Speaker
Description
In the digital era, artificial intelligence (AI) is transforming how industries operate—powering breakthroughs in image analytics, large language models (LLMs), deep learning, reinforcement learning, hybrid modeling, and real-time decision-making.
This talk will reframe the narrative around AI, not as a hype or threat, but as an opportunity for the statistics community to lead with purpose and clarity. Statistical thinking is the backbone of Trustworthy and Responsible AI principles, core statistical concepts are critical such as:
Garbage In, Garbage Out (GIGO): emphasizes that data need to be of sufficient quality and trusted for model building
Outlier detection and extrapolation awareness: Preventing misleading conclusions and confirmation bias.
Model complexity and generalization: Balancing bias and variance to avoid overfitting or underfitting.
Uncertainty quantification: Making informed decisions under uncertainty
As AI continues to evolve, the statistics community must embrace a growth mindset. This talk will offer practical strategies:
Engage proactively with domain experts and AI practitioners.
Communicate the value of statistical rigor in business and engineering contexts.
Teach others how to define “good enough” in decision-making, grounded in uncertainty and risk.
Statistics is the language of data and the foundation of Industrial AI. The future of AI needs statisticians as leaders, let’s shape the future together.