Like The New Yorker, I was born in 1925. Somewhat to my surprise, I decided to keep a journal of my hundredth year. The author, who was born on December 17, 1925, notes that the magazine's first issue came out ten months before he did. Old age is no joke, but it can feel like one. You look everywhere for your glasses, until your wife points out that you're wearing them. I turn a hundred this year. People act as though this is an achievement, and I suppose it is, sort of. Nobody in my family has lived this long, and I've been lucky. I'm still in pretty good health, no wasting diseases or Alzheimer's, and friends and strangers comment on how young I look, which cues me to cite the three ages of man: Youth, Maturity, and You Look Great. On the other hand, I've lost so many useful abilities that my wife, Dodie, and I have taken to calling me Feebleman. Look, up in the sky! No, it's Dodie doesn't want me to know how old she is, but she's nearly three decades younger than I am, and I become ...

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80% accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

Artificial intelligence (AI) is finding its way into many areas of healthcare, now including mobile devices, thanks to an application designed by the Georgia Institute of Technology to guide and support cancer patients. The mobile app, which runs on a tablet computer, gives 50 breast cancer patients in rural Georgia personalized recommendations on everything from side effects to insurance, and the information regularly changes based on each patient's progress. Artificial intelligence could be a game-changing technology for a host of healthcare applications. Sutter Health, Ada Health, Innovaccer and QuartzClinical, among others introduced technologies or offerings infused with AI at HIMSS19. MyPath starts with a mobile library of resources compiled from the American Cancer Society and other reputable organizations, and can be personalized with each patient's diagnosis and treatment plan, along with dates for specific procedures.

You are free to share this article under the Attribution 4.0 International license. A new app is using artificial intelligence to guide and support some 50 breast cancer patients in rural Georgia, giving them personalized recommendations on everything from side effects to insurance. The app, called MyPath, adapts to each stage in a patient's cancer experience. So the information available on the app--which runs on a tablet computer--regularly changes based on each patient's progress. Are you scheduled for surgery?

Note: This essay is a response to "Machine Morality and Human Responsibility." The essays in this symposium were first delivered at the second conference in the series "Stuck with Virtue." Sponsored by the University of Chicago's New Science of Virtues project, this conference examined the various Cartesian, Lockean, and Darwinian premises that help shape and inform the ethics and ethos of modern technological democracy. Held in April 2011 at Berry College in Rome, Georgia, the conference featured four main speakers: Ronald Bailey, Charles T. Rubin, Patrick J. Deneen, and Robert P. Kraynak, with responses to Mr. Bailey by Benjamin Storey and to Professor Rubin by Adam Keiper (left, here joined by Ari N. Schulman). The symposium is introduced by Peter Augustine Lawler and Marc D. Guerra. Should we care about machine morality at all? Do the issues that Charles T. Rubin so ably raises merit scholarly time and public attention? Or are they just frivolities -- material suited for science fiction romps in books and movies but unworthy of serious consideration? This is a difficult question to answer readily.