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) …
The demographics of Canada are changing quickly. By 2050, 26% of Canada's population is expected to be aged 65 or better, up from 18% today. With smaller families, busier schedules, and tighter budgets, the pressure is on to find solutions to ensure this growing group of people receives quality care. Fortunately, artificial intelligence is helping the retirement industry serve up innovative solutions to meet the burgeoning need. Though results from our 2019 Sklar Wilton AI tracker* indicate that 57% of people aged 65 and older don't understand the current state of artificial intelligence, 71% believe AI may affect them.
Based on our interactions and the results of this study, we expect to see organizations not only adopt AI--but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.
Staying ahead of sudden deterioration and chronic illnesses is a goal shared by many providers. AI enables healthcare organizations to do just that by serving as a backbone for technologies such as predictive analytics and clinical decision support systems. Such systems aid clinicians in sifting through and making sense of the vast amount of health data collected by modern medical devices. They alert providers of any potential problems with patients and even go as far as suggesting next steps for treatment. This predictive approach can be used to detect early warning signs for conditions such as sepsis, which, if left untreated, can quickly lead to organ failure and septic shock.
ORO VALLEY, AZ / ACCESSWIRE / February 18, 2020 / Tautachrome, Inc. (OTC PINK:TTCM) announces Google's TensorFlow artificial intelligence (AI) for image classification in ARknet release 1.3.4. ARknet will have the ability to classify incoming images utilizing TensorFlow, Google's artificial intelligence (AI) engine. The deployment of TensorFlow AI image recognition enables ARknet to crowdsource its userbase for machine learning purposes. Since ARknet can have segmented datasets, the ability to develop specialized machine learning models will also allow services to be provided to a wide range of business applications, connected devices, and enterprise services. TensorFlow is planned as the first of many AI frameworks that will be introduced into the ARknet platform in the future.
AI ethics is a hot topic these days, so you see all kinds of rhetoric zooming around. Complaints range from "the robots took my job" to "your computer system is just as biased as you are (you jerk)." Why aren't we talking about what makes ML/AI uniquely more dangerous than other technologies? The topics that come up in connection with AI ethics are vital, timely, and necessary. I just wish we wouldn't use the term AI ethics whenever it… isn't even about AI.
Gabriel is a professional transcriber, and for years he earned a middle-class living. He'd sit at his desk, "knock it out" for hours using custom keystrokes, and watch the money roll in. "I sent my son to private schools and university on transcribing," he tells me. "It was a nice life." But in the past decade, the bottom fell out.
Today organizations have to deal with so many emergent behaviors that the notion of central control as the only coping mechanism seems to be receding as a dominant management model. Freedom must be doled out further from the centrist idea by creating goals, constraints, boundaries and allowable edge behaviors. Someday software and hardware agents will negotiate their contribution to business outcomes on their own, but until then organizations will have to prepare themselves by managing coordinated autonomy. Edge computing is a form of distributed computing which brings computation and data storage closer to the location where it is needed, to improve response times and provide better actions. Now, AI on Edge, can offer a whole lot of new possibilities.
Over the past decade, we have witnessed notable breakthroughs in Artificial Intelligence (AI), thanks in large part to the development of deep learning approaches. Healthcare, finance, human resources, retail, there is no field in which AI has not proven to be a game-changer. Who would have said just a few years ago that there would be autonomous vehicles on public roads, that large-scale facial recognition would no longer be science fiction, or that fake news could have such an impact socially, economically, and politically? Some statistics related to AI are dizzying. According to Forbes, 75 countries are currently using AI technology for surveillance purposes via smart city platforms, facial recognition systems, and smart policing.
The Defense Department will soon adopt a detailed set of rules to govern how it develops and uses artificial intelligence, officials familiar with the matter told Defense One. A draft of the rules was released by the Defense Innovation Board, or DIB, in October as "Recommendations on the Ethical Use of Artificial Intelligence." Sources indicated that the Department's policy will follow the draft closely. "The Department of Defense is in the final stages of adopting AI principles that will be implemented across the U.S. military. An announcement will be made soon with further details," said Lt. Cmdr.
Have you ever wondered how to demonstrate that one machine learning model's test set performance differs significantly from the test set performance of an alternative model? This post will describe how to use DeLong's test to obtain a p-value for whether one model has a significantly different AUC than another model, where AUC refers to the area under the receiver operating characteristic. This post includes a hand-calculated example to illustrate all the steps in DeLong's test for a small data set. It also includes an example R implementation of DeLong's test to enable efficient calculation on large data sets. An example use case for DeLong's test: Model A predicts heart disease risk with AUC of 0.92, and Model B predicts heart disease risk with AUC of 0.87, and we use DeLong's test to demonstrate that Model A has a significantly different AUC from Model B with p 0.05.