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The Future of Artificial Intelligence in Healthcare

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With this explosion in innovation, it's important for healthcare professionals and other stakeholders to understand the regulations set in place for the effective development and deployment of these technologies. "We can't just put out all of these applications of artificial intelligence without getting approval from regulatory authorities," Dr. Weber said. Agencies have started thinking about how their regulatory framework can adapt to new and evolving technologies, he said. For example, the FDA introduced a new framework last year that enables it to pre-approve manufacturing of adaptive AI-powered software. "It allows for more testing and more rapid approval, and so you'll see faster turnover, much like the tech industry with smartphones," Dr. Weber said.


Designing an AI ethics framework

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More than 60 years after the discipline's birth,2 artificial intelligence (AI) has emerged as a preeminent issue in business, public affairs, science, health, and education. Algorithms are being developed to help pilot cars, guide weapons, perform tedious or dangerous work, engage in conversations, recommend products, improve collaboration, and make consequential decisions in areas such as jurisprudence, lending, medicine, university admissions, and hiring. But while the technologies enabling AI have been rapidly advancing, the societal impacts are only beginning to be fathomed. Until recently, it seemed fashionable to hold that societal values must conform to technology's natural evolution--that technology should shape, rather than be shaped by, social norms and expectations. For example, Stewart Brand declared in 1984 that "information wants to be free."3 In 1999, a Silicon Valley executive told a group of reporters, "You have zero privacy … get over it."4 In 2010, Wired magazine cofounder Kevin Kelly published a book entitled What Technology Wants.5 "Move fast and break things" has been a common Silicon Valley mantra.6 But this orthodoxy has been undermined in the wake of an ever-expanding catalog of ethically fraught issues involving technology. While AI is not the only type of technology involved, it has tended to attract the lion's share of discussion about the ethical implications.


Human Intelligence and AI for Oncology advancement - Vatican News

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The increase in the number of cancer cases worldwide is a major cause for concern for the medical community. Doctor Alexandru Floares, a speaker at a 3-day workshop organized by the Pontifical Academy for Life on Ethics and Artificial Intelligence (AI), spoke to Vatican Radio on the potential for larger strides in the field of oncology and medical research through the efficiency that AI provides. Dr. Floares, a Neurologist, specialist in AI applications in Oncology, and President of Solutions of Artificial Intelligence Applications (SAIA), gave a presentation titled "AI in Oncology." In his interview with Vatican Radio, Dr. Floares spoke on issues bordering on access to data for medical research, solutions to the emerging issues surrounding the use of AI in healthcare, and the revolutionary role of AI in the field of medicine. "The problems related to applying AI to medicine and oncology can be solved relatively easily," he said.


Knowledge Management joint with Subject Analysis and Access -- IFLA WLIC 2020

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Digital technologies of all its sorts have given rise to wider considerations and applications of Artificial Intelligence (AI) in library and information environments. In particular, the exponential growth of data, information and knowledge can no longer be managed by libraries, research centres and similar institutions via traditional means. AI operates via algorithms that restrict freedom of choice, changing the ways in which public users (externally) and library professionals and workers (internally) access resources. Historically, two important aspects of our profession have been to understand: a) the user's information needs and b) ways in which users interact with resources. AI-based solutions may assist or altogether replace manual procedures traditionally developed and performed by trained, educated professionals.


The potential merge of artificial intelligence (AI) and robotics - ELE Times

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In today's global manufacturing sector, there are a few main ways in which AI is deployed along with robotics. AI is a highly useful tool in robotic assembly applications. When combined with advanced vision systems, AI can help with real-time course correction, which is particularly useful in complex manufacturing sectors like aerospace. AI can also be used to help a robot learn on its own which paths are best for certain processes while it's in operation. Robots are machines or mechanical human beings that are designed to assist humans with laborious and complex tasks.


Responsible and Ethical AI -- Building Explainable Models - DZone AI

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Ethical AI, in simple words, is about ensuring your AI models are fair, ethical, and unbiased. So how does bias get into the model? Let's assume you are building an AI model that provides salary suggestions for new hires. As part of building the model, you have taken gender as one of the features to suggest salary. The model is trying to discriminate salary based on gender.


Appen High-Quality Training Data for Machine Learning

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Our skilled project managers use multiple quality control methods and mechanisms to meet and exceed quality standards for training data. Quality assurance is built into both the platform and processes at Appen. With a crowd of over 1 million skilled contractors operating in 130 countries and 180 languages and dialects, Appen can collect and label high volumes of image, text, speech, audio, and video data used to build and improve artificial intelligence systems. Our platform and solutions are purpose-built to handle large-scale data collection and annotation projects, on demand. With deep expertise planning and recruiting to meet a variety of uses cases for our clients, we can quickly ramp up new projects in new markets.


How To Implement Artificial Intelligence In Mobile App Development

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Artificial Intelligence (AI) is one of the few emerging technologies that promise to bring about some striking transformations in the blooming world of Android app development. When it comes to improving business relations, growth, and expectations, this technology has got the highest spotlight that cannot be overlooked by anyone looking to make a meaningful impact in the business world through technology. It is interesting to see how AI is growing rapidly to become the next big thing the world has ever known. Today, many app development companies around the world are not only interested in adopting AI but are also focused on putting the technology into the hands of people. Basically, they are looking to introduce it through apps in their mobile devices.


Deep Learning in the Cosmos: Ranking 3 Machine Learning (ML) Applications

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Deep learning has helped advance the state-of-the-art in multiple fields over the last decade, with scientific research as no exception. We've previously discussed Deepmind's impressive debut in protein folding prediction, as well as a project by Stanford students studying protein complex binding operations, which are both examples of using deep learning to study very small things. Deep learning has likewise found applications in scientific research at the opposite end of the scale spectrum. In this post we'll discuss some recent applications of deep learning used to study cosmology, aka the study of the universe. As you might imagine, this topic encompasses a wide variety of sub-categories.


How AIOps Conquers Performance Gaps on Big Data Pipelines - The New Stack

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If your data pipelines are growing in complexity and beyond the point where you can manage them, you're not alone. Today, they have become so massive and are crisscrossed by so many dependencies that it can be hard to see how all the components fit together, and hard to identify issues and opportunities that impact app performance and availability. Data stacks combine many disparate elements for data gathering and analysis, among other functions -- and exponential data growth in most organizations only adds to the challenge. In such an environment, simply monitoring performance and taking reactive measures when performance lags is no longer a viable approach. Today, with AIOps (Artificial Intelligence for IT Operations), a correlated data model helps you discover the full context of your apps and system resources so that you can adequately plan, manage, and improve performance.