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The Consciousness Conundrum

The wetware that gives rise to consciousness is far too complex to be replicated in a computer anytime soon

14 min read
Image: Courtesy Pablo de Heras Ciechomski, Ph.D. Copyright all rights reserved 2006–2008, Visualbiotech Sarl (www.visualbiotech.ch), Switzerland
Image: Courtesy Pablo de Heras Ciechomski, Ph.D. Copyright all rights reserved 2006–2008, Visualbiotech Sarl (www.visualbiotech.ch), Switzerland

This is part of IEEE Spectrum's Special Report: The Singularity

I'm 54, with all that entails. Gray hair, trick knee, trickier memory. I still play a mean game of hockey, and my love life requires no pharmaceutical enhancement. But entropy looms ever larger. Suffice it to say, I would love to believe that we are rapidly approaching “the singularity." Like paradise, technological singularity comes in many versions, but most involve bionic brain boosting. At first, we'll become cyborgs, as stupendously powerful brain chips soup up our perception, memory, and intelligence and maybe even eliminate the need for annoying TV remotes. Eventually, we will abandon our flesh-and-blood selves entirely and upload our digitized psyches into computers. We will then dwell happily forever in cyberspace where, to paraphrase Woody Allen, we'll never need to look for a parking space. Sounds good to me!

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Dialing Down a Quantum Compute Glitch by 100,000x

A low-key solution to qubits’ cosmic ray problem

3 min read
Conceptual computer artwork of electronic circuitry contained within spheres against beams of light, representing how data may be controlled and stored in a quantum computer.
Mehau Kulyk/Science Source

The kind of quantum computers that IBM, Google and Amazon are building suffer catastrophic errors roughly once every 10 seconds due to cosmic rays from outer space. Now a new study reveals a way to reduce this error rate by nearly a half-million-fold to less than once per month.

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Deep Learning Gets a Boost From New Reconfigurable Processor

The ReAAP processor allows AI to be faster, more efficient

2 min read
different colored beams of light shooting up
iStock

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Deep learning is a critical computing approach that is pushing the boundaries of technology – crunching immense amounts of data and uncovering subtle patterns that humans could never discern on their own. But for optimal performance, deep learning algorithms need to be supported with the right software compiler and hardware combinations. In particular, reconfigurable processors, which allow for flexible use of hardware resources for computing as needed, are key.

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Learn how WiPL-D software suite can be efficiently used for biomedical applications

1 min read

Microwave Imaging (MWI) has attracted massive attention in the medical research field over the last decade due to its standout qualities of utilizing harmless non-ionizing radiation and affordable components. At present, conventional technologies (CT and MRI) which provide high-resolution images, still have several limitations such as their long examination time, non-portability, high cost, and also ionizing radiation.

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