In this day and age, we all know someone who has met their other half online. Whether its swiping through dating apps like Tinder and Bumble, or'sliding into DMs' on Instagram, there are plenty of ways to try and bag a date. Some celebrities – including Joe Jonas and Sophie Turner – even met over the internet. But couples whose relationship started online are less happy in love and have lower levels of marital satisfaction, according to a new study. What's more, they even experience love less intensely than those who met in person, the findings say.

Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation (HCT). However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal barriers (e.g., relational difficulties with their care partner, who is often involved in medication management). To optimize the effectiveness of a three-component digital intervention targeting both members of the dyad as well as their relationship, we propose a novel Multi-Agent Reinforcement Learning (MARL) approach to personalize the delivery of interventions. By incorporating the domain knowledge, the MARL framework, where each agent is responsible for the delivery of one intervention component, allows for faster learning compared with a flattened agent. Evaluation using a dyadic simulator environment, based on real clinical data, shows a significant improvement in medication adherence (approximately 3%) compared to purely random intervention delivery. The effectiveness of this approach will be further evaluated in an upcoming trial.

Artificial intelligence (AI) may not be programmed to love, but it can determine the top predictors for a happy relationship. In a first-of-its-kind study, researchers conducted a machine learning analysis of over 11,000 couples and found romantic success is achieved when partners believe the other person is fully committed. The technology revealed other elements including feeling close, appreciated and sexual satisfaction all lead to a successful partnership. On the other hand, the data also showed factors that run the risk of a doomed romance – depression and insecure attachment. The study was conducted by a team from University of California and other researchers around the world, who analyzed 11,196 couples across 43 distinct self-reported datasets.

The most reliable predictor of a relationship's success is partners' belief that the other person is fully committed, a Western University-led international research team has found. Other important factors in a successful relationship include feeling close to, appreciated by and sexually satisfied with your partner, says the study – the first-ever systematic attempt at using machine-learning algorithms to predict people's relationship satisfaction. "Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity," Western Psychology professor Samantha Joel said. "But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories." The massive machine-learning study, conducted by Joel, Paul Eastwick from University of California, Davis, and 84 other scholars from around the world, delved into more than 11,000 couples and 43 distinct self-reported datasets on romantic couples.

A first-of-its-kind artificial intelligence (AI) study of romantic relationships based on data from thousands of couples has identified the top predictors that make partners feel positively about their relationship – and the findings show romantic happiness is about a lot more than simply who you're with. Researchers conducted a machine-learning analysis of data collected from over 11,000 couples, and found that relationship-specific characteristics (personal evaluations of the relationship itself) were significantly more powerful predictors of relationship quality overall than variables based on individual characteristics. In other words, the type of relationship you build with a partner may be more important to your happiness than either of your individual characteristics - in the study, they looked at traits like how satisfied a person was with life, how anxious they were, or whether their parents' marriage worked out. "Relationships-specific variables were about two to three times as predictive as individual differences, which I think would fit many people's intuitions," says lead researcher and psychologist Samantha Joel from Western University in Canada. "But the surprising part is that once you have all the relationship-specific data in hand, the individual differences fade into the background."

The most reliable predictor of a relationship's success is partners' belief that the other person is fully committed, a Western University-led international research team has found. Other important factors in a successful relationship include feeling close to, appreciated by and sexually satisfied with your partner, says the study – the first-ever systematic attempt at using machine-learning algorithms to predict people's relationship satisfaction. "Satisfaction with romantic relationships has important implications for health, wellbeing and work productivity," Western Psychology professor Samantha Joel said. "But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories." The massive machine-learning study, conducted by Joel, Paul Eastwick from University of California, Davis, and 84 other scholars from around the world, delved into more than 11,000 couples and 43 distinct self-reported datasets on romantic couples.