Lifeboat Foundation

The Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) is home to many interdisciplinary projects which benefit from the synergy of a wide range of expertise available at the institute. One such project is the study of black holes that could have formed in the early universe, before stars and galaxies were born.

Such primordial black holes (PBHs) could account for all or part of dark matter, be responsible for some of the observed gravitational waves signals, and seed supermassive black holes found in the center of our Galaxy and other galaxies. They could also play a role in the synthesis of heavy elements when they collide with neutron stars and destroy them, releasing neutron-rich material.

In particular, there is an exciting possibility that the mysterious dark matter, which accounts for most of the matter in the universe, is composed of primordial black holes. The 2020 Nobel Prize in physics was awarded to a theorist, Roger Penrose, and two astronomers, Reinhard Genzel and Andrea Ghez, for their discoveries that confirmed the existence of black holes. Since black holes are known to exist in nature, they make a very appealing candidate for dark matter.

Nothing to see here-just our future robot overlords dancing to retro PMJ tracks before they take over the world.

En un video, Boston Dynamics presumió el avance que ha alcanzado con sus robots al ejecutar tareas, antes limitadas a los humanos.

Quantum computer: One of the obstacles for progress in the quest for a working quantum computer has been that the working devices that go into a quantum computer and perform the actual calculations, the qubits, have hitherto been made by universities and in small numbers. But in recent years, a pan-European collaboration, in partnership with French microelectronics leader CEA-Leti, has been exploring everyday transistors—that are present in billions in all our mobile phones—for their use as qubits. The French company Leti makes giant wafers full of devices, and, after measuring, researchers at the Niels Bohr Institute, University of Copenhagen, have found these industrially produced devices to be suitable as a qubit platform capable of moving to the second dimension, a significant step for a working quantum computer. The result is now published in Nature Communications.

Quantum dots in two dimensional array is a leap ahead

One of the key features of the devices is the two-dimensional array of quantum dots. Or more precisely, a two by two lattice of quantum dots. “What we have shown is that we can realize single electron control in every single one of these quantum dots. This is very important for the development of a qubit, because one of the possible ways of making qubits is to use the spin of a single electron. So reaching this goal of controlling the single electrons and doing it in a 2-D array of quantum dots was very important for us”, says Fabio Ansaloni, former Ph.D. student, now postdoc at center for Quantum Devices, NBI.

The head scientist for Alexa thinks the old benchmark for computing is no longer relevant for today’s AI era.

A multitasking nanomachine that can act as a heat engine and a refrigerator at the same time has been created by RIKEN engineers. The device is one of the first to test how quantum effects, which govern the behavior of particles on the smallest scale, might one day be exploited to enhance the performance of nanotechnologies.

Conventional heat engines and refrigerators work by connecting two pools of fluid. Compressing one pool causes its fluid to heat up, while rapidly expanding the other pool cools its fluid. If these operations are done in a periodic cycle, the pools will exchange energy and the system can be used as either a heat engine or a fridge.

It would be impossible to set up a macroscale machine that does both tasks simultaneously—nor would engineers want to, says Keiji Ono of the RIKEN Advanced Device Laboratory. “Combining a traditional heat engine with a refrigerator would make it a completely useless machine,” he says. “It wouldn’t know what to do.”

Japanese company Sumitomo Forestry has announced a joint development project with Kyoto University to test the idea of using wood as a component in satellite construction. As part of the announcement, officials with Sumitomo Forestry told reporters that work on the project will begin with experiments designed to test different types of wood in extreme environments.

Some of the major components in most satellites include aluminum, Kevlar and aluminum alloys, which are able to withstand both temperature extremes and constant bombardment by radiation—all in a vacuum. Unfortunately, these characteristics also allow satellites to remain in orbit long after their usefulness has ended, resulting in constant additions to the space junk orbiting the planet. According to the World Economic Forum, there are currently approximately 6000 satellites circling the Earth but only 60% of them are still in use. Some in the field have predicted that nearly 1000 satellites will be launched into space each year over the coming decade. Considering their lifespan, this suggests there could be thousands more dead satellites orbiting the planet in the coming years. This space debris poses a significant threat to other satellites (they all travel thousands of miles per hour) and also to manned space missions.

Albert Einstein once said, “You have to learn the rules of the game, and then you have to play better than anyone else.” That could well be the motto at DeepMind, as a new report reveals it has developed a program that can master complex games without even knowing the rules.

DeepMind, a subsidiary of Alphabet, has previously made groundbreaking strides using reinforcement learning to teach programs to master the Chinese board game Go and the Japanese strategy game Shogi, as well as chess and challenging Atari video games. In all those instances, computers were given the rules of the game.

But Nature reported today that DeepMind’s MuZero has accomplished the same feats—and in some instances, beat the earlier programs—without first learning the rules.

Over the past few years, artificial intelligence (AI) tools, particularly deep neural networks, have achieved remarkable results on a number of tasks. However, recent studies have found that these computational techniques have a number of limitations. In a recent paper published in Nature Machine Intelligence, researchers at Tübingen and Toronto universities explored and discussed a problem known as ‘shortcut learning’ that appears to underpin many of the shortcomings of deep neural networks identified in recent years.

“I decided to start working on this project during a science-related travel in the U.S., together with Claudio Michaelis, a dear colleague and friend of mine,” Robert Geirhos, one of the researchers who carried out the study, told TechXplore. “We first attended a deep learning conference, then visited an animal research laboratory, and finally, a human vision conference. Somewhat surprisingly, we noticed the very same pattern in very different settings: ‘shortcut learning,’ or ‘cheating,’ appeared to be a common characteristic across both artificial and biological intelligence.”

Geirhos and Michaelis believed that shortcut learning, the phenomenon they observed, could explain the discrepancy between the excellent performance and iconic failures of many deep neural networks. To investigate this idea further, they teamed up with other colleagues, including Jörn-Henrik Jacobsen, Richard Zemel, Wieland Brendel, Matthias Bethge and Felix Wichmann.