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Category: security – Page 50

ChatGPT launched a tide wave of interest in AI. For many consumers, AI is finally living up to long overdue expectations. The accomplishments of ChatGPT in a short period of time are phenomenal. But what is yet to come when AI is combined with robotics will change everything.

I have been promoting the advances in robotics for several years. I even called 2022 the year of robotics, partially because of the growing need to overcome shortages in labor and to handle tasks beyond the physical or mental capability of humans, and partially because of the continued advances that AI, accelerated processing, semiconductor, sensors, wireless connectivity, and software technologies are enabling to develop advanced, autonomous machines. Robots are no longer just for the manufacturing floor. They are hazardous material handlers, janitors, personal assistants, food preparers, food deliverers, security guards, and even surgeons that are increasingly autonomous. Essentially, they are AI in the physical world. As a result, robot competitions are heating up from middle schools to Las Vegas.

As seen at CES, robotics technology is advancing rapidly with advances in technology. My favorite examples were the multi-configurable Yarbo outdoor robot and the John Deere See & Spray. Yarbo can be a mower, a leaf blower, or a snow blower. If it could dispose of animal excrement and the annoying neighbor, it would be perfect yard tool. On the other end of the spectrum was the John Deere See & Spray Ultimate, a tractor with up to a 120-foot (36.6m) reach that uses AI/ML to detect weeds smaller than the size of a smart phone camera and spray herbicide accordingly. John Deere also offers self-drive tractors.

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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.

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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 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 , by capturing multiple images of a scene with lateral movements of the camera.

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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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Check out all the on-demand sessions from the Intelligent Security Summit here.

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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Dr Ben Goertzel is the Founder and CEO of SingularityNET and Chief Science Advisor for Hanson Robotics.

He is one of the world’s leading experts in Artificial General Intelligence (AGI), with decades of expertise in applying AI to practical problems like natural language processing, data mining, video gaming, robotics, national security and bioinformatics.

He was part of the Hanson team which developed the AI software for the humanoid Sophia robot, which can communicate with humans and display more than 50 facial expressions. Today he also serve as Chairman of the AGI Society, the Decentralized AI Alliance and the futurist nonprofit organisation Humanity+.

Watch the FULL EPISODE here: https://londonreal.tv/e/dr-ben-goertzel/

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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.

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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.

Quiet sounds can be obliterated easily in most cases, but Resemble figured out a way to hide its identification tones within the sounds of speech. As people talking is the point of Resemble’s services, the audio watermark is much more likely to come through an edit unscathed. Resemble takes advantage of how humans tend to focus on specific frequencies and how louder sounds can hide quieter noises that are close in frequency. The combination masks and protects the watermark sound from humans noticing or being able to extract the audio watermark. Resemble’s machine learning model can determine where to embed the quiet sonic tag, generate the appropriate sound, and put it in place. The diagram below illustrates how the watermark hides in plain sight, or sound in this case.

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