How has AI revolutionized the concept of Socially Intelligent Bots since 1956 ? -written by Poonam Chanchlani (BCA, Data Science)

Since its inception in 1956,  AI research has drawn inspiration from human intelligence, leading to remarkable progress. The 1990s marked a pivotal moment when the term "intelligence" was coined to describe "cognitive" attributes resembling human intelligence. This expansion, termed "classical AI," has progressed significantly, yet new avenues emerged, including social intelligence in robots.

 

In the realm of AI and robotics, the integration of social intelligence has gained prominence. The demand for interactive capabilities is evident across contexts where robots collaborate with humans or other robots. The study of human-robot interaction (HRI) probes the intricacies of interactive behavior in both entities.

 

Robots' connection to human intelligence extends to sensorimotor skills that mirror human actions, like walking and object manipulation. Such skills, considered fundamental, underline the synergy between mind, body, and environment. This evolved perspective diverges from the earlier perception of robots as mere "computers on wheels." Modern AI-driven robots are inherently embedded, responsive, and intertwined with their surroundings.

 

AI's evolution sparked a shift in robotics research. This approach embraced diverse inspirations, not limited to human models. Insects, slugs, or salamanders provided viable behavioral models. This paradigm shift acknowledged the importance of balancing "body," "mind," and "environment" complexity.

 

The robotics landscape in the 1990s showcased the behavioral repertoire of "nouvelle AI" robots, featuring actions such as wandering, obstacle avoidance, and photo-sensitive behavior. These robots enabled investigations into machine learning for robot controllers, with applications like obstacle avoidance and light-source seeking.

 

Affective computing emerged as a broad study domain encompassing systems that simulate or interpret human emotions. Virtual assistants exemplify this, aiming to replicate emotional dynamics and facilitate human-computer interaction. However, this simulation might create unrealistic expectations about computer agent intelligence.

 

The journey continued with emotional computing, including textual and multimodal sentiment analysis projects using AI to classify emotions. This continuous exploration underscores the transformative potential of AI, shaping a future where technology mirrors human interaction intricately.