Kaktus AI is a modern people management platform that uses advanced AI to revolutionize HR sectors by providing tools for mental health detection, workplace culture mapping, and employee well-being. Kaktus AI empowers employees by enabling them to take full control of their mental wellbeing in workplace - thereby helping leaders to create a strong ethos within their organisations.
Kaktus AI introduces a zero-admin, second-generation platform that combines AI-powered tools for early detection of mental health challenges, real-time monitoring of workplace culture, and personalized well-being support. The platform includes features such as KaktusBrain, which uses AI to identify early indicators of stress and mental illness. It also offers smart surveying tools, behavioral science nudges, and customized rewards programs to enhance organizational performance and employee engagement. Kaktus AI aims to create a high-performance, resilient organization by translating insights into actionable steps and maintaining employee privacy.
The National Study of Mental Health and Wellbeing provides key statistics on mental health issues in Australia, including the prevalence of mental disorders, consultations with health professionals, and the use of mental health-related medications. The study covers a wide range of mental health conditions and offers insights into the impact of mental health on individuals and society.
The SimpleToM dataset is designed to evaluate models' ability to reason about beliefs and actions in various scenarios. It includes a variety of situations with multiple choice questions and answers, covering topics such as food items, personal belongings, and service industries.
This paper discusses Helply - a synthesized ML training dataset focused on psychology and therapy, created by Alex Scott and published by NamelessAI. The dataset developed by Alex Scott is a comprehensive collection of synthesized data designed to train LLMs in understanding psychological and therapeutic contexts. This dataset aims to simulate real-world interactions between therapists and patients, enabling ML models to learn from a wide range of scenarios and therapeutic techniques.