Lifeboat Foundation: Safeguarding Humanity
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Blog – Page 3579

The silicon microchips of future quantum computers will be packed with millions, if not billions of qubits—the basic units of quantum information—to solve the greatest problems facing humanity. And with millions of qubits needing millions of wires in the microchip circuitry, it was always going to get cramped in there.

But now engineers at UNSW Sydney have made an important step toward solving a long-standing problem about giving their more breathing space—and it all revolves around jellybeans.

Not the kind we rely on for a sugar hit to get us past the 3pm slump. But jellybean quantum dots—elongated areas between qubit pairs that create more space for wiring without interrupting the way the paired qubits interact with each other.

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In the United States, the first step on the road to exascale HPC systems began with a series of workshops in 2007. It wasn’t until a decade and a half later that the 1,686 petaflops “Frontier” system at Oak Ridge National Laboratory went online. This year, Argonne National Laboratory is preparing for the switch to be turned on for “Aurora,” which will be either the second or the third such exascale machine in the United States, depending on the timing of the “El Capitan” system at Lawrence Livermore National Laboratory.

There were delays and setbacks on the road to exascale for all of these machines, as well as technology changes, ongoing competition with China, and other challenges. But don’t expect the next leap to zettascale – or even quantum computing – to be any quicker, according to Rick Stevens, associate laboratory director of computing for environment and life sciences at Argonne. Both could take another 15 to 20 years or more.

Such is the nature of HPC.

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Artificial Intelligence and Machine Learning are two terms that are commonly used interchangeably. But they are not the same thing. Artificial Intelligence is a field which contains a lot of sub-fields, including Machine Learning.

In this article I hope to comprehensively differentiate between AI and Machine Learning. I’ll explain how Machine Learning is not the same thing as Artificial Intelligence, but rather a part of it – like a cog amongst many cogs that makes up the machine which is Artificial Intelligence.

To begin, I’ll discuss the two concepts separately, describe their subsets, and then state the relationship binding the two of them. I’ll explain how Machine Learning, as a cornerstone concept, fits into AI as a field.

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Exceptionally well-preserved fossils from the Cambrian period have helped fill a gap in our understanding of the origin and evolution of major animal groups alive today.

A new analysis of fossils belonging to an extinct invertebrate called Rotadiscus grandis have helped place this species in the animal tree of life, revealing how some characteristics of living species may have evolved independently rather than originating in a single common ancestor.

Half a billion years ago, an unusual-looking animal crawled over the sea floor, using tentacles to pick up food particles along the way.

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