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In this talk, Kurzweil explores the history and trajectory of advances in computing and Information Technology to project how he believes Artificial Intelligence (AI) may enhance our natural biological intelligence in the future.

Kurzweil spoke at the Nobel Week Dialogue on December 9, 2015 in Gothenburg, Sweden.

Nobel Week Dialogue is a free of charge, full-day event and part of the official Nobel Week programme. The event aims to stimulate discussion at the highest level on a topical science-related theme by bringing together Nobel Laureates, the world’s leading scientists and experts, key opinion leaders, policy makers and the general public, online as well as on site. By bridging science and society, it’s an opportunity to stimulate thinking, excite imagination and inspire greatness! http://www.nobelweekdialogue.org

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Fast radio bursts (FRBs) are millisecond-long cosmic explosions that each produce the energy equivalent to the sun’s annual output. More than 15 years after the deep-space pulses of electromagnetic radio waves were first discovered, their perplexing nature continues to surprise scientists – and newly published research only deepens the mystery surrounding them.

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Harvard Medical School scientists and colleagues at Stanford University have developed an artificial intelligence diagnostic tool that can detect diseases on chest X-rays directly from natural-language descriptions contained in accompanying clinical reports.

The step is deemed a major advance in clinical AI design because most current AI models require laborious human annotation of vast reams of data before the labeled data are fed into the model to train it.

Get more HMS news here.

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China Launches World’s Fastest Quantum Computers | China’s Advancement In Quantum Computers #technology.

“Techno Jungles”

In 2019, Google announced that its 53-qubit Sycamore processor had finished a task in 3.3 minutes that would have taken a conventional supercomputer at least 2.5 days to accomplish. According to reports, China’s 66-Qubit Zuchongzhi 2 Quantum Processor was able to complete the same task 1 million times faster in October of last year. Together with the Shanghai Institute of Technical Physics and the Shanghai Institute of Microsystem and Information Technology, a group of researchers from the Chinese Academy of Sciences Center for Excellence in Quantum Information and Quantum Physics were responsible for the development of that processor.

According to NDTV, the Chinese government under Xi Jinping has spent $10 billion on the country’s National Laboratory for Quantum Information Sciences. This demonstrates China’s significant commitment to the field of quantum computing. According to Live Science, the nation is also a world leader in the field of quantum networking, which involves the transmission of data that has been encoded through the use of quantum mechanics over great distances.

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As humanity reaches out to the stars and make new homes on strange new worlds, how will our genetics & DNA change under those alien planets?

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Listen or Download the audio of this episode from Soundcloud: Episode’s Audio-only version: https://soundcloud.com/isaac-arthur-148927746/genetic-divergence-civilization.

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Companies and research labs across the globe are working towards getting their nascent quantum technologies out of the lab and into the real world, with the US technology giant IBM being a key player. In May this year, IBM Quantum unveiled its latest roadmap for the future of quantum computing in the coming decade, and the firm has set some ambitious targets. Having announced its Eagle processor with 127 quantum bits (qubits) last year, the company is now developing the 433-qubit Osprey processor for a debut later this year, to be followed in 2023 by the 1121-qubit Condor.

But beyond that, the company says, the game will switch to assembling such processors into modular circuits, in which the chips are wired together via sparser quantum or classical interconnections. That effort will culminate in what they refer to as their 4158-qubit Kookaburra device in 2025. Beyond then, IBM forecasts modular processors with 100,000 or more qubits, capable of computing without the errors that currently make quantum computing a matter of finding workarounds for the noisiness of the qubits. With this approach, the company’s quantum computing team is confident that it can achieve a general “quantum advantage”, where quantum computers will consistently outperform classical computers and conduct complex computations beyond the means of classical devices.

While he was in London on his way to the 28 th Solvay conference in Brussels, which tackled quantum information, Physics World caught up with physicist Jay Gambetta, vice-president of IBM Quantum. Having spearheaded much of the company’s advances over the past two decades, Gambetta explained how these goals might be reached and what they will entail for the future of quantum computing.

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An information engine uses information to convert heat into useful energy. Such an engine can be made, for example, from a heavy bead in an optical trap. A bead engine operates using thermal noise. When noise fluctuations raise the bead vertically, the trap is also lifted. This change increases the average height of the bead, and the engine produces energy. No work is done to cause this change; rather, the potential energy is extracted from information. However, measurement noise—whose origin is intrinsic to the system probing the bead’s position—can degrade the engine’s efficiency, as it can add uncertainty to the measurement, which can lead to incorrect feedback decisions by the algorithm that operates the engine. Now Tushar Saha and colleagues at Simon Fraser University in Canada have developed an algorithm that doesn’t suffer from these errors, allowing for efficient operation of an information engine even when there is high measurement noise [1].

To date, most information engines have operated using feedback algorithms that consider only the most recent bead-position observation. In such a system, when the engine’s signal-to-noise ratio falls below a certain value, the engine stops working.

To overcome this problem, Saha and colleagues instead use a “filtering” algorithm that replaces the most recent bead measurement with a so-called Bayesian estimate. This estimate accounts for both measurement noise and delay in the device’s feedback.

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