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This is the BMW electric wingsuit 2021.

BMW Group will present the first electric drive system for a wingsuit with which the centuries-old dream of flying can be realised in a completely novel way. The innovative drive module and the likewise entirely newly designed wingsuit were developed in a cooperation between BMW i, Designworks and the professional wingsuit pilot Peter Salzmann from Austria. His maiden flight with the Electrified Wingsuit by BMW i was visually captured in an elaborately staged video documentation. The spectacular film, which will be seen for the first time in the run-up to the #NEXTGen 2020, shows impressively how BMW eDrive technology is able to make a lasting change to the individual mobility experience.

The long-term experience and the BMW i brand’s pooled development competence have now been utilised in a completely innovative way. The result is an extremely powerful, compact and light drive and energy storage package for a unique flying experience with the Electrified Wingsuit by BMW i. The fly unit of the Electrified Wingsuit by BMW i comprises two encased carbon propellers, so-called impellers, each delivering a power output of 7.5 kW, a speed of around 25,000 rpm and a total output of 15 kW, which is available for approximately 5 minutes. The design of the drive unit and the likewise exclusively developed wingsuit is the result of a cooperation between Peter Salzmann and the creative team from Designworks. The BMW Group subsidiary has strongly influenced the BMW i brand’s design from the outset, applying its creative capabilities not only in the automotive sector, but also on behalf of numerous international clients from other branches of industry.

Around 3 years passed from the idea of a wingsuit flight with electric extra boost to the premiere in the Austrian alps. During this time the air sports pioneer Peter Salzmann and his partners at BMW i and Designworks worked jointly on the details of the suit and the drive system. The electric twin-propeller drive system including an energy storage unit were perfectly integrated into the front of the wing suit. The tests in the wind tunnel at the BMW Group Aerodynamics Testing Centre in Munich were a significant part of the development programme for the project.

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When U.S. Rep. Jake Auchincloss decided to deliver a speech on a bill that would create a U.S.-Israel artificial intelligence center, he opted to let the AI do the talking.

The brief two-paragraph speech read by the Massachusetts Democrat on the floor of the U.S. House on Wednesday was generated by the online AI chatbot ChatGPT. His staff said they believe it’s the first time an AI-written speech was read in Congress.

Auchincloss said he prompted the system in part to “write 100 words to deliver on the floor of the House of Representatives” about the legislation. Auchincloss said he had to refine the prompt several times to produce the text he ultimately read.

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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 visual2,3 and semantic4,5,6,7 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.

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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.

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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.

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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).

Existing market maps describing the landscape for generative AI lack a convincing organization, instead seeming to be random boxes based on functionality. Since most of my readers are interested in the technologies and companies powering things like games, simulations and metaverse applications — you’ll find this map helpful in charting who is moving these specific experiences forward.

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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.

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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 (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 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.

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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 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 of them—whether there are many 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.

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