Unleashing the Power of Social Intelligence in AI

Table of Contents

1. Introduction
2. The Next Milestone: Social Intelligence
   • 2.1 Understanding Human Social Cues
   • 2.2 Engaging in Social Conversation
3. Applications of Social Intelligence
   • 3.1 Understanding Multimedia Content
   • 3.2 Building Intelligent Personal Assistants
   • 3.3 Interactive Robots
4. Foundations of Social Intelligence
   • 4.1 Multi-modal Perception
   • 4.2 Multi-modal Interaction
5. Challenges in Multi-modal Learning
   • 5.1 Fusion, Representation, Alignment, and Translation
   • 5.2 Co-learning from Multi-modal Data
   • 5.3 Robustness, Fairness, and Privacy
6. Real-world Applications of Social Intelligence
   • 6.1 Healthcare
   • 6.2 Education
7. Benchmarks for Multi-modal Learning
   • 7.1 Multi-modal Effective Recognition
   • 7.2 Introducing Multibench
8. Insights and Paradigms in Multi-modal Learning
   • 8.1 Using Tensors for Multi-modal Interactions
   • 8.2 Factorized Representations
   • 8.3 Learning through Translation
9. Fairness in Social Intelligence
   • 9.1 Mitigating Exposure Bias
   • 9.2 Measuring and Mitigating Social Biases
10. Privacy-preserving Multi-modal Learning
    • 10.1 Real-world Mental Health Assessments
    • 10.2 Pushing the Performance and Privacy Frontier
11. Conclusion
12. References

👉 The Next Milestone: Social Intelligence

Artificial Intelligence (AI) has made significant progress in various domains, including physical intelligence and digital intelligence. However, the next milestone for AI is social intelligence, which involves understanding and engaging in human social cues, conversations, and social norms. This poses a grand challenge for current machines. In this article, we will delve into the significance of social intelligence and its applications in various fields.

2.1 Understanding Human Social Cues

Understanding human social cues is crucial for building socially intelligent AI. Social cues include verbal and non-verbal signals used by humans to convey their intentions. AI agents need to comprehend these cues, including intense and effective states, personalities, and references to the broader environment. Through multi-modal perception, AI can receive and interpret human communication. By modeling the interactive loop between social perception and action, AI agents can effectively engage in social interactions.

2.2 Engaging in Social Conversation

Engaging in social conversation is another vital aspect of social intelligence. AI should be able to communicate through human behaviors and take actions in an embodied environment. This ability enables AI to build intelligent personal assistants that can interact with people through social engagement. Additionally, interactive robots can monitor human behavior and provide assistance in various settings such as schools, hospitals, and the workplace. The applications of social intelligence are diverse and impactful.