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Edge Artificial Intelligence Market Research Report by Processor, by Component, by Source, by End-Use, by Application, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19


GNW The Global Edge Artificial Intelligence Market size was estimated at USD 572.00 million in 2020 and expected to reach USD 701.73 million in 2021, at a CAGR 23.35% to reach USD 2,014.99 million by 2026. Market Statistics: The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period. Market Segmentation & Coverage: This research report categorizes the Edge Artificial Intelligence to forecast the revenues and analyze the trends in each of the following sub-markets: Based on Processor, the market was studied across ASIC, CPU, and GPU.

Which Jobs Will Likely Be Replaced by Robots?


Artificial intelligence (AI) will replace receptionists, manufacturing workers, proofreaders, bookkeeping clerks, basic retail jobs, receptionists and cashiers. Artificial intelligence (AI) is transforming how businesses operate and execute their operations. The rise of artificial intelligence during Covid-19 has also led to a change in skills requirements for organizations including blue collar and white collar workers. Artificial intelligence won't replace jobs which require conflict resolution, negotiation, emotional intelligence, and empathy. Researchers have created robots that are capable of carrying out specific tasks automatically.

COVID-19 patients in Japan recovering in robot-staffed hotels

The Japan Times

Across the street from one of the busiest train stations in Tokyo, the Shinagawa Prince Hotel is in a bustling complex filled with restaurants, sports facilities and entertainment options like a dolphin show. The only formal greeting guests receive is from Softbank Corp.'s robot, Pepper. They're given written instructions on their rooms and stay. That's because the new arrivals all have one thing in common: they're infected with the coronavirus. In Japan, some COVID-19 patients get a hotel booking -- and can enroll in clinical trials during their stay -- with their positive test results.

How can Earth Observation and Artificial Intelligence help people in need?


In a world where we produce enough food to feed everyone, 811 million people still go to bed hungry each night; that's one in every 10 people worldwide. The COVID-19 pandemic, climate change, and complex social-economic factors make the situation even more dire. By contrast, space, satellite, and Artificial Intelligence (AI) technologies have radically transformed humanity's ability to observe and model Earth's systems. It's inevitable then that we pose the question: how can Earth observation (EO) and AI help those in need? To find an answer, the Φ-lab at the European Space Agency, together with the World Food Programme (WFP) Innovation Accelerator, are launching the new EO & AI for SDGs Innovation Programme.

AI System Identifies COVID-19 Patients Who Require ICU


A new artificial intelligence (AI) system developed by researchers at the University of Waterloo and DarwinAI, an alumni-founded startup company, could help doctors efficiently utilize limited resources during the COVID-19 pandemic. The system is able to identify patients who require intensive care unit (ICU) treatment. The AI system predicts this necessity of ICU admission through the use of 200 clinical data points, which include blood test results, medical history, and vital signs. Alexander Wong is a professor of systems design engineering and Canada Research Chair in AI and Medical Imaging at Waterloo. "That is a very important step in the clinical decision support process for triaging patients and developing treatment plans," Wong said.

Pinaki Laskar on LinkedIn: #artificialintelligence #AI #robots


AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How does biological intelligence differ from #artificialintelligence? Comparing BI with #AI. 1. Biological intelligence engages all the conscious and unconscious knowledge of a human being. That immense field stretches from genetics to culture to society and psychology. Much of it is hardly understood. Your mother's arm that holds you in an embrace, the lover's hand that gently touches your cheek, and the little gestures that tell you're loved will prove hard work for #robots. You have an immune system, a cardiovascular system, a hormonal system, a muscular system, dozens of interconnected systems.

Top Remarkable Artificial Intelligence Developments that Happened in 2021


The year 2021 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated. It typically takes years, if not decades, to develop a new vaccine. But by March 2020, vaccine candidates to fight covid-19 were already undergoing human tests, just three months after the first reported cases.

How to Safeguard Humanity in a Context of Excessive Automation? - MedicalExpo e-Magazine


Jean-Michel Besnier is a French philosopher who teaches at Sorbonne University in Paris. His research focuses on the philosophical and ethical impact of science and technology on individual and collective representations and imagination. We met with him to talk about the consequences of the explosion of robotics and artificial intelligence (AI) in the healthcare sector, especially since the beginning of the Covid-19 pandemic. MedicalExpo e-magazine: Can you give us your definition of artificial intelligence? Jean-Michel Besnier: I have the same definition that everyone has. I am more attentive to the conceptual extension of the notion of artificial intelligence, which at the beginning referred to something rather simple, that is to say the implementation of devices capable of solving problems in an automatic or algorithmic way.

Will The Rise of Facial Recognition Technology in Surveillance Signal the End of Privacy?


Facial-recognition technology (FRT) is mainly deployed in the cybersecurity and surveillance sectors. It has long been in use at airport borders and on smartphones, and as a tool to help police identify criminals. But it is now creeping further into private and public spaces. From Quito to Nairobi, Moscow to Detroit, hundreds of municipalities have installed cameras equipped with FRT, sometimes promising to feed data to central command centres as part of'safe city' or'smart city' solutions to crime. The COVID-19 pandemic might accelerate their spread.

How statistics can aid in fight against misinformation


An American University math professor and his team created a statistical model that can be used to detect misinformation in social posts. The model also avoids the problem of black boxes that occur in machine learning. With the use of algorithms and computer models, machine learning is increasingly playing a role in helping to stop the spread of misinformation, but a main challenge for scientists is the black box of unknowability, where researchers don't understand how the machine arrives at the same decision as human trainers. Using a Twitter dataset with misinformation tweets about COVID-19, Zois Boukouvalas, assistant professor in AU's Department of Mathematics and Statistics, College of Arts and Sciences, shows how statistical models can detect misinformation in social media during events like a pandemic or a natural disaster. In newly published research, Boukouvalas and his colleagues, including AU student Caitlin Moroney and Computer Science Prof. Nathalie Japkowicz, also show how the model's decisions align with those made by humans.