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New machine-learning approach brings digital photos back to life

#artificialintelligence

Every day, billions of photos and videos are posted to various social media applications. The problem with standard images taken by a smartphone or digital camera is that they only capture a scene from a specific point of view. But looking at it in reality, we can move around and observe it from different viewpoints. Computer scientists are working to provide an immersive experience for the users that would allow them to observe a scene from different viewpoints, but it requires specialized camera equipment that is not readily accessible to the average person. To make the process easier, Dr. Nima Kalantari, professor in the Department of Computer Science and Engineering at Texas A&M University, and graduate student Qinbo Li have developed a machine-learning-based approach that would allow users to take a single photo and use it to generate novel views of the scene.


24 Best (and Free) Books To Understand Machine Learning - KDnuggets

#artificialintelligence

"What we want is a machine that can learn from experience" There is no doubt that Machine Learning has become one of the most popular topics nowadays. According to a study, Machine Learning Engineer was voted one of the best jobs in the U.S. in 2019. Looking at this trend, we have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field. Even paid books are seldom better. A good introduction to the Mathematics, and also has practice material in R. Cannot praise this book enough.


Sharpening Its Edge: U.S. Postal Service Opens AI Apps on Edge Network

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In 2019, the U.S. Postal Service had a need to identify and track items in its torrent of more than 100 million pieces of daily mail. A USPS AI architect had an idea. Ryan Simpson wanted to expand an image analysis system a postal team was developing into something much broader that could tackle this needle-in-a-haystack problem. With edge AI servers strategically located at its processing centers, he believed USPS could analyze the billions of images each center generated. The resulting insights, expressed in a few key data points, could be shared quickly over the network.


Insights into black box of artificial intelligence

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At many banks, insurance companies and online retailers, self-learning computer algorithms are used to make decisions that have major consequences for customers. However, just how algorithms in artificial intelligence (AI) represent and process their input data internally is largely unknown. They have published their results in the journal Neural Networks. 'What we call artificial intelligence today is based on deep artificial neural networks that roughly mimic human brain functions,' explains Dr. Patrick Krauss from the Cognitive Computational Neuroscience Group at FAU. As is the case in children learning their native language without being aware of the rules of grammar, AI algorithms can learn to make the right choice by independently comparing a large amount of input data.


How the USPS is deploying AI to help track 7.3B packages a year

ZDNet

The US Postal Service runs a massive operation, processing 129 billion pieces of mail a year, including 7.3 billion packages. So when a package gets lost, there's a lot of sorting involved in finding it. What is AI? Everything you need to know about Artificial Intelligence With a new, Nvidia-powered AI program, the USPS has built a way to dramatically reduce the time it takes to find lost packages, down from several days to just two hours. Package sorting is just the beginning -- the USPS now has ideas for dozens of applications it could power with its new edge AI deployment, spanning everything from mail sorting to marketing. "There are not many enterprise-wide AI/ML projects that have been deployed at this scale across the whole enterprise, especially not in the case of government," Anthony Robbins, VP of Nvidia's federal government business, told reporters this week.


Top 20 Artificial Intelligence Research Labs In The World In 2021

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Artificial intelligence is continuously evolving and propagating across every industry. With much of the groundbreaking innovations moving the industry forward, the technology is continuously making headlines every day. AI refers to software or systems that perform intelligent tasks like those of human brains such as learning, reasoning, and judgment. Its applications range from automation and translation systems for natural languages that people use daily, to image recognition systems that help identify faces and letters from images. Today, AI is used in different forms including digital assistants, chatbots, and machine learning, among others.


Complete Machine Learning & Data Science Bootcamp 2021

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This is a brand new Machine Learning and Data Science course just launched and updated this month with the latest trends and skills for 2021! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 400,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. You will go from zero to mastery!


Artificial Intelligence and the COVID-19 Pandemic - Future of Privacy Forum

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Machine learning-based technologies are playing a substantial role in the response to the COVID-19 pandemic. Experts are using machine learning to study the virus, test potential treatments, diagnose individuals, analyze the public health impacts, and more. Below, we describe some of the leading efforts and identify data protection and ethical issues related to machine learning and COVID-19, with a particular focus on apps directed to health care professionals that leverage audio-visual data, text analysis, chatbots, and sensors. "Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care." Now – with the development of the pandemic resulting from the spread of the coronavirus (COVID-19), medical providers, institutions, and commercial developers are all considering whether and how to apply machine learning to confront the threat of this current crisis.


Ethics of AI: Benefits and risks of artificial intelligence

ZDNet

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.


Ethics of AI: Benefits and risks of artificial intelligence

#artificialintelligence

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.