Welcome to the very first episode of our podcast, where we dive deep into the fascinating world of data, artificial intelligence, and cybersecurity. I'm Gürol Canbek. In this episode, we’ll explore one of the most critical concepts in AI: Garbage In, Garbage Out, or GIGO.

We often focus on building smarter algorithms, but what happens when the data we feed into these systems is flawed or incomplete? Like using spoiled ingredients in a recipe, bad data can lead to disastrous results. In this episode, I'll discuss my latest research on how data quality affects AI's ability to generate insights and how we can avoid those "bad ingredients."

We’ll talk about patterns, data fingerprints, and even some surprising parallels between natural phenomena like earthquakes and your smartphone apps! 🧐

For full access to the research behind this episode, you can read the paper here: bit.ly/GIGOpaper.

Also, be sure to check out more of my work at gurol.canbek.com.

Join me as we uncover how clean, well-structured data can make all the difference in AI, and why GIGO is more relevant than ever in our increasingly data-driven world.

👉 Please cite my article as follows: Canbek, G. (2022). Gaining insights in datasets in the shade of “garbage in, garbage out” rationale: Feature space distribution fitting. WIREs Data Mining and Knowledge Discovery, 12(3), 1–18. https://doi.org/10.1002/widm.1456