Beyond the Hype of AI

The integration of Artificial Intelligence across healthcare,energy and manufacturing has moved past the pilot stage and into mission critical infrastructure. While high level benefits are heavily celebrated - such as super human diagnostic triage,dynamic grid balancing and automated quality control - they often obscure severe operational risks.

Traditional software engineering frameworks treat code as a static,deterministic asset. Machine learning systems,however,are probabilistic,volatile and highly sensitive to environmental context.

We will examine the mechanics of 'data drift' - the process by which live AI models silently degrade over time without triggering standard system alerts - and the optimization hazards of 'reward hacking'.

Additionally the lecture will address the security vulnerabilities of poisoned data pipelines ,the human-factor risks of automation bias and the compliance challanges posed by black-box un-interpretability.