SOBRE ESTE EPISÓDIO
How do we design robots and autonomous vehicles that understand the unwritten rules of human behavior? Kyle speaks with Cornell Tech professor Wendy Ju about implicit interaction, "Wizard of Oz" prototyping, and what studying pedestrians, self-driving cars, and even robotic furniture can teach us about designing technology that behaves the way people expect.
Inglês
Estados Unidos da América
TRANSCRIÇÃO 🔗
Are you the producer of this podcast?
Add a podcast transcript
Need Audio-to-Text?
Transcribe with Listen411 in Just 60 Seconds
PESQUISAR EPISÓDIOS PASSADOS
Pesquisar episódios anteriores de Data Skeptic.
OUTROS EPISÓDIOS NESTE PODCAST
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 matter more than differences between books. The episode also explores how to model reader preferences,…
Aaron Payne, an MBA student at Georgia Tech studying business analytics and a Senior Insights Analyst at Chick-fil-A, joins Kyle Polich to talk about turning analytics into decisions that matter. They unpack a real-world forecasting project with Comfama in Colombia, including messy data realities, …
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 matters. They unpack key dimensions of responsible AI, including robustness to adversarial attacks, pr…
How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers recommendation tran…
Ervin Dervishaj, a PhD student at the University of Copenhagen, discusses his research on disentangled representation learning in recommender systems, finding that while disentanglement strongly correlates with interpretability, it doesn't consistently improve recommendation performance. The conver…
Aviso: O podcast e a arte incorporada nesta página são de Kyle Polich, que é propriedade de seu proprietário e não é afiliado ou endossado por Listen Notes, Inc.
EDITAR
Obrigado por ajudar a manter a base de dados dos podcasts atualizada.