If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A medical AI expert shares views from his experiences at the seminar. More than 30 local government representatives and experts in academic, medical, and industrial fields were invited to explore the pressing issues, pain points, and future development of artificial intelligence (AI) application in medicine in Nanning, Guangxi Zhuang autonomous region. Held by the Chinese Health Information and Big Data Association (CHIBDA) and the Big Data Development Bureau of Guangxi Zhuang Autonomous Region, the seminar aimed to promote the AI application in medical treatment. Participants conducted a discussion on the challenges encountered in the innovative cooperation of medical AI in its use, production, learning, and research, exploring the cooperation models between AI enterprises and hospitals from various perspectives. Combined with the local conditions in Guangxi, they also provided valuable experience and advice for the development of medical AI.
The World Economic Forum (WEF) recently released a report detailing the ten "world-changing technologies that are poised to rattle the status quo." Let's see for ourselves what these technologies have to offer. Some developments in the bioplastics industry allow lignin, a component of wood, to be broken down into its simpler components using engineered solvents. With this possible, plastics can then be made from it. Lignin is found in wood waste and agricultural byproducts which otherwise doesn't have any other function.
Time to explain ergodicity, ruin and (again) rationality. Recall from the previous chapter that to do science (and other nice things) requires survival t not the other way around? Consider the following thought experiment. First case, one hundred persons go to a Casino, to gamble a certain set amount each and have complimentary gin and tonic –as shown in the cartoon in Figure x. Some may lose, some may win, and we can infer at the end of the day what the "edge" is, that is, calculate the returns simply by counting the money left with the people who return. We can thus figure out if the casino is properly pricing the odds.
WAUKESHA, Wis.--(BUSINESS WIRE)--GE Healthcare today announced the Food and Drug Administration's 510(k) clearance of Critical Care Suite, an industry-first collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device. Built in collaboration with UC San Francisco (UCSF), using GE Healthcare's Edison platform, the AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung. "X-ray – the world's oldest form of medical imaging – just got a whole lot smarter, and soon, the rest of our offerings will too," says Kieran Murphy, President & CEO, GE Healthcare. "GE Healthcare is leading the way in the creation of AI applications for diagnostic imaging and taking what was once a promise and turning it into a reality. By integrating AI into every aspect of care, we will ultimately improve patient outcomes, reduce waste and inefficiencies, and eliminate costly errors. Critical Care Suite is just the beginning."
Technology tools such as artificial intelligence (AI), machine learning (ML) and cloud-based analytics platforms, along with aggregated "big data" organized into informational dashboards, may have cracked the code for improving worker productivity. Data about how employees work and behave can be analyzed, predicted and subsequently used to drive decisions to allocate resources, monitor performance and make the workplace better. These solutions have evolved to shape the way workers work. Vadim Tabakman is the "technical evangelist" at Nintex, a Bellevue, Wash., firm providing end-to-end process management and workflow automation. He said AI and ML are used in many ways to improve performance by learning employee work patterns and habits.
Jellyfish are about 95% water, making them some of the most diaphanous, delicate animals on the planet. But the remaining 5% of them have yielded important scientific discoveries, like green fluorescent protein (GFP) that is now used extensively by scientists to study gene expression, and life-cycle reversal that could hold the keys to combating aging. Jellyfish may very well harbor other, potentially life-changing secrets, but the difficulty of collecting them has severely limited the study of such "forgotten fauna." The sampling tools available to marine biologists on remotely operated vehicles (ROVs) were largely developed for the marine oil and gas industries, and are much better-suited to grasping and manipulating rocks and heavy equipment than jellies, often shredding them to pieces in attempts to capture them. Now, a new technology developed by researchers at Harvard's Wyss Institute for Biologically Inspired Engineering, John A. Paulson School of Engineering and Applied Sciences (SEAS), and Baruch College at CUNY offers a novel solution to that problem in the form of an ultra-soft, underwater gripper that uses hydraulic pressure to gently but firmly wrap its fettuccini-like fingers around a single jellyfish, then release it without causing harm.
AI is rocking medical diagnosis with its potential to incite drastic improvements to hospital processes. AI can process images and patient health records with more accuracy and expediency than humans are capable of, lessening physician workload, reducing misdiagnosis, and empowering clinical staff to provide more value. While early moving hospitals are already extracting value from AI in medical diagnosis, most US hospitals are at the very early stage of the AI transformation curve -- and they risk falling behind if they don't move now. In this report, Business Insider Intelligence examines the value of AI applications in three high-value areas of medical diagnosis -- imaging, clinical decision support, and personalized medicine -- to illustrate how the tech can drastically improve patient outcomes, lower costs, and increase productivity. We look at US health systems that have effectively applied AI in these use cases to illustrate where and how providers should implement AI.
Central Learning, a web-based coding assessment and education application, released the results of the 4th annual nationwide ICD-10 coding contest. Central Learning is part of the Pena4, Inc. suite of health information and revenue cycle technology solutions for healthcare organizations. Manny Peña, RHIA, Founder and CEO of Pena4, Inc., announced today that Kristin Iovino from Lexington, Massachusetts, received $1,000 for achieving the highest average accuracy and productivity scores for outpatient cases. This year's contest focused on outpatient coding performance to address some of the challenges associated with the surge in outpatient reimbursement, coding errors and claim denials, with the goal of helping HIM, coding and revenue cycle teams pinpoint opportunities for improvement. Four years of coding contests have resulted in over 10,000 real medical record cases using Central Learning, a real-time, online coder assessment tool for HIM.
We treat athletes as if they are real-life superheroes that overcome physical challenges to achieve greatness in their respective sports. Today's athletes are physically faster, stronger and more agile than the generation before, but something is wrong. We have not made the same progress in improving athletes' mental skills and health as we have physical skills and health. The focus of any individual or team sport is to maximize player performance. In our sports culture, we are obsessed with team and player statistics using traditional measures in each sport.
Digital agriculture increasingly relies on the generation of large quantity of images. These images are processed with machine learning techniques to speed up the identification of objects, their classification, visualization, and interpretation. However, images must comply with the FAIR principles to facilitate their access, reuse, and interoperability. As stated in recent paper authored by the Planteome team (Trigkakis et al, 2018), "Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy." In this third Ontologies Community of Practice webinar, Justin Preece, Senior Faculty Research Assistant Oregon State University, presents the module developed by the Planteome project using the Bio-Image Semantic Query User Environment (BISQUE), an online image analysis and storage platform of Cyverse.