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The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over 6 benchmark datasets of different scales, showing that the proposed NFM models achieve predictive performance comparable to or sometimes surpassing state-of-the-art survival models.

The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence.

While assistive robots have much potential to help older people with frailty-related needs, there are few in use. There is a gap between what is developed in laboratories and what would be viable in real-world contexts. Through a series of co-design workshops (61 participants across 7 sessions) including those with lived experience of frailty, their carers, and healthcare professionals, we gained a deeper understanding of everyday issues concerning the place of new technologies in their lives. A persona-based approach surfaced emotional, social, and psychological issues. Any assistive solution must be developed in the context of this complex interplay of psychosocial and environmental factors. Our findings, presented as design requirements in direct relation to frailty, can help promote design thinking that addresses people's needs in a more pragmatic way to move assistive robotics closer to real-world use.

Recently, deep neural network (DNN) has provided a major breakthrough to enhance prediction in various areas (LeCun et al., 2015; Goodfellow, 2016). The DNN models allow extensions of Cox proportional hazards (PH) models (Kvamme et al., 2019; Sun et al.,2020). Recently, subject-specific prediction of the DNN models has been studied by including random effects in neural network (NN) predictor (Tran et al., 2020; Mandel et al., 2022). However, these DNN random-effect models have been studied for only complete data. In this paper we propose a new DNN-FM. To the best of our knowledge, there is no literature on the DNN-FM for censored survival data. Lee and Nelder (1996) introduced the h-likelihood for the inference of general models with random effects and Ha, Lee and Song (2001) extended it to the semi-parametric frailty models. We reformulate the h-likelihood to obtain maximum likelihood estimators (MLEs) for fixed unknown parameters and best unbiased predictors (BUPs; Searle et al., 1992; Lee et al., 2017) for random frailties by a simple joint maximization of the profiled h-likelihood, which is constructed by profiling out 1

For much of the past decade, Elon Musk has regularly voiced concerns about artificial intelligence, worrying that the technology could advance so rapidly that it creates existential risks for humanity. Though seemingly unrelated to his job making electric vehicles and rockets, Musk's A.I. Cassandra act has helped cultivate his image as a Silicon Valley seer, tapping into the science-fiction fantasies that lurk beneath so much of startup culture. Now, with A.I. taking center stage in the Valley's endless carnival of hype, Musk has signed on to a letter urging a moratorium on advanced A.I. development until "we are confident that their effects will be positive and their risks will be manageable," seemingly cementing his image as a force for responsibility amid high technology run amok. Existential risks are central to Elon Musk's personal branding, with various Crichtonian scenarios underpinning his pitches for Tesla, SpaceX, and his computer-brain-interface company Neuralink. But not only are these companies' humanitarian "missions" empty marketing narratives with no real bearing on how they are run, Tesla has created the most immediate--and lethal--"A.I. risk" facing humanity right now, in the form of its driving automation.

An inflammatory ageing clock can predict how strong your immune system is and when you'll become frail by analysing your blood, according to its developers. The AI-driven device can diagnose life-threatening illness years before any symptoms begin to develop, allow for early treatment and improved recovery. The system can also determine frailty levels in old age seven years in advance, say researchers from the Buck Institute for Research on Aging in Novato, California. The US team analysed blood samples from 1,001 individuals aged eight to 96 years as part of a project called '1000 Immunomes', to create a prediction score. It's even more accurate than the number of candles on your birthday cake, say scientists from Stanford University School of Medicine, who also worked on its development, as it is based on blood-borne proteins that drive chronic inflammation.

A new AI system that predicts the health spans of mice could help develop life-extension interventions for humans, according to the tool's inventors. The system analyzes established measures of frailty to gauge a mouse's chronological age and their so-called biological age -- the condition of their physical and mental functions. It was created by researchers from Harvard Medical School's Sinclair Lab, who say it's the first study to track a mouse's frailty for the duration of its life. They plan to use the predictions to quickly test interventions intended to extend the mice's lives and move towards doing the same in humans. "It can take up to three years to complete a longevity study in mice to see if a particular drug or diet slows the aging process," said study co-first author Alice Kane, a research fellow in genetics at Harvard Medical School's Sinclair Lab.

The idea sounded good in theory: Rather than giving away full-boat scholarships, colleges could optimize their use of scholarship money to attract students willing to pay most of the tuition costs. So instead of offering a $20,000 scholarship to one needy student, they could divide the same amount into four scholarships of $5,000 each and dangle them in front to wealthier students who might otherwise choose a different school. Luring four paying students instead of one nonpayer would create $240,000 in additional tuition revenue over four years. The widely used practice, called "financial aid leveraging," is a perfect application of machine learning, the form of predictive analytics that has taken the business world by storm. But it turned out that the long-term unintended consequence of this leveraging is an imbalance in the student population between economic classes, with wealthier applicants gaining admission at the expense of poorer but equally qualified peers. Machine learning, a branch of artificial intelligence, applies specialized algorithms to large data sets to discover factors that influence outcomes that might be invisible to humans because of the sheer quantity of data involved. Researchers are using machine learning to tackle a wide variety of tasks of unimaginable complexity, such as determining harmful drug interactions by correlating millions of patient medication records or identifying new factors that contribute to equipment failure in factories. Web-scale giants such as Facebook Inc., Google LLC and Microsoft Corp. have stoked the frenzy by releasing robust machine learning frameworks under open-source licenses.