Lifeboat Foundation

Imagine if we could use strong electromagnetic fields to manipulate the local properties of spacetime—this could have important ramifications in terms of science and engineering.

Electromagnetism has always been a subtle phenomenon. In the 19th century, scholars thought that electromagnetic waves must propagate in some sort of elusive medium, which was called aether. Later, the aether hypothesis was abandoned, and to this day, the classical theory of electromagnetism does not provide us with a clear answer to the question in which medium electric and magnetic fields propagate in vacuum. On the other hand, the theory of gravitation is rather well understood. General relativity explains that energy and mass tell the spacetime how to curve and spacetime tells masses how to move. Many eminent mathematical physicists have tried to understand electromagnetism directly as a consequence of general relativity. The brilliant mathematician Hermann Weyl had especially interesting theories in this regard. The Serbian inventor Nikola Tesla thought that electromagnetism contains essentially everything in our universe.

In 2014, physicists found evidence of a deep connection between quantum error correction and the nature of space, time and gravity. Generally, gravity is defined as the fabric of space and time but beyond Einstein’s theory, there must be a quantum origin from which the space-time somehow emerges.

The three physicists at the origin of this discovery, Ahmed Almheiri, Xi Dong and Daniel Harlow, suggested that a holographic “emergence” of space-time works just like a quantum error-correcting code. In their paper “Bulk Locality and Quantum Error Correction in AdS/CFT” published in its first version in November 2014, they showed that space-time emerges from this quantum error correction code in an anti-de Sitter (AdS) universes.

Continue reading “Is spacetime a quantum code?” »

In part because the technologies have not yet been widely adopted, previous analyses have had to rely either on case studies or subjective assessments by experts to determine which occupations might be susceptible to a takeover by AI algorithms. What’s more, most research has concentrated on an undifferentiated array of “automation” technologies including robotics, software, and AI all at once. The result has been a lot of discussion—but not a lot of clarity—about AI, with prognostications that range from the utopian to the apocalyptic.

Given that, the analysis presented here demonstrates a new way to identify the kinds of tasks and occupations likely to be affected by AI’s machine learning capabilities, rather than automation’s robotics and software impacts on the economy. By employing a novel technique developed by Stanford University Ph.D. candidate Michael Webb, the new report establishes job exposure levels by analyzing the overlap between AI-related patents and job descriptions. In this way, the following paper homes in on the impacts of AI specifically and does it by studying empirical statistical associations as opposed to expert forecasting.

We’ve been trying for a long time to make a tiny Sun on Earth, one that would sustainably produce energy by nuclear fusion of hydrogen or similar atoms. Come to think of it, I’d like one for my basement.

Fusion requires quite a bit of heat to get going, but once it does, it starts producing its own heat. If you can keep that system contained so it doesn’t expand too much or allow too much heat to escape, further fusion happens. If it reaches a point where self-heating becomes the primary driver of fusion, you have yourself a “burning plasma”.

Continue reading “Burning plasma! Controlled nuclear fusion on Earth briefly sustains itself” »

“It is what I would call a dippy process,” Richard Feynman later wrote. “Having to resort to such hocus-pocus has prevented us from proving that the theory of quantum electrodynamics is mathematically self-consistent.”

Justification came decades later from a seemingly unrelated branch of physics. Researchers studying magnetization discovered that renormalization wasn’t about infinities at all. Instead, it spoke to the universe’s separation into kingdoms of independent sizes, a perspective that guides many corners of physics today.

Renormalization, writes David Tong, a theorist at the University of Cambridge, is “arguably the single most important advance in theoretical physics in the past 50 years.”

As Jess Bezos becomes the latest billionaire to attempt to conquer ageing, Gemma Askham meets those on the front line of the quest to delay death.

Mathematician and computer scientist, is considered one of the fathers of Artificial Intelligence. He was Toshiba Professor of Media Arts and Sciences at the Massachusetts Institute of Technology; cofounder of MIT’s Artificial Intelligence Laboratory; and the author of eight books, including The Society of Mind. Recorded: 2002.

While renewable energy sources like wind and solar have become more common across the United States, fossil fuels remain the main source of energy. According to the U.S. Information Administration’s (EIA) International Energy Outlook 2021 (IEO2021), the global supply of fossil fuels and biofuels is expected to adequately meet global demands for liquid fuel through 2050. Renewable energy technologies have improved to become more efficient and less costly, however most renewable energies are unable to provide a constant supply of energy.

This issue inspired Shriya Tailor, a middle school student from Duluth, Georgia, to find a constant renewable energy supply that created energy at all hours of the day and in any weather condition.

She looked to space for this solution, leading her to design a prototype for a “Solar Energy Station.” Shriya says the station, made of solar cells, would need to be around 50 miles away from earth for the energy waves to be transmitted back to earth via electromagnetic fields, then collected by an antenna and converted back to electricity. Consisting of a small solar panel, switching circuit, and transmitting and receiving coils, Shriya’s prototype allowed her to test her process here on earth.

Executives are missing out on a crucial pathway to smarter and more efficient decision-making with explainable machine learning.