that can “solve at once” or come up with a whole answer to a problem in a single go. However, this is not how most individuals approach difficult situations. Mathematical reasoning is significantly more challenging to formalize and measure.

Meta AI has made an important development at the intersection of artificial intelligence and mathematics. The neural theorem prover developed by the team has completed five times as many IMO problems as any other AI system before it, totaling ten. Concerning miniF2F, a popular mathematics test, the AI model outperforms the state of art by 20% and outperforms Metamath by 10%.

For more than half a century, researchers around the world have been struggling with an algorithmic problem known as “the single source shortest path problem.” The problem is essentially about how to devise a mathematical recipe that best finds the shortest route between a node and all other nodes in a network, where there may be connections with negative weights.

Sound complicated? Possibly. But in fact, this type of calculation is already used in a wide range of the apps and technologies that we depend upon for finding our ways around—as Google Maps guides us across landscapes and through cities, for example.

Now, researchers from the University of Copenhagen’s Department of Computer Science have succeeded in solving the single source shortest path problem, a riddle that has stumped researchers and experts for decades.

After shocking the mathematics community with a major result in 2013, Yitang Zhang now says he has solved an analogue of the celebrated Riemann hypothesis.

Spin glasses are alloys formed by noble metals in which a small amount of iron is dissolved. Although they do not exist in nature and have few applications, they have nevertheless been the focus of interest of statistical physicists for some 50 years. Studies of spin glasses were crucial for Giorgio Parisi’s 2021 Nobel Prize in Physics.

The scientific interest of spin glasses lies in the fact that they are an example of a complex system whose elements interact with each other in a way that is sometimes cooperative and sometimes adversarial. The mathematics developed to understand their behavior can be applied to problems arising in a variety of disciplines, from ecology to machine learning, not to mention economics.

Spin glasses are magnetic systems, that is, systems in which individual elements, the spins, behave like small magnets. Their peculiarity is the co-presence of ferromagnetic-type bonds, which tend to align the spins, with antiferromagnetic-type bonds, which tend to orient them in opposite directions.

In 1994, the computer scientist Peter Shor discovered that if quantum computers were ever invented, they would decimate much of the infrastructure used to protect information shared online. That frightening possibility has had researchers scrambling to produce new, “post-quantum” encryption schemes, to save as much information as they could from falling into the hands of quantum hackers.

Earlier this year, the National Institute of Standards and Technology revealed four finalists in its search for a post-quantum cryptography standard. Three of them use “lattice cryptography” — a scheme inspired by lattices, regular arrangements of dots in space.

Lattice cryptography and other post-quantum possibilities differ from current standards in crucial ways. But they all rely on mathematical asymmetry. The security of many current cryptography systems is based on multiplication and factoring: Any computer can quickly multiply two numbers, but it could take centuries to factor a cryptographically large number into its prime constituents. That asymmetry makes secrets easy to encode but hard to decode.

Like most physicists, I spent much of my career ignoring the majority of quantum mechanics. I was taught the theory in graduate school and applied the mechanics here and there when an interesting problem required it … and that’s about it.

Despite its fearsome reputation, the mathematics of quantum theory is actually rather straightforward. Once you get used to the ins and outs, it’s simpler to solve a wide variety of problems in quantum mechanics than it is in, say, general relativity. And that ease of computation—and the confidence that goes along with wielding the theory—mask most of the deeper issues that hide below the surface.

Deeper issues like the fact that quantum mechanics doesn’t make any sense. Yes, it’s one of the most successful (if not the most successful) theories in all of science. And yes, a typical high school education will give you all the mathematical tools you need to introduce yourself to its inner workings. And yes, for over a century we have failed to come up with an alternative theory of the subatomic universe. Those are all true statements, and yet: Quantum mechanics doesn’t make any sense.

With mathematical modeling, a research team has now succeeded in better understanding how the optimal working state of the human brain, called criticality, is achieved. Their results mean an important step toward biologically-inspired information processing and new, highly efficient computer technologies and have been published in Scientific Reports.

“In particular tasks, supercomputers are better than humans, for example in the field of artificial intelligence. But they can’t manage the variety of tasks in everyday life —driving a car first, then making music and telling a story at a get-together in the evening,” explains Hermann Kohlstedt, professor of nanoelectronics. Moreover, today’s computers and smartphones still consume an enormous amount of energy.

“These are no sustainable technologies—while our brain consumes just 25 watts in everyday life,” Kohlstedt continues. The aim of their interdisciplinary research network, “Neurotronics: Bio-inspired Information Pathways,” is therefore to develop new electronic components for more energy-efficient computer architectures. For this purpose, the alliance of engineering, life and natural sciences investigates how the human brain is working and how that has developed.

An in-depth survey of the various technologies for spaceship propulsion, both from those we can expect to see in a few years and those at the edge of theoretical science. We’ll break them down to basics and familiarize ourselves with the concepts.
Note: I made a rather large math error about the Force per Power the EmDrive exerts at 32:10, initial tentative results for thrust are a good deal higher than I calculated compared to a flashlight.

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His work will be published soon.

Shanghai-born Zhang Yitang is a professor of mathematics at the University of California, Santa Barbara. If a 111-page manuscript allegedly written by him passes peer review, he might become the first person to solve the Riemann hypothesis, The South China Morning Post (SCMP)

The Riemann hypothesis is a 150-year-old puzzle that is considered by the community to be the holy grail of mathematics. Published in 1,859, it is a fascinating piece of mathematical conjecture around prime numbers and how they can be predicted.

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