The 6 Best Data Skeptic Podcast Episodes
1) AI Decision-Making
Making a decision is a complex task. Today's guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts...Show More
2) P vs NP
In this week's episode, host Kyle Polich interviews author Lance Fortnow about whether P will ever be equal to NP and solve all of life’s problems. Fortnow begins the discussion with the example quest...Show More
3) [MINI] Bayesian Belief Networks
A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between them. It's an intuitive way of encoding y...Show More
4) [MINI] Big Oh Analysis
How long an algorithm takes to run depends on many factors including implementation details and hardware. However, the formal analysis of algorithms focuses on how they will perform in the worst case...Show More
5) Artificial Intelligence, a Podcast Approach
This episode kicks off the next theme on Data Skeptic: artificial intelligence. Kyle discusses what's to come for the show in 2018, why this topic is relevant, and how we intend to cover it.
6) The Death of a Language
USC students from the CAIS++ student organization have created a variety of novel projects under the mission statement of "artificial intelligence for social good". In this episode, Kyle interviews Za...Show More
7) The Future is Agentic in Recommender Systems
Kyle Polich sits down with Yashar Deldjoo, research scientist and Associate Professor at the Polytechnic University of Bari, to explore how recommender systems have evolved and why trustworthiness mat...Show More
8) Book Ratings and Recommendations
Goodreads star ratings can be misleading as measures of "book quality," and research from Hannes Rosenbusch suggests that for many professionally published books, differences between readers often mat...Show More
9) Disentanglement and Interpretability in Recommender Systems
10) Collective Altruism in Recommender Systems
Ekaterina (Kat) Filadova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research r...Show More