Background: Adolescents are particularly vulnerable to mental disorders, with over 75% of cases manifesting before the age of 25. Research indicates that only 18 to 34% of young people experiencing high levels of depression or anxiety symptoms seek support. Digital tools leveraging smartphones offer scalable and early intervention opportunities. Objective: Using a novel machine learning framework, this study evaluated the feasibility of integrating active and passive smartphone data to predict mental disorders in non-clinical adolescents. Specifically, we investigated the utility of the Mindcraft app in predicting risks for internalising and externalising disorders, eating disorders, insomnia and suicidal ideation. Methods: Participants (N=103; mean age 16.1 years) were recruited from three London schools. Participants completed the Strengths and Difficulties Questionnaire, the Eating Disorders-15 Questionnaire, Sleep Condition Indicator Questionnaire and indicated the presence/absence of suicidal ideation. They used the Mindcraft app for 14 days, contributing active data via self-reports and passive data from smartphone sensors. A contrastive pretraining phase was applied to enhance user-specific feature stability, followed by supervised fine-tuning. The model evaluation employed leave-one-subject-out cross-validation using balanced accuracy as the primary metric. Results: The integration of active and passive data achieved superior performance compared to individual data sources, with mean balanced accuracies of 0.71 for SDQ-High risk, 0.67 for insomnia, 0.77 for suicidal ideation and 0.70 for eating disorders. The contrastive learning framework stabilised daily behavioural representations, enhancing predictive robustness. This study demonstrates the potential of integrating active and passive smartphone data with advanced machine-learning techniques for predicting mental health risks.

An issue across every sector, from market research to employee engagement and government relations, is how to truly understand large groups of people across political, geographical, and cultural divides and amplify their collective voice. This problem is intensified when challenging issues arise and sending out a survey doesn't provide the opportunity to discover what you don't know to ask. On the other hand, focus groups don't represent enough people to justify action. We've come a long way in learning how to better understand massive groups of people utilizing artificial intelligence (AI). This piece outlines key advantages of AI in market research resulting from my work with a number of organizations facing the challenge of transitioning from traditional market research to modern representative intelligence; that is intelligence capable of engaging, understanding and authentically representing massive groups of stakeholders (customer, employees, citizens, etc.).

An issue across every sector, from market research to employee engagement and government relations, is how to truly understand large groups of people across political, geographical, and cultural divides and amplify their collective voice. This problem is intensified when challenging issues arise and sending out a survey doesn't provide the opportunity to discover what you don't know to ask. On the other hand, focus groups don't represent enough people to justify action. We've come a long way in learning how to better understand massive groups of people utilizing artificial intelligence (AI). This piece outlines key advantages of AI in market research resulting from my work with a number of organizations facing the challenge of transitioning from traditional market research to modern representative intelligence; that is intelligence capable of engaging, understanding and authentically representing massive groups of stakeholders (customer, employees, citizens, etc.).

Data collection at a massive scale is becoming ubiquitous in a wide variety of settings, from vast offline databases to streaming real-time information. Learning algorithms deployed in such contexts must rely on single-pass inference, where the data history is never revisited. In streaming contexts, learning must also be temporally adaptive to remain up-to-date against unforeseen changes in the data generating mechanism. Although rapidly growing, the online Bayesian inference literature remains challenged by massive data and transient, evolving data streams. Non-parametric modelling techniques can prove particularly ill-suited, as the complexity of the model is allowed to increase with the sample size. In this work, we take steps to overcome these challenges by porting standard streaming techniques, like data discarding and downweighting, into a fully Bayesian framework via the use of informative priors and active learning heuristics. We showcase our methods by augmenting a modern non-parametric modelling framework, dynamic trees, and illustrate its performance on a number of practical examples. The end product is a powerful streaming regression and classification tool, whose performance compares favourably to the state-of-the-art.