Lifeboat Foundation

In Einstein’s theory of general relativity, gravity arises when a massive object distorts the fabric of spacetime the way a ball sinks into a piece of stretched cloth. Solving Einstein’s equations by using quantities that apply across all space and time coordinates could enable physicists to eventually find their “white whale”: a quantum theory of gravity.

In a new article in The European Physical Journal H 0, Donald Salisbury from Austin College in Sherman, USA, explains how Peter Bergmann and Arthur Komar first proposed a way to get one step closer to this goal by using Hamilton-Jacobi techniques. These arose in the study of particle motion in order to obtain the complete set of solutions from a single function of particle position and constants of the motion.

Three of the four fundamental forces —strong, weak, and electromagnetic—hold under both the ordinary world of our everyday experience, modeled by classical physics, and the spooky world of quantum physics. Problems arise, though, when trying to apply to the fourth force, gravity, to the quantum world. In the 1960s and 1970s, Peter Bergmann of Syracuse University, New York and his associates recognized that in order to someday reconcile Einstein’s theory of general relativity with the quantum world, they needed to find quantities for determining events in space and time that applied across all frames of reference. They succeeded in doing this by using the Hamilton-Jacobi techniques.

Simulations of a quantum computer made of six rubidium atoms suggest it could run a simple brain-inspired algorithm that can learn to remember and make simple decisions.

The US has retaken the top spot in the world supercomputer rankings with the exascale Frontier system at Oak Ridge National Laboratory (ORNL) in Tennessee.

The Frontier system’s score of 1.102 exaflop/s makes it “the most powerful supercomputer to ever exist” and “the first true exascale machine,” the Top 500 project said Monday in the announcement of its latest rankings. Exaflop/s (or exaflops) is short for 1 quintillion floating-point operations per second.

Frontier was more than twice as fast as a Japanese system that placed second in the rankings, which are based on the LINPACK benchmark that measures the “performance of a dedicated system for solving a dense system of linear equations.”

Machine-learning researchers make many decisions when designing new models. They decide how many layers to include in neural networks and what weights to give inputs at each node. The result of all this human decision-making is that complex models end up being “designed by intuition” rather than systematically, says Frank Hutter, head of the machine-learning lab at the University of Freiburg in Germany.

A growing field called automated machine learning, or autoML, aims to eliminate the guesswork. The idea is to have algorithms take over the decisions that researchers currently have to make when designing models. Ultimately, these techniques could make machine learning more accessible.

The material of the future could make an imaginative concept of the past real.

Brief history of the space elevator

Continue reading “A New Method for Making Graphene has an Awesome Application: A Space Elevator!” »

Many say that human beings have destroyed our planet. Because of this these people are endeavoring to save it through the help of artificial intelligence. Famine, animal extinction, and war may all be preventable one day with the help of technology.

The Age of A.I. is a 8 part documentary series hosted by Robert Downey Jr. covering the ways Artificial Intelligence, Machine Learning and Neural Networks will change the world.

Continue reading “Saving the world one algorithm at a time | The Age of A.I.” »

When users want to send data over the internet faster than the network can handle, congestion can occur—the same way traffic congestion snarls the morning commute into a big city.

Computers and devices that transmit data over the internet break the data down into smaller packets and use a special algorithm to decide how fast to send those packets. These congestion control algorithms seek to fully discover and utilize available network capacity while sharing it fairly with other users who may be sharing the same network. These algorithms try to minimize delay caused by data waiting in queues in the network.

Over the past decade, researchers in industry and academia have developed several algorithms that attempt to achieve high rates while controlling delays. Some of these, such as the BBR algorithm developed by Google, are now widely used by many websites and applications.

It took researchers about 62 minutes to crack a late-stage Post-Quantum Encryption candidate algorithm using a single-core CPU.

Multivariable calculus, differential equations, linear algebra—topics that many MIT students can ace without breaking a sweat—have consistently stumped machine learning models. The best models have only been able to answer elementary or high school-level math questions, and they don’t always find the correct solutions.

Now, a multidisciplinary team of researchers from MIT and elsewhere, led by Iddo Drori, a lecturer in the MIT Department of Electrical Engineering and Computer Science (EECS), has used a neural network model to solve university-level math problems in a few seconds at a human level.

The model also automatically explains solutions and rapidly generates new problems in university math subjects. When the researchers showed these machine-generated questions to university students, the students were unable to tell whether the questions were generated by an algorithm or a human.