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In the last video in this series we discussed the differences between deep learning and machine learning, how and when the field of deep learning was officially born, and it’s rise to mainstream popularity. The focus of this video then will be on artificial neural networks, more specifically – their structure.

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The FCC authorized Microsoft to perform proof-of-concept demonstrations of a service that would connect its cloud computing service Azure with a ground station the company proposed to build.

“If the demonstrations result in significant market interest, Microsoft will file an application for regular earth station authority with the International Bureau (IB) to support future commercial operations,” the company wrote in the filings.

The company’s strategy will put it in competition with Amazon, which in November 2018 launched its similar AWS Ground Station service.


Microsoft is looking to compete with Amazon with a service that connects satellites directly to a cloud computing network.

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Magnetism offers new ways to create more powerful and energy-efficient computers, but the realization of magnetic computing on the nanoscale is a challenging task. A critical advancement in the field of ultralow power computation using magnetic waves is reported by a joint team from Kaiserslautern, Jena and Vienna in the journal Nano Letters.

A local disturbance in the magnetic order of a magnet can propagate across a material in the form of a wave. These waves are known as spin waves and their associated quasi-particles are called magnons. Scientists from the Technische Universität Kaiserslautern, Innovent e. V. Jena and the University of Vienna are known for their expertise in the called ‘magnonics,’ which utilizes magnons for the development of novel types of computers, potentially complementing the conventional electron-based processors used nowadays.

“A new generation of computers using magnons could be more powerful and, above all, consume less energy. One major prerequisite is that we are able to fabricate, so-called single-mode waveguides, which enable us to use advanced wave-based signal processing schemes,” says Junior Professor Philipp Pirro, one of the leading scientists of the project. “This requires pushing the sizes of our structures into the nanometer range. The development of such conduits opens, for example, an access to the development of neuromorphic computing systems inspired by the functionalities of the human brain.”

Materials scientists at Duke University have uncovered an atomic mechanism that makes certain thermoelectric materials incredibly efficient near high-temperature phase transitions. The information will help fill critical knowledge gaps in the computational modeling of such materials, potentially allowing researchers to discover new and better options for technologies that rely on transforming heat into electricity.

The results appear online on September 4 in the journal Nature Communications.

Thermoelectric materials convert heat into electricity when electrons migrate from the hot side of the material to the cold side. Because providing a temperature difference between its two sides is required, researchers are interested in trying to use these materials to generate electricity from the heat of a car’s tailpipe or recovering energy lost as heat in power plants.