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Many businesses struggle with demand forecasting. Whether you run a small business or a large enterprise, the challenge of predicting customer behavior and stock levels never gets easier. Even major organizations like Target and Walmart that are able to afford teams of data scientists have recently reported struggles with excess inventory due to poor demand forecasting.

During this time of global uncertainty, many businesses have adopted a just-in-case mindset. They’ve relied on archaic methods of forecasting, scouring old data and drawing poor conclusions based on past problems.

Optical computing has been gaining wide interest for machine learning applications because of the massive parallelism and bandwidth of optics. Diffractive networks provide one such computing paradigm based on the transformation of the input light as it diffracts through a set of spatially-engineered surfaces, performing computation at the speed of light propagation without requiring any external power apart from the input light beam. Among numerous other applications, diffractive networks have been demonstrated to perform all-optical classification of input objects.

Researchers at the University of California, Los Angeles (UCLA), led by Professor Aydogan Ozcan, have introduced a “time-lapse” scheme to significantly improve the image classification accuracy of diffractive optical networks on complex input objects. The findings are published in the journal Advanced Intelligent Systems.

In this scheme, the object and/or the diffractive network are moved relative to each other during the exposure of the output detectors. Such a “time-lapse” scheme has previously been used to achieve super-resolution imaging, for example, in security cameras, by capturing multiple images of a scene with lateral movements of the camera.

Researchers have captured the signal of neutrinos from a nuclear reactor using a water-filled neutrino detector, a first for such a device.

In a mine in Sudbury, Canada, the SNO+ detector is being readied to search for a so-far-undetected nuclear-decay process. Spotting this rare decay would allow researchers to confirm that the neutrino is its own antiparticle (see Viewpoint: Probing Majorana Neutrinos). But while SNO+ team members prepare for that search, they have made another breakthrough by capturing the interaction with water of antineutrinos from nuclear reactors [1]. The finding offers the possibility of making neutrino detectors from a nontoxic material that is easy to handle and inexpensive to obtain, key factors for use of the technology in auditing the world’s nuclear reactors (see Feature: Neutrino Detectors for National Security).

The SNO+ detector was inherited from the earlier Sudbury Neutrino Observatory (SNO) experiment. Today the detector is filled with a liquid that lights up when charged particles pass through it. But in 2018, to calibrate the detector’s components and to characterize its intrinsic radioactive background signal after the experiment’s upgrade, it contained water. The antineutrino signal was observed when, after completing those measurements, the researchers took the opportunity to carry out additional experiments before the liquid was switched out.

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In a new season of Tech Tonic, FT tech journalists Madhumita Murgia and John Thornhill investigate the race to build a quantum computer, the impact they could have on security, innovation and business, and the confounding physics of the quantum world.

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Data management and integration veteran Talend today debuted the winter ‘23 release of its core platform, providing enhanced observability, automation and connectivity for enterprises’ data assets. The update comes over a month after the company announced it is being acquired by Qlik in a transaction set to close in the first half of 2023.

Talend started in 2004 as a data integrator, but gradually expanded to offer Talend Data Fabric, a unified solution that works across any cloud, hybrid or multicloud environment. The solution combines enterprise-grade data discovery, integration, quality (automatic cleaning and profiling) and governance capabilities. It’s is intended to reduce the effort involved in working with data, while providing teams with clean and uncompromised information for decision-making.

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GOOGLE has urged millions to download Chrome’s new security update or risk their data being vulnerable.

On Thursday, Google released a new security update for all desktop versions of its Chrome web browser.

The update targets 10 security issues in the web browser – one of which is rated at the most critical level.

But the computing power necessary for a company to adopt in-house AI capabilities is enormous, and that’s where Nvidia’s new service offering comes in. Dubbed “DGX Cloud,” Nvidia is offering an AI supercomputer accessible to its customers via a web browser. The company partnered with various cloud providers, including Microsoft, Google, and Oracle, to launch the service.

“Nvidia AI as a service offers enterprises easy access to the world’s most advanced AI platform, while remaining close to the storage, networking, security and cloud services offered by the world’s most advanced clouds,” Huang explained.

“Nvidia AI is essentially the operating system of AI systems today,” Huang also said.

Synthetic speech and voice cloning startup Resemble AI has introduced an “audio watermark” to tag AI-generated speech without compromising sound quality. The new PerTh Perceptual Threshold) Watermarker embeds the sonic signature of Resemble’s synthetic media engine into a recording to mark its AI origin regardless of future audio manipulation, yet subtle enough that no human can hear it.

Audio Watermarking

Visual watermarking hides one image within another, invisible without a computer scanner in the case of particularly high-security documents. The same principle applies to audio watermarks, except it’s a very soft sound that people won’t notice but encoded with information that a computer could decipher. The concept isn’t new, but Resemble has leveraged its audio AI to make PerTh more reliable without compromising the realism of its synthetic speech creation.

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