Elon Musk's AI, Grok, has been telling users the world's richest person is smarter and more fit than anyone in the world, in a raft of recently deleted posts. Elon Musk's AI, Grok, has been telling users the world's richest person is smarter and more fit than anyone in the world, in a raft of recently deleted posts. Elon Musk's Grok AI tells users he is fitter than LeBron James and smarter than da Vinci Thu 20 Nov 2025 23.25 ESTLast modified on Thu 20 Nov 2025 23.27 EST Elon Musk's AI, Grok, has been telling users the world's richest person is smarter and more fit than anyone in the world, in a raft of recently deleted posts that have called into question the bot's objectivity. Users on X using the artificial intelligence chatbot in the past week have noted that whatever the comparison - from questions of athleticism to intelligence and even divinity - Musk would frequently come out on top. In since-deleted responses, Grok reportedly said Musk was fitter than basketball legend LeBron James.

Time-to-event analysis (survival analysis) is used when the response of interest is the time until a pre-specified event occurs. Time-to-event data are sometimes discrete either because time itself is discrete or due to grouping of failure times into intervals or rounding off measurements. In addition, the failure of an individual could be one of several distinct failure types, known as competing risks (events). Most methods and software packages for survival regression analysis assume that time is measured on a continuous scale. It is well-known that naively applying standard continuous-time models with discrete-time data may result in biased estimators of the discrete-time models. The Python package PyDTS, for simulating, estimating and evaluating semi-parametric competing-risks models for discrete-time survival data, is introduced. The package implements a fast procedure that enables including regularized regression methods, such as LASSO and elastic net, among others. A simulation study showcases flexibility and accuracy of the package. The utility of the package is demonstrated by analysing the Medical Information Mart for Intensive Care (MIMIC) - IV dataset for prediction of hospitalization length of stay.

Jon the Robot uses artificial intelligence to determine where to jump next in his human-written script. Jon can tell a joke has fallen flat, Fitter says, and then make a quip on the joke's failure, attempting to repair the interaction. "It might be poking fun at the audience, trying to guess why they didn't like the joke," Fitter says. The majority of the time, when the robot tried to rescue the joke, it improved the audience's reaction, a result Fitter finds "promising."

Executing the perfect exercise move is as much about technique as it is strength. And fitness enthusiasts have been paying personal trainers for decades in order to learn how to correctly pull off the right moves. Now there's a free app that offers to do the same thing by using motion-tracking technology and artificial intelligence to help you perform the perfect squat. Called the Perfect Squat Challenge, it was developed by digital therepy company Kaia Health who consulted with physiotherapists and sport scientists to figure out a squat that most of us can achieve. Once you fire up the app, you're guided through the motion by a virtual assistant called Kaia.

We had to go through a whole process of development and discovery, and, as a result of computer experts working hand in hand with domain experts over the course of 15 to 20 years, computers and specialized software were developed to suit different needs. Most people now are familiar with conversion rate optimization (CRO), where site operators try to maximize conversions by testing new ideas for design, messaging, user experience, and more. The operator sets parameters and goals, but the AI decides the combination of ideas, always trying to find a better answer and better results against that goal. And just like computerization, AI enablement will only be fully achieved once all of us can be considered AI experts by today's standards.

Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than both the associated unprocessed Monte Carlo estimate and an estimate based on a functional fit to the MC samples. These experiments run over a wide variety of integration spaces, numbers of sample points, dimensions, and fitting functions. In particular, we apply StackMC in estimating the expected value of the fuel burn metric of future commercial aircraft and in estimating sonic boom loudness measures. We compare the efficiency of StackMC with that of more standard methods and show that for negligible additional computational cost significant increases in accuracy are gained.