From Deep Seek, Remember Modern Causal Inference of Judea Pearl

In early 2020, the Boston Global Forum (BGF) honored Professor Judea Pearl with the 2020 World Leader in AI World Society (AIWS) Award for his groundbreaking contributions to artificial intelligence. Professor Pearl pioneered modern causal inference, revolutionizing how AI systems process and understand cause-and-effect relationships. His model significantly reduces data requirements and lowers the power demands on chips and computing systems, making AI more efficient, sustainable, and capable of advanced reasoning.

Recognizing his transformative impact, the AI World Society (AIWS) introduced and promoted his model, naming him the “Father of Modern AI” in acknowledgment of his visionary work and his seminal book, “The Book of Why.”

In his acclaimed book, The Book of Why: The New Science of Cause and Effect, Professor Judea Pearl (UCLA) describes the profound significance of causal inference in AI:

“The ideal technology that causal inference strives to emulate is in our own mind.
All because we asked a simple question: ‘Why?’

Causal inference is all about taking this question seriously. It posits that the human brain is the most advanced tool ever devised for managing causes and effects. Our brains store an incredible amount of causal knowledge, which, supplemented by data, could be harnessed to answer some of the most pressing questions of our time.

More ambitiously, once we truly understand the logic behind causal thinking, we could emulate it on modern computers and create an “artificial scientist.”
This would be a smart AI system capable of:

• Discovering yet unknown phenomena
• Finding explanations for unresolved scientific dilemmas
• Designing new experiments
• Continuously extracting more causal knowledge from the environment

A Vision for AI Governance and the Future

By integrating Modern Causal Inference into AI governance and applications, AIWS promotes a responsible, intelligent, and efficient AI ecosystem that enhances society while minimizing risks associated with data overload and excessive computational power consumption.

Deep Seek may apply Modern Causal Inference of Judea Pearl to reduce data requirements and machine dependency, enhancing efficiency and sustainability in AI systems.

For more details, visit:
https://aiws.net/practicing-principles/modern-causal-inference/judea-pearls-works/bio-and-honors/introduction-for-the-section-modern-causal-inference-on-aiws-net/