The Age of AI has also ushered in the Age of Debates About AI. And Yuval Noah Harari, author of Sapiens and Homo Deus, and one of our foremost big-picture thinkers about the grand sweep of humanity, history and the future, is now out with Nexus: A Brief History of Information Networks from the Stone Age to AI. Harari generally falls into the AI alarmist category, but his thinking pushes the conversation beyond the usual arguments. The book is a look at human history through the lens of how we gather and marshal information. For Harari, this is essential, because how we use--and misuse--information is central to how our history has unfolded and to our future with AI. In what Harari calls the "naïve view of information," humans have assumed that more information will necessarily lead to greater understanding and even wisdom about the world.

In critical care, intensivists are required to continuously monitor high dimensional vital signs and lab measurements to detect and diagnose acute patient conditions. This has always been a challenging task. In this study, we propose a novel self-correcting deep learning prediction approach to address this challenge. We focus on an example of the prediction of acute kidney injury (AKI). Compared with the existing models, our method has a number of distinct features: we utilized the accumulative data of patients in ICU; we developed a self-correcting mechanism that feeds errors from the previous predictions back into the network; we also proposed a regularization method that takes into account not only the model's prediction error on the label but also its estimation errors on the input data. This mechanism is applied in both regression and classification tasks. We compared the performance of our proposed method with the conventional deep learning models on two real-world clinical datasets and demonstrated that our proposed model constantly outperforms these baseline models. In particular, the proposed model achieved area under ROC curve at 0.893 on the MIMIC III dataset, and 0.871 on the Philips eICU dataset.