Lifeboat Foundation

Zero-knowledge proof (ZKP) is a cryptographic tool that allows for the verification of validity between mutually untrusted parties without disclosing additional information. Non-interactive zero-knowledge proof (NIZKP) is a variant of ZKP with the feature of not requiring multiple information exchanges. Therefore, NIZKP is widely used in the fields of digital signature, blockchain, and identity authentication.

Since it is difficult to implement a true random number generator, deterministic pseudorandom number algorithms are often used as a substitute. However, this method has potential security vulnerabilities. Therefore, how to obtain true random numbers has become the key to improving the security of NIZKP.

In a study published in PNAS, a research team led by Prof. Pan Jianwei and Prof. Zhang Qiang from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, and the collaborators, realized a set of random number beacon public services with device-independent quantum random number generators as entropy sources and post-quantum cryptography as identity authentication.

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

Continue reading “Quantum Advantage: A Physicist Explains The Future of Computers” »

Japan’s Nippon Telegraph and Telephone Corporation (NTT) is applying its Deep Anomaly Surveillance (DeAnoS) artificial intelligence tool, originally designed for telecom networks, to predict anomalies in nuclear fusion reactors.

DeAnoS is like a detective, trying to understand which part of the equation is making things weird.

Atomic fusion reactors are at the forefront of scientific innovation, harnessing the enormous energy released by atomic nuclei fusion. This process, which is similar to the Sun’s power source, involves the union of two light atomic nuclei, which results in the development of a heavier nucleus and the release of a massive quantity of energy.

DeepMind’s new machine learning algorithm takes less than a minute to make its forecasts and can run on a desktop. But it won’t replace traditional forecasts anytime soon.

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In the grand theater of the cosmos, amidst a myriad of distant suns and ancient galaxies, the Fermi Paradox presents a haunting silence, where a cacophony of alien conversations should exist. Where is Everyone? Or are we alone?

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Continue reading “The Fermi Paradox Compendium of Solutions & Terms” »

I’ve posted a number of times about artificial intelligence, mind uploading, and various related topics. There are a number of things that can come up in the resulting discussions, one of them being Kurt Gödel’s incompleteness theorems.

The typical line of arguments goes something like this: Gödel implies that there are solutions that no algorithmic system can accomplish but that humans can accomplish, therefore the computational theory of mind is wrong, artificial general intelligence is impossible, and animal, or at least human minds require some as of yet unknown physics, most likely having something to do with the quantum wave function collapse (since that remains an intractable mystery in physics).

Continue reading “Gödel’s incompleteness theorems don’t rule out artificial intelligence” »

Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that were previously thought to be limited to human intelligence. AI is changing the way humans approach problems and bringing revolutionary transformations and solutions to almost every industry. Teaching machines to learn from massive amounts of data and make decisions or predictions based on that learning is the basic idea behind AI. Its application in scientific endeavors has given rise to some amazing tools that are gaining massive popularity in the AI community.

In Artificial Intelligence, Symbolic Regression has been playing an important role in the subtleties of scientific research. It basically focuses on algorithms that allow machines to interpret complicated patterns and correlations found in datasets by automating the search for analytic expressions. Scientists and researchers have been putting in efforts to explore the possible uses of Symbolic Regression.

Diving into the field of Symbolic Regression, a team of researchers has recently introduced Φ-SO, a Physical Symbolic Optimization framework. This method navigates the complexities of physics, where the presence of units is crucial. It automates the process of finding analytic expressions fitting complex datasets.

The tests assessed the use of AI-based navigation sensors and enhanced algorithms for autonomous formation flight.

Airbus.

Continue reading “Airbus tests autonomous air-to-air refueling with AI-guided drones” »

Open-source supercomputer algorithm predicts patterning and dynamics of living materials and enables studying their behavior in space and time.

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called “active matter.” The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Scientists from the Max Planck Institute of Molecular Cell.