For the thousands of tech workers recently laid off in California and across the country, the future may not be as bleak as it looks right now: Many are likely to retrain fairly quickly for new jobs in the burgeoning field of artificial intelligence. The massive rounds of layoffs at tech giants and many smaller companies were largely the result of stricter investor demands -- what managers saw as over-hiring during the pandemic and a stock market that rewarded those personnel cuts. But the industry also was clearing the way to focus on AI, which is expected to revolutionize computer-related technology and work in the years ahead -- even as it displaces jobs, previously handled by humans, in areas as varied as coding and background acting. Not only is AI taking over more standard computer programming once done entirely by humans, it is also starting to spur waves of new applications -- and with them, jobs, both tech and non-tech, in a wide range of industries, including in Southern California. "What we're seeing is a lot of tech companies are actually monetizing the AI solution," said Jenn Longnion, the Los Angeles-based founder of See & Free Consulting, which helps businesses grow sustainably.

Zeerak Ahmed has spent years in the U.S., working for some of the world's biggest tech companies. But one thing he has grown frustrated with is how "computing treats non-Latin languages as second class citizens." One such language is his mother tongue, Urdu, the national language and lingua franca of Pakistan, which is also widely spoken in India. Ahmed, who is from Lahore, has had many conversations with his friends and family about the difficulties of trying to use existing Urdu keyboards or read Urdu type. And he has witnessed many young people instead resorting to English or so-called Roman Urdu, using the Latin script to produce a phonetic transliteration, in the absence of a better solution.

Deep neural networks for survival prediction outper-form classical approaches in discrimination, which is the ordering of patients according to their time-of-event. Conversely, classical approaches like the Cox Proportional Hazards model display much better calibration, the correct temporal prediction of events of the underlying distribution. Especially in the medical domain, where it is critical to predict the survival of a single patient, both discrimination and calibration are important performance metrics. Here we present Discrete Calibrated Survival (DCS), a novel deep neural network for discriminated and calibrated survival prediction that outperforms competing survival models in discrimination on three medical datasets, while achieving best calibration among all discrete time models. The enhanced performance of DCS can be attributed to two novel features, the variable temporal output node spacing and the novel loss term that optimizes the use of uncensored and censored patient data. We believe that DCS is an important step towards clinical application of deep-learning-based survival prediction with state-of-the-art discrimination and good calibration.