One of the main reasons to integrate AI in the current school curriculum is to make the upcoming generation familiar with technology. The Government of India and the educational board have been pushing for more artificial intelligence to be integrated into the education system, not from the perspective of enhancing it, but also with the intention of making young minds more aware and skilled when it comes to artificial intelligence. Today, children are curious about the smart conversational devices and AI used in applications like Siri and Alexa; some of them even wonder how Netflix gives them precise recommendations. Gradually, they will grow curious and try to learn what algorithms are, what a neural network is, and how they work. The Government of India and the educational board have been taking measures to make the existing school curriculum more AI-centric with a firm belief that the students will learn about AI, have fun and also take India forward.
Working at Royal Dutch Shell's Deepwater division in New Orleans gives Barbara Waelde a front-row seat to how the right data can unlock crucial information for the oil giant. So when her supervisor asked her last year if she was interested in a program that could sharpen her digital and data science capabilities, Waelde, 55, jumped at the chance. Since she began her online coursework, the seven-year Shell veteran has learned Python programming, supervised learning algorithms and data modeling, among other skills. Shell began making these online courses available to U.S. employees long before COVID-19 upended daily life. And according to the oil giant, there are no plans to halt or cancel any of them, despite the fact that on March 23 it announced plans to slash operating costs by $9 billion.
The recent success of deep neural networks at tasks such as language modelling, computer vision, and speech recognition has attracted considerable interest from industry and academia. Achieving a better understanding and widespread use of such models involves the use of Knowledge Representation and Reasoning together with sound Machine Learning methodologies and systems. The goal of this special track, which closed in 2017, was to serve as a home for the publication of leading research in deep learning towards cognitive tasks, focusing on applications of neural computation to advanced AI tasks requiring knowledge representation and reasoning.
Tunisia deployed a police robot to patrol streets of the capital and enforce a lockdown imposed to contain coronavirus spread. Known as PGuard, the "robocop" which is remotely operated and is equipped with thermal imaging cameras is seen calling out to suspected violators in a video, "What are you doing? You don't know there's a lockdown?"
NEW YORK: Scientists have developed an artificial intelligence (AI) tool that may accurately predict which patients newly infected with the virus that causes Covid-19 would go on to develop severe respiratory disease. The study, published in the journal Computers, Materials & Continua, also revealed the best indicators of future severity, and found that they were not as expected. "While work remains to further validate our model, it holds promise as another tool to predict the patients most vulnerable to the virus, but only in support of physicians' hard-won clinical experience in treating viral infections," said Megan Coffee, a clinical assistant professor at New York University (NYU) in the US. "Our goal was to design and deploy a decision-support tool using AI capabilities -- mostly predictive analytics -- to flag future clinical coronavirus severity," said Anasse Bari, a clinical assistant professor at New York University. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds, and who can safely go home, with hospital resources stretched thin," Bari said.
Deep neural networks are being mustered by U.S. military researchers to marshal new technology forces on the Internet of Battlefield Things. U.S. Army and industry researchers said this week they have developed a "confidence metric" for assessing the reliability of AI and machine learning algorithms used in deep neural networks. The metric seeks to boost reliability by limiting predictions based strictly on the system's training. The goal is to develop AI-based systems that are less prone to deception when presented with information beyond their training. SRI International has been working since 2018 with the Army Research Laboratory as part of the service's Internet of Battlefield of Things Collaborative Research Alliance.
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A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, weren't very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs. "Here's a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate," said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. "These results show us that machine learning isn't magic; there are clearly other factors at play when it comes to predicting the life course." The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data.
The Frontier Development Lab (FDL) Europe applies AI technologies to science to push the frontiers of research and develop new tools to help solve some of the biggest challenges that humanity faces. These range from the effects of climate change to predicting space weather, from improving disaster response, to identifying meteorites that could hold the key to the history of our universe. FDL brings researchers from the cutting-edge of AI and data science, and teams them up with their counterparts from the space sector for an intensive eight-week research sprint, based on a range of challenge areas. The results far exceed what any individual could develop in the same time period, or even in years of individual research. A key aspect of our success is the careful formation of small interdisciplinary teams focused on tackling specific challenges.
Researchers at the University of California, San Francisco have recently created an AI system that can produce text by analyzing a person's brain activity, essentially translating their thoughts into text. The AI takes neural signals from a user and decodes them, and it can decipher up to 250 words in real-time based on a set of between 30 to 50 sentences. As reported by the Independent, the AI model was trained on neural signals collected from four women. The participants in the experiment had electrodes implanted in their brains to monitor for the occurrence of epileptic seizures. The participants were instructed to read sentences aloud, and their neural signals were fed to the AI model.