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Computational imaging holds the promise of revolutionizing optical imaging with its wide field of view and high-resolution capabilities. Through the joint reconstruction of amplitude and phase — a technique known as “coherent imaging or holographic imaging” — the throughput of an optical system can expand to billions of optically resolvable spots. This breakthrough empowers researchers to gain crucial insights into cellular and molecular structures, making a significant impact on biomedical research.

Despite the potential, existing large-scale coherent imaging techniques face challenges hindering their widespread clinical use. Many of these techniques require multiple scanning or modulation processes, resulting in long data collection times to achieve a high resolution and signal-to-noise ratio. This slows down imaging and limits its feasibility in clinical settings due to tradeoffs between speed, resolution, and quality.

The crushing demand for AI has also revealed the limits of the global supply chain for powerful chips used to develop and field AI models.

The continuing chip crunch has affected businesses large and small, including some of the AI industry’s leading platforms and may not meaningfully improve for at least a year or more, according to industry analysts.

The latest sign of a potentially extended shortage in AI chips came in Microsoft’s annual report recently. The report identifies, for the first time, the availability of graphics processing units (GPUs) as a possible risk factor for investors.

On Wednesday, Meta announced it is open-sourcing AudioCraft, a suite of generative AI tools for creating music and audio from text prompts. With the tools, content creators can input simple text descriptions to generate complex audio landscapes, compose melodies, or even simulate entire virtual orchestras.

AudioCraft consists of three core components: AudioGen, a tool for generating various audio effects and soundscapes; MusicGen, which can create musical compositions and melodies from descriptions; and EnCodec, a neural network-based audio compression codec.

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The recent Ant-Man movie did a great job of putting quantum up in lights, but the future of quantum science shines even brighter than fiction. One application, quantum sensors, is already the basis of some of the most important systems and technologies in our world — global positioning systems (GPS) and magnetic resonance imaging (MRI) scanners are prime examples.

Quantum sensors and quantum AI are just the beginning: Robots are now getting the quantum sensor treatment too. Quantum sensors will supercharge the way robots work and how we apply them to important 21st-century challenges.

A team of researchers from British universities has trained a deep learning model that can steal data from keyboard keystrokes recorded using a microphone with an accuracy of 95%.

When Zoom was used for training the sound classification algorithm, the prediction accuracy dropped to 93%, which is still dangerously high, and a record for that medium.

Such an attack severely affects the target’s data security, as it could leak people’s passwords, discussions, messages, or other sensitive information to malicious third parties.

It reduces water wastage and carbon emissions.

“When it comes to photovoltaics, dust is the enemy. This is not a trivial concept, even if it may seem so at first glance; actually, the problem of soiling – the accumulation of dust, dirt or sand on PV panels – can decrease, sometimes significantly, the performance of solar power systems,” stated an Enel Green Power press release published on Friday.

Desert areas

“It’s an issue that’s particularly important in desert areas, areas with low rainfall, and those characterized by the presence of very dusty soil, where soiling can have a heavy impact on energy yield, but in any case, it’s something that concerns solar power everywhere, because regardless of location, cleaning the panels still involves costs, including environmental ones.”