Computer Scientist Explains: How to Use AI Without Cheating | Faheem Ullah | AFP 52

About this episode

The episode discusses the impact of AI on academic research and how to use it without compromising integrity.

Is the traditional literature review dead? In an era where AI can summarize 1,000 papers in seconds, the boundary between "working smart" and "losing depth" has never been thinner. As researchers, we are facing a fundamental shift: AI is no longer a futuristic concept—it is a pervasive reality that touches every domain of science. But as we automate our workflows, are we sacrificing the very creativity and learning curves that define a PhD? In this episode, Jeroen Schreel sits down with Faheem Ullah, an Assistant Professor in Computer Science at the University of Adelaide, to dismantle the hype and provide a technical roadmap for the modern academic. Faheem breaks down the hierarchical differences between AI, Machine Learning, and Deep Learning, while offering a pragmatic framework for using these tools without compromising your academic integrity. We dive deep into: - The 1-Hour Literature Review: How to turn a week-long manual search into a high-productivity hour using iterative search strings and pilot studies. - The Technical "Black Box": Understanding feature selection, hyperparameters, and why Deep Learning is uniquely suited for automation. - The Cost of "Free" AI: Why…

People in this episode

Host: Jeroen Schreel

Guest: Faheem Ullah

Topics covered

• AI in research
• academic integrity
• literature review
• Machine Learning
• Deep Learning
• automation
• bias in AI

Keywords

• AI
• academic integrity
• literature review
• Machine Learning
• Deep Learning
• automation
• bias
• research productivity
• feature selection
• hyperparameters

Mentioned in this episode

Organizations: University of Adelaide

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