Lifeboat Foundation

New research from the University of Kent has demonstrated that quantum information could eventually be used to coordinate the actions of devices that can move, such as drones or autonomous vehicles. This could lead to more efficient logistics, which could make deliveries cheaper, and better use of limited bandwidth for the likes of self-driving cars.

By carrying out “real world” experiments on a quantum computer, the team of quantum physicists (led by Ph.D. student Josh Tucker in the University of Kent’s School of Physics and Astronomy), found that if the two devices share a pair of quantum coins (qubits), the devices can continue to influence each other even after they have been separated and can no longer communicate.

The experiments simulated the phenomenon using real qubits inside a quantum computer developed by IBM. The qubits are made of superconducting material and kept at temperatures colder than the interstellar void. This allows them to behave according to the laws of quantum physics that defy common sense—including the ability to influence each other without coming into contact and without sending signals.

A new study demonstrates that even simple single-cell organisms, such as ciliates and amoebae, exhibit habituation, a basic form of learning previously thought to be exclusive to more complex beings.

This revelation not only changes our understanding of cellular capabilities but also opens up possibilities for applications in cancer immunology, suggesting that our immune cells might be reprogrammed to better recognize and attack cancer cells.

A dog learns to sit on command. A person tunes out the steady hum of a washing machine while engrossed in a book. The ability to learn and adapt is a cornerstone of evolution and survival.

Scientists have developed a novel technique using high-energy particle collisions at the Relativistic Heavy Ion Collider (RHIC), a U.S. Department of Energy (DOE) Office of Science user facility for nuclear physics research located at DOE’s Brookhaven National Laboratory. Detailed in a newly published paper in Nature, this method complements lower-energy approaches for studying nuclear structure. It offers deeper insights into the shapes of atomic nuclei, enhancing our understanding of the building blocks of visible matter.

“In this new measurement, we not only quantify the overall shape of the nucleus — whether it’s elongated like a football or squashed down like a tangerine — but also the subtle triaxiality, the relative differences among its three principle axes that characterize a shape in between the ‘football’ and ‘tangerine,’” said Jiangyong Jia, a professor at Stony Brook University (SBU) who has a joint appointment at Brookhaven Lab and is one of the principal authors on the STAR Collaboration publication.

Deciphering nuclear shapes has relevance to a wide range of physics questions, including which atoms are most likely to split in nuclear fission, how heavy atomic elements form in collisions of neutron stars, and which nuclei could point the way to exotic particle decay discoveries. Leveraging improved knowledge of nuclear shapes will also deepen scientists’ understanding of the initial conditions of a particle soup that mimics the early universe, which is created in RHIC’s energetic particle smashups. The method can be applied to analyzing additional data from RHIC as well as data collected from nuclear collisions at Europe’s Large Hadron Collider (LHC). It will also have relevance to future explorations of nuclei at the Electron-Ion Collider, a nuclear physics facility in the design stage at Brookhaven Lab.

Researchers have linked the origins of fast radio bursts to magnetars, highly magnetized neutron stars, which often arise from the mergers of massive stars in star-forming galaxies.

By utilizing the Deep Synoptic Array-110, they’ve localized 70 FRBs, discovering that these bursts are more frequent in massive, metal-rich galaxies. This suggests that the environmental conditions conducive to FRB occurrence are also ideal for magnetar formation.

Unveiling the mystery of fast radio bursts.

Amid heated debates about the potential pitfills of artificial intelligence, the technology has finally taken a form we can probably all get behind — an “AI granny” created expressly to waste scammers’ time.

British telecom company Virgin Media O₂ on Thursday introduced Daisy, a custom-made human-like chabot that answers calls in real time, keeping fraudsters on the phone as long as possible in a bid to annoy and frustrate them, just as they do to consumers worldwide. Daisy (that’s “dAIsy”) automates the practice of “scambaiting,” which involves people posing as potential victims to squander scammers’ time and resources, publicly expose their wily ways, gather information useful to law enforcement and even confuse the con artists’ devices.

Daisy, newly dubbed O2’s “head of scammer relations,” impersonates an older adult, making her part of a demographic that’s particularly vulnerable to scams. Unlike human scambaiters who need to sleep and shower once in a while, Daisy can spend all day and night on the phone with swindlers. “While they’re busy talking to me they can’t be scamming you, and let’s face it, dear, I’ve got all the time in the world,” Daisy says in the introductory video from O₂ embedded below. The video personifies her as a photorealistic AI-generated woman with gray hair, glasses and pearls talking on a pink landline.

A team of astrophysicists, led by our Institute for Computational Cosmology, have developed a new model that could estimate how likely it is for intelligent life to emerge in our Universe and beyond.

In the 1960s, American astronomer Dr Frank Drake came up with an equation to calculate the number of detectable extraterrestrial civilisations in our Milky Way galaxy.

More than 60 years on, researchers at Durham, the University of Edinburgh and the Université de Genève, have produced a new model based on the conditions created by the acceleration of the Universe’s expansion and the amount of stars formed instead.

Join us at ploutos.dev.

#AI #topology #language #computation #neuroscience

Researchers have long observed that neurons in the brain tend to be organized in clusters, with neighboring neurons often sharing similar functions. This phenomenon is also seen in the brain’s language system, where certain areas respond to different aspects of language, such as syntax (sentence structure) or semantics (meaning). However, the exact mechanisms behind this organization remain a mystery.

Continue reading “TopoLM: brain-like spatio-functional organization in a topographic language model. Researchers have long observed that neurons in the brain tend to be” »

From superorganisms to superintelligences, how studying crabs could reveal that we are unintentionally building an artificial world brain.

The risk for thrombosis on equipment within coronary arteries during PCI – and the potential dangerous complications – has led to nearly 50 years of targeted research on the mechanisms of normal and pathologic thrombosis. This research has in turn led to the development of blood-thinning drug treatments to prevent thrombosis during and after PCI. However, the blood thinning (‘anti-thrombotic’) therapies can also lead to life-threatening excessive bleeding. Research to identify the optimal balance of anti-thrombotic drugs that minimises both pathologic thrombosis and excessive bleeding continues through today.

Dr Scott Denardo at Duke University Medical Center in the USA has modelled the behaviour of platelets inside blood vessels and near medical device surfaces. Some of his observations are just now entering the contemporary understanding of thrombosis. Denardo believes that applying these observations can refine existing anti-thrombotic therapies to improve their safety (less bleeding) while not compromising their effectiveness (preventing thrombosis on PCI equipment, including stents).