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Checking blood pressure in a heartbeat, using artificial intelligence and a camera

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Australian and Iraqi engineers have designed a system to remotely measure blood pressure by filming a person's forehead and extracting cardiac signals using artificial intelligence algorithms. Using the same remote-health technology they pioneered to monitor vital health signs from a distance, engineers from the University of South Australia and Baghdad's Middle Technical University have designed a non-contact system to accurately measure systolic and diastolic pressure. It could replace the existing uncomfortable and cumbersome method of strapping an inflatable cuff to a patient's arm or wrist, the researchers claim. In a new paper published in Inventions, the researchers describe the technique, which involves filming a person from a short distance for 10 seconds and extracting cardiac signals from two regions in the forehead, using artificial intelligence algorithms. The systolic and diastolic readings were around 90 per cent accurate, compared to the existing instrument (a digital sphygmomanometer) used to measure blood pressure, that is itself subject to errors.


Creepy AI reveals plan for world domination and gives users sick tips on committing crimes including how to make BOMBS

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A NEW creepy AI technology has revealed a plan for world domination to its users along with instructions on how to shoplift and make bombs. The tech company OpenAI created a new bot called ChatGPT, which generates convincing dialogue from a short writing prompt. While this technology is meant to formulate helpful solutions, with the right prompt, it can also give you criminal responses. ChatGPT's safeguards, which are meant to prevent the AI from using offensive content, can be removed, depending on what the user says. Vice gave a few examples of safeguard overrides.


Luke Soon on LinkedIn: Beware the Dark Side: There's Good Friction and There's Bad Friction: How…

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What kind of #Metaverse strategy should companies have? Forbes covers four ways business leaders can build a successful and sustainable approach PwC Roberto Hernandez Kok Weng SAM Greg Unsworth Demystifying the #Metaverse: what business leaders need to know or do PwC The #metaverse may profoundly change how businesses and consumers interact with products, services and each other. But what kind of #metaverse strategy, if any, should your company have? We take a detailed look and outline 6 ways to start preparing for the metaverse era. Our quick-start guide breaks down the 5 key categories of #digital assets that u need to know: #crypto, #NFTs, #stablecoins, #CBDCs https://lnkd.in/gPZPU9yt


Deep learning algorithm predicts Cardano (ADA) price for December 31, 2022

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As traders kick off the last month of 2022, Cardano (ADA) is still among the top ten biggest cryptocurrencies by market capitalization, despite being affected similarly to the rest of the market by the aftermath of the FTX crash. Currently, the ninth largest asset by market cap is predicted to increase its value by over 30% by the end of the month, according to NeuralProphet's PyTorch-based price prediction algorithm that uses an open-source machine learning framework. The deep learning algorithm forecasts that ADA will trade at $0.42 by December 31, 2022, a 32% increase from its price of $0.318 at the time of publication. Even though it is not a perfect forecast of future values, the model spanning exhibited a fair track record of accuracy up until the sudden market collapse of the algorithm-based stablecoin project TerraUSD. Since the cryptocurrency market has recently shown signs of recovery, investors are looking to the performance of assets like ADA to make predictions about its price over the next month. Cardano is changing hands at $0.318, up 1.48% in the last 24 hours and 1.67% across the previous week, with a total market cap of $10.9 billion, according to Finbold data.


Automated material analysis offers cut to cost and human error

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Rietveld analysis is frequently employed for data tasks and involves firing X-rays at a crystal that interact with the geometric arrangement of its particles and are diffracted in many directions. The resulting pattern of rays depends on the crystal's precise structure and is key to identification. However, while this is effective and often used process for revealing the three-dimensional atomic structure of new materials, the patterns' complexity requires expert assessment as does the intensity of the diffracted X-rays before it is possible to determine accurately the crystal's internal arrangement. Now Japan's National Institute for Materials Science (NIMS) has outlined in Science and Technology of Advanced Materials: Methods how it has harnessed robotics and machine learning to reduce labour intensity and circumvent risk of human error. "We have developed a robotic process automation (RPA) system that we apply to an existing Rietveld analysis program called RIETAN-FP," explained spokesperson Ryo Tamura of the NIMS team.


This story was created using Artificial Intelligence – Manila Bulletin

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ArtificiaI Intelligence or AI is having a major impact on the world of journalism, allowing journalists to better keep up with the ever-changing and growing amounts of news. AI technology is able to quickly sort through data and provide more accurate and timely news stories and insights that would have otherwise been impossible to uncover. This technology is helping journalists stay ahead of the curve, ensuring that news stories are as accurate and relevant as possible. To demonstrate how journalists can use AI in their work, blockchain developer and MB Tech consultant Alvin Veroy created this story using Artificial Intelligence with a very minimal human intervention. And by the way, AI also created this introduction and the image. In recent years, blockchain and cryptocurrency have become increasingly popular among tech-savvy investors, startups, and large corporations alike.


Deep Learning Visually Explained. Understanding deep learning using…

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Deep learning is one of the most powerful AI techniques, however, it can be difficult to understand. In this blog, I will attempt to explain deep learning using visuals and examples. Deep learning architecture is inspired by how our brain works. It is a connection of neurons. Deep learning models can have many parameters.


EE Times Europe - Polyn Looks to Speed ML Adoption at the Edge

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Israeli fabless semiconductor company Polyn has announced the availability of neuromorphic analog signal processing (NASP) models for Edge Impulse, a development platform for machine learning on edge devices. Edge impulse provides a way for developers to compare models and their performance, and Polyn is making its models available on the platform to enable such evaluations, CEO and founder Aleksandr Timofeev said in an interview with EE Times Europe. "Polyn is comfortable with this comparison, as it is confident in its promise of offering chips that consume 100 microwatts of power, and no other competitor offers the same," said Timofeev, adding that the company pays a licensing fee to make models available on Edge Impulse. Current ML implementation methods rely on digitizing the generated data and then running them through digital ML frameworks, a process that involves considerable computational power. Processing raw sensor data in analog form can lead to decreased power consumption and increased accuracy for all applications compared with traditional, digital algorithm-based computing, Timofeev said.


How Artificial Intelligence in Music is doing the Unthinkable!

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New Delhi: Are you thinking about who is the sensational music composer on the latest trending songs? It might just be Artificial Intelligence. Progress in Artificial Intelligence and music is on fire, thanks to the creative researchers who are innovating and working hard to bring AI into the music world. A researcher from United States, have devised a new approach – Music Prediction method, using which music can be generated with a very high accuracy. Composer is the main backbone of any soothing or funky tunes that brings a song to life.


Breaking the scaling limits of analog computing

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As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform computations. Conventional digital computers are struggling to keep up. An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy. However, these analog devices are prone to hardware errors that can make computations less precise. Microscopic imperfections in hardware components are one cause of these errors.