BIRD: Behaviour Inspired Reinforcement Development

At NeuronX, our AI research is distinguished by its integration of cutting-edge technologies and methodologies designed to understand, model, and influence human behaviour on a deep level. Our work is structured around three primary domains: Behaviour Identification, Behaviour Reconciliation, and Behaviour Dissemination. Each domain leverages specific AI and machine learning technologies to address complex challenges.

Behaviour Identification

Objective: To accurately identify and classify human behaviours using a multifaceted approach that incorporates cultural, social, and individual contexts. This process involves the collection and analysis of large datasets to understand behaviour patterns.

Technologies and Methodologies:

• Deep Learning: Utilizing convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to process and analyse complex behavioural data from diverse sources such as social media, video feeds, and textual communications.
• Natural Language Processing (NLP): Applying advanced NLP techniques like sentiment analysis, named entity recognition (NER), and language models (e.g., Transformers, BERT) to understand the nuances of human communication and its implications on behaviour.
• Data Analytics: Leveraging big data technologies and analytics platforms (e.g., Apache Hadoop, Spark) to handle and interpret vast amounts of behavioural data, enabling real-time behaviour analysis and insights.

Behaviour Reconciliation

Objective: To address and mitigate negative or conflicting behaviour patterns, promoting healthier interactions and mental well-being. This involves developing models that can predict the impact of certain behaviours and suggest interventions.

Technologies and Methodologies:

• Reinforcement Learning: Employing reinforcement learning algorithms to train models that can propose optimal interventions in various behavioural scenarios, maximizing positive outcomes.
• Agent-based Modelling: Creating simulations of complex social systems to understand and predict the effects of behavioural changes, facilitating the design of more effective reconciliation strategies.
• Psychometric AI: Integrating psychological theories and metrics into AI models to better understand the motivations behind behaviours and to tailor interventions more effectively.

Behaviour Dissemination

Objective: To propagate positive behaviour patterns across communities, utilizing AI to encourage constructive interactions and societal well-being.

Technologies and Methodologies:

• Social Network Analysis (SNA): Analysing social networks using graph theory to identify key influencers and nodes through which positive behaviours can be most effectively disseminated.
• Generative Adversarial Networks (GANs): Using GANs to generate content (e.g., text, images, videos) that promotes positive behaviours, tailored to the preferences and consumption habits of targeted audiencies.
• Behavioural Nudging: Implementing AI systems that can deliver personalized messages and content at strategic times, nudging individuals towards positive behaviour patterns based on their unique behavioural profiles and contexts.