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Why Engineers Still Need the Humanities

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Since last September, Ive been spending seven hours a day, five days a week happily researching the history of women in electrical engineering. So far Ive uncovered the names of more than 200 women who contributed to electrical engineering, the first step in an eventual book project. I could keep going. Who exactly are we?

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When Does an AI Image Become Art?

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IEEE Spectrum spoke with Paul about Cohens iconic AI program, digital art curation, and the relationship between art and technology. Whether painting or photography or print, we all look at the sophistication of a concept and how it is translated into a medium. So my curatorial choices are not driven by the technology.

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A Guide to IEEE Education Week’s Events

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As technology evolves, staying current with the latest advancements and skills remains crucial. The Creating Impactful Community Service Learning Projects webinar from EPICS in IEEE on 7 April discusses how to develop engineering solutions for community challenges, as well as the grant application process.

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Bertrand Piccard’s Big Hydrogen Adventure

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Now hes in the midst of what looks like his most technologically ambitious mission yet: to fly around the planet in a green-hydrogen fuel-cell aircraft. In 1946 he was joined by his son, Jacques, a marine engineer, with whom he made a series of record descents. This is the concept of the plane. So that was a fantastic example.

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Get Ready for the Stellarator Showdown

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Proxima detailed its fully integrated stellarator power plant concept, called Stellaris, in the journal Fusion Engineering and Design. Proxima Fusion This is the first time anyone has put all the elements together in a single, fully integrated concept, says Proxima cofounder and chief scientist Jorrit Lion. billion (US $1.5

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Startup’s Analog AI Promises Power for PCs

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Naveen Verma s lab at Princeton University is like a museum of all the ways engineers have tried to make AI ultra-efficient by using analog phenomena instead of digital computing. It combines two key concepts that should make machine learning massively less energy intensive. Neither has a commercial future, according to Verma.

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Prototype Computer Uses Noise to Its Advantage

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Were focusing on algorithms that are able to leverage noise, stochasticity, and non-determinism, says Zachery Belateche , silicon engineering lead at Normal Computing. Although this was a successful proof-of-concept, the Normal Computing team acknowledges that this prototype is not scalable.

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