Lifeboat Foundation

Gas clouds across the universe are known to absorb the light produced by distant massive celestial objects, known as quasars. This light manifests as the so-called Lyman alpha forest, a dense structure composed of absorption lines that can be observed using spectroscopy tools.

Over the past decades, astrophysicists have been assessing the value of these absorption lines as a tool to better understand the universe and the relationships between cosmological objects. The Lyman alpha forest could also potentially aid the ongoing search for dark matter, offering an additional tool to test theoretical predictions and models.

Researchers at University of Nottingham, Tel-Aviv University, New York University, and the Institute for Fundamental Physics of the Universe in Trieste have recently compared low-redshift Lyman alpha forest observations to hydrodynamical simulations of the intergalactic medium and dark matter made up of dark photons, a renowned dark matter candidate.

Perception generally feels effortless. If you hear a bird chirping and look out the window, it hardly feels like your brain has done anything at all when you recognize that chirping critter on your windowsill as a bird.

In fact, research in Psychological Science suggests that these kinds of audio cues can not only help us to recognize objects more quickly but can even alter our visual perception. That is, pair birdsong with a bird and we see a bird—but replace that birdsong with a squirrel’s chatter, and we’re not quite so sure what we’re looking at.

“Your brain spends a significant amount of energy to process the sensory information in the world and to give you that feeling of a full and seamless perception,” said lead author Jamal R. Williams (University of California, San Diego) in an interview. “One way that it does this is by making inferences about what sorts of information should be expected.”

Researchers at Tohoku University, the University of Messina, and the University of California, Santa Barbara (UCSB) have developed a scaled-up version of a probabilistic computer (p-computer) with stochastic spintronic devices that is suitable for hard computational problems like combinatorial optimization and machine learning.

Moore’s law predicts that computers get faster every two years because of the evolution of semiconductor chips. While this is what has historically happened, the continued evolution is starting to lag. The revolutions in machine learning and artificial intelligence means much higher computational ability is required. Quantum computing is one way of meeting these challenges, but significant hurdles to the practical realization of scalable quantum computers remain.

A p-computer harnesses naturally stochastic building blocks called probabilistic bits (p-bits). Unlike bits in traditional computers, p-bits oscillate between states. A p-computer can operate at room-temperature and acts as a domain-specific computer for a wide variety of applications in machine learning and artificial intelligence. Just like quantum computers try to solve inherently quantum problems in quantum chemistry, p-computers attempt to tackle probabilistic algorithms, widely used for complicated computational problems in combinatorial optimization and sampling.

Researchers have used a quantum processor to make microwave photons uncharacteristically sticky. They coaxed them to clump together into bound states, then found that these photon clusters survived in a regime where they were expected to dissolve into their usual, solitary states. The discovery was first made on a quantum processor, marking the growing role that these platforms are playing in studying quantum dynamics.

Photons—quantum packets of electromagnetic radiation like light or microwaves—typically don’t interact with one another. Two crossed flashlight beams, for example, pass through one another undisturbed. But in an array of superconducting qubits, microwave photons can be made to interact.

In “Formation of robust bound states of interacting photons,” published today in Nature, researchers at Google Quantum AI describe how they engineered this unusual situation. They studied a ring of 24 superconducting qubits that could host microwave photons. By applying quantum gates to pairs of neighboring qubits, photons could travel around by hopping between neighboring sites and interacting with nearby photons.

Good Morning, 2033 — A Sci-Fi Short Film.

What will your average morning look like in 2033? And who hacked us?

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A grand vision in robot learning, going back to the SHRDLU experiments in the late 1960s, is that of helpful robots that inhabit human spaces and follow a wide variety of natural language commands. Over the last few years, there have been significant advances in the application of machine learning (ML) for instruction following, both in simulation and in real world systems. Recent Palm-SayCan work has produced robots that leverage language models to plan long-horizon behaviors and reason about abstract goals. Code as Policies has shown that code-generating language models combined with pre-trained perception systems can produce language conditioned policies for zero shot robot manipulation. Despite this progress, an important missing property of current “language in, actions out” robot learning systems is real time interaction with humans.

Ideally, robots of the future would react in real time to any relevant task a user could describe in natural language. Particularly in open human environments, it may be important for end users to customize robot behavior as it is happening, offering quick corrections (“stop, move your arm up a bit”) or specifying constraints (“nudge that slowly to the right”). Furthermore, real-time language could make it easier for people and robots to collaborate on complex, long-horizon tasks, with people iteratively and interactively guiding robot manipulation with occasional language feedback.

Women are playing a significant role in the field of Artificial Intelligence. Take a look at the well-known women personalities in the AI field here.

How far away could an artificial brain be? Perhaps a very long way off still, but a working analogue to the essential element of the brain’s networks, the synapse, appears closer at hand now.

That’s because a device that draws inspiration from batteries now appears surprisingly well suited to run artificial neural networks. Called electrochemical RAM (ECRAM), it is giving traditional transistor-based AI an unexpected run for its money—and is quickly moving toward the head of the pack in the race to develop the perfect artificial synapse. Researchers recently reported a string of advances at this week’s IEEE International Electron Device Meeting (IEDM 2022) and elsewhere, including ECRAM devices that use less energy, hold memory longer, and take up less space.

The artificial neural networks that power today’s machine-learning algorithms are software that models a large collection of electronics-based “neurons,” along with their many connections, or synapses. Instead of representing neural networks in software, researchers think that faster, more energy-efficient AI would result from representing the components, especially the synapses, with real devices. This concept, called analog AI, requires a memory cell that combines a whole slew of difficult-to-obtain properties: it needs to hold a large enough range of analog values, switch between different values reliably and quickly, hold its value for a long time, and be amenable to manufacturing at scale.

In 1916, Einstein finished his Theory of General Relativity, which describes how gravitational forces alter the curvature of spacetime. Among other things, this theory predicted that the Universe is expanding, which was confirmed by the observations of Edwin Hubble in 1929. Since then, astronomers have looked farther into space (and hence, back in time) to measure how fast the Universe is expanding – aka. the Hubble Constant. These measurements have become increasingly accurate thanks to the discovery of the Cosmic Microwave Background (CMB) and observatories like the Hubble Space Telescope.

Astronomers have traditionally done this in two ways: directly measuring it locally (using variable stars and supernovae) and indirectly based on redshift measurements of the CMB and cosmological models. Unfortunately, these two methods have produced different values over the past decade. As a result, astronomers have been looking for a possible solution to this problem, known as the “Hubble Tension.” According to a new paper by a team of astrophysicists, the existence of “Early Dark Energy” may be the solution cosmologists have been looking for.

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