Auditing LLMs and Twitter

In part two of the Data Skeptic Recommender Systems season finale, Kyle asks a deceptively difficult question: what should recommender systems actually optimize for? Drawing on conversations from across the season, the episode explores engagement, filter bubbles, popularity bias, ...  Show more

Where did recommender systems come from, and how do we know when they're actually working? In part one of Data Skeptic's three-part Recommender Systems finale, Kyle traces the field from collaborative filtering and the Netflix Prize to matrix factorization and modern approaches, ...  Show more

Graphs, but not as you would expect them: Graph analytics guru Amy Hodler speaks to Jon Krohn about the graph data structure and graph applications, graph algorithms, graph RAG, and graphs as memory systems for AI agents. We can use graphs in a surprising number of ways. Money la ...  Show more

In episode nine we talk with George Dahl, of  the University of Toronto, about his work on the Merck molecular activity challenge on kaggle and speech recognition. George recently successfully defended his thesis at the end of March 2015. (Congrats George!) We learn about how net ...  Show more

On today’s episode, we get a reality check on all the hype surrounding AI with guest Phil Gervasi. Phil provides background on Large Language Models (LLMs) and their applications, as well as the current state of AI technology. We also delve into practical use cases for AI in netw ...  Show more

State space models (SSMs), granite models, and Mamba: Dell’s Tyler Cox and Shirish Gupta discuss with Jon Krohn why state space models can process information so efficiently, and how Dell’s AI factory helps enterprises manage custom AI workloads. Hear the latest on the Dell Pro A ...  Show more