It s a common assumption among marketers that if you can customize any form of marketing, particularly mobile advertising, you ll get better results. With this in mind, mobile marketing relies significantly on user tracking data as a cornerstone advertising strategy.
A researcher from Kanazawa University devised a way to speed up a fundamental task in computer vision and graphics known as non-rigid point set registration. Unlike previous registration techniques, the proposed method is computationally efficient even for large data sets. Moreover, the computing times for this method are shorter than those for a state-of-the-art approach. The results of this study could have implications for various fields, such as autonomous driving, medical imaging, and robotic manipulation.
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DANVILLE, Pa. - A team of scientists from Geisinger and Tempus have found that artificial intelligence can predict risk of new atrial fibrillation (AF) and AF-related stroke.
Atrial fibrillation is the most common cardiac arrhythmia and is associated with numerous health risks, including stroke and death. The study, published in
Circulation, used electrical signals from the heart measured from a 12-lead electrocardiogram (ECG) to identify patients who are likely to develop AF, including those at risk for AF-related stroke. Each year, over 300 million ECGs are performed in the U.S. to identify cardiac abnormalities within an episode of care. However, these tests cannot generally detect future potential for negative events like atrial fibrillation or stroke, said Joel Dudley, chief scientific officer at Tempus. This critical work stems from our major investments in cardiology to generate algorithms that make existing cardiology tests, such as ECGs, smarter and capab
Credit: World Scientific
Human beings are living inside an ocean of signals. These signals enable human beings to demonstrate intelligence. Similarly, machines are also surrounded by signals. Artificial intelligence (AI) or machine intelligence has become a hot topic for many decades due to the high expectation of the public on the emergence of smart machines and smart products.
However, the AI research community is still struggling to determine the foci of machine intelligence. For instance, the low-level focus of AI is to advocate computer-aided human intelligence. We believe that the more challenging focus should aim at creating machines that can achieve human-like self-intelligence by themselves.