HSE University and Sber Researchers to Make AI More Empathetic
Researchers at the HSE AI Research Centre and Sber AI Lab have developed a special system that, using large language models, will make artificial intelligence (AI) more emotional when communicating with a person. Multi-agent models, which are gaining popularity, will be engaged in the synthesis of AI emotions. The article on this conducted research was published as part of the International Joint Conference on Artificial Intelligence (IJCAI) 2024.
The system is based on effective AI models of computer vision developed by the authors for recognising user facial expressions, which can be run directly on their devices. While using these models during a dialogue the emotions of the interlocutor are analysed in real time, including AI responses, which can help in building special datasets for training and improving the system for generating emotional responses to user requests.
One of the article authors, Associate Professor at the Department of Information Systems and Technologies at HSE University in Nizhny Novgorod
‘In our system, the recognised emotion is used to select a more empathic AI response, which is generated simultaneously by several language models tuned to basic emotions: joy, sadness, fear or anger. As a result, the received response contains a more detailed analysis of the recommendations issued by AI, their advantages and disadvantages in terms of the emotional reaction of users to them.’
The HSE University and Sber solution can be applied to situations when it is important to take into account the emotional state of users: in company chatbots, support services, educational applications and other areas where contact with the client is required. As a result, suggestions and recommendations of products and services will look more natural and empathetic. This will allow companies to better communicate with customers, give researchers a new tool for exploring emotions, and let users interact with AI more effectively. Today, AI models have already been applied in the development of GigaPevt, a medical diagnostic assistant based on the large GigaChat language model, to analyse the user's emotional state.
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