Lifeboat Foundation

Neural decoding models attempt to identify the current mental state of an individual from recordings of their neural activity¹. In recent years, neural decoders have been developed to identify numerous different types of mental activity from many neuroimaging modalities. These decoders were first developed to decode visual^2,3 and semantic^4,5,6,7 information from the brain, while more recent examples of neural decoders have been developed to decode a diverse set of activities, including, but not limited to, affective states⁸, visual imagery during sleep⁹, and story meaning¹⁰.

Neural decoding models have been developed that make use of many different types of neuroimaging techniques including, but not limited to, functional magnetic resonance imaging (fMRI), electrocortiography (ECoG), electroencephalogram (EEG), and functional near infrared spectroscopy (fNIRS). Depending on the type of neuroimaging technique the neural decoder uses different types of mental processes may be decoded. For example, fMRI provides a recording of activity throughout the entire brain with a very high spatial resolution, allowing a neural decoder the ability to decode mental states involving sub-cortical brain regions¹¹. However, this comes at the cost of poor time resolution, which prevents decoding of mental activity over very short time scales.

Few of us give much thought to Earth’s swirling, spinning contents until some sudden movement, an earthquake or a volcanic eruption, jolts us to our senses.

Geoscientists, though, are a little more clued into the dynamics of Earth’s guts, and have just discovered that Earth’s solid inner iron core – which usually spins within a near-frictionless molten outer envelope – appears to have slowed to a grinding halt.

Before anybody panics and searches for a copy of a terrible 20-year-old science fiction movie predicting such an event in hopes of inspiring a solution, it’s not the first time record of such an event. It’s not even the first in recent history.

Stay ahead of the game with top-notch cybersecurity measures. The attacks may be becoming more severe, but so are our defenses.

How can we ensure that the technologies currently being developed are used for the common good, rather than for the benefit of a select few?

When ChatGPT was asked what advances in artificial intelligence mean for the human condition, it responded to our inquiry that AI will “change the way people view their own abilities and skills and alter their sense of self.” It could “impact people’s sense of identity and purpose.” It could “change the way people form and maintain relationships and impact their sense of community and belonging.” The progress made in 2022 by generative AI, which includes large language models such as ChatGPT and image generators such as Dall-E, is awe-inspiring. When it eloquently warns us about its own potential impact on the human sense of self, purpose, and belonging—we are naturally impressed. But large language models can only give back to us what we have fed to them.

Someday soon, we’ll speak entire universes into existence.

This article is a guide to the companies building the generative artificial intelligence technology that will lead to these virtual worlds (games, simulations, metaverse applications).

Continue reading “Market Map: Generative AI for Virtual Worlds” »

Warm dense matter (WDM) measures thousands of degrees in temperature and is under the pressure of thousands of Earth’s atmospheres. Found in many places throughout the universe, it is expected to have beneficial applications on Earth. However, its investigation is a challenge.

Even the temperature of a material under WDM conditions is anything but easy to determine. A team of researchers led by Dr. Tobias Dornheim from the Center for Advanced Systems Understanding (CASUS) at HZDR has demonstrated a mathematical solution that allows an accurate assessment of the temperature.

As the team points out in the journal Nature Communications, their method can readily be used at experimental facilities of matter research around the world and expedite the gain of scientific knowledge.

Sometimes, the most complex problems can be solved with the simplest approaches. Such was the case for researchers at UC Santa Barbara as they tried to resolve a longstanding issue of fluid friction—the resistance between an object moving through fluid, or conversely, a stationary object with fluid flowing around or through it. It’s also known as drag.

“We had built a theory, but it was a very messy problem,” said mechanical engineering professor Paolo Luzzatto-Fegiz. Their problem dealt in particular with superhydrophobic surfaces (SHS), which are seen as a potential solution to the problem of drag, a phenomenon that reduces the efficiency of things traveling through water, like cargo ships, and increases the energy expenditure to pump liquids through pipes.

Their calculations for an effective SHS encompassed 10 complex parameters, but as it turns out, the ability to predict if an SHS will perform as intended boils down to just one. Their research is published in the Proceedings of the National Academy of Sciences.

Only by knowing the average number of friends each person has, scientists at Complexity Science Hub (CSH) were able to predict the group sizes of people in a computer game. For this purpose, they modeled the formation of social groups on an example from physics, namely the self-organization of particles with spin.

Sociologists have focused on how social groups are forming and the mechanism behind it for a long time. The urge to avoid stress, as well as homophily—the tendency of people to join groups with others who share similar features, traits, or opinions—have been observed in many different contexts.

“Although multiple models have been studied, little is known about how homophily and stress avoidance affect the formation of human groups, and in particular the size distribution of them—whether there are many small groups or few large ones, for example,” explains Jan Korbel from CSH and first author of the study. By using two contemporary fields from physics, called self-assembly and spin glasses, scientists now shed new light on social group formation.

Researchers have theorized a new mechanism to generate high-energy “quantum light,” which could be used to investigate new properties of matter at the atomic scale.

The researchers, from the University of Cambridge, along with colleagues from the U.S., Israel and Austria, developed a theory describing a new state of light, which has controllable quantum properties over a broad range of frequencies, up as high as X-ray frequencies. Their results are reported in the journal Nature Physics.

The world we observe around us can be described according to the laws of classical physics, but once we observe things at an atomic scale, the strange world of quantum physics takes over. Imagine a basketball: observing it with the naked eye, the basketball behaves according to the laws of classical physics. But the atoms that make up the basketball behave according to quantum physics instead.