Secure AI for Mental
Health and Development

As large language models (LLMs) like ChatGPT become increasingly integrated into therapy applications and mental health chatbots, ensuring their safety is crucial. The rapid advancement of these technologies in sensitive areas demands rigorous safety protocols. This focus on safety is essential for the responsible deployment of AI in mental health care, aiming to protect vulnerable users while enhancing the effectiveness and reliability of the support provided.

01

AI as a Rigorous Science

Drawing on the principles of universal scaling phenomena from statistical physics, we are committed to devising scaling laws to promote systematic and data-driven research. Our primary focus is to identify and establish uncomplicated relationships between data, computations, parameters, and the performance of large-scale networks. Utilizing these relationships, we aim to optimize the predictability and efficiency of network training, along with improving the effectiveness of our performance assessments. Furthermore, our ongoing research is oriented towards exploring scaling laws relevant to AI system safety, a critical area that will profoundly influence our future research directions.

02

Resilience and Growth

At SuisseAI, we place a premium on safety research pertaining to advanced AI models. With each advancing year, we see the development of increasingly large neural networks that outshine their antecedents. However, this enhanced capacity also presents unique safety challenges. We are committed to thoroughly investigating and proactively addressing safety issues linked to large models, with an objective to bolster their dependability. Additionally, we are dedicated to sharing our findings with the broader community to improve safe deployment practices across the field. Currently, our main focus lies in developing systems that amalgamate these safety techniques with text analysis tools, allowing for a comprehensive safety evaluation and analysis.

03

Tools and Measurements

We fundamentally believe in critically evaluating the societal ramifications of our work. Our strategy is centered on creating tools and metrics that allow us to thoroughly assess and understand the capabilities, limitations, and potential societal impacts of our AI systems. We invite you to delve into some of the projects we have led or participated in to gain a deeper understanding of our research direction in this sphere. These initiatives underscore our commitment to comprehending and addressing the wider societal implications of AI technology: AI and Efficiency, Measurement in AI Policy: Opportunities and Challenges, the AI Index 2021 Annual Report, and Microscope.

04

Focused, Collaborative Research Initiatives

Collaboration is at the heart of our research projects, and we strive to find a balance between top-down and bottom-up research planning strategies. While adhering to a distinct and focused research agenda, we prioritize inclusivity by involving a diverse set of stakeholders, including researchers, engineers, societal impact specialists, and policy analysts, to guide our work. We are actively seeking collaborations with other laboratories and researchers, acknowledging that the most thorough and insightful research on these systems emerges from a wide, interconnected network of researchers working in unison.

Alignment

Safe artificial intelligence for mental healthcare and self development for everyone!

Happygroup is dedicated to making AI safe and clinically effective for mental healthcare and self develpment.

May 7, 2024